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Kerno is an AI backend runtime QA engineer that writes and runs backend tests against your running application and its real dependencies. It independently tests your critical flows locally against your real stack, catching runtime and security bugs inside the agent loop before they merge. Kerno is built for engineering teams and coding agents that ship backend code and need fast, trustworthy verification of every change, and its stated purpose is to verify and secure everything your agents ship — locally. In most backend teams, the bottleneck is not writing code, it is proving it works at runtime. Verification typically happens hours later in CI, where failures pile up, draining engineering time, forcing expensive rework and slowing releases. The content illustrates this with simulated DORA-style metrics where lead time, production incidents, change failure and weekly rework all rise as verification is delayed. Static review reads your code and unit tests run on mocks, so neither can see runtime behavior such as double charges across services, authorization leaks across tenants, SSRF through a callback URL hitting internal metadata, broken contracts, resilience failures and MCP tools that return an empty body instead of an error. Kerno is positioned as the runtime quality and security layer for your backend, giving every developer and agent staff-level testing knowledge to catch the expensive runtime issues DIY testing and code review miss, right at the source. Kerno organizes its work into three modules. Module A, Real evidence, verifies every code change against your live stack and dependencies, catching the functional and security bugs that only surface at runtime, before they ship. Module B, E2E coverage, verifies every critical flow a code change touches: Kerno maps a change's blast radius and verifies each flow end to end. Module C, Consistent quality, brings the same high-quality verification loop to every engineer by giving coding agents structured signals and persistent code context on every change. The runtime test floor follows four steps. Step 01, Map: Kerno builds a graph of your codebase and docs, maps every entry point and the dependencies behind it, and maps your critical user flows with you. Step 02, Test: it tests each user flow as one journey across your APIs, then covers every entry point behind it in depth. Step 03, Verify: it re-runs every flow a change touches and shows the step that broke, with the outcome the user expected and what actually happened. Step 04, Heal: it updates entry-point tests as your product changes and re-generates flows when behavior changes on purpose, so the suite evolves with your code. In practice this means a diff on a flow such as checkout step 03 on POST /payments is surfaced as a specific change — for example charges going from 1 to 2 — and self-healing records intended changes such as updated tests and added flows from a pull request. Kerno catches the bugs that only appear at runtime inside the agent loop, across a set of verification gates. End-to-end user flows verify complete user journeys across your APIs, from the first request to the outcome read back from your system. Contract and schema validation checks request and response structures, data types, required fields, serialization rules and version compatibility. Authorization and authentication reviews token validation, role-based access rules, permission scopes, session handling and credential-related flows. Security validation covers broken object- and function-level authorization, injection, excessive data exposure, mass assignment and SSRF against the OWASP Top 10. Boundary and edge cases validate payload size limits, pagination behavior, null or empty values, malformed inputs and constraint-based validation. Error handling and resilience validate status codes, error body formats, retry behavior, backoff procedures, timeout handling and controlled fallbacks. MCP server testing verifies each tool an MCP server exposes, including returned content, structured output, schema conformance and error handling. AI agent testing, listed as coming soon, is intended to verify how your agent calls tools, chains model steps and handles streaming responses, with model providers mocked for deterministic runs. Kerno connects to the services and datastores you already run and manages the data layer so it can exercise them the way your application does. Kerno runs locally: `kerno init` starts the agent, indexes your endpoints, and runs a Kerno MCP server that your coding agents connect to, while `kerno status` reports indexing and application reachability. Kerno learns your business and standards: you can define custom rules in plain English, scoped to the repos and paths you choose, and it applies them on every run in scope. It also learns from every test run and the feedback you give it, building a sharper model of your codebase and standards over time through a lessons store that records discovered behaviors. Setup follows a clear schedule. In the first hour you install Kerno, point it at your repo, map every entry point, connect it to your running app and its dependencies, and map your critical user flows together. By the first day, Kerno covers your critical flows and builds baseline tests for every API, task and event behind them, and adding Kerno instructions to your agent guidance file enables validation of every change. Within the first week you roll out to your team, with the Kerno MCP connected to your agents, validating every code change and re-running the flows it touches before they reach production. A runtime control console at portal.kerno.io provides Analytics, Coverage and Behaviour Drift views across all repositories, showing flows covered, entry points covered, code changes tested, potential issues caught, tests in your suite and active developers, plus per-repo breakdowns. The stated benefits are concrete. The content reports 6× faster merges for PRs tested by Kerno, 48% more runtime issues caught, and 10 hours freed up per engineer every week to build. Verified customers describe Kerno as being up and running in minutes with no manual work, making API migrations smoother by instantly validating that every change made by Cursor worked as expected, providing quick feedback with no manual effort when used with Claude Code while saving debugging of integration issues post-merge, catching side effects and issues that would definitely break backends, and acting as a force multiplier that lets teams ship releases much faster without the manual overhead of QA. Kerno fits into everyday delivery workflows for backend teams. A team maps and tests its critical flows, such as checkout, refund-an-order and team-invite, then uses the console to track coverage, analytics and behavior drift across repositories. When an agent adds a feature, for example partial refunds to checkout, Kerno validates the affected flows and endpoints and reports failing steps such as a double charge on POST /payments before a fix brings all checks back to passing. Bugs are caught inside the agent's session before a PR is opened, and every PR is then re-checked in CI. Engineers can review reported potential issues such as a PATCH with no changed fields returning 200 and bumping updated_at, an IDOR attempt blocked when a customer changes another customer's id, mass assignment protection, payment details never appearing in a response, and an email change that stays inactive until confirmed. It also supports testing of MCP tools such as get_order_status and consumer entries such as charge.retry and order.shipped. Kerno is aimed at engineering teams, individual engineers and coding agents shipping backend code. It connects over MCP to any coding agent, including Claude Code, Codex, Cursor and GitHub Copilot. It supports eleven languages (TypeScript, JavaScript, Python, Java, Go, Rust, C#, C, Kotlin, Ruby and PHP), eight data and messaging systems (PostgreSQL, MySQL, MariaDB, MongoDB, ClickHouse, Redis, Kafka and RabbitMQ) and three clouds (AWS, Azure and Google Cloud). Security is a stated priority: Kerno encrypts everything end to end, never retains your code, and a SOC 2 Type 2 audit is in progress, with private code never stored or used for training. A free starting path is offered through `kerno init` and a free signup at portal.kerno.io, and the content references free-plan organizations retaining event data for 30 days versus 90 days for paid plans, indicating both free and paid tiers. Kerno's core promise is simple: verify and secure everything your agents ship, locally. By moving runtime verification into the agent loop instead of waiting for late-stage CI, Kerno gives every developer and agent staff-level testing knowledge, catches expensive functional and security issues at the source, and helps teams merge faster with fewer regressions reaching production.

Reelbird is a free, open-source screen recorder for macOS 15 and later. It records your Mac screen, camera and microphone, and a share link is on your clipboard the moment you stop recording. Later you can add auto-zoom, a smooth cursor and captions, then update that same link. It is built for Apple silicon Macs, requires no account, and is MIT licensed. Its main purpose is to combine the fast, link-on-stop workflow of Loom with the polished auto-zoom look of Screen Studio in a single take, for free, with the files kept on your Mac. The problem it solves is a split in existing screen recording tools. Loom gets you a link fast, but on its free Starter plan it stops each video at 5 minutes, keeps 25 videos, records up to 720p and offers no downloads, and it has no auto-zoom. Screen Studio produces polished auto-zoom videos but costs $29 a month, or $9 billed yearly, and its share link only exists after you export. Reelbird does both from one take, for free: the link is ready when you stop, the polish can come later under the same link, and every take is kept as MP4 files on your Mac. Reelbird sets no limits of its own. Where the alternatives still do more: Loom has comments, view counts and apps for every platform, and Screen Studio has a more mature editor and a signed download. Recording starts with a simple choice. You pick a display, a window or an area, and the shortcut ⌥⇧R starts a recording from any app. The picker shows windows to choose from, including ones on other desktops, along with switches for microphone, camera, system audio and Share while recording. Camera, microphone and system audio record as separate tracks, so screen, camera, microphone and system audio stay apart. Keeping those tracks separate means you can move the camera bubble or mute system sound after recording instead of committing at capture time. The recording bar shows the timer, microphone level, pause, restart, delete and Stop. Share while recording is the core of the workflow. With it turned on, Reelbird uploads a 1080p stream to your own bucket as you record. When you stop, a Shared popover shows the link with Copy, Open in Browser and Update Link, and the link is already on your clipboard. You can paste it anywhere and it plays in any browser a moment later, with no app and no sign-in. The link never changes, even when you update the video: after editing, Update link replaces the video and the URL stays the same, so everyone you already sent it to sees the new version. Polishing happens under that same link, whenever you have time. Auto-zoom follows your clicks and typing, with a Subtle to Punchy slider in the Zoom inspector and zoom regions marked on the timeline; a real export zooms in on each click, then out again, planned from the clicks in the recording. The cursor is redrawn from the log so it glides smoothly instead of jittering, and you can resize or hide it after recording. Preview and export use the same Metal renderer, so the file looks exactly like what you watched. Framing options include background, padding, rounded corners, shadow, and a camera bubble you can move after the fact. Audio and captions are handled on the Mac. Auto level measures your voice and sets it to −16 LUFS, so a quiet take comes out at a normal level. Voice enhance uses Apple's sound isolation to take out room noise and hum where your Mac supports it, then applies a gentle compressor, de-esser and presence lift. Ducking dips system audio about 12 dB while you speak and brings it back when you stop. Captions are made on your Mac by Apple's speech recognition; you can burn them in or offer them as a track viewers can turn on. Cut Long Silences finds pauses and removes them in one go, and any cut can be restored. Safe peaks put a limiter on every track and on the mix with a −1.5 dBFS ceiling so nothing clips. Reliability and output formats are designed around not losing work. Every track is written to disk in 2-second segments from the first frame, so if your Mac crashes Reelbird recovers the take on next launch, minus at most two seconds. Warnings for a silent microphone, stalled capture, audio drift, a filling disk or uploads falling behind show up during the recording rather than after it. Exports come in the usual formats: MP4 (H.264), HEVC, ProRes and GIF with hardware encoding, in landscape, square, vertical or 4:3. Overall, the architecture is deliberately simple. Your Mac records and uploads; your bucket — Cloudflare R2, S3, B2 or MinIO — stores the files; any browser plays them. Setup takes about ten minutes, once: a bucket, a scoped API token and a public URL, entered through endpoint, bucket, region, access keys, public URL and prefix fields and verified with Test Connection. Reelbird is native and light: Swift, ScreenCaptureKit and Metal, with no Electron and no third-party dependencies. A take is an open project format — a .reelbird folder of standard MP4s, JSON and an event log — so nothing is locked in. Privacy follows the same principle: nothing leaves your Mac unless you share it. Recordings, edits, captions and transcripts stay in ~/Movies/Reelbird. Captions and titles come from Apple's on-device frameworks, with no API keys and no cloud. Shares go only to the bucket you set up, signed with your own keys, and the secret lives in your Keychain. There is no account, analytics, telemetry, crash reporting or update checks. Links are unlisted, not secret: anyone with a link can watch, so share accordingly, and Stop sharing deletes every file under that link. Reelbird can also be driven by an MCP helper: adding it to an assistant exposes list_sources, start_recording, stop_recording and share, so you can ask for a 20-second demo of a settings page you just changed and get the polished share link back. The benefits follow from that workflow. Because the link arrives the moment you stop, you can share immediately and improve the video later without resending anything. Because the polish happens under the same link, you avoid re-exporting and re-sharing. Because the files live on your Mac and your own bucket, you control storage and cost — Cloudflare R2's free tier of 10 GB with free egress covers most people. Typical scenarios include recording a quick demo of a feature you just shipped, turning a raw take of a task app or settings page into a walkthrough with zooms and captions, recording a voiceover with system audio and ducking or muting it afterwards, and capturing automated demos on request through the MCP integration. Installation, platform and pricing are straightforward. Reelbird is free and MIT licensed, with no paid tier, watermark or export limit. Download the zip from the download page, unzip it, and drag Reelbird to Applications; it is signed with an Apple Developer ID and notarized by Apple, so it opens like any other app. You can also build it from source with git clone and make install using Apple's Command Line Tools, with no Xcode needed. It runs on macOS 15 or later on Apple silicon Macs, and version 0.1.1 shipped in October 2026. It does not work on Windows or Linux, and that is not planned, because Reelbird is built on Apple frameworks throughout. In short, Reelbird gives Mac users a free, open-source way to record the screen, camera and microphone, get a share link the moment they stop, and polish that same take later with auto-zoom, a smooth cursor, captions and audio cleanup under a link that never changes. With your own storage bucket, no account and no telemetry, it is a privacy-conscious alternative that combines Loom's speed with Screen Studio's polish.

siliXon is a web platform that turns natural language into electronics. You describe the board you want to build in plain English, and siliXon takes it all the way to a routed, manufacturable PCB built on real, purchasable components. The company's framing is simple: natural language in, electronics out, for the whole world, not just engineers. The platform is built so that anyone with an idea can create their own electronics regardless of electronics background. Its design agent, Rocky, guides the process and lets you decide how much of the work it hands you. As siliXon puts it, if you can describe what you want, you can build it. Electronics design is locked behind years of studying and experience. According to siliXon, less than 0.01% of the world can design a circuit board, which means the vast majority of people with an idea for a device have no practical way to turn it into working hardware. Hardware is hard, and electronics is harder. The company also reports that after talking to over 100 engineers, it found engineering teams can take, on average, 2–3 months to get a PCB from concept to market-ready. siliXon's stated goal is to make electronics accessible to anyone: describing what you want should be enough to start building it, rather than requiring knowledge of components, schematics and copper routing. The workflow siliXon presents has four steps: Describe, Clarify, Generate and Manufacture. In the first step you provide just your ideas in simple English, with no constraints required. You can type a prompt such as "I want a garden sensor that texts me when the soil is dry." In the second step, siliXon's assistant Rocky chats with you along the way to help you figure out what you want your electronics to look like. Rocky asks you questions to verify your intent and gives you options to choose from, with the option it thinks is better recommended. Beta users have highlighted this behaviour: a student and hobbyist said they liked the self-validation and how it explains everything too, which makes it easier to cross-reference and learn on the go, while a founder appreciated that the response asks questions to verify and offers answers to choose from with the recommended option marked. In the third step, siliXon's system determines the best components and their placement, draws the schematic and routes the board. Rocky picks real, purchasable parts, and every component on the board exists on the market. The generated board remains visible and editable in both 2D and 3D. You can move parts, draw or remove traces, add vias, and revise the design through conversation — editing directly or simply asking Rocky. Rocky v1.5 is described as more accurate, far more precise in how it changes your board, and able to actually see the layout it is working on. The platform's placement and routing engine is highlighted as a world-class autorouter and autoplacer: siliXon placed third on comma.ai's PCB Golf contest, whose purpose was to get the smallest board possible given a set of components and a scoring algorithm, and it was the only autonomous entry on the podium. The fourth step is manufacture. Your board must pass all of siliXon's engineering checks before it goes to manufacture; the company runs physics and manufacturing tests and states that every board must pass them before being sent off. Once that is done, you send it off to manufacture and, as the site puts it, it is in your hands in a few days ready to use. You own the design, and siliXon provides all the necessary files to manufacture your board, including downloadable files. The Pro plan adds KiCad import and export as well as private PCBs, and the ability to upload silkscreen images is listed as coming soon. siliXon positions itself as more than an AI wrapper. Asked whether it is just another piece of "AI slop" software, the company answers that the product is built on sound electronic engineering principles, that AI helps speed up parts of the design process, that every component on the board exists on the market, and that each board is checked against physics and manufacturing tests before being sent off to the manufacturer. It states that the product is built to allow anyone to create their own electronics without needing to understand the intricacies of electronics design, and that the system checks to make sure your board can work and be manufactured. Because it is in early beta, siliXon currently allows users to create 2- and 4-layer boards and encourages users to play with mostly digital designs, as analogue circuit capabilities are limited. The headline benefits are speed and accessibility. siliXon promises end-to-end design in minutes: describe a board and get real parts, placement, routing, checks and files to manufacture. It also claims the fastest board generation in the world — going from a real prompt to a manufacturable circuit board in minutes, then sending it off to the manufacturer. For engineers, the benefit is iteration: iterate on your board quickly and make sure you only need to manufacture once, with the guidance that the more specific your prompt is, the better the final result. For beginners and hobbyists, the benefit is confidence and learning: the system teaches you along the way, and you can start with an idea and make changes as you see fit. siliXon addresses three broad user situations. A beginner who has an idea but no idea what a resistor is can use the platform to create their own electronics without understanding the intricacies of electronics design, with the system checking that the board can work and be manufactured. A hobbyist who designs and builds for fun but finds electronics tricky to grasp can start with an idea and make changes as they see fit, being taught along the way. An electronics expert can prompt the system with exact components, communication protocols and size to get a more precise result and iterate quickly. The site also offers real example boards to explore rather than only starting from a sentence: a Temperature indicator (27.5 × 32 mm, 2 layers, 18 parts, 14 vias), a Bluetooth remote (69.5 × 65.5 mm, 2 layers, 20 parts, 18 vias) and a USB Macro Pad (57 × 42.5 mm, 2 layers, 29 parts, 57 vias), each with a public preview link. siliXon is explicitly built for anyone with an idea, not only engineers — beginners, hobbyists and experienced electronics engineers alike. It is a web product: users sign up through app.silixon.io, and the platform is described as being in early beta. Pricing has two tiers. Free is £0 per month and lets you try siliXon on a real board with one end-to-end project every two weeks, limited Community access with two remixes, and public projects only; no card is needed. Pro, marked as most popular, is £14.99 per month with unlimited projects, Community access, unlimited remixes, private PCBs and KiCad import and export, while uploading silkscreen images is listed as coming soon; Pro is billed monthly and can be cancelled anytime. siliXon also notes a Product Hunt launch promotion of 48 hours of Pro, free, with no card needed. Overall, siliXon's core promise is that a sentence is enough to get started with electronics. By combining a conversational design agent, real purchasable components, an autorouter and autoplacer, physics and manufacturing checks, and fully owned design files you can send to manufacture, it lowers the barrier that has kept circuit board design in the hands of a tiny fraction of the world — natural language in, electronics out.

SuperLocalMemory is a governed memory brain for AI agents, positioned as one local-first memory shared by every agent a person or team runs. According to its listing, Claude Code, Codex, Cursor, ChatGPT and any MCP or CLI agent share the same governed memory, so context learned in one tool is available to the others. The product's stated purpose is to give agents durable memory that is trustworthy rather than approximate. Instead of guessing, recall tells you whether a memory actually answers the question, is only related to it, or is missing entirely. It is aimed at people running one or more coding agents on the same project, and at builders who need memory to be governed, with profiles, roles, an audit trail and verified erasure wrapped around a core that runs on the user's own machine. The project is open source under AGPL-3.0 and built by Qualixar, described as an AI Reliability Engineering lab behind 10 open-source products. The maker's relaunch post describes a rebuild around failures that showed up only after the first versions could already save and retrieve context. The harder failure was that a retrieval layer usually returns the closest thing it has, even when that thing does not actually answer the agent's question. In a chain of agents this compounds: one agent saves a half-related note, the next one treats it as fact, and the error travels onward. A second class of problem appeared once several agents shared durable state. The team needed to know when a fact was true, which agent wrote it, and who was allowed to read it, and whether deleting it really removed the derived copies. Those questions, accuracy of recall and governance of shared memory, are the two axes the relaunch was organised around. The headline capability is governed recall that refuses to bluff. Queries return one of three labels: supported, related, or no answer. Memories that are merely similar are handed back separately and labelled as related, while something is only marked as an answer when a stored memory actually supports it. When several agents share one memory, that label travels with the handoff, so a downstream agent inherits the uncertainty instead of a false certainty. An optional Answer Check verifies recall, checked by Laya on the user's Mac or by Jev online. One commenter singled out this distinction as the most valuable part of the design, noting that returning no answer is more useful than passing a vaguely relevant fact to the next agent. SuperLocalMemory is presented as one memory brain for every agent rather than a separate store per tool. Claude Code, Codex, Cursor, ChatGPT and any MCP or CLI agent connect to the same governed memory, whether through the Model Context Protocol or the command line. The system is described as mesh-connected, and the relaunch is accompanied by a separate mesh product listing from the same maker. The stated benefit is continuity across tools: what one agent learns does not have to be re-explained to the next one. The stated risk, which the makers explicitly invite feedback on, is where shared memory helps and where it becomes dangerous when more than one coding agent works on the same project. That open question is the reason access control and labelling sit in the shared layer instead of being left to individual agents to handle. Beyond text, the product includes optional local picture and PDF memory. Users can find pictures and PDFs by describing them, using Google's EmbeddingGemma 2. Memory is also time-aware: recall takes time into account, and every fact keeps a record of who wrote it and when, and the system tracks when each fact was true. The research behind the product describes related machinery, including a full cognitive memory taxonomy, adaptive forgetting, a memory lifecycle modelled as Langevin dynamics rather than hand-tuned decay, a retrieval metric drawn from information geometry, a distance metric that prefers full-fidelity embeddings over quantized ones, and a formal way to detect contradictions between memories. Governance is the V4 layer of the system. It enforces profiles, scopes, roles and role-based access, maintains a hash-chained audit trail, and offers verified erasure so that deleting a memory can be shown to have removed it. The first preprints describe local-first memory for multi-agent AI with a defence against memory poisoning through isolation and Bayesian trust scoring, and no cloud or LLM calls. Reliability claims are presented with unusual candour: the V4 paper opens with a negative result, reporting ten mechanisms that were implemented and reachable on a live call path yet ineffective at their final connection, plus two mechanical checks added to tell implemented, reachable and effective apart. Fault-injection testing of the write path covered eleven scenarios each run 200 times, holding 2,199 of 2,200 scoped properties. Measured properly, a governed write takes 11.0 ms, and most of that is described as the cost of durability rather than governance. The overall approach is local-first and evidence-led. The core memory path runs on the user's machine, so memory does not have to leave it. Memory is stored as facts rather than loose text; facts are ranked so that recency and relevance are respected, and old memories are forgotten. Everything is open source under AGPL-3.0, and the design is backed by four arXiv preprints published between February and August, covering local-first memory for multi-agent AI, the mathematical foundations of retrieval and forgetting, the cognitive taxonomy of the living brain, and the V4 governed memory system. According to the co-authors, each paper added something the product actually ships. Benefits stated or directly implied include a reduction in compounding errors across agent chains, because related-but-wrong context is labelled instead of silently promoted to fact. Users get continuity: one memory shared across Claude Code, Codex, Cursor, ChatGPT and other MCP or CLI agents, so context carries over between tools and sessions. They get provenance for every fact, the ability to know when a fact was true, and an audit trail of what happened. They get to enforce who can read what, and to demonstrate that data was erased. They also retain control over where the core runs, since it sits on their own machine, and they can inspect the implementation because it is open source. Concrete scenarios described in the content include running more than one coding agent on the same project and sharing memory between them; finding pictures and PDFs by describing them rather than by filename or folder; recalling a fact while knowing whether it is still true and which agent wrote it; and applying roles and scopes so that different people or agents see different parts of a shared memory. Another scenario is the handoff between agents, where the supported, related or no-answer label is passed along so the receiving agent does not treat an approximate memory as an answer. The makers also invite technical readers to examine the evaluation of the write path under fault injection, which speaks to teams that need reliability evidence before trusting governed memory in production workflows. The audience is developers and teams working with AI coding agents, particularly people running more than one agent on the same project, along with anyone building on MCP or CLI agents who needs shared, governed memory. Integrations named in the content are Claude Code, Codex, Cursor, ChatGPT, and any MCP or CLI agent. Google's EmbeddingGemma 2 powers picture and PDF retrieval, while Laya and Jev perform Answer Checks locally and online respectively. The launch is listed as Free and categorised under AI Coding Agents and LLM Memory, with topics including Open Source, Developer Tools, Artificial Intelligence and GitHub. The project is open source under AGPL-3.0, with code hosted on GitHub under Qualixar, and makers Arun Pratap Bhardwaj, Varun Pratap Bhardwaj and Garima Singh are listed on the launch. SuperLocalMemory's primary value proposition is memory an agent can be trusted with. By refusing to answer when the stored evidence does not support a claim, by keeping provenance and time attached to every fact, and by wrapping a locally running core in roles, an audit trail and verified erasure, it aims to make shared agent memory both useful and safe across the tools a team already uses.

Actually Asked is an AI FAQ app for websites and Shopify stores. Rather than adding a floating chat bubble, it places a FAQ section directly inside your page, styled in your own fonts and colours, so it looks like part of the site. Visitors ask their question in their own words and receive answers taken only from your own pages, your help center and files you upload. The questions visitors ask most then join your FAQ on their own. The problem it addresses is a familiar one: businesses answer the same questions again and again by email. Sizing, shipping times, caffeine content, whether a jacket is truly waterproof, what a cancellation policy covers, what to bring to a first appointment. The answers usually already exist somewhere on a product page, in a help centre article or inside a PDF, but visitors either cannot find them or do not want to dig. Actually Asked is aimed at any site where people ask before they buy, book or sign up. The company puts the filter simply: if people email you the same questions again and again, it is for you; if nobody asks you anything yet, it is not. Three features carry the core experience. The first is an inline FAQ: a FAQ section that lives in your page rather than a chat bubble, and that is styled like your site. The marketing content shows the same widget rendered in the look of three different shops, a linen shirt store, a coffee roaster and an outdoor gear shop, each with its own typography and colours. The second is highlight to ask. Visitors select any sentence on the page and ask a question about it, which turns a specific line of copy, such as a sizing note or a care instruction, into an entry point for an answer. The third is the self-improving FAQ. The questions visitors ask most join your FAQ on their own, with nothing for you to approve. You can still edit, pin or remove any entry, and the product states that your edits are never overwritten. Answers are restricted to your own sources and nothing else. On the Sources page you connect your site, its pages, and on Shopify your products and store policies, which are read again every 7 days. You can add the address of a help center or knowledge base, and you can upload PDF or Word files such as size charts, care guides, manuals and price lists, including files that are not public. Answers can quote a file but never link to it. The onboarding view shows a site of 38 pages, a Shopify catalogue of 212 pages and four policy pages, all read at 04:00 with the next read scheduled for 04:00 the following day. The weekly gap report arrives as one email every Monday. It lists the questions asked most, the ones your pages do not answer, and what was added to your FAQ. A sample report shows 214 questions asked in the week, 87% of them answered and nine with no answer to write about. A separate leads feature captures visitors who leave an email address, so you have someone ready to reply to. Finally, an MCP integration lets you manage your FAQ by asking Claude, ChatGPT or another assistant, for example publishing FAQ entries through a tool call. Setup is presented as three steps. First, you add your domain, plus your help center or PDF and Word files if you have them; those sources are read, private files included, and your first FAQ entries are written from them. Second, you paste two lines, a div carrying your site key and an async script tag from the Actually Asked CDN, and the FAQ appears where you put it; on Shopify you add it as a block in the theme editor instead. Third, visitors ask, and your FAQ grows. The distinguishing choices are the absence of a chat bubble, the tight sourcing rule that answers come only from your pages, and the fact that popular questions are promoted into the FAQ automatically rather than waiting on a human editor. The outcome the product describes is a FAQ that looks like yours and learns from visitors. Popular questions are answered once and added permanently, so the same email does not need to be written twice. Because answers are grounded only in your own material, the reply a visitor sees reflects what you already publish. The Monday report gives a regular view of the questions your pages fail to answer, which points at content worth writing. Leads are collected for follow-up, and the whole FAQ can be maintained from a dashboard, the Shopify admin or an AI assistant. Four use cases are documented. Online stores, including Shopify, WooCommerce, Squarespace and custom shops, use it for product and policy questions such as whether a size runs small. SaaS and apps use it on pricing pages and documentation for plan and "does it do X" questions, illustrated by a per-seat pricing example where an annual discount question is answered from the /pricing page. Travel and hospitality businesses such as hotels, rentals and tours handle check-in, cancellations and what is included, illustrated by a room booking page answering whether breakfast is included from the /rooms page. Services and clinics, including agencies, clinics, studios and coaches, cover booking, pricing and what to expect, illustrated by a first-visit page that answers what to bring. Actually Asked was built first for Shopify stores but works on any website whose visitors have questions before they buy, book or sign up. Integration paths include a Shopify theme block, a WordPress Custom HTML block or footer script, a Webflow Code Embed, a Framer Marketplace plugin or pasted component, a Squarespace Code block, and static HTML, React, Next.js or custom templates. There is also a free gap-finder tool to find FAQ gaps and an MCP route for managing the FAQ through Claude or ChatGPT. Pricing starts free with 50 AI answers a month on one site, then $12 a month; every plan includes unlimited team members, and you pay for the answers you use and the sites you run. Actually Asked replaces the chat bubble with something more durable: a branded, inline FAQ that answers from your own sources and quietly grows with the questions visitors actually ask. It is free to start, live on your site in about ten minutes, and aimed at any business that keeps answering the same question by email.

withHuman is the approval layer for AI agents. It lets you put your most consequential agent actions behind human approvals so that your agents can do real work. Its core behaviour is interception: every tool call an agent makes is held before it runs, checked against your rules, and either approved or denied automatically or routed to the right person on your team. withHuman is framework-agnostic, open source and built on open standards, and it works across Claude Code, Codex and agents built with LangGraph, Pydantic AI and more through an open protocol called AAP. Crucially, it gives you one set of rules for every agent your company runs. Agents are increasingly trusted with actions that carry real consequences: refunding a customer, paying an invoice, merging a pull request, rolling back a production deployment or sending a marketing campaign. Once such a call is made, it is often impossible to take back. The site describes the purpose of withHuman as covering "the actions you can't take back" by letting agents draft everything while shipping nothing until someone accountable has looked. Without a control layer, teams must choose between blocking agents from useful work entirely or letting them act unsupervised. withHuman exists to remove that trade-off, so agents can be put to work accountably rather than being held back. Every tool call, whatever the agent. Each tool call is intercepted by AAP, the Agent Approval Protocol, and sent to withHuman for approval. If the agent supports AAP, withHuman just works. Concretely, the product ships SDKs for LangGraph, CrewAI, Mastra and other agent frameworks, hooks for Claude Code, Codex and other coding agents, and the open Agent Approval Protocol for your own agents. In the worked example, a support agent's stripe.refund call is held by withHuman before it reaches Stripe, so nothing leaves the agent runtime until a decision has been made. This interception layer is what makes everything else in the product possible: because the call is paused rather than executed, withHuman can inspect it, apply policy, notify a person and only then let it run. No approval fatigue. The site is explicit that approval fatigue is the enemy, and the way to avoid it is to write policy rules that approve or deny routine requests automatically, sending only the tough stuff to humans. An approval pipeline can express conditions on the agent, the tool, the arguments and the agent context; an AI judge handles calls that rules can't describe; and webhooks hand decisions to your own policy service. In a live example pipeline, kubernetes.pods_list is approved by a rule for read-only tools, an email reply is approved by an AI judge as a routine support reply, db.drop_table is denied by a rule against schema changes, and a vendor.pay call over the team budget is sent to a person through a webhook, meaning three of four calls were decided without a person. The refund example shows the same idea in miniature: refunds under $20 are automatically approved, and anything more asks a human. Escalation paths decide which requests go to which team or person. You write rules that route by agent, tool, arguments or team; teams set their own escalation order and on-call flows; and requests escalate when nobody answers in time. In the illustrated example an under-$1,000 refund goes to the support team, a $2,400 refund goes to finance lead Sam Alvarez, and an out-of-hours request goes to Priya Nair, who is on call. Reviewers are then met where they already work: requests arrive in Slack and Discord, in iPhone and Android apps with push, and by email, text message and WhatsApp, so each reviewer's notification rules pick the channels. Every decision is kept on the record. withHuman maintains an append-only audit log run history: each entry records the request, the decision and the reviewer, such as db.delete_rows approved by d.chen, stripe.refund approved by s.alvarez with a note verifying with finance, k8s.apply denied by m.okafor, and crm.update_contact approved by d.chen. The log keeps reviewer identity on every row and the full request with each decision, so when someone asks who approved a given action, the answer is available. Overall, withHuman acts as a pipeline between the agent and the tool it wants to call. In the site's step-by-step example, a customer asks for a refund and the support agent decides to issue it; the agent's tool call is intercepted and held by withHuman before it reaches Stripe; the rules check the call and either approve or deny it automatically or ask a human to look; the request is sent to the Payments team, where Chris is on call; the human is notified on their preferred platform; the human sees the tool call and request context and decides; and the approved refund finally reaches Stripe while the agent carries on. The distinctive architectural choice is that integrations are made with AAP rather than with withHuman, so agents and frameworks wait on human decisions through an open spec, and you can swap providers without rewriting a single agent. The benefit is straightforward: withHuman lets teams put agents to work without giving up accountability. Because routine calls are decided automatically by rules or an AI judge, reviewers are not buried in approvals. Because risky calls are routed to the right person together with the full request context, decision-makers see exactly what is being proposed before it happens. Because every request, decision and note is kept in an append-only history with reviewer identity, the question of who approved something always has an answer. And because the protocol and the core are open source, teams avoid lock-in and can leave whenever they choose, running the queue, audit trail and web UI on their own infrastructure or letting withHuman host it. Use cases centre on consequential actions: an AI SRE agent requesting a kubernetes.rollout_undo to roll payments-api back to v2.41.0 after the 5xx rate crossed 2%; coding agents running Bash commands; customer support agents issuing stripe.refund calls; finance and accounting agents running bill.pay; sales agents using docusign.send; marketing agents sending campaign.send; and personal assistants creating calendar events with calendar.create_event. In each case the pattern holds: the agent prepares the action, withHuman pauses it, a rule or a person decides, and only then does the action execute. withHuman is aimed at teams running agents in production across engineering, support, finance, sales, marketing and SRE functions; the example reviewers include a Payments team member who is on call, a finance lead and an SRE. Integration is broad. Installing the CLI connects every supported agent on your machine, covering Claude Code, Codex, OpenClaw, Pi, Hermes and DeepSeek, and the supported list also includes the MCP gateway, LangGraph, Deep Agents, Pydantic AI, OpenAI Agents, Claude Agent SDK, Google ADK, CrewAI, AI SDK, Mastra and an HTTP API with POST /api/aap/v1/requests. withHuman is open source with a hosted version, and the Product Hunt listing notes a promotion giving three months of the Scale plan free with the code PRODUCTHUNT. In summary, withHuman is the approval layer for AI agents: it intercepts tool calls, applies automatic policy for routine actions, escalates the consequential ones to the right accountable human, records every decision, and does so through an open, self-hostable protocol that avoids lock-in. Its value proposition is simple and stated plainly on the site: put your agents to work, accountably.

Nostaliga is an iPhone and iPad app that turns your listening history into a personal timeline. Pick any song and see the day around it: your photos, places, runs, check-ins, shows and posts. The app brings your music, photos, workouts, walks, the places you have been, and the movies and shows you have watched into one timeline, so that a single track becomes an entry point into a period of your life rather than just a piece of audio. Nostaliga is made for anyone who has ever heard a song and been pulled straight back to a specific time and wanted to see that moment again, the photos that were taken, the route that was ridden, the place that was checked in to, and not only feel a vague sense of nostalgia. The app grew out of a very specific memory. The maker describes hearing "1-800-273-8255" by Logic and being instantly back in 2017, the first year he was cycling, doing long rides with that song on repeat. The song remembered those rides better than he did, but the routes and the photos from them were scattered across Strava, his camera roll and Last.fm. That fragmentation is exactly the problem Nostaliga sets out to solve. The music that triggers a memory lives in one service, the photographic evidence lives in another, the workout data in a third, and the check-ins, shows and social posts somewhere else again. Nostaliga anchors all of those threads to listening history, so instead of hunting through several apps for the same day, you can start from the song and let the app reassemble the context around it. The central interaction is deliberately simple: pick any song and Nostaliga shows you the day around it. The app looks through your listening history and brings back the photos, places, rides, walks and moments from the times you played that track before. Photos, places, runs, check-ins, shows and posts all sit in the same view, arranged around the music rather than around a date. This matters because most memory tools ask you to remember a date or scroll a photo library; Nostaliga starts from the thing that actually triggered the memory. The song is the index, and everything else, where you were, what you photographed, what you were doing, hangs off it. Nostaliga also includes an On This Day experience that works across the years. Instead of looking only at a single song, you can go back to the same date in earlier years and see what belongs to it. That gives the timeline a second axis: one direction from a song into a day, and another from a calendar date back through the years. Together they turn the app into something closer to a personal archive with two ways in, so the memory can be found whether you remember the music or the date. The app draws on a broad set of sources so the timeline is not limited to music. It works with Apple Music and Last.fm for listening history, Photos for imagery, Apple Health and Strava for runs, rides, workouts and walks, Swarm for check-ins, Trakt for the movies and shows you have watched, and Calendar for events. If your listening history does not live in those services, you can import your Spotify listening history, and you can also import your Twitter, Instagram or Facebook archives. Each of these sources contributes a different kind of evidence about a day, where you went, what you did, what you watched, what you shared, and Nostaliga's job is to line them up against the music. Underneath, Nostaliga's approach is defined as much by what it does not do as by what it does. There is no account, and there is no analytics or tracking. Your timeline is built and stored on your device, so the assembled record of your listening, photos, places and activity stays with you instead of being uploaded and held by a service. That makes the app a local, personal archive rather than a social or cloud product. The result is a timeline you can explore without signing up, without a profile, and without the sense that your memories are being collected by someone else. The benefits follow from that design. A song stops being only a song and becomes a way back into a particular stretch of life, the year you were cycling, the city you were in, the film you were watching that week. Because the sources are gathered rather than scattered, you do not have to remember which app holds which fragment; the timeline does it for you. And because the listening history is the organizing principle, the memory is triggered by the same thing that would have triggered it in real life: hearing the track. Finally, the on-device, no-account model means the value of the archive is not tied to a subscription or a login. Concretely, Nostaliga is used when a song brings back a period and you want to see it rather than only recall it. You might pick a track that was on repeat during a first year of cycling and use it to pull up the routes and photos from those long rides. You might use On This Day to revisit the same date in earlier years and see what you were listening to, where you checked in and what you were watching. You might start from a film or show in Trakt and work back to the music of that month, or start from a check-in in Swarm and see what was playing. If your history is spread across Apple Music, Last.fm, Strava, Photos and social archives, importing those sources is the first step in building the timeline you then browse by song or by date. Nostaliga is an iOS app for iPhone and iPad, listed under iOS, Music and Photography. Its connected sources include Apple Music, Last.fm, Photos, Apple Health, Strava, Swarm, Trakt and Calendar, along with imports for Spotify listening history and Twitter, Instagram or Facebook archives. It is free to use. Because it requires no account and performs no analytics or tracking, there is nothing to sign up for before you begin: the timeline is assembled and kept on your own device. The takeaway is that Nostaliga treats your listening history as the thread that ties your memories together. By combining music with photos, places, runs, check-ins, shows, posts and calendar events in one on-device timeline, and by letting you enter it either from a song or from a date, it makes rediscovering the days behind your favorite songs a matter of picking a track and seeing what comes back.

Afterparty is an AI agent product that does the work that comes after a meeting. Your note taker records the call and hands over the transcript, and Afterparty finds every action in it and writes the draft that action calls for: the recap email, the Slack message, the calendar invite, the CRM update. It works on top of the note taker you already use rather than replacing it, and it draws context from your email, calendar and Slack so each draft goes to the right person in the right thread. It is built for anyone who leaves a meeting holding a list of follow-ups, and its purpose is to turn that list into drafts you simply approve. The product is framed around an everyday problem: note takers give you a list, and Afterparty does the list. Recording a call and producing a tidy set of action items is the first half of the work. The second half — writing the recap to the client, posting the update in Slack, sending the invite for the next session, moving the deal in the CRM — is still manual, and it is where meeting momentum is usually lost. Afterparty is aimed squarely at that second half, so the follow-through happens right when the meeting ends instead of waiting for someone to find the time. The first thing Afterparty does is catch every action, even the ones said in passing. From a sample kickoff transcript it pulls out commitments such as sending a list of store managers, sending a recap and pilot proposal with pricing by Friday, writing up a one page plan for leadership, setting up a sandbox account, checking with Dana about onboarding capacity, and booking a room for the prep session. If you do not have a note taker, you can paste a transcript or upload a .txt, .vtt or .srt file instead, so the same workflow applies to any meeting you can capture. Context is what makes the drafts usable. Before writing, Afterparty reads your email and Slack so each draft goes to the right person in the right thread. The example on the site shows an action to "Send Priya the recap and the pilot proposal" turning into a draft addressed to Priya Shah with the subject line "Pilot recap and proposal," written in your voice and referencing the twelve-week pilot across Harbor Street, Eastgate and Millbrook, the priced proposal coming by Friday, the one page plan, and the kickoff workshop on October 14. Afterparty also skips what is already done and asks when it needs something. For an action to send an NDA, it searches Gmail for "in:sent NDA Kestrel" to see whether the document has already gone out. For a one page pilot plan in Notion it raises a question — "What budget should the plan assume?" — with an answer field, plus "Answer and draft" and "Skip" options, so you can supply the missing detail or drop the action entirely. Nothing happens without you. Every action waits in Afterparty, and approving an email simply places a draft in Gmail or Outlook — you still press send. One click moves each approved item into the right app, and the agents move on to the next one. The product also learns from your edits. When you approve, edit or dismiss a draft, Afterparty notes who people are and how you like things written, and the next meeting's drafts start from that. A visible memory panel in the example captures details such as "Tom Becker: handles the sandbox and anything technical" and "You sign off with 'Talk soon, Alex'," and the rewrite example shows a bloated opening being trimmed down. You can read, change or clear this memory in Settings. Once your recorder is connected, the whole flow runs after every call. You connect the recorder a single time, every transcript arrives on its own, and the agents get to work the moment you hang up. Supported meeting tools include Granola, Fireflies, Fathom, Zoom, tl;dv, Google Meet and Gong. Under the hood, Afterparty's agents are strictly read-only. They look at your email, calendar and CRM for context, but they cannot send, post or change anything. Reading is also targeted: for each action the agents look up only what that draft needs, such as recent threads with the people from the meeting, your calendar around the dates that came up, or the CRM contact — and they only read while a meeting is being drafted. Drafts are written in the language the meeting was held in. Privacy is treated as a core promise. Your meetings, drafts and app data are never used to train AI models, and no other user can see them. Deleting your account in Settings removes everything with it: meetings, drafts, memory and app connections. The full details live in the privacy policy. The benefit is speed and completeness rather than another tool to manage. Recap emails, Slack posts, invites and CRM notes are ready before you are back at your desk, written with context pulled from your own tools, and every one of them waits for your approval. You keep the final say on every send, post and update. Use cases described on the site include sales and client meetings, where a kickoff call generates a recap email, a priced proposal follow-up, a one page plan for leadership and a CRM stage update in HubSpot; internal coordination, where an action becomes a Slack message asking a colleague about onboarding capacity; scheduling, where a follow-up session turns into a calendar invite; and document work, where an action to produce a one page plan is drafted in Notion. Each of these is drafted from the transcript plus the context already sitting in your inbox, calendar and chat. Afterparty connects to meeting recorders, email, calendar, chat, CRM and project tools, listing Gmail, Google Calendar, Outlook, Slack, Microsoft Teams, HubSpot, Salesforce, Attio, Notion, Linear, Jira, Asana, ClickUp, monday.com, Google Docs, Google Drive, Confluence, OneDrive, Granola, Zoom, Google Meet, Gong, Fireflies, tl;dv, Fathom, Zendesk, Intercom and GitHub, plus "many more." Pricing starts free at $0 a month for email and calendar drafts and five meetings a month with no credit card. Pro is $29 a month (or $290 a year, saving $58) for sends to every app, auto import, learning how you write, and 20 meetings a month. Max is $79 a month (or $790 a year, saving $158) for frontier models, your own instructions, six months of context and 60 meetings a month. Prices are in USD, and every plan includes the approval inbox and memory you control. Afterparty is, in short, the second half of the meeting. Your note taker delivers the list; Afterparty turns each item into a finished draft in the right app, with the context to send it to the right person, and leaves the final click to you — so the actions from a call get done while the call is still fresh.

Daddy Issues is a companion app for dads, described on its own site as a way to "think ahead" and "fight less." At its core it delivers a weekly family briefing, built by AI around a dad's actual week. The product is aimed squarely at fathers who are involved, who care, and who already do a lot, but who still find themselves reacting instead of anticipating. Its stated purpose is to help dads stay one step ahead each week, before anyone has to remind them. To do that, it connects the elements that make up family life — kids, calendar, responsibilities and tasks — and surfaces what is coming up, what might be missing, and what is worth handling now. The problem Daddy Issues addresses is not a lack of effort or intention — the site says so directly. The failure point it names is timing. A dad may be present and helpful, yet still hear "Did you think about this?", still find out about things too late, and still end up reacting rather than anticipating. The app draws a sharp line between helping and owning: "If someone has to remind you, you're not ahead. You're catching up." It lists the roles a dad does not want to play — the one who gets reminded, the one who reacts late, the one who "helps" but doesn't lead — and sets the opposite as the goal: being the one who already handled it. Because that gap between effort and timing shows up as last-minute stress, repeated reminders and small disagreements, the product treats planning ahead as the thing worth fixing. The central feature is the weekly briefing. It is presented not merely as a summary but as an AI that "connects the dots you don't see." Each week it lays out what's coming up, what actually matters, what the AI spots early, and what to handle now. The briefing is built around the user's real week rather than a generic checklist, and it is designed to be acted on immediately: the user can turn briefing items into actions and add them to their calendar, then be done. The interface example on the site shows a week for Monday, April 14 containing a kindergarten trip on Thursday with a snack needed, a doctor follow-up that is overdue, and a birthday on Sunday with a gift still missing, summarised as "3 items need attention this week." A second core capability is that the app learns how a family works. As the user creates tasks, the AI understands patterns and begins anticipating what matters earlier. The stated result is that things stop slipping — including things the user had not been tracking before. This is a meaningful distinction from a static reminder list or a shared calendar: rather than depending on the dad to enter every future obligation, the system uses the tasks and routines he does create to anticipate what is likely to matter next. Over time that shifts the dynamic from catching up on what was already missed toward surfacing it while there is still time to act. The site describes a deliberately short path to value in three steps. First, connect your basics: calendar, kids and routines. Second, get your weekly briefing, which the AI builds around your actual week. Third, act early, before things pile up. The onboarding is positioned as lightweight — "Takes 1 minute · No setup stress" — with calls to action to "Start being ahead this week" or to "Show me my first week." The emphasis throughout is that the product should fit into an already busy life rather than adding another system to maintain; the dad supplies the basics, and the briefing carries the ongoing thinking work. Daddy Issues frames its approach as owning rather than helping. Its origin story reinforces that: it didn't start as an app but as a personal fix, built by a dad who was tired of being behind — trying to keep track of everything, still missing things, still being reminded. He built something that helps you stay ahead, described honestly as "not perfectly. But consistently." That combination — AI that connects the dots across kids, calendar, responsibilities and tasks, plus a weekly briefing rhythm tied to a real calendar week — is the product's distinctive method. Rather than issuing a stream of constant notifications, it consolidates anticipation into one recurring habit. The promised outcomes are relational as much as logistical: less stress, fewer reminders, more trust. The site sums this up by saying the user doesn't just help more — they take ownership. Read out, those benefits follow from the mechanics: because the briefing surfaces what is coming before it becomes urgent, there is less last-minute scrambling; because the dad is no longer the person who needs reminding, the recurring friction of who remembered what is reduced; and because he acts early, he is seen to be ahead rather than catching up. The tagline "Think ahead. Fight less." captures the same two-sided promise — better preparation and calmer family life. The example week on the site doubles as a set of concrete use cases. A kindergarten trip on Thursday surfaces early in the week, with the detail that a snack is needed — exactly the kind of small item that is easy to discover the night before. A doctor follow-up is flagged as overdue, turning a vague worry into a specific action. A birthday on Sunday is flagged because the gift is still missing, giving the dad lead time to sort it out. Elsewhere the site asks the question "backup clothes for daycare?" — another everyday logistical detail that matters only if it is handled before the morning it is needed. In each case the pattern is the same: the briefing names the item, then the dad turns it into an action and adds it to his calendar. The target user is explicit: dads. The site describes them as involved, caring and already doing a lot, and notes the product is "built for dads - by dads" and "built from real life. Made for yours." The expected starting task is to connect basics such as calendar, kids and routines. On privacy, the site states that it uses anonymous statistics to see how the site is used, with no ad tracking and no profiles, and that essential login storage always stays on, with a linked privacy policy at daddyissues.app/privacy. The product is accessible on the web at daddyissues.app, with "Log in" for existing users and a "Start being ahead this week" path for new ones. In short, Daddy Issues is not trying to make dads try harder; it is trying to change their timing. By connecting kids, calendars, responsibilities and tasks, learning from the tasks they create, and returning a single weekly briefing covering what's coming up, what matters and what to handle now, it aims to replace reacting with anticipating. The result it promises is less stress, fewer reminders and more trust — and the shift from helping out to taking ownership. For a dad who is already doing a lot and still hearing "Did you think about this?", that is the whole proposition.

Navige is a governed layer that sits between your AI agents and everything those agents pay for — the language models, tools and metered APIs each run depends on. The stated promise is straightforward: every run gets cheaper and faster, with the same result. Navige intercepts the calls your agents make, decides whether the step really needs the model that is about to be used, reuses answers that are still valid, trims oversized context and tool payloads, and blocks repeated failures before they add to the bill. It is built for teams running programmatic AI workloads in production — agent developers working with frameworks such as LangChain, LangGraph, CrewAI, OpenAI Agents, MCP and Agentforce, and the organisations paying for the models behind them from Anthropic, OpenAI, Google, AWS Bedrock, Mistral and Meta Llama. The problem Navige addresses is that AI agents spend money continuously while they work, and most cost tooling only reports on that spending after the fact. The site frames the failure modes plainly: runs that never finish, where agents keep running without producing an output; big model, small task, where a step is answered by far more intelligence than the outcome required; chasing, not remembering, where the same work is redone instead of reused; one budget, many agents, where multiple agents touch the same work; and misread, then paid for, where an incorrect result still costs money. As the site puts it, everyone else shows you what the run cost after it has run, while Navige changes the step before it runs. That distinction matters because the bill is effectively decided before the dashboard ever sees it — by the routing, context and tool-call decisions taken while the agent is still working. The first two cost-control mechanisms are Cache & Reuse and Context trimming with tool payload capping. Cache & Reuse is described as “Never Pay Twice”: instead of paying for another model call or tool call, Navige reuses a verified answer when it is still valid. This targets the repetitive portion of agent work — the same conclusion reached the same way many times — which the site says carries the most opportunity for savings. Context trimming and tool payload capping is summarised as “Only Pay For What’s Used”. Before sending the next request to the model, Navige limits oversized tool results and trims accumulated history, so the request contains what the result is actually built from rather than everything that has piled up. Together these two mechanisms attack the two most common sources of wasted tokens: paying twice for information you already hold, and paying to transmit context that no longer contributes to the answer. The second pair of mechanisms is Loop & failure guard and Smart routing. The loop and failure guard is designed to “Stop Runaway Jobs” — it blocks repeated failures before another call adds to the bill, which directly addresses agents that keep retrying indefinitely and never produce an output. Smart routing is described as “The Right Model, once it’s earned”, choosing a model based on the complexity of the task. The underlying idea is labelled Sufficiency Intelligence: send suitable requests to smaller models, and if the system is unsure, keep your originally chosen model. Crucially, the site is explicit that this is not discounting — where a step needs a frontier model, sufficiency keeps it there. Nothing about the approach proposes a cheaper answer to the same question; it proposes the same answer, reached at the level of intelligence it actually needed. Beyond per-call optimisation, Navige adds operating controls. According to the product description, teams can set budgets, catch runaway loops and approve sensitive actions before they execute. Cost and savings are reported by job, with estimates clearly labelled so that measured figures are never confused with projections. Changes can be previewed before the corresponding controls are switched on, which lets teams see the effect of a routing, caching or trimming policy before it governs live traffic. One stat on the site highlights that tool calls are checked against your rules before they run, not flagged afterwards — governance is positioned as preventative rather than retrospective. Architecturally, Navige is a single governed layer. Your agents sit on one side and the models they pay for sit on the other; you plug both in once and every call runs through that layer. For platform connectivity the site says you change one line, and your frameworks, keys and providers stay exactly as they are. Navige also includes a sufficiency calculator: you enter monthly programmatic AI spend (models, agent traffic and metered AI tools, excluding employee seat licences) and describe whether most of the work is repetitive, mixed, complex or unknown, and which behaviours sound familiar — premium models used by default, large prompts or context, retries, loops or repeated calls, paid tools, search or data APIs, or multiple agents touching the same work. The result is deliberately presented as a range, never a single number, and the page is clear that the calculator estimates opportunity while Navige measures reality once connected. The measurement approach is the part that guards against silent quality loss. Connected, Navige leaves a slice of your jobs running untouched and compares the two, so the difference is observed rather than assumed. Results are checked as well as cost: a step that became cheaper and started being wrong is reported as a failure, never as an opportunity taken. For users, the stated outcome is the same destination with no detours — same result, smaller bills, and runs that get lighter in four distinct ways while your existing frameworks, keys and providers remain unchanged. The site also reports that tool calls are governed before they run, that AI spend is managed priced to the millionth of a dollar and live, and that over 51 agents are already connected. Use cases follow directly from the failure modes and mechanisms described. Repetitive workloads benefit most from Cache & Reuse, since the same conclusion reached the same way many times can be served from a verified answer. Workloads with large prompts or heavy context benefit from context trimming and tool payload capping. Multi-agent setups where several agents touch the same work, or where several agents draw on one budget, benefit from the governed layer and budget controls. Teams suffering retries, loops or repeated calls benefit from the loop and failure guard. Workloads that pay for search, data APIs and other metered tools benefit when a held answer is still good enough. And any team that wants a human approval gate before sensitive actions run can use that control. Navige targets developers and organisations running programmatic AI at scale. Supported frameworks and platforms listed on the site include LangChain, LangGraph, CrewAI, OpenAI Agents, MCP and Agentforce, and the models span Anthropic, OpenAI, Google, AWS Bedrock, Mistral and Meta Llama. Onboarding is described as a one-line change that leaves existing frameworks, keys and providers untouched. Navige is positioned to sit between your agents on one side and the models they pay for on the other, with the calculator inviting teams to compare an illustrative opportunity estimate against the measurement Navige produces once connected. Pricing detail is not spelled out on the pages captured here beyond the statement that Watchtower is free forever, plus a dedicated pricing page and an enterprise option listed in the footer. Navige’s core proposition is sufficiency rather than frugality: sufficiency isn’t about using less AI, it’s about using exactly enough. By sitting in the path of every agent call — routing, caching, trimming, guarding and governing — it aims to cut AI agent and LLM spend without changing the output, and to control runaway spending before the bill is written rather than explaining it afterwards.

Apps and files on an infinite canvas where people and AI agents work together.

Pancake is a go-to-market platform that its makers describe as "The Vercel for GTM." The product gives your agent the brain, the data and the tools to run your go-to-market, and it is pitched at teams who want to deploy GTM ideas that bring in customers rather than build more software. It provides the building blocks to deploy any go-to-market idea from a favourite AI agent such as Claude or Codex: a GTM Brain, sending infrastructure, and data providers and enrichment, all accessible through one MCP connection. The core promise is simple — one prompt in, leads keep coming. You type a GTM idea, Pancake builds the Play, and one click deploys it. The positioning starts from a specific observation carried on the site: building got 10x easier, but selling didn't. While shipping software has become faster and cheaper, the work of actually finding and winning customers has not kept pace. Pancake's answer is to treat go-to-market as something you deploy rather than something you grind through manually. Instead of stitching together research tabs, spreadsheets, enrichment tools and an outreach sequencer by hand, Pancake packages the whole motion — who to target, where to find them, what to say and how to follow up — into reusable, running Plays. The result is a system that keeps searching for people who fit rather than a one-off list you exhaust in a week. The first step is the GTM Brain. You add your website and Pancake builds the Brain from it: from your website, Pancake learns who buys from you, what to say, and where to find them, and the site states that this stays always up to date. The Brain covers competitors, ICPs, outreach, influencers, market research and a market profile. That research profile includes details such as the company, its offering and its ideal clients — for example "websites for startups" and "funded B2B startups." Because everything downstream reads from the Brain, outreach and targeting stay consistent with how the company actually positions itself. It is the brain that the rest of the product draws on when it reads your Brain and your Plays to decide what a good lead looks like. The second step is the Play. A Play is one GTM idea put to work: it keeps searching for people who fit. You say who you want and Pancake builds the Play for you; you can start one in Pancake, in Claude or in Codex. A Play is defined by a handful of parts: who you are looking for, the signal that qualifies them, how many you want, and when it runs. In the demo, that looks like founders and heads of marketing at B2B startups of 10 to 50 people, with the signals "seed round" and "hiring marketing," running daily at 25 leads a week. Pancake also offers suggested Plays as starting points, such as "Funding — just raised a round," "Hiring — hiring a marketing lead," and "Keyword — posting about a rebrand." Building a Play is free, so teams can try any GTM idea without committing. Plays are powered by data sources you can trust. Pancake reads funding rounds, open roles and tech stacks from providers such as Dealroom, TheirStack, Wappalyzer, Apify, Firecrawl and Coresignal, and the page notes 30+ data providers (the Product Hunt description cites 40+ data providers and enrichment). Every lead shows why it fits and where that came from — a lead card can include the person's role, company, industry, location, seniority, and a "why they fit" line such as "Quillfern raised a seed round this month and is hiring a marketing lead." Leads can be added to a sequence or marked "not a fit." Because the sourcing is evidence-based, the person reviewing a lead can see the signal behind it rather than trusting an opaque score. Once leads are approved, Pancake handles the outreach. It writes a personal message for every lead, opening with their signal — a post, an open role, a funding round — and each sequence has its own language and tone. Drafts can be edited before they send, and messages go out in your voice under your name. You approve every lead before anyone is contacted. Pancake then follows up until they answer and reads the reply; a yes gets your calendar link, sent from your account, while every other answer comes back to you. The product's stated goal at that stage is that Pancake gets you the meeting and you close it. Pancake's distinctive approach is that the whole system is exposed to an AI agent. Connect Pancake to Claude or Codex with a browser sign-in, and you can build Plays, review leads and edit messages from the chat you already use. The same request works from Claude or Codex through Pancake's MCP server — "Set up Pancake by reading https://pancake.ai/install.md and following its instructions." In the demos shown on the site, the agent reads the GTM Brain, searches funding news and hiring data through Pancake, and then asks permission to use a tool such as "Deploy Play" or "Approve leads." Pancake is therefore both an app and a set of tools an agent can call. Pancake also learns from what wins. Approving or rejecting a lead teaches the Brain who fits, and each Play tracks contacts, replies and wins so you can see what works. The site shows this as a "what worked" panel — for example leading with the seed round, shorter intros, and asking for Thursdays — followed by "Brain updated." Reported outcomes from users include $50K of pipeline in 7 days, a 10%+ reply rate, and 15 meetings booked in a week, alongside a reply rate improvement of 56% versus the previous period. Practically, that means less time spent rebuilding lists and more time spent in the conversations that turn into revenue. Pancake is aimed at founders, marketing leads and go-to-market teams at B2B startups — the same kinds of people the Plays are built to find — as well as teams that already work inside Claude or Codex. The site lists customers and supporters including Kong, Hyperspell, AgentMail, Fleet, Requesty, Alpic, Praxis, Kinro, Vector Legal, Covera, Spacefill and Kardinal. Pricing starts from $99 a month per workspace with your whole team included, built to land 2 to 3 customers a month. That plan includes 60,000 credits a month with every lead checked against your ICP, unused credits rolling over in a slow month, free Play building, a personal message for each lead in your voice, and approval of every lead before anyone is contacted. A free start includes 15,000 free credits valid for 7 days, and you can also book a demo. In short, Pancake packages go-to-market into deployable units. It builds a GTM Brain from your website, turns a single prompt into a Play that keeps sourcing qualified leads, writes and follows up in your voice, and lets you run the whole thing from Pancake, Claude or Codex. For teams that have found building easy and selling hard, it is a way to treat customer acquisition as infrastructure you deploy rather than a manual chore you repeat.

Mira Tours is an AI tour guide that turns a walk through any city into a narrated audio tour. Mira tells you the story of what is around you as you walk, and every tour is researched and written for you, wherever you happen to be. It offers walking audio tours anywhere in the world, with narration chosen per tour. The product is aimed at people who want to understand the place they are walking through, whether they are visiting a city for the first time, living somewhere they have never fully explored, or simply dreaming about a destination from home. Most walking tours require planning: choosing a route, finding a guide, joining a group and following a fixed schedule. Mira takes a different approach, built around four ways to start walking and a promise that you can plan it out or plan nothing at all. You can shape a route in conversation, start walking and let Mira find something interesting near you, head towards a destination you were going to anyway, or walk a tour someone else has shared. Rather than one fixed route for everyone, every tour is researched and written for you, so the stories you hear at each stop relate to the walk you are actually on and to what you said you were curious about. The four ways to start walking cover different intentions. The first is making your own tour: you say what you are in the mood for and build the route together in a chat, adding, dropping and reordering stops until the plan feels right. The second is wandering: there is nothing to plan or pick, you tap start, Mira finds something interesting near you and keeps finding stops for as long as you feel like walking. The third is heading somewhere: if you are going somewhere anyway, Mira routes you there and narrates what you pass, and if you give an arrival time she paces the walk to make it. The fourth is walking someone else's tour: others have shared the best of their cities, so you can walk one near you or listen from home to a city you are dreaming about. Building your own audio tour works like planning a day out with a friend who knows every street. You tell Mira what you are curious about and build the tour together. You can say what you are in the mood for, whether that is hidden history, film spots or something the kids will love, and any idea works. Mira then proposes real places near you, in walkable order, and you add, drop and reorder stops until the plan feels right. In the example shown on the site, a walker says "I have an hour and I love hidden history", and Mira responds by offering a route through the oldest corners of Chinatown starting a short walk away: Portsmouth Square, Old Saint Mary's Cathedral, Waverly Place and Ross Alley, about an hour on foot. When the walker asks for coffee near the end, Mira adds Caffe Trieste. Then you just walk, and every stop is narrated as you arrive, researched and written for your tour. As you walk, stories arrive when you do: you can lock your phone, put it in your pocket and keep walking, with narration delivered as you reach each stop. Your interests shape what you hear at every stop, because you can tell Mira what you love and what you can skip, and every stop leans your way; a topics screen lets you turn subjects such as Legends and Ghosts up and Politics down. At a stop you can see what else a place has to offer, hear another story, or just keep walking. You can also ask anything, anytime, about something you just heard or something you just walked past. Ask your guide, get a real answer, and turn it into more audio if you want to keep going. Tours are also designed to be shared. You can share a link and everyone walking with you hears the same stops you do, live as you walk. They install Mira, tap the link and follow the whole tour without spending any of their own minutes, because walking along is free for your friends. Tours are narrated in 23 languages, chosen per tour: Arabic, Bangla, Dutch, English, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Marathi, Polish, Portuguese, Romanian, Russian, Spanish, Tamil, Telugu, Thai, Turkish, Ukrainian and Vietnamese. Overall, Mira's approach is to research and write each tour for the person walking it, whether that route was shaped in a chat, discovered stop by stop while wandering, or planned to finish at a particular place at a particular time. The main benefit is that you do not have to plan, book or study to get the story of a place. You can begin with no plan at all, or shape one in a short conversation, and hear researched narration at each stop instead of reading it. Because Mira keeps finding stops for as long as you feel like walking, a walk does not end when a fixed itinerary does, and you can stop whenever you like. You can put your phone away and simply listen as you arrive, and you can bend the tour towards your own interests at every stop, leaning into legends and ghosts or turning politics down. If you are walking with others, everyone hears the same stories live rather than one person reading aloud from a guide. Concrete uses follow directly from the four modes. A visitor with an hour and a taste for hidden history can ask for that in a chat and immediately receive a walkable route with real stops and a coffee finish. Someone already heading somewhere can have Mira route them there and narrate what they pass, with the walk paced to an arrival time. A local with no plan can tap start and keep walking while Mira finds something interesting nearby, and keep going for as long as they like. Someone dreaming about a distant city can listen from home to a tour others have shared, and a group of friends walking together can follow one shared link so everyone hears the same stops live. Anyone who wants to try it first can walk a real city in street view in the browser as Mira tells them about it, with nothing to install. Availability and pricing are stated plainly: your first 30 minutes are free, and after that you buy listening time when you want it, with no subscription and no monthly fee. Thirty minutes for a quick tour costs $0.99, two hours costs $2.99 and saves 24%, and five hours costs $5.99 as the best value. Time never expires, and it is only spent on narration and research you actually use. Prices are in US dollars, and the app shows your store's local price. Mira runs in the browser at mira.tours/wander as well as in mobile apps, with downloads on the App Store and on Google Play, and Product Hunt lists it under Android, Travel, Artificial Intelligence and Google Cloud Run. In short, Mira Tours is a personal tour guide in audio form: an AI guide that plans a route with you or finds one for you, narrates it stop by stop as you arrive, adapts to your interests, answers your questions, and can be shared so everyone walking with you hears the same stories.

Winq is a macOS app that turns your Mac's built-in camera into a hands-free input device. It lives in your menu bar and runs entirely on your Mac. Glance at a display to focus it, wink to click, and raise an eyebrow or make a face to fire a shortcut. Winq is made for people whose hands are busy, including multi-monitor power users, developers and creators, and people who want a lighter alternative on their hands because of RSI or limited hand mobility. It runs on macOS 13 or later on both Apple silicon and Intel Macs, and it needs no hardware beyond the camera your Mac already has. The problem Winq tackles is interruption. Hunting for the window you meant, reaching across displays or grabbing the mouse all pull your hands away from the keyboard, the pen tablet or the instrument you are holding. Winq replaces a few of those small physical actions with signals a camera can read reliably. Rather than acting as a full assistive replacement, it positions itself as a lighter option that works alongside macOS Head Pointer and Voice Control. The distinction from Head Pointer is deliberate: Head Pointer moves a cursor with your head, while Winq jumps focus between displays and windows, clicks with either eye, and runs app-aware shortcuts. Gaze focus is the first of Winq's three inputs. You look at a screen and it becomes focused. Gaze picks a display, or a window, not a pixel: settle on a target for a moment and Winq raises it and puts the pointer where you are looking, so your next keystroke or click lands there. The app describes it as like Command-Tab, but with your eyes. You can switch whole screens, or individual windows and panes when you have two to four big windows per screen. Both your head and your eyes count, so glancing with your eyes alone works too. An optional confirm keeps the target highlighted until you raise your eyebrows, and Winq never switches while you type or use the mouse, and never flickers at the edges. Because you look where you click, every click quietly improves accuracy. Wink clicks are the second input. A left wink is a left click and a right wink is a right click, so the two eyes are separate actions. A normal blink closes both eyes, so it is never treated as a click. Calibration learns how open and closed each of your eyes looks, and Winq keeps learning how far you really wink, even if you cannot wink one eye cleanly. A double wink double-clicks and a long wink drags. Glasses mode reads through reflections and glare. If you cannot wink at all, you can dwell to click by resting the pointer, or pull a mouth corner instead; the mouth pull left and mouth pull right gestures ship unbound and can be tried for left and right click. Face shortcuts are the third input, and Winq ships an eleven-gesture vocabulary that you can rebind. Each gesture can fire a keyboard shortcut, a Shortcuts shortcut, an AppleScript, a shell command or a Winq action, either globally or per app, so a nod can save in your editor and send in your mail app. Thresholds are relative to your own calibrated neutral face, and gestures pause while you are talking unless you turn on arming mode. Per-app profiles exist for your editor, browser, design tool and calls. The default bindings are: eyebrow raise, which also confirms a gaze switch, for Mission Control; head tilt left and right, done as a tilt and back, for previous and next Space; nod for Return; head shake for Escape; holding an open mouth to toggle dictation; holding a smile to take a screenshot to the clipboard; pucker, calibrated to you, to play or pause media; and mouth pull left and right, which are unbound by default. Setup is a dot calibration. A dot glides to a few spots on each of your screens, you follow it the way you normally look, and Winq learns how your head and your eyes move together. It takes about two minutes, and the app keeps learning from every click afterwards. While you calibrate, Winq rests: nothing clicks or switches. If you plug in a new screen, Winq offers to calibrate it from the notch, and a live Try it map shows the screen you are facing before you finish. The notch island shows clicks, gestures and switches briefly around the camera notch, and live view shows exactly what Winq reads from your face, as numbers. Under the hood, the pipeline is deliberately narrow. Frames come from your built-in camera through AVFoundation at up to 1280 by 720 and 15 frames per second. Apple's Vision framework performs on-device machine learning for face landmarks, right on your Mac. Winq's core then applies filters, detectors and your bindings to numbers, not pictures: what moves through the app is a small description of your face per frame, angles and eye openness, never an image. The resulting focus, click or shortcut is delivered through macOS Accessibility. Frames are processed in memory and thrown away, with no cloud, no disk, no account and no uploads, so zero frames are saved to disk or sent anywhere. The only things stored are your settings and calibration: head angles and eye measurements on your Mac, never images. The camera light is honest, and Winq's menu-bar icon shows when it is watching and when it is paused. The camera stops when your Mac locks or sleeps. Every setting lives in a plain config.toml that you can change in the settings window or your own editor. It supports global bindings plus per-app profiles by bundle ID, and every threshold is a setting, including settle time, typing pause and mouse guard. Shift-Command-G toggles Winq instantly from anywhere, and Winq also pauses when your Mac locks or sleeps. For tinkerers, this means the behaviour of gaze, clicks and gestures is fully inspectable and editable as text. The payoff is a lighter workflow. Gaze tracking is accurate enough to pick a display or a large window, which is all Winq asks of it; it does not try to point at pixels, because a webcam cannot do that reliably. Updates are signed and install from inside Winq, and every future update is included. Uninstalling is clean: removing Winq from Settings removes the app, its data and every permission. It works with glasses, and when the picture is too poor to be confident, Winq simply does nothing rather than guess. It asks for two permissions, Camera and Accessibility, each with a stated reason at setup. The scenarios Winq is built around are concrete. Multi-monitor power users stop hunting for the window they meant and simply look at the screen they want before typing. Developers and creators keep their hands on the keyboard, the pen tablet or the instrument while their face handles the switching. For RSI or limited hand mobility, Winq offers a lighter alternative to full assistive setups that works alongside macOS Head Pointer and Voice Control. Day to day, that translates into app-aware gestures: a nod that saves in VS Code, an eyebrow raise for Mission Control, an open mouth to toggle dictation during calls, or a pucker to play and pause media. Winq is a Mac app with one price. You get every feature free for one day from the first launch, with no account and no card. If it earns its place, you buy once: the listed price is $5.66, down from $7.98, described as a first-100 offer at 29 percent off. The licence includes gaze focus, wink clicks and all eleven gestures, every future update, and one Mac per licence, which you can move any time. The licence key arrives by email right away, and a portal is available to find your key again. Winq requires macOS 13 or later. It is not on the Mac App Store because App Store sandboxing restricts the Accessibility control Winq needs, so it ships as a download with automatic updates. In short, Winq turns the camera that is already on your Mac into three new inputs: gaze focus, wink clicks and face shortcuts, all processed on-device and controlled through a plain text config file. Look, wink, done.

Rambox is a desktop workspace manager that keeps your apps and accounts organized in ready-to-use workspaces, so you can switch contexts and stay focused. Instead of scattering work across browser tabs, windows and logins, Rambox brings the web apps you already use into one dedicated desktop application, where each client, project or role lives in its own clearly separated space. It is designed for people who work with many tools, accounts and contexts every day, whether on their own or as part of a team, and its core premise is stated plainly on its website: browsers are for browsing, Rambox is for working. Work today is spread across too many apps, too many accounts and too much switching. Rambox describes the challenge in four parts. Tab chaos buries important tools in a maze of browser tabs and windows. Account juggling means that moving between a work Gmail, a personal Gmail and a client Gmail forces you to log out, log back in and lose your flow. Notification noise breaks concentration throughout the day with constant alerts. And the question "where was that file?" becomes a daily puzzle when the file you need could be sitting in Slack, Jira, Notion or Drive, forcing you to search every app separately. According to Rambox, it should not take so much effort just to get back to what you were doing, and that friction is exactly what the product is built to remove. More than 80,000 users across 5,000 companies of all sizes have chosen Rambox worldwide, and its trust logos include Oracle, Amazon, Google, Atlassian and Adobe. The first pillar of Rambox is organization through workspaces. You group apps into workspaces for each client, project or role, and then switch the entire context with one click. Rambox calls these workspaces ready-to-use because they adapt to the way you work rather than forcing you into a single fixed layout. You can open tabs while working inside an app and return to where you left off later, and you can keep different contexts clearly separated so that client work never bleeds into personal or internal work. The product page emphasizes context persistence and improved tabs as part of the workspace experience, meaning the state of your work survives the switches instead of being reset every time you move between tasks. Rambox's second pillar is running multiple accounts without friction. The application lets you use multiple instances of the same app with different logins at the same time, so several mail accounts, messaging workspaces, social profiles or brand accounts can sit side by side. For freelancers, agencies and marketing teams this is the difference between working and continually signing in and out: rather than logging out of one account to reach another, each account gets its own instance inside the workspace it belongs to. The website frames this as perfect for managing several clients, brands or accounts side by side, and it is the feature that most directly attacks the account juggling problem described on the homepage. Finding things is the third pillar. Rambox provides one search bar, reachable with Ctrl K, to find what you need across apps like Slack, Jira, Notion, GitHub and Google Drive, rather than searching each application separately. This Unified Search is one of the newly added capabilities highlighted in the current release, and it is joined by the Command Center, which gives you quick access to your apps and common actions without disrupting your flow. The redesigned interface supports all of this with a cleaner layout, simpler navigation and fewer clicks to get things done, reducing the number of clicks needed to move between apps, workspaces, tasks and contexts. Rambox also notes a redesigned onboarding experience that makes the product easier to set up and understand, plus improved tabs and context persistence, and faster access to what you need. Focus is the fourth pillar, and it is where Rambox's built-in tools come together. Focus Mode and Pomodoro help you reduce distractions and organize your day into focused work sessions. You can mute everything and hide the apps you are not using, so the machine, and your attention, can breathe again instead of being pulled at by constant alerts. An AI Side Panel keeps your AI tools within easy reach while you work in your apps, so you do not have to leave your current context to use them. Desktop integration is what makes the whole approach possible: your web apps run in a dedicated desktop environment, so you are not constantly returning to the browser. The Product Hunt release notes for this major redesign add Quick View to the list of new tools alongside Unified Search, Command Center and the new focus features. Rambox's overall approach is additive rather than disruptive. The company states directly that Rambox does not try to replace the tools you already use, it helps you bring order to them. You keep your existing apps and services; you simply add them to Rambox and keep going. Setup begins with a free download for macOS, Windows or Linux, and no credit card is required to start. From there, you organize workspaces and accounts your own way, deciding which apps belong to which client, project or role. Advanced features are available when you need more control, so the product can start simple and grow with the way you work. The result is a single desktop home for all of your work apps, where context, not just the app itself, is the unit you switch between. The intended outcome is productivity without the noise: fewer tabs, less switching and more clarity in how you work every day. Instead of spending effort getting back to what you were doing, you spend it on the work itself. Because each workspace preserves its tabs and context, returning to a client or project restores the state you left, which reduces the mental reload cost of context switching. Because multiple accounts run side by side, you stop losing your flow to logins. Because Unified Search spans your apps, locating a message or file stops being a separate hunt through every tool. And because Focus Mode, muting and the AI Side Panel live inside the same workspace, protecting your attention does not require leaving your tools. Rambox also emphasizes that it is simple to start and powerful as you grow, with a free starting point and advanced features when more control is needed. Rambox lists several concrete groups it is built for. Freelancers and independent professionals use it to work with multiple clients, tools and accounts without mixing everything up. Marketing, social and analytics teams use it to manage several brands, platforms and logins at the same time, with each context clearly organized. Operations, support and admin roles use it to keep daily tools organized and switch between tasks quickly, without tab overload. Developers use it to switch between repos, environments and tools without losing context, staying focused on their code. The homepage also demonstrates a Personal workspace where chats sit side by side with unread counts and filters for all, unread, favorites and groups, illustrating how one workspace can replace a pile of browser tabs for everyday messaging. Rambox is used by people who work with many tools, accounts and contexts every day, whether on their own or as part of a team. For teams, Rambox offers a dedicated path that brings the same structure to the whole team, with shared setups and better control over apps and accounts. The application runs on macOS, Windows and Linux as a downloadable desktop workspace. Pricing follows a free-to-start model: the site repeatedly states that no credit card is required, that you can start free and upgrade later, and that advanced features unlock when you need more control. Downloads, plans and setup are handled through the official site at rambox.app. The takeaway is straightforward. Rambox turns a scattered collection of web apps and logins into a structured desktop workspace where each client, project or role is one click away, complete with its own accounts, tabs and focus settings. It does not ask you to abandon your tools; it asks you to stop letting them run your attention. Fewer tabs, less switching and more clarity, delivered through organized workspaces, multiple accounts without friction, cross-app search and built-in focus tools, all inside one desktop environment designed for getting work done rather than browsing.

Epherem is a free source code security scanner for apps built with AI. It is made for people building with tools such as Lovable, Bolt, v0, Replit, Cursor and Claude Code, and it is especially aimed at builders who do not read much code themselves. You upload your project or connect a repository, and Epherem reports the security mistakes it finds in your code, each with a suggested fix and a prompt you can paste into your own AI coding assistant. The product is in early access and is free while it is being built. Most apps built with AI assistants are created quickly, and the same handful of failures show up again and again at ordinary trust boundaries. Epherem's own research review, What actually goes wrong in vibe-coded apps, draws on seventeen sources that describe the same recurring problems, and its security checklist distils that work into fourteen checks worth running before real users arrive. The problem Epherem addresses is straightforward: people building with AI tools often ship without a security review, particularly when they do not read much code themselves, and the mistakes that matter are usually common ones such as exposed keys, open database rules, missing login and ownership checks, and unsafe input handling. Scanning starts with your project. You upload a ZIP of your app or connect GitHub and choose a repository, including a private one. Epherem reads your source code, your settings files and your package list, and it never runs your app. The scanning is layered, meaning your code, your settings files and your packages are each checked, and findings point to the exact file and line where the problem sits. Each scan produces a new report of your code as it is right now, so the recommended loop is to change something and then scan again. The scanner runs 55 checks that look for exposed keys, open database tables, missing login and ownership checks, and unsafe input. A settings-file finding might be an open Firebase rule such as allow read, write: if true; at a named line, and a code finding might be an SQL injection or a hardcoded secret with the file that contains it. On top of the code checks, your packages are checked against published advisories. Packages with exact versions, taken from a lockfile, are matched against advisories such as GHSA-fr5h-rqp8-mj6g for next 14.1.0, GHSA-wf5p-g6vw-rhxx for axios 1.5.1 and GHSA-qwph-4952-7xr6 for jsonwebtoken 8.5.1. If there is no lockfile, the report says which packages it could not check. Every report sorts what was found into four groups. Known issues are published advisories that match a package version you use. Likely issues are the ones where the scanner saw the risky code itself, and these are the ones to start with. Findings that need checking are cases where Epherem looked for a protection and did not see it, or where the match depends on context it cannot see, so the protection may exist somewhere the scan could not look. Anything it could not read is named in the report and never counted as clean. Each finding also shows the file, the line and the code, and explains what someone could actually do with it, so you know where to start. Findings are written for whoever built the app. Each one says in plain words what is wrong, then what someone could do with it, how to fix it, and the file and line. Because Epherem never changes your code, the fix arrives as a prompt for Cursor, Lovable or Claude Code that you paste into your AI tool, let it make the change, and then scan again. A typical finding might explain that a Supabase service-role key sits in code the browser downloads, describe how anyone opening the site could copy that key and read, change or delete every row, recommend using the anon key in browser code and keeping the service-role key in a server-only variable, and then supply a prompt that lists the files it changed. Epherem also notes which steps still need you: your AI tool cannot rotate a leaked key, so that part is done yourself in your Supabase dashboard. The free scan works with fixed checks and a public advisory database, and no code is sent to an AI. Uploaded code is used only to run the scan and is then deleted, while your report is kept in your account until you delete it. That report contains file paths, line numbers and short code excerpts, with anything that looks like a key or password masked. Epherem is direct about its limits. It will not make your app hack-proof, and no scanner can show that an app is secure. It points out common mistakes and tells you what it could not check. It reads your code without running your app, so it cannot confirm a code finding for you, and it cannot see your live site or settings outside your code, such as your Supabase dashboard. It works best on JavaScript and TypeScript, Python is partly covered, and other languages are listed in your report as not analysed. Every report states what was not checked, so a quiet report never pretends to be a clean one. The natural workflow is to scan before you deploy, see what your AI may have left exposed, fix it with your AI tool, and then scan again. That loop suits the people Epherem names as its audience: vibe coders, indie hackers, AI builders, solo founders, startup teams and weekend shippers. You do not need to know about security to use it, because each finding explains in plain words what is wrong, what someone could do with it and how to fix it, and the guides, research articles, security checklist and docs linked from the site go deeper if you want the background. Access is simple. Epherem is free during early access, each account can run 5 scans a day, and you can sign in and scan your app free. The site states that if the pricing ever changes it will say so there first, and that Epherem will never charge you unless you choose to pay. Epherem gives AI-assisted builders a way to ship with their eyes open: a free, plain-English scan of the code, settings and packages an AI wrote for them, a suggested fix and a copy-ready prompt for the AI tool that wrote it, and an honest list of everything the scan could not check.

Specreel is a tool for making product demos that update themselves. You can paste your app's URL, or point Specreel at the Playwright traces your tests already produce, and it turns those recorded browser flows into narrated, shareable demos. The goal is simple: a demo should always show the app as it is today, not as it was on the day someone recorded it. Specreel is built for people who show software to other people — marketing and sales engineers who need current demo material, and developers who already maintain end-to-end tests. It comes as an open-source CLI that runs on your machine and in your CI, plus a hosted Cloud version that needs no setup, so teams can choose the workflow that fits them. Product demos rot. A Loom recording, a click-through tour, a screenshot in the docs — each one shows your app on the day it was recorded. The UI moves every sprint, and you usually find out the demo is wrong when a prospect does. The insight behind Specreel is that a demo and an end-to-end test are the same artifact: a recorded browser flow. Most teams keep them as two separate things, and the demo half quietly drifts away from reality. Specreel makes them one thing, so a demo can't drift without a test turning red. That ties demo freshness to a signal the engineering team already watches every day: the build. The core workflow starts with Playwright traces. Turn tracing on by setting trace: 'on' in your Playwright config, and every test run writes a trace.zip. Running specreel test-results/ then builds a gallery of narrated demos from those traces. Captions are written from the recorded clicks and assertions, with no AI involved, so the narration describes what the test actually did rather than an interpretation of it. Because the source is your existing test output, adding demos does not require a separate recording session or a dedicated demo environment — the material is a byproduct of tests you are already running. If you don't have tests yet, Specreel still gives you a starting point. The specreel recommend command crawls your running app, ranks the flows worth showing, and scaffolds a runnable Playwright script. That means a team with no end-to-end suite can begin with a URL and end up with both a demo and the test that keeps it honest. The recommendation step is about deciding what is worth showing — Specreel ranks the candidate flows for you rather than leaving you to guess which paths make a good tour. Freshness is enforced by a publishing loop. A GitHub Action republishes the gallery to the same URL on every passing build, so the demo is never older than your last green build. Flows whose steps changed but still pass get an amber "updated" badge, which tells viewers that something about that flow has moved since they last looked. A flow that breaks turns the build red instead of publishing a wrong demo — the failure surfaces in the same place your team already looks for failures. Together these two behaviors mean the gallery is either current or visibly broken, never quietly out of date. Sharing is deliberately lightweight. Specreel produces one self-contained gallery.html that you can email, or you can publish it to a URL and embed it with an in docs, a site, or an internal wiki. The gallery plays like a product tour: real screenshots rather than recreated mockups, step captions, read-aloud narration, a "play all" tour that runs the sequence end to end, and light or dark presentation. You can export an MP4 with a real voiceover track when you need a video file. That combination of a single file, a hosted URL and an embeddable frame means the same gallery can be used in a sales email, on a landing page, or inside documentation without re-recording anything. Nothing in Specreel requires an AI model, but there is an optional path. Optional AI narration rewrites captions into friendly, sales-engineer narration. It is opt-in, you bring your own Anthropic key, and it costs under a cent per flow. Separately, Specreel can be driven from your AI tool: there is a hosted MCP server, a JSON API and agents.md. In practice you say "make a demo of my app" and the agent runs the whole loop against your deployed site. The open-source CLI also ships an MCP server and a Claude Code skill. This makes demo creation something an agent can trigger, rather than a manual chore someone has to remember. The unique approach here is unification. Instead of treating the demo as a marketing artifact and the test as an engineering artifact, Specreel treats the recorded browser flow as the single source for both. Captions come from clicks and assertions. The publishing cadence comes from the CI build. The freshness signal comes from test results. Because all of it is derived from the same recorded flow, there is no separate demo pipeline to maintain, and no gap where the demo can quietly diverge from the product while every other signal stays green. For users, the outcome is that demos stop being a recurring project. Marketing and sales no longer need to schedule re-recordings after a UI change, because the gallery republishes itself on passing builds. Developers get a reason for their end-to-end tests to be visible to the rest of the company, since the same traces now produce something prospects and customers actually see. Viewers get material that matches the live product, with amber badges calling out flows that changed. And because the CLI is open source and runs on your machine, a team can adopt the whole loop without committing to a hosted service first. Concrete scenarios include a sales engineer pasting a demo URL into an outreach email, where the single self-contained gallery.html plays like a tour without any login; a documentation team embedding an iframe so the screenshots in the docs are the real product; a developer pushing a green build so the GitHub Action republishes the gallery automatically; a team with no Playwright suite running specreel recommend and getting both a demo and a scaffolded test; and an AI agent invoking the hosted MCP server to build a demo against a deployed site on request. On Cloud, the no-suite path is a pasted URL, and the result is a hosted gallery with break alerts, review links and analytics. Specreel comes in two forms. The open-source CLI is self-hosted under AGPL and free forever; it includes the full CLI for render, gallery and MP4, the recommend flow discovery, the single-file shareable bundle, publishing to GitHub Pages with embeds, the GitHub Action freshness loop, and the MCP server and Claude Code skill. The hosted Cloud version is free for one project with unlimited viewers and no card, and it runs on Google Cloud Run. The first 14 days include the team, no card. Team is $39/mo for unlimited projects, scheduled checks and alerts, and Scale is $149/mo where they run the browsers and offer a custom domain. Cloud adds hosted galleries with no CI required, stable per-project share URLs, private galleries with access control, view and engagement analytics, team workspaces and notifications, and the ability to point it at a deployed URL with no setup. The takeaway: Specreel makes a product demo and an end-to-end test the same artifact, so the demo cannot go stale without something turning red. If you already have Playwright traces, you are one command away from a narrated, shareable gallery. If you don't, a URL is enough to start. Either way, the demo stays as current as your last green build.

AirJelly is an always-on desktop AI agent that its makers describe as the world's first context-aware proactive agent. It is aimed at people whose work moves faster than their notes, and its stated purpose is to bring scattered context together so that agents such as Codex and Claude Code can act with the full picture. According to the website, AirJelly reads across every app you use, including Slack, Zoom, Google Docs, Calendar and email, following the simple rule that if something is on your screen, AirJelly can see it. It is positioned as a second brain that organizes itself: the Product Hunt listing calls it a proactive, self-organizing second brain, while the site emphasizes that the agent knows who you are and what you are working on by observing your on-screen activity. 'The problem AirJelly addresses is context sprawl. The website frames it bluntly: you probably have twenty tabs open right now, and AirJelly is for anyone whose work moves faster than their notes. The fragments that matter — a confirmation in a team chat, a deadline mentioned in passing, a saved note you have forgotten about — get scattered across apps and rarely make it into a to-do list. Most AI assistants wait for you to ask before doing anything, which means the user still has to notice and remember. AirJelly's stated difference is that it watches your workflow continuously and acts before you ask, surfacing follow-ups, capturing tasks from conversations, and preparing briefs at exactly the right moment. It also tracks your personal interactions, so you never miss a follow-up or forget a promise. The product is built on three stated steps: watches, remembers and acts. Watching means capturing authorized screen context across your work apps, so the agent has visibility into the conversations, documents and meetings you actually work in. Remembering means building a living timeline of your work, where every task, meeting and message is searchable, forever. Together these two steps create the memory layer: a full log of everything AirJelly observed, across every app, which you can query at any time. The website describes this as moving from context to memory — gathering the scattered signals of a working day into one continuous record that does not depend on you writing anything down. Acting is the part users notice most. AirJelly surfaces what matters and then does something about it: it creates tasks, prepares briefs and sends reminders before you are asked, and its proactive alerts deliver follow-ups, reminders and briefs at what the site calls exactly the right moment. Because the agent has already watched and remembered the relevant context, an alert can arrive with the material attached — for example, a task created from a message, complete with due time and the conversation it came from. The company contrasts this with assistants that only respond to prompts, positioning AirJelly as a system that continuously works in the background rather than one that waits for instructions before it does anything at all. The website lists five core features built around how you work. The Daily Report is an end-of-day summary of what happened, what is pending and what comes next. The Timeline is a full log of everything AirJelly observed, across every app, so past work remains reviewable. Tasks are auto-captured from conversations, meetings and messages, complete with due dates and without the user writing anything down. Memory lets you ask about anything from your past work, anytime. Proactive alerts pull the other features forward by delivering follow-ups, reminders and briefs at the right moment. AirJelly's FAQ adds that task capture happens automatically from conversations, meetings and messages, with context and due dates included. AirJelly's overall approach can be summarized by the site's own progression: from context to memory, and then to action. The home page shows the loop as context for the agent, an activity log and deadline alerts, and it describes the mechanics in three numbered steps — watches, remembers, acts. One highlighted capability is bringing scattered context together so that Codex and Claude Code can act with the full picture. Practically, that means the agent converts your ongoing screen activity into structured context and memory, and then uses that memory to decide when a task, brief or reminder is warranted. The result is described not as a tool you operate but as an always-on companion that observes, retains and intervenes on your behalf. The stated outcomes are time and reliability. By managing everything in your life, AirJelly frees your time for what AI cannot do, or simply for you to enjoy. In practical terms, users get tasks that no longer depend on being written down, follow-ups that are not forgotten, briefs that are ready before a meeting, and a searchable record of past work that can be queried at any moment. The site groups the beneficiaries into four rough buckets: product, operations and engineering teams; founders and executives; researchers and PhD students; and anyone juggling more context than one brain can hold. The unifying benefit is that the user stops having to act as the memory system for their own work. AirJelly's site offers three concrete scenarios. In a product and operations setting, a meeting message became a task: the team confirmed a review in chat, and AirJelly caught it, created the task and attached the conversation, with the task created, the due time set and the context attached. In research and academia, fourteen tabs became one page: tabs spread across papers, docs and notes were read through and turned into a single research summary, including a note the user had forgotten they saved, with key points extracted and the past note surfaced. For founders and executives, an interview in thirty minutes was already prepared: AirJelly saw it on the calendar and prepared a brief with background, recent work and talking points. The FAQ adds that AirJelly also extracts tasks from conversations, meetings and messages automatically. AirJelly is a free download for Apple Silicon Macs and Windows x64 PCs, with Linux support described as coming soon. It reads across every app you use, with Slack, Zoom, Google Docs, Calendar and email named explicitly, and the rule that if it is on your screen, AirJelly can see it. Privacy is addressed directly: screenshots are encrypted and stored on your device, capture can be paused at any time, and specific apps can be excluded. The intended audience is anyone whose work moves faster than their notes — product, operations and engineering teams, founders and executives, researchers and PhD students, and anyone juggling more context than one brain can hold. There is also a Discord community linked from the site for users to join. AirJelly's pitch is simple to restate: instead of waiting for a prompt, an always-on desktop agent watches your workflow, remembers it as a searchable timeline, and acts — creating tasks, preparing briefs and sending reminders before you ask. For people whose work scatters across tabs, chats and calendars, that means less to remember and fewer dropped follow-ups. The product is available today as a free download for Apple Silicon Macs and Windows x64 PCs, with Linux support coming soon. The site's framing sums it up: stop asking, start working.

daily.dev for agents is a marketplace of plugins that teach you or your agent new workflows on top of the daily.dev API. daily.dev is where millions of developers find out what's next, and this product extends that reach to your agent: you connect Claude, Cursor, Codex or any MCP client, and your agent can read personalized feeds, search posts, learn from discussions, save bookmarks and tune your feed. Every listing in the catalog is published either as a Plugin or as an Agent skill, and the marketplace is built and shared by developers. Alongside the plugin marketplace there is also an apps ecosystem for people who want to build on daily.dev rather than only consume from it, with a documented path for connecting your agent and submitting a plugin. Most of the value a developer collects from daily.dev does not disappear, it just goes stale. Bookmarks pile up and rot in a list you never reopen. Notifications stack into an undifferentiated inbox of replies, mentions, squad reviews and fresh content. The feed follows tags that may no longer match what you are actually building, while your git branch touches libraries, APIs and patterns your feed has never heard about. When an incident fires, when a dependency gets bumped, or when you want to know whether a tool is genuinely worth adopting, the answers live scattered across posts and comment threads that you have no time to read. The marketplace exists to route that knowledge into the surfaces you already work in — your terminal, your editor, your notes folder, your agent — so the content finds you instead of waiting to be found. The reading plugins bring daily.dev into wherever you already look. Unofficial dailydev CLI reads daily.dev from your terminal: your feed, popular and discussed posts, search, comments and bookmarks, and every command has a --json flag so scripts and coding agents can use it too. It installs with npm install -g @devopsdaily/dailydev-cli. daily.dev TUI for Terminal is a Text User Interface made in Go that calls the Daily.dev API into the terminal. daily.dev statusline for Claude Code rotates daily.dev headlines at the bottom of Claude Code while you wait, combining curated major headlines with the community's most-upvoted posts of the day, clickable in your terminal and requiring no API token. daily.dev RSS lets you read daily.dev in any RSS reader: sign in with daily.dev and get a private RSS or JSON Feed URL for your personalized feed. daily.dev Hello world is a starter app for building on daily.dev that signs in with daily.dev and shows your profile, personalized feed and latest bookmarks, pulled live from the public API, ready to fork. Bookmark, notification and profile workflows are covered by their own set of plugins. Bookmarks to Notes exports your daily.dev bookmarks to Markdown notes for Obsidian, Logseq or any folder of files: one note per post with summary, tags, source and links, and later runs sync only what's new so your second brain stays current without duplicates. Bookmark Recap turns what you bookmarked this week into a short reading report you can paste into standup, Slack or a newsletter, grouping your saves by topic, pulling the one-line summaries and telling you what is worth reading first. Inbox Zero summarizes your daily.dev notifications in one short briefing, split into what needs you (replies, mentions, squad reviews), what's social, and what's just new content, with links on everything actionable, and it marks them all read only when you say so. Profile Autofill takes your CV, LinkedIn export or a short career summary and has your agent fill in your daily.dev work history, education, projects, certifications and tech stack, after you approve the plan first. daily.dev Roulette spins the cylinder on a forgotten bookmark: one comes up, and you either spare it and go read it, or pull the trigger and it is deleted from daily.dev for good. Feed tuning and research plugins turn daily.dev into a decision surface. Feed Tuner compares what you bookmark on daily.dev with the tags and sources you follow, then proposes follow, unfollow and unblock changes and applies only the ones you approve. Stack Sync points your agent at a repository, reads the dependency manifests, updates your daily.dev tech stack and follows the matching tags so your feed matches what you actually build, showing you the plan before anything changes. Feed Builder takes a plain-English description such as "Rust backend and observability, 50+ upvotes, no videos" and produces a daily.dev custom feed with the right tags, sources and thresholds, with your agent resolving the tags, creating the feed and applying the filters. Branch Reads reads your git branch, spots the libraries, APIs and patterns you are touching, and finds the 3-5 daily.dev posts worth reading for that exact change. Trend Radar compares daily.dev's popular feed against last week's snapshot and surfaces what broke in, what cooled off and the posts that drove the change. Release Radar is a weekly changelog for your stack, finding release notes, breaking changes, deprecations and migration posts for the tags you follow, writing a digest that leads with what breaks, and saving posts to a bookmark list. Upgrade Scout pulls migration guides, gotchas and community reactions from daily.dev for a library and version and gives you a verdict before you run the upgrade. Community Check reads the most-discussed posts and their comment threads on a topic and summarizes the consensus, the dissent and the production gotchas, with sources. On-call Lookup takes an error or a log line from an incident, has your agent search daily.dev for posts about the same failure, reads the comments for corrections, and gives you the likely causes, the checks to run first and the fixes, each with its source, sending only generic terms and never hostnames or secrets. Hot Take Drafter finds the most-argued post on daily.dev this week, lays out both sides from the comment thread and drafts three takes — the contrarian, the synthesis and the sharp question. daily.dev Roulette rounds out the catalog with a playful but concrete way to clear your backlog. The marketplace is built on the daily.dev API, and the entry point is connecting your agent, with documented instructions for doing so and for how plugins work. A recurring pattern across the agent skills is an approval-first design: Profile Autofill has you approve the plan before anything is written, Stack Sync shows you the plan before anything changes, Feed Tuner applies only the changes you approve, and Inbox Zero marks notifications read only on your instruction. Hot Take Drafter drafts takes but you press send, not the agent. The plugins run in surfaces you already use — the terminal, an editor statusline, an RSS reader, a notes folder or an MCP client — rather than asking you to learn a new destination. The stated outcomes focus on keeping your knowledge current and your attention where it matters. Bookmarks stop piling up and rotting, and your second brain stays current without duplicates. Notifications collapse into one short briefing, with links on everything actionable. Your feed matches what you actually build, so the posts you see relate to your branch, your repository and your tech stack. Incidents get a starting point with sources attached. Dependency bumps come with a verdict before you run them. Adopting a tool comes with the community consensus and the production gotchas up front. Because all of it is delivered where you already work, you spend less time searching and more time on the change in front of you. Concrete workflows show how these plugins fit together. During an incident, you paste an error or a log line into On-call Lookup and get likely causes, first checks and fixes with sources, without leaking hostnames or secrets. Before bumping a dependency or reviewing a PR that does, you run Upgrade Scout for migration guides, gotchas and community reactions. Each week you can run Release Radar for a changelog of your stack that leads with what breaks, then Bookmark Recap to turn your saves into a report for standup, Slack or a newsletter. If you keep a second brain, Bookmarks to Notes exports bookmarks to Obsidian or Logseq and syncs only what's new. When you start work on a branch, Branch Reads finds the 3-5 posts worth reading for that exact change, and Stack Sync keeps your daily.dev tech stack aligned with the repository. Before adopting a tool or technique, Community Check gives you the consensus, the dissent and the production gotchas. Between all of that, Inbox Zero triages your notifications and Feed Builder or Feed Tuner keeps your feed pointed at what you follow and what you actually read. The product targets developers who already rely on daily.dev and who now work through agents and MCP clients such as Claude, Cursor and Codex, along with anyone building on the daily.dev API. The ecosystem is explicitly built and shared by developers, so contributors and plugin authors are part of the audience too. Integrations named in the content include Claude, Cursor, Codex and any MCP client, CSS readers via RSS or JSON feed, Obsidian and Logseq for notes, Slack for sharing recaps, and the terminal and Claude Code statusline as reading surfaces. Technologies that appear explicitly include an MCP server, a Text User Interface written in Go, and npm distribution for the CLI. On pricing, the marketplace itself is a catalog of plugins, and a limited-time promotional offer is noted on the site: a Product Hunt week promotion gives 25% off with the discount already applied, ending October 20th, redeemable through Get Plus. Taken together, daily.dev for agents is the bridge between the developer news that daily.dev curates for millions of developers and the agents that now do much of the day-to-day work. It keeps your feed, bookmarks, notifications, profile and research habits connected to the places you already work, and it does so with plugins and agent skills that are built and shared by developers themselves.

Table Canon is a tool for tabletop RPG game masters that turns uploaded game-night audio into a session recap, an outline, and a prep desk. It is built for the person running the game — the game master who wants to stay in the scene while playing and then sit down later with the whole night in one place. The product states its promise plainly: play the session, prep from the recap. Rather than asking a GM to write notes while the story is moving, Table Canon takes the recording of the session and turns it into written material the GM can actually use when preparing the next game. Callbacks from earlier sessions show up when you prep, so the campaign's own history becomes the source of future hooks. The site frames the whole product around a simple idea: solid foundation, lasting stories. The problem Table Canon addresses is stated directly on its site: you meant to write it down, but the scene had already moved on. Prep night should not start by reconstructing the last session from memory. Notes capture only what you had time to scribble during play, and a general chatbot only knows what you paste into it. Table Canon puts the recap, the outline, and the open threads in one desk, so a promise made three sessions ago can become next week's hook without pausing the table to record it. The goal is to let the GM focus on the game and then prep from what already happened, instead of losing the thread between sessions or hunting through old chats and documents for details that may never have been written down at all. The process begins with uploading a recording. The audio is the notes: you played, and the recording stands in for the note-taking you did not do. Today, on the web, a game master uploads an audio file from game night. Live recording on the mobile app and in the browser is described as coming soon. Also marked coming soon are optional GM notes for secrets and corrections, layered on top of the audio — a way to add information the recording alone would not capture. This approach keeps the GM inside the scene rather than at the edge of it with a notebook, and it means the record of the session is created automatically rather than depending on how much time anyone had to write things down. Once a recording is uploaded, the night lands in one place. Table Canon produces a transcript, speaker labels, an outline, and a Recap. The site states that this material is usually ready in about 15 to 20 minutes, depending on session length, and that users see a "Building your campaign…" indicator until the session is ready. Having the transcript, the speakers, the outline, and the recap assembled together means the GM does not have to piece the evening back together from scattered files, memory, or half-finished notes. The recap and outline are designed to be read before planning the next scene, so preparation starts from a written account of what actually happened at the table. Preparation then starts from that desk. The prep desk surfaces open threads, hooks, and the people who mattered during the session. Earlier nights resurface as callbacks, which is how continuity across a long campaign is maintained without manual bookkeeping. This is described on the site as "the desk you open on prep night" — a single place where the loose ends from previous sessions are already visible. Instead of asking the GM to remember which promise, faction, or unresolved question is still live, the prep desk collects those elements from the campaign's own history and puts them in front of the person planning the next game. The campaign's world is also kept current. Table Canon maintains NPCs, places, and factions that are updated from play, described as "a world that keeps up." When the AI gets something wrong, the site says that this happens and provides tools to fix it: voice samples can be calibrated for better speaker labels, and on any World dossier a GM can use GM corrections to fix names, blurbs, aliases, and mistaken duplicates, merging or marking them distinct. Reviewing the session Recap before sharing it is recommended, and corrections stick for Ask and future sessions. When a fact is fuzzy, the GM can ask — for example, "What did they promise the tavern keeper?" — and the answers cite the sessions they came from. That citation behaviour is a stated differentiator from pasting text into a chatbot: the memory is derived from play, with evidence. The site positions Table Canon against do-it-yourself approaches with a direct comparison. Taking notes while you play means you leave the scene; Table Canon keeps audio as the record and notes optional. Reconstructing the last session before you can even prep leads to scattered memory and old chats; Table Canon puts recap, outline, and prep in one place. Coming back next week and pasting into a new chat causes names to drift; Table Canon keeps the same campaign desk, updated from each night. Building a campaign wiki by hand drifts from what players actually did; Table Canon's memory is derived from play, with evidence. And asking "what did they promise in session 4?" normally means hunting old chats and docs, while Table Canon lets you ask with citations into your actual sessions. The stated outcomes for users follow from that design. After game night you get the night in one place: a recap and outline you can read before planning the next scene. You get a prep desk with open threads and hooks, including callbacks from earlier nights. And you get a world that keeps up, with NPCs, places, and factions updated from play. Together these pieces mean prep time is spent planning forward rather than reconstructing backward. The memory of the campaign evolves each session rather than being frozen at whatever was written down at the time, and answers about past events are backed by the sessions they came from. Concrete use cases appear throughout the site. A game master can upload a recording after game night and, roughly 15 to 20 minutes later, read the recap and outline before planning the next scene. During prep, they can open the desk to see open threads and hooks, including callbacks from earlier nights that become next week's hooks. When a detail is fuzzy, they can ask a question such as what the party promised the tavern keeper and get an answer citing their sessions. They can browse a real campaign from a live table — World, Story, and night-by-night Recaps — and try Ask on the sample campaign, including questions that pull from an earlier night, before uploading anything themselves. After a session is processed, they can review the Recap before sharing it, calibrate voice samples for better speaker labels, and use GM corrections on World dossiers to fix names, blurbs, aliases, or merge mistaken duplicates. Table Canon is built for the table and works with the systems game masters already run, listing Dungeons & Dragons, Pathfinder, Call of Cthulhu, and indie TTRPGs. The site notes that memory stays campaign-shaped, not system-locked. Pricing starts with a free tier: 6 hours of processed audio lifetime, no login and no credit card required, with the option to try as a Guest. Free access includes the full desk — campaigns, World, Story, Prep, and Ask — and the sample campaign tour can be explored without uploading anything. Plans are priced on processed audio hours once a user outgrows Free. The site states that session audio and campaign content run on your account only and are not used for public model training, and that live recording on the mobile app and in the browser is coming soon, while the web beta today supports uploading a session recording. Table Canon's core value proposition is that the session itself becomes the campaign's memory. By turning uploaded game-night audio into a transcript, speakers, outline, Recap, a prep desk of open threads and hooks, and an evolving world of NPCs, places, and factions — all searchable with answers that cite your sessions — it removes the need to take notes during play or reconstruct the last session from memory before prep. Game masters stay in the scene while playing, then open one desk on prep night where earlier nights surface as callbacks, corrections stick, and the story keeps its foundation from session to session.

Spinachi is a personal health manager that runs on WhatsApp. Rather than asking you to install and learn yet another app, it meets you in the messaging app you already open every day: you forward your lab reports to Spinachi on WhatsApp, and it reads them, sorts them, and keeps your health records — and your family's — in one place. From there you can see where you stand, how things are changing over time, and when your next test is due. Spinachi describes itself simply as "your health, finally in one place," and it is explicit that it is built for organising and tracking health information, never for issuing a diagnosis. The problem Spinachi addresses is a familiar one: health records do not stay in one place. A blood test arrives as a PDF in a family WhatsApp group, a thyroid panel is emailed by a lab, a knee MRI lives on a hospital portal, and vaccination slips sit in a drawer. When you want to know what your last reading was, whether things have changed since the previous test, or when you were last screened, you have to hunt through messages, downloads and paper folders. Multiply that by every member of a household and the picture gets messier still. Spinachi's premise is that the answer should live somewhere you already are, and that the record-keeping should happen for you rather than being another chore you have to maintain yourself. The delivery channel is the product's defining choice. Spinachi works through WhatsApp, so there is no app to download, no account to keep logging into, and no new interface to learn. You sign up for Spinachi and then simply forward your reports as you receive them. Because the entry point is a messaging thread, the workflow fits the way people already share medical documents with their families — the same PDF that lands in your chat can be passed straight to Spinachi instead of being lost in a scroll of messages. Once a report arrives, Spinachi reads it and sorts it. The website shows this organisation in action, with health records gathered together and individual files filed alongside labels such as Lab report, Prescription, Scan report, and Full body checkup. Example documents used on the site include Blood Test 2025.pdf, Knee MRI.pdf, Thyroid Panel.pdf, and Chest X-ray.pdf, sitting under folder-style headings for Health Records, Prescriptions, Lab Results, and Vaccinations. The point is not just storage but structure: instead of a pile of attachments, you get categorised records that can be found again when you need them. Beyond filing, Spinachi is designed to help you understand where you stand. It keeps your records in a form that shows how things are changing over time, so that a single reading stops being an isolated data point and becomes part of a pattern. It also tracks timing: Spinachi reminds you when a retest is due, so follow-up tests do not quietly slip past. This combination — a stored history, a view of change, and a nudge for the next test — is the core of what the product offers. The overall approach is deliberately minimal. You forward a lab report on WhatsApp; Spinachi reads it; Spinachi sorts it for you and your family; the record is kept in one place. There is no app to install, and there is no diagnosis. Spinachi states plainly that it never provides a diagnosis, which keeps its role clearly defined: it is a manager and organiser of the health information you already have, not a source of medical opinion. That boundary matters, because it means the product can be useful for storing and tracking records without making clinical claims. For users, the benefits follow directly from those choices. Records for the whole family end up in a single place rather than being distributed across chats, inboxes and drawers. You can see where you stand and how things are changing, which makes it easier to arrive at an appointment with context rather than guesswork. Retest reminders reduce the chance that a recommended follow-up is forgotten. And because everything happens inside WhatsApp with no app, the effort required to keep the record up to date is close to zero — the act of forwarding a file is the act of filing it. Concrete scenarios follow directly from the document types shown on the site. After a full body checkup, you forward the lab report and Spinachi files it so the results are not lost among other messages. If a household member has a thyroid panel done, the record joins a lab results history that can be compared against earlier tests. Scan reports such as a knee MRI or a chest X-ray are stored alongside the rest of the family's records instead of sitting in a hospital portal. Prescriptions are kept in their own place, and vaccination records are collected together so the household's paperwork is complete. Spinachi is aimed at individuals and families who want their health records organised in one place. Access is through WhatsApp, and getting started means signing up at spinachi.in/start. The service is presented without a download, which makes it approachable for people who do not want to manage another piece of software, and for households where one person ends up holding everyone's paperwork. The takeaway: Spinachi turns WhatsApp into a personal health manager. Forward your lab reports, and they are read, sorted and stored with your family's records in one place. You see where you stand, how it is changing, and when your next test is due — with no app to install and never a diagnosis. The value proposition is straightforward: your health, finally in one place, maintained with the smallest possible amount of effort.

Scape is a macOS application for orchestrating fleets of AI coding agents, and Argus is the loop orchestrator that ships with it. In Scape's own definition, an orchestrator is an agent whose job is to run other agents on a loop: you hand it an objective and a heartbeat, it wakes up on that heartbeat, does a round of work, files what it found, and goes back to sleep until the next one. Scape's example orchestrator is named Lloyd, and it manages some of the engineering work on Scape itself. The page exists to show a real working example rather than a diagram, so people can see what one of these orchestrators actually looks like for an engineer and designer who has spent 20+ years building software. The problem the orchestrator addresses is that someone still has to decide what work should happen, keep those priorities in view, and notice when something is wrong. Scape's answer is to give the agent a fixed objective, a cadence for acting, a memory of its own, and a short standing checklist — the same way you would keep priorities in front of a person you manage. Because an orchestrator can do anything, Scape argues that giving it a database to manage its own memory has compounding value over time, and that the whole shape works precisely because it is simple. Memory is the first piece. Lloyd's primary goal is managing its own internal tickets table, which in this case is a simple SQLite table. It has managed over 600 tickets that it can query, functioning like its own internal Jira that the owner can click through. Every time a new ticket arrives, the orchestrator can look up previous, related tickets — the same behavior you would expect from a person picking up newly assigned work. That gives the agent a database of tribal knowledge that can be passed to any model. In Scape, the Internal Tickets table is viewable as a table, a kanban, or a gantt, and it holds plain-language summaries of the bugs and spikes the orchestrator filed. The heartbeat is the second piece, and Scape points to it as where the value visibly comes from. Lloyd wakes up on a heartbeat the owner sets — a bit over two hours in this example, shown as a HEARTBEAT card set to 147m — and the owner caps how many child agents it is allowed to run at once, shown as a MAX CHILDREN card set to 16. Each time it wakes, it works through the same short list of standing checks the owner gave it. That list is the pulse. In Lloyd's mission note, the Every-Pulse Actions include running a "Bug Reports from Feedback" playbook, checking for new bugs and thread replies, checking the backlog for items that were not staffed, and determining whether any docs need updating. The pulse in practice covers four things. First, Lloyd runs a playbook — an automation script — to check email for any new bug reports from customers, and it checks for previous context before staffing a ticket. Second, it checks whether any docs need to be updated on the website. Third, it checks the app's own running logs to see what is up; Scape calls this a very important practice because it surfaces bugs nobody reported and that never produced an error, such as a noisy problem that never crashed anything. Fourth, it adds its own tickets for bugs and enhancements along the way, which gives the agent a voice to surface ideas and problems for the owner to triage. Scape highlights that last part as the one it likes most: the agent has the agency to create its own work and put it on the table for triage. When the orchestrator decides work needs doing, it staffs it. Scape gives every project its own SQLite tables, synced to your iCloud. Playbooks are the automations the orchestrator runs. The fleet of child agents is the orchestrator staffing the work across isolated worktrees, and each child runs an adversarial review on its work before it reaches the owner. Scape names the orchestrator Argus, after the hundred-eyed watcher, because keeping a pulse on the whole fleet is the entire job. The whole shape of the system is a table the orchestrator owns, a heartbeat, and a short list of things it checks every time it wakes up. Scape states that this works because it is simple: every pulse it puts the priorities back in front of the model, the same way you would keep them in front of a person you manage. That simplicity is also what makes the operation legible — the mission note shows the heartbeat and the Every-Pulse Actions in the center, with the live fleet of running agent sessions down the right, so the owner can see the state of the whole thing at a glance. The outcome for the user is an agent that remembers, repeats, and reports. Its memory means new work is staffed with the benefit of everything that came before. Its heartbeat means priorities are re-presented on a fixed cadence instead of depending on someone remembering to re-prompt. Its log checks mean problems surface before anyone reports them. Its self-filed tickets mean the owner gets a steady stream of ideas and problems to triage rather than silence. And its adversarial reviews mean child-agent work is checked before it reaches the owner, so the orchestrator is not simply generating volume. Concrete uses described in the content include running engineering work on a project: Lloyd manages some of the engineering work on Scape and has filed over 600 tickets. Another is monitoring customer feedback, where a playbook checks email for new bug reports from customers and looks up previous context before staffing a ticket. Another is documentation upkeep, checking whether any docs on the website need updating. Another is log review, watching the app's own running logs to catch noisy problems nobody reported. Another is backlog triage, checking for items that were never staffed. And another is parallel delivery, where child agents work in isolated worktrees and each runs an adversarial review. Scape runs on macOS and is local-first: the code never leaves your machine. It brings its own set of supported agent harnesses — Claude Code, Codex, Copilot, and Cursor are listed alongside OpenCode and omp — and you bring your own subscription. Projects get their own SQLite tables synced to iCloud. Argus ships with Scape, and you can download it to start with no account, no credit card, and no license key: it is free to try for 7 days on macOS, with pricing available on the site. A sample mission note can be cloned from a shared link if you already use Scape. In short, Scape turns agent management into a loop you can watch: one table the orchestrator owns, one heartbeat that wakes it, and one short pulse of standing checks that keeps priorities in front of the model — so bugs, docs, backlog gaps, and new work are surfaced continuously instead of waiting to be reported.

Kai is an AI executive assistant that proactively manages your calendar, email, and tasks. Built by Morgen, the team behind the calendar app 248,000 people already trust, Kai works across your email, calendar, and tasks: it triages your inbox and drafts replies, schedules around your availability, records and transcribes your meetings, then turns them into summaries and action items. It is aimed at solopreneurs, freelancers, and startup founders, and also serves founders and executives, teams, AI power users, and people with ADHD. Kai is available on web, Mac, iPhone, and Android. Kai frames the problem in blunt terms: an assistant costs $6,000 a month, and most people cannot hire one. A chat assistant helps only when you open it and ask, which means the work still waits on you. Kai is connected to your mailbox, so when an email lands — at 1am included — it is already sorted, the task is filed, and a reply is drafted in your voice by the time you look. The company describes Kai as an embedded assistant, not a chatbot: it connects directly to your email, calendar, and meeting tools and works proactively, surfacing what needs attention without waiting to be asked. On email, Kai sorts your inbox by what each thread needs from you, using four states: Needs Reply, FYI, Waiting Reply, and Done. Replies are drafted in your voice, and the send decision stays yours. Drafts are written from how you actually write to that person, so a note to a client does not read like a note to a colleague. Kai also tracks your email, meetings, and DMs and chases what you promised. Product Hunt lists the product tagline as an autonomous assistant designed to handle meetings and email. Everything arrives in a triage workspace where you accept, edit, or reject the AI's suggestions before anything is sent or synced. On meetings, Kai covers three phases. Before the call, the brief is already written. During the call, there is a live transcript, with no bot joining. After the call, a summary and tasks land. Kai captures Zoom, Google Meet, and Teams calls without a bot joining the meeting. The company notes that recording, summaries, and share links are table stakes that most notetakers do well; Kai's stated difference is what happens after the call. Action items land as real tasks on the same list your email feeds, each linked to its meeting, and the follow-up email is drafted from how you actually write to that person. Task management and calendar are the other two pillars. Kai keeps your tasks on track in a ranked, single list so nothing slips, and hands them over so your agent gets the briefing. Every action item arrives with the thread or meeting it came from attached, so you act with the context in front of you. Today Kai does not sync with external task tools, so it works best as the home for the commitments Kai catches. On the calendar, Kai runs every account in one grid, spots a clash, and moves it, and can block focus time on your calendar. Kai also gives you a booking page that reads free/busy from every calendar marked as busy in Kai, across Google and Outlook accounts, so you share one link and people pick a time that actually works. Prefer email? Kai drafts the reply suggesting times, in your voice, and you stay the sender. Getting started is deliberately minimal: no imports and no new workflow to learn. You connect your email and calendars — Google, Microsoft, one account or several — and Kai works across all of them at once. Kai then analyses your inbox and starts building your memory: it reads what is already there and begins remembering the people, the projects, and the preferences. Next, Kai handles your email and listens in your meetings, with the inbox arriving sorted, replies drafted in your voice, and notes taken from your device. You approve the drafts, the follow-ups, and the tasks: nothing sends without you, and accepted items become tasks linked to their source. This approval step is central to the design. Everything goes through triage first, Kai does not create a task or block calendar time without your approval, and if it miscategorizes an email or suggests the wrong action item you edit or reject it. Kai learns from your corrections over time. The promised outcome is straightforward: Kai clears the inbox and keeps the calendar honest so you get back to the thing only you can do. Rather than adding another surface to check, it works in the background and hands tasks to Claude or ChatGPT with the context of the meeting or email they came from. For AI users specifically, the value is not having to paste your day into a prompt: ask ChatGPT what is due and it reads Kai, or tell Claude to move the meeting and you approve. Built by the team behind Morgen, Kai brings the calendar expertise of an app already used by 248,000 people. Kai describes several concrete scenarios. Founders and executives: before a call, Kai has what you last agreed with them; after it, what was agreed becomes tasks; and you can ask Kai to hold your building time. Freelancers and solopreneurs: mail that needs you is sorted with the reason attached, a reply is drafted in your voice and you press send, and what you promised yourself sits in the list. Teams: meetings are recorded on each person's own laptop with no bot; mail is sorted by what needs a reply; and each person starts with a brief on their day. AI nerds: ask ChatGPT what is due and it reads Kai, tell Claude to move the meeting and you approve, with no pasting your day into the prompt. People with ADHD: everything you owe today sits in one ranked list, say it once and Kai holds it for later, and it flags a task before it slips. Kai connects to Gmail, Google Calendar, Microsoft Outlook, Zoom, Google Meet, and Microsoft Teams, and supports Google and Microsoft email and calendar accounts, one or several at once. It runs on web, Mac, iPhone, and Android, and offers MCP for the AI tools you already use, so an existing Claude or ChatGPT subscription can reach your live day. Pricing starts from $20 per user, per month, and the product is free to start with no credit card required — the site's phrasing is "Free until I've earned it." Product Hunt sign-ups receive 2x trial credits. The product earned 194 upvotes and 48 comments on Product Hunt, listed under Email, Productivity, and Artificial Intelligence. Kai's core value proposition is simple: an executive assistant that works in the background across email, meetings, calendar, and tasks, and never acts without your approval. It triages, drafts, transcribes, summarises, and files, then hands the result to whichever AI tool you already use. For anyone whose day is spent chasing commitments rather than doing the work, Kai is designed to hold the chores so you can hold the focus.

The Drive AI is an AI file assistant that organizes, searches, and creates documents in plain English, in its own workspace or in connected Google Drive, OneDrive, SharePoint, and Dropbox accounts. The website describes it as the AI assistant that organizes and searches your team's files: you connect the drives your company already uses, and the assistant reads and organizes them in place, so nothing has to move. It is built for teams and individuals who want to stop filing by hand and instead ask for what they need in a single sentence, whether that is sorting a folder, finding a clause, sharing a report, or asking a question across thousands of files. The problem it addresses is one almost every team recognizes. Files are usually named by whoever happened to save them, which is why the homepage points at collections full of scan_0412.pdf and Final FINAL v3.docx, where the filename tells you nothing about what the document actually contains. Once a drive reaches that state, search stops being reliable, duplicates accumulate, and the only dependable way to find anything is to open files one at a time. The Drive AI's answer is to read every file by what it says rather than by what it is called. As the Product Hunt description puts it, most AI tools can read your files; The Drive AI does the work after reading. Rather than stopping at summarizing or chatting about a document, it takes the next step and reorganizes, renames, merges, shares, and sends files on your behalf. Organizing is the first job the assistant takes on. You can ask it to sort every invoice by client and year, to sort a year's client documents by entity and fiscal year, or to rename every invoice that lands in Finance to vendor, date, and amount. It also finds duplicates and can organize thousands of files in a single prompt. Crucially, it proposes the work as a plan rather than mutating a drive silently: a sample request to sort the Clients folder by client and year produces a proposal showing 1,264 changes and 24 new folders, flagged as needing your OK. For accounting firms the same plan covers every file at once, and an undo puts it all back. This plan-first approach is what makes a large-scale reorganization of thousands of files practical rather than terrifying. Alongside organizing, the assistant answers questions across the entire collection rather than one document at a time. You can ask which vendors raised prices this year, and it reads the files to find out. In the real estate example, asking which invoices for 14 Elm Street came in over $2,000 this year makes it read every invoice for the property and answer with a table. For law firms, asking which NDAs renew in the next 90 days makes it read each agreement and list them with their notice periods. That turns a folder of unstructured PDFs into something you can interrogate directly, without opening anything yourself. The assistant also creates and acts on documents, not just sorts them. It can turn the Q3 board notes into a two-page PDF summary, merge three contracts into one PDF, and build a new document such as a closing binder from 20 PDFs. In the human resources example, asking it to split Priya's onboarding packet makes it split the PDF, name each form, and file them under the employee. It handles sharing too: you can ask it to share the Q4 report with Sarah, view-only, or send the Acme NDA to both signers. The workspace that ships on every plan adds version history, permissions by file and folder, file requests from anyone, and e-signature, so document workflows that normally span several tools can happen in one place. The website summarizes the workflow in three verbs: connect it, ask it, approve it. In the connect step, you link Google Drive, SharePoint, OneDrive, Dropbox, or upload files directly, and every file is read by what it says rather than what it is called. In the ask step, one sentence is enough to sort a folder, find a clause, share a report, or ask a question across thousands of files. In the approve step, it shows the plan: small changes apply at once, big ones wait for your OK, and everything can be undone. Three explicit rules govern this. Small changes apply at once and can be undone. Large changes, and anything sent to another person, wait for your OK. Admins decide where that line sits for the whole workspace. On top of that, workflows let you describe a job once and have it run on every file that arrives afterward, wherever it arrives from; you see every step before it runs, and any run can be replayed. An activity log records every change, who made it, and who it was for, including restores, moves, shares, and renames. The benefits follow directly from that design. Work that used to mean hours of dragging, renaming, and cross-referencing becomes a single sentence and an approval. Renames like scan_0412.pdf becoming Acme MSA 2024.pdf mean files are findable by the next person, not just by the person who saved them. Questions that previously required opening dozens of PDFs are answered with a table. Because the assistant reads and organizes files in place, nothing has to move between systems. And because every change is visible, approvable, and reversible, teams can let an agent act on real business documents without losing control of them. The product is explicitly framed around document-heavy industries, with concrete scenarios for each. Real estate teams use it to read every invoice for a property and answer spend questions with a table, and to file documents from different senders under one address in the order the deal happened, so a listing agreement, purchase contract, and inspection report all land in the right place. Law firms use it to read each agreement and surface NDAs renewing in the next 90 days with their notice periods. Accounting firms use it to sort a year's client documents by entity and fiscal year in one approved plan. Property management teams use it to put every lease under its property and unit, named with tenant and start date, proposing the moves and renames as a single change. HR teams use it to split onboarding packets and file each form in the right employee folder. Finance teams use it to rename every invoice that lands in Finance to vendor, date, and amount, and to organize invoices by client and year. New files can arrive from uploads, Gmail, Outlook, Slack, and Microsoft Teams, while the connected drives it works on are Google Drive, OneDrive, SharePoint, and Dropbox. Files can alternatively live in The Drive AI's own workspace, which is on every plan, while connected drives require the Max or Team plan. The product is free to start, with a book-a-demo option through Cal.com. On trust, the site states that data is encrypted, scanned for malware, and never used to train AI, and lists CASA Tier 2 certification, Microsoft Verified Partner status, AES-256 encryption, real-time malware detection, and enterprise infrastructure. A Chrome extension lets you use it right inside your existing drive. The takeaway is simple: The Drive AI positions itself as the assistant that does the work on your files rather than just reading them. Find anything, file nothing by hand — connect a drive or upload a folder, ask in plain English, approve the plan, and let every new file be read and filed where it belongs.

HarnessHive is an AI-native, collaborative, browser-based and versioned design ecosystem for wiring harness engineers. It is a next-generation wiring harness CAD software built to fit inside pre-existing workflows while enabling real-time collaboration in the cloud. Its central idea is a single live document that holds schematic, wiring, bill of materials and a to-scale formboard view, with an AI assistant available at every step to help engineers find parts, place them and connect cavities. The product is aimed at the people who actually build harnesses: startups and machine builders, student teams, and harness shops and freelancers. Traditionally, harness design work is split across separate tools and documents: a schematic in one place, physical routing in another, a spreadsheet for the BOM and cut list, and a separate manufacturing drawing for the build board. Keeping those artefacts consistent is manual work, and a change in one rarely propagates to the others automatically. HarnessHive addresses this by making all four views part of the same live document, so routes, cut lengths, the BOM and a 1:1 formboard all derive from one model. The ecosystem is also designed for integration within pre-existing workflows and for the reality that harnesses are rarely built by one person: they are shared between engineers, handed over between seasons, and sent to build benches for manufacturing. Schematic and wiring views form the two halves of the model. The schematic view is the logical netlist: engineers place components and splices, connect their pins, and put the conductor on the connection itself. The wiring view represents the physical side of the same harness, with connectors, branch points and the routes between them, so a team can decide where the bundle splits and add coverings to a route. Because these are two views of one document rather than two files, editing one and having the others follow removes the manual rework of synchronising logic and physical layout. The BOM and cut list view holds parts and cut lengths that are derived live from the model. When the harness is fixed, the numbers follow, and the workbook can be exported to XLSX. The formboard view is the manufacturing drawing at true 1:1 scale, kept in sync with the wiring, and can be exported as a full-size PDF for the build board. Together these two views mean the manufacturing output is generated from the design rather than rebuilt by hand, and the shop receives a document that matches the model. Collaboration is built into the document rather than bolted on. Everyone edits the same live harness, seeing cursors, drags and changes as they happen. Undo only reverts your own work, so one engineer's correction does not roll back a colleague's progress, and every action lands in a history that anyone can open in read-only mode. Access is managed through Viewer, Editor and Admin roles, and history is organised by day with read-only snapshots, giving teams a record of what changed and when. The AI assistant reads the harness itself. It can be asked to find real parts from trusted supplier sources, add them to your library, place them and connect the cavities. It also reads CSV wire lists you attach, creates parts from supplier pages, and can generate a whole harness as a single proposal. The safety model is explicit: every edit arrives as a proposal, and nothing changes until you apply it. Applied changes then show up in the history alongside manual edits, so AI-assisted work is traceable in the same way as human work. Integrations are designed to sit inside an existing toolchain. Teams can bring wire lists, supplier data and whole harnesses into HarnessHive, and send full-scale drawings, BOMs and a complete JSON back out. Concretely, the formboard exports as a PDF at true 1:1 scale, the BOM and cut list export as XLSX, and the harness JSON supports round-trip use. KiCad and Onshape importers are described as coming soon, which reflects the goal of integration within pre-existing workflows. Parts are treated as real, versioned objects. Connector housings, terminals, conductors, seals and splices live in organisation libraries and local parts, with cavity counts defined on the part definition. Every occurrence in a harness pins the exact version it was built with, so updating a library never silently changes an existing design, and BOM rows follow the model. This matters because harness designs are long-lived: version pinning keeps a released harness reproducible even as the parts library evolves. HarnessHive also ships an MCP server so teams can bring their own agent. Claude, ChatGPT, Cursor or a custom agent can be pointed at the workspace to read harnesses, BOMs, empty cavities and libraries. Every token gets eight read tools, and writes sit behind an explicit opt-in. Access is scoped to a single workspace and a token can be revoked instantly. The site lists Claude, ChatGPT, Gemini, Cursor and Muse as tools it works with, positioning the MCP server as a way to expose harness data to the AI assistants engineers already use. For the teams using it, the outcomes described are practical. Startups and machine builders can ship their first harness without buying a seat of legacy EDA. Student teams can hand the harness over each season with its full history intact. Harness shops and freelancers can send a 1:1 formboard and a clean BOM straight to the build bench. Because the design lives in the browser, there is nothing to install, and the free trial requires no credit card. In practice, a harness workflow in HarnessHive starts with a design: components and splices with connected pins in the schematic, then connectors, branch points and routes in the wiring view, including where the bundle splits and which routes carry coverings. A CSV wire list can be attached for the AI assistant to read, supplier pages can be turned into parts in the library, and the assistant can propose an entire harness that a user reviews before applying. The resulting BOM and cut list are exported to XLSX, and the formboard is exported as a full-size PDF. Alongside the visual editor, an MCP-connected agent can read harnesses, BOMs, empty cavities and libraries for further automation or analysis. Typical users include startups and machine builders, Formula Student and other student teams, and harness shops and freelancers. Onboarding is deliberately lightweight: a 25-minute demo can be booked with the team, and a free trial gives 14 days of access with no credit card and nothing to install. The product runs in the browser, with sign-up handled at app.harnesshive.com. Community is part of the product's development model, with a Discord server plus accounts on X, LinkedIn and Instagram, and a newsletter for new features and blog posts. HarnessHive states that it is a small team building with the people who use it, and sponsors Formula Student teams across the globe. HarnessHive's core promise is simple: one harness, four views, one live document. Schematic, wiring, BOM and cut list, and a true 1:1 formboard all stay in sync, collaboration and history are part of the document, real parts are versioned, and an AI assistant proposes changes that a human applies. For wiring harness engineers who want AI-native, browser-based, collaborative and versioned design without necessarily buying a seat of legacy EDA, HarnessHive is positioned as the ecosystem that ties logic, physical routing, manufacturing data and AI assistance together in one place.

MentionBird is an AI visibility tool that shows brands why AI assistants recommend their competitors instead of them — and what to do about it. When buyers ask ChatGPT, Perplexity, or Gemini what to buy, the answer is assembled from a small set of sources that the model trusts for that specific question. MentionBird finds those sources for the prompts that drive a company's sales and gives a concrete plan to get the brand into them. It is built for any business whose customers research products or services online, and it positions itself as the "Ahrefs for AI Search." Buyers no longer start with ten blue links. They ask an AI assistant, get two or three brand names, and shop from that shortlist. The shortlist now forms inside the answer itself, and if AI does not say your name, you were never in the running — and you would never know. AI answers are built from a small set of pages, reviews, and videos that the model trusts for that specific question. Get into those sources and you get recommended; stay out, and your competitor takes the customer. Because most categories have no clear winner in AI answers yet, starting early can help a brand become the default answer. The first step is picking the prompts that bring customers. Users add the real questions buyers ask — examples on the site include "Best running shoes for daily use," "Best marathon shoes for men," "Best running shoes for flat feet," and "Most comfortable shoes for long runs." MentionBird then tracks exactly which sources AI cites for each one. For a given prompt, it lists the sites the models check and marks each as cited or not cited — showing, for example, that runnersworld.com and runrepeat.com are cited while a brand's own listings are not. That visibility into the underlying citations is the difference between knowing you are losing and knowing where to go to fix it. For any tracked prompt, MentionBird generates AI Ranking Advice. It reviews the answers, the named brands, and the cited sources, then recommends practical steps and shows the effort required for each one. Advice can include getting featured in trusted roundups and comparison guides, publishing guides that answer the questions buyers ask before choosing, adding clearer product facts such as fit and durability details to product pages, or earning videos and mentions from trusted websites. Each recommendation is labelled with impact and effort — for example, "Medium effort · High impact" — so teams know what to fix first. The platform checks and updates AI visibility data once every 24 hours. It tracks ChatGPT, Perplexity, Gemini, and Claude, as well as Google AI Overviews, Grok, Microsoft Copilot, and DeepSeek. Tracking is prompt by prompt: cited, not cited, or no brand named yet. Users see share of AI recommendations versus each competitor week over week, and every score change is traced to the exact prompt, model, or citation that caused it. A weekly brand visibility share view can show, for instance, one brand up four points while another drops three. MentionBird describes itself as built around the question that matters: what do I do to get mentioned for this prompt? It contrasts itself with other AI visibility trackers that show a mentions chart, tell you when you dropped, and leave the "now what?" to you. MentionBird instead shows the sources behind every answer, gives a per-prompt plan to win them, and proves the plan worked with daily tracking. It also distinguishes itself from SEO rank tracking: SEO tools show a webpage's position in Google search results, while MentionBird checks whether AI answers mention your brand, which sources AI cites, and how your visibility compares with competitors. The outcome MentionBird promises is movement from "never mentioned" to the brand AI often mentions. Users can watch themselves become the answer, because every plan they execute shows up in the numbers. Teams quoted on the site describe concrete gains: Risely discovered it won the category questions but lost the comparison ones, which it called exactly backwards for a buying decision; Facets learned the exact questions where engineering buyers saw it as invisible; Recooty found AI was recommending an ATS to every buyer who asked, just never theirs; Superfans reported being in 83% of "alternatives to the category leader" answers after MentionBird found the gap; and Helixo, despite 3300+ reviews, could pinpoint the questions where AI still was not recommending it. Typical use cases start with evidence gathering: a brand runs the free AI Visibility Report to see whether AI recommends its competitors, then adds its brand, prompts, and competitors to track the questions that matter. Marketing and SEO teams use prompt-level data to decide which pages, guides, or product details to fix first. Agencies manage several clients in one workspace, using tags to organize prompts by brand, product, or project. E-commerce and software companies watch comparison and category prompts separately, since the two can behave very differently. Multi-brand teams keep several brands or product lines in a single workspace, and anyone entering a new category can move early to become the default AI answer before a clear winner emerges. MentionBird works for any business whose customers research online, including e-commerce stores, software companies, healthcare providers, law firms, financial services, real estate, travel, and education, plus home services, automotive companies, and local businesses. Agencies, marketing teams, and SEO teams are explicitly supported, and the site suggests starting with 20 to 50 real buying questions rather than hundreds of broad prompts. No installation is required — there is no code, plugin, or tracking tag to add to a website, and setup takes only a few minutes. Pricing includes a free AI Visibility Report tool and a Starter plan at $19/mo for three months with the Product Hunt code PH19. Plans can be changed anytime, with upgrades applying immediately and downgrades beginning with the next billing period, while saved prompts, labels, and past data remain available. MentionBird's core value proposition is simple: it turns AI visibility from a chart you cannot act on into a per-prompt plan you can execute and verify. By revealing the sources behind every AI answer, explaining why competitors get recommended, and tracking progress daily across the major AI platforms, it helps brands get into the shortlist that now forms inside the answer — before their competitors do.

PostNitro for Agents is a social media API and design engine that lets AI agents create and publish social media content. Most social APIs an agent can reach will publish text or pre-made media, but PostNitro gives the agent the design layer too: on-brand carousels, single-image posts and animated video built from more than 100 templates and the user's own brand kit. The service then schedules and publishes that output natively to LinkedIn, Instagram, TikTok, Threads, Facebook and Pinterest. It is built for creators, marketers and teams who want one continuous chain, from prompt to designed, scheduled and published post, with no human in the loop unless they choose to add one. The site states the product is used by 85,000+ creators, with its editor embedded in 200+ platforms. PostNitro frames the problem it addresses directly: most social APIs that an agent can reach will only publish text and pre-made media. An agent can therefore write a caption and push a finished file, but it cannot produce a designed post on its own, because the design engine lives outside the API in a graphic design tool that a human has to operate. PostNitro's stated purpose is to give your AI agent a design engine, not just a publishing API, so that the agent can own the entire chain. That matters because the formats that carry a social presence, such as multi-slide carousels, consistently branded image posts and short vertical video, are precisely the ones that normally require manual design work. Moving design into the API layer means a single prompt can produce a publishable, on-brand asset rather than only a caption or a pre-made image. The creation capability covers carousels, single-image posts and short video, generated from several kinds of input: a topic, raw text, an article URL, or an X post. The agent can also add AI-generated images and bake them into slides as part of the same job. A separate import path lets the agent bring its own slide content and have PostNitro design it using the same templates and the same brand kit, with no AI writing step involved. That distinction matters for teams that already have approved copy: the design engine can be used purely as a layout and branding layer, so the agent's role can be limited to formatting and rendering rather than authoring. Combined, these two paths mean content can either be written by the model or supplied by the user, and either way it comes out designed rather than plain. Everything PostNitro generates is meant to be on-brand, and the brand capability is exposed through the API rather than handled by a prompt. Agents can list, read, create and update brand kits, which cover fonts, colours, logos and headshots. The practical value is that output is on-brand without the model needing a prompt that describes the brand each time; the agent simply points a generation job at the right brand kit and the design engine applies it. Because brand kits can be created and updated programmatically, an agent or an automated pipeline can manage them as data, keeping multiple brands, sub-brands or client identities separate and current. This is what makes the output of an unattended agent something a marketing team can publish rather than something that needs to be redesigned from scratch before it is usable. Scheduling and publishing are first-class capabilities in the toolset. Agents can create, read, update and delete scheduled posts, connect and inspect social accounts, and publish natively to six networks. Publishing destinations are LinkedIn, Instagram, TikTok, Threads, Facebook and Pinterest; X (Twitter) is supported as a content source and as an export format, but not as a publishing destination. The site also notes a recent addition, publishing straight to Facebook Pages and Pinterest boards. Alongside scheduling, a browse capability lets an agent list templates, AI presets and audio tracks so it can pick deliberately rather than accept a default. That gives an agent control over the look, the motion preset and the soundtrack of a video, all through the same interface it uses to generate and schedule the post. Generation jobs are handled in two ways, which matters for how an agent behaves. Every generator has a fire-and-forget form and an and-wait form, plus status and output polling, so an agent can choose between streaming and blocking depending on its own design. A long design job does not have to stall the agent, and a short one does not have to be polled unnecessarily. A second, more unusual capability is the handoff: if the agent asks for the DESIGN output, PostNitro returns an editable design instead of a rendered file. The agent can do the work at 3am, and the human opens the result in the editor, changes the headline, and publishes. PostNitro describes this as the one place where the human and the agent share a workspace rather than a handover, because every publish-only API returns something you cannot edit. Access works through six interfaces that share one engine, one account, one brand kit and one template library. Claude connects in one click via Claude's Connector Directory, and ChatGPT installs in one click via ChatGPT's plugin directory; both use OAuth, so there is no API key to fetch. Any MCP client can be pointed at mcp.postnitro.ai, which exposes the whole engine as 35 tools. The CLI installs with npm i -g @postnitro/cli and is described as JSON in, designed post out, script-safe and built for CI and automations. The REST API, whose design and publish chain is exposed at POST /post/initiate/generate, is the layer the others are built on. Finally, a Skill for OpenClaw and Claude Code installs with npx skills add postnitro/postnitro-agent so an agent can drive PostNitro end to end. The core outcome the product promises is a shorter chain from idea to published post. The site's demo line is 56 seconds: prompt, designed on your brand kit, scheduled, published. An agent can generate, schedule and publish in a single chain with no review step, or it can save posts as drafts on the calendar so a human approves them and hits schedule. Output can be delivered as PNG, PDF, MP4 or an editable design, which lets the user choose between a finished asset and a working file. One benefit PostNitro calls out specifically concerns video: output video is the carousel slides rendered as an MP4 with animated slide text and an audio track the user chooses. It is a design feature rather than a generative video model, and the company notes this is how carousels reach TikTok, which static-export tools cannot do. Concrete workflows are illustrated with sample prompts on the site. Turning a latest blog post into a LinkedIn carousel produces a carousel and a PDF. Announcing a launch as an Instagram post on a chosen brand kit produces an image post. Making a 20-second TikTok video from five tips produces video as MP4. Planning a week of content, three carousels from Monday to Wednesday at 9am, saves three drafts for review. Each of these runs through the same engine: the agent is handed a topic, article or X thread and returns an on-brand, designed asset, which is then either scheduled or held as a draft. Because the CLI is script-safe and the REST API accepts HTTP requests, the same jobs can also be wired into pipelines and automation tools without a human typing prompts. PostNitro is designed for anything that speaks MCP, anything that can run a command, and anything that can make an HTTP request. Named MCP clients include Claude Desktop, Claude Code and the Claude web app, Cursor, ChatGPT and VS Code, along with agent frameworks that support MCP servers. The CLI is usable from OpenClaw via the Skill, n8n, Make and CI pipelines. Agent access is priced with the rest of the product: included in Creator+ at $30/month with 200 credits per month, and Team+ at $60/month with 500 credits, or available on the Starter plan as a $10/month add-on with 250 credits. Credit volume can be increased. An imported slide costs 1 credit and an AI-generated slide costs 2 credits, and the site states there is no free tier for the API. Taken together, PostNitro for Agents positions design as the missing layer in agent-driven social publishing. Instead of asking an agent to write a caption for media that already exists, it exposes a design engine through MCP, CLI and REST so the agent can produce on-brand carousels, image posts and video, schedule them to six networks, or hand back an editable draft. The same engine, the same brand kit and the same templates sit behind every interface, and OAuth removes the key step for Claude and ChatGPT. For teams that want an AI agent to carry social content from a raw idea to a scheduled, publishable, on-brand post, that combination of design plus publishing in one API is the product's central value proposition.

CLI Manager is a free, open-source macOS desktop app that puts every AI command-line agent a developer uses into a single dashboard. Instead of scattering Claude Code, Codex CLI, Gemini CLI and plain shells across separate terminal windows, it keeps them side by side in one sidebar, where projects can be grouped into folders, each session can be named for the role it plays, and every session keeps running even while you look elsewhere. It is built for developers who juggle multiple AI agents and lose context switching between them, and its stated purpose is to keep that work organized in one powerful workspace while leaving the user in control of the desktop agents. Developers working with AI coding agents increasingly run more than one of them. Claude Code, Codex CLI and Gemini CLI each arrive as their own command-line tool, and a typical working day can mean several terminal windows, several projects, and a steady loss of context as the developer moves between them. Sessions get confused, tasks get forgotten, and there is no obvious place to note what a particular terminal was for. CLI Manager addresses that by treating agent sessions as organized objects rather than anonymous terminal tabs. The problem it solves is context: keeping every agent, every project and every running task visible in one place so the developer does not have to remember which window is doing what. In one sidebar, CLI Manager places Claude Code, Codex CLI, Gemini CLI and plain shells next to each other. Projects can be grouped into folders, and each session can be named by the role it plays, so a glance at the sidebar shows what is running and why. Sessions can be switched with a shortcut, and as the site states, every session keeps running while you look elsewhere. Sidebar folders can also be dragged into any order, and that ordering is kept across restarts. Because any CLI tool can be added, including Claude Code, Gemini CLI, Codex and even apps, there is no vendor lock-in; the developer switches freely between tools. The AI Control API, introduced in v1.9.0 and highlighted from v1.10, lets one AI agent open and drive another agent's terminal. Claude Code, Codex or any script can open sessions inside CLI Manager, send prompts, wait and read the screen, in ordinary terminals the developer can watch. Sessions an AI is driving turn green in the sidebar and are marked "AI connected"; the orchestrator gets a crown. At any time the user can type into those sessions or take them back, and "Disconnect AI" stops the agent in that session, while switching the API off ends its access entirely. The API is off by default, local only, running as REST plus MCP on 127.0.0.1, and requires a token. An accompanying agent skill can be installed with a single npx command so a coding agent can open visible sessions in CLI Manager, run another agent there, wait for it and read the screen. CLI Manager includes an in-app diff review. A developer can open the diff for any workspace, select lines, and send a comment, with file and line numbers, straight back to the agent's terminal. Worktrees are compared against the branch they forked from, including new files. Git is built in as well: creating a Git worktree makes it its own workspace with its own sessions, and the Source Control panel lets the developer stage, commit, push and browse history without remembering commands. Worktrees can also be hidden entirely from Settings, terminal file links can be turned off, and file paths can be set to open only on Command-click; reloading worktrees no longer wipes terminal scrollback. Session status is driven by official Claude Code and Codex hooks rather than guessed from terminal output, so a permission prompt is never mistaken for a finished task. Usage alerts warn before the 5-hour or weekly limit cuts an agent off, and usage-limit meters are shown alongside. Every session has a memo pad: pressing Command-J opens a place to write down what a session is for; memos save as you type, stay with the session across restarts, and an AI working through the Control API can read them. A separate Loop Dashboard window is dedicated to watching Claude Code /loop sessions per project, showing iteration count, status and timing at a glance. CLI Manager's approach is to act as a management layer above the agents rather than a replacement for them. The tools remain ordinary terminals: the sessions an AI opens are the same terminals a person can type into. That is why the AI Control API is off by default, bound to localhost, and token-protected, so the agent's access is something the user explicitly grants and can revoke. Status information comes from the vendors' own hook events rather than screen scraping, and everything, including the memos and the git operations, lives inside the same workspace. Local-only processing is a stated design principle: the app does not send data externally, and it is Apple notarized. The stated benefit is staying in control while multiple agents work. Developers stop switching between terminals and losing context, because everything is organized in one sidebar where folders, session names and shortcuts make the landscape readable. Because sessions keep running in the background and status comes from official hooks, the developer can be told when an agent actually needs attention instead of watching it. Usage alerts protect against an agent being cut off mid-task by a 5-hour or weekly limit. And because an AI can be given controlled access to open, prompt and read sessions that remain visible and take-back-able, orchestration happens without hiding what is going on. Concrete scenarios include running an orchestrating agent: Claude Code or Codex opens a session in CLI Manager, runs another agent there, waits and reads the screen while the developer watches, then hands control back. Another is multi-project work: a developer keeps several projects grouped in folders with their own worktree workspaces and sessions, switching with a shortcut while each one keeps running. A third is reviewing agent output: opening a workspace diff, selecting lines and sending line-numbered comments back to the agent's terminal. A fourth is long-running Claude Code /loop sessions, watched from the Loop Dashboard for iteration counts and timing. Finally, developers can write per-session memos to record what each session is for, and let a Control API agent read them. CLI Manager is aimed at developers who juggle multiple AI agents, described on the site as built for developers who want to stop switching between terminals and losing context. It integrates with Claude Code, Codex CLI, Gemini CLI and plain shells, and can add any CLI tool or app. Status hooks come from official Claude Code and Codex integrations. The AI Control API is local only, exposed as REST and MCP on 127.0.0.1, requires a token, and is off by default; the companion agent skill works with Claude Code, Codex and other agents. Pricing is free: the app is open source under the MIT license since v1.5.0, distributed for macOS (Apple Silicon ARM64 and macOS Intel x64, current version v1.13.0) and available on GitHub. Windows is coming soon; Linux is not yet on the roadmap. CLI Manager's value proposition is straightforward: it gathers Claude Code, Codex CLI, Gemini CLI and plain shells into one macOS dashboard, keeps projects, sessions, worktrees, git operations, memos and status information in that same place, and optionally allows an AI to open and drive terminals the developer can watch and reclaim. Free, open source, local-only and Apple notarized, it is a management layer for developers whose daily work is already a crowd of AI agents.

Toby is an AI mock interviewer built to help people prepare for real job interviews. You paste a job description, and Toby builds the questions a real panel would ask — from the warm-up to the curveball — then interviews you on a Google Meet, Microsoft Teams or Zoom-style video call. Toby Higgins, the AI hiring manager, asks the questions, listens, and tells you exactly what to fix. It is aimed at candidates who have an interview coming up, whether that is a recruiter screen, a hiring manager conversation, a case round or a values interview, and its purpose is to let you practise the hard questions before they count so that the real interview feels familiar. Interview preparation usually means reading a generic list of questions, rehearsing alone, or memorising answers that collapse under pressure. Toby's stated approach is different: it is not a generic list of questions. Toby researches the company, maps every round and plays the interviewer, then shows you exactly what to fix. The product frames itself as "the interview before the interview." The idea is that useful practice should resemble the interview you have actually got, happen on the kind of call you will actually take, and produce feedback that tells you precisely what to change rather than vague encouragement. The site explicitly contrasts this with generic question lists, positioning company-specific research and round-by-round mapping as the core of the preparation. The first thing Toby does is build your prep brief so that you know the company cold. Tell it the role and it produces a picture of what the company does, how it makes money, what has happened lately and how the team works. The brief presents stage, size, HQ and business model at a glance. The worked example on the site is a Senior Product Manager, Payments role at Fernleaf: spend management for UK and EU small businesses, covering company cards, bill pay and expenses in one account, at Series B with £45m raised, roughly 200 staff, headquartered in London. The brief also lists recent news and culture signals, dated — for example, a new CFO hired in August 2026 from a listed payments company, and a June 2026 launch of bill pay in Ireland and the Netherlands. Finally, it suggests talking points worth raising in the room: asking how SEPA failure rates compare with Faster Payments, noting that interchange is most of their revenue so authorisation rate is a revenue metric, and referencing their memo culture by offering a short written plan for your first 90 days. The point of this section is that you arrive already knowing the business model, the recent context and the signals that show you have done the work. Toby then maps the interview process so you can see every round coming. It sets out each round, how long it runs and what it tests, and then lists the questions most likely to come up in each one. In the example this is a 30-minute recruiter screen, a 45-minute hiring manager interview, a 60-minute product case and a 45-minute values round, with six questions listed per round. Each question is tagged by competency and difficulty and marked when real candidates have reported it. So "What made you apply to Fernleaf, and why this payments role in particular?" is tagged customer focus, motivation, easy and reported, while "This role leads a squad of six engineers without line management. How have you led teams in that set-up?" is tagged cross-functional leadership, leadership, medium and reported. There is also a browsable library of company interview guides linked from the site. This gives you a structured view of what each stage is assessing rather than one undifferentiated bank of questions. The practice itself is designed to feel like the day. You pick the round — recruiter screen, hiring manager, case or values — and how long you have got: 10, 20 or 30 minutes, whatever you've got. You then choose a mode. Real mode runs it straight through, uninterrupted, like the day itself, with the full debrief at the end. Coach mode gives you a note or two after each answer and a chance to try again; coach notes only appear once you have finished talking, so you are not interrupted mid-answer. The call runs in a Google Meet, Teams or Zoom-style format so the real thing feels familiar, and you can have your camera on or off. Toby will wait while you think, and as the site puts it, he is very patient. Overall, Toby works as a three-step loop: tell Toby the role, take the call, get the debrief. Everything is anchored to the job description you pasted, which is what makes the questions and the feedback specific to the role rather than generic. Toby Higgins plays the interviewer throughout, and the model is built around reproducing the conditions of the real interview — the round type, the length, the video-call format — and then converting that performance into structured notes. The outcome is a debrief that tells you exactly what to fix. Toby gives you a score against the role's rubric — the example shows 60 out of 100 with the verdict "Solid examples, light on measurable impact" — and then breaks down every answer: what worked, what to fix, and a stronger version built only from what you said. Each competency is scored 1–5 with the quote behind it, for instance customer focus at 3/5 because the answer was a real example but had no numbers to size the impact. It also measures delivery: talk time at 35%, pace at 136 wpm against an ideal of 130–160, and one filler word, "actually", used once. Because the stronger version is built only from what you said, you are not handed a generic script — you get your own answer rebuilt, with brackets to fill in with your real details, such as adding the result as a number: from what, to what, over what time. The full transcript, question by question, is included as well. The overall promise is simple: walk in having done it already. In practical terms, the use cases follow the rounds Toby maps. A candidate with a recruiter screen coming up can run a short 10-minute coach-mode session to warm up on motivation and background questions. Someone facing a hiring manager round can run a longer session in Real mode to get the uninterrupted feel of the day, then read the debrief. A candidate with a 60-minute product case or a values round can pick that specific round and rehearse against the questions most likely to come up in it. Before applying or interviewing, you can use the prep brief to learn the company's business model, recent news and talking points. And after a bad answer, you can review the transcript and the stronger version, fill in the brackets with your real numbers, and try again. Toby is aimed at job candidates preparing for interviews, from those practising a recruiter screen through to people preparing for case and values rounds at specific companies. It runs on the web. On price, Toby is free to start: your first 5 minutes on a call are free, which is enough to warm up and see how the feedback works, and you save a card to see your debrief — it isn't charged. The free tier includes a full prep brief for your role, likely questions for every round, Real or Coach mode, and a debrief on what you answered. After that it is £0.50 a minute; you buy minutes in packs from £5, they are only used on a call, and they last 6 months. There is no subscription. As an illustration, a 10-minute mock interview is £10.00, and both modes, prep briefs and debriefs are included, and you can stop whenever you like. Toby's core value proposition is that it turns interview preparation into a realistic rehearsal against the specific role and company you are targeting, then converts that rehearsal into concrete, measurable fixes — pacing, filler words, talk time, structure and stronger versions of your own answers. Rather than hoping the real interview goes well, you walk in having already done it.

OpenCode is an open source AI coding agent that helps you write code in your terminal, IDE, or desktop. The project describes itself simply as an open source agent, and its stated purpose is to sit inside the environment where developers already work so that an AI assistant is available without leaving that environment. It is available as a terminal interface, a desktop app, and an IDE extension, and the Product Hunt listing notes it can also be used as a web app. Free models are included out of the box, or you can connect any model from any provider, including Claude, GPT, Gemini and more. That combination of an open source codebase and flexible model access is the core of what the product offers. The problem OpenCode addresses is visible in how developers currently work with AI coding assistants. Most assistants tie you to a single vendor's models, a single interface, or a single subscription, and the site notes that teams and individuals end up worrying about inconsistent performance and quality across providers. OpenCode takes a different route: because it is open source, it can be inspected and self-directed, and because it connects to many providers rather than one, the choice of model stays with the developer. The site also highlights a privacy dimension, stating that OpenCode does not store any of your code or context data so that it can operate in privacy sensitive environments. Taken together, these choices reflect a demand for coding agents that are transparent, portable across providers, and safe to run on code that cannot leave a controlled environment. The first major capability group in OpenCode is model access and provider choice. The site states that free models are included and that you can connect any model from any provider, including Claude, GPT, Gemini and more. Beyond those named providers, OpenCode supports any model across 75+ LLM providers through Models.dev, and that list includes local models, which matters for developers who want to run inference on their own hardware. Two subscription integrations are called out explicitly: you can log in with GitHub to use your Copilot account, and you can log in with OpenAI to use your ChatGPT Plus or Pro account. That means an existing AI subscription does not go to waste, and you avoid being locked into one vendor's model lineup. The second capability group concerns where and how the agent runs. OpenCode is described as available as a terminal interface, a desktop app, and an IDE extension, and the Product Hunt description adds that it is available as a web app as well. The site summarizes this under "any editor." It is LSP enabled, meaning OpenCode automatically loads the right Language Server Protocols for the LLM, which gives the model accurate, language-aware context about the code it is working on. It also supports multi-session use, so you can start multiple agents in parallel on the same project, and share links, which let you share a link to any session for reference or to debug. Together these features make OpenCode fit into real workflows rather than a single chat window. The third capability group is around privacy and model quality. On privacy, the site is explicit: OpenCode does not store any of your code or context data, which is what allows it to be used in privacy sensitive environments, and a dedicated privacy page for enterprise use is linked. On model quality, OpenCode offers Zen, described as access to a handpicked set of AI models that OpenCode has tested and benchmarked specifically for coding agents. The stated benefit of Zen is that you do not need to worry about inconsistent performance and quality across providers, because you are using validated models that are known to work well for agentic coding. Between the privacy stance and Zen's validated models, OpenCode is aiming at two of the most common hesitations about adopting a coding agent. Overall, OpenCode works as an agent that lives in your development environment and talks to whichever model you choose. Installation is handled through common package channels — the site shows a curl install command and lists npm, bun, brew, paru and yay as installation options — so the setup step fits into whatever toolchain a developer already uses. Once running, the agent loads the appropriate LSPs for the language in front of it, which serves as the context layer for the LLM. Model routing is handled through Models.dev, the provider directory that gives OpenCode access to 75+ LLM providers, including local models, while Zen sits alongside that as a curated, benchmarked set of models chosen specifically for coding agents. The same agent is then reachable from a terminal, a desktop app, an IDE extension, or the web, which means the underlying approach stays consistent even when the surface changes. The benefits OpenCode claims are backed by adoption numbers on the site. It reports over 208,000 GitHub stars, 950 contributors, and over 13,000 commits, and states that OpenCode is used and trusted by over 16 million developers every month. For an individual developer, the practical benefits are the freedom to pick a model and provider without rewriting a workflow, the ability to reuse an existing Copilot or ChatGPT Plus/Pro subscription, and the option to keep everything local or self-hosted when working with sensitive code. For teams, the privacy stance — no storage of code or context data — is a way to adopt an AI coding agent in environments where sending code to an unknown third party is not acceptable. Concrete use cases follow directly from those capabilities. A developer can run OpenCode in the terminal to write and modify code without leaving the command line, or install the IDE extension to work inside their editor. With multi-session support, they can start several agents in parallel on the same project — for example, one working on a frontend task while another handles a backend refactor. When something goes wrong, they can generate a share link to any session and send it to a colleague for reference or debugging. Developers with an existing GitHub Copilot or ChatGPT Plus/Pro account can log in and use that subscription instead of paying for a separate one, and teams operating in privacy sensitive environments can use OpenCode because it does not store code or context data. OpenCode is aimed at developers and development teams: anyone who writes code and wants an AI agent inside their terminal, IDE, desktop, or browser. Its integrations are listed plainly — GitHub Copilot accounts via GitHub login, ChatGPT Plus and Pro accounts via OpenAI login, and 75+ LLM providers through Models.dev, including local models. It installs via curl, npm, bun, brew, paru, or yay. The site states that free models are included and that OpenCode is open source, and it links to a privacy page for enterprise users, but it does not publish specific pricing tiers or plan details in the content provided here. In short, OpenCode is an open source AI coding agent built around three ideas: an agent that meets you in your terminal, IDE, desktop, or browser; open access to models from any provider, including free and local ones; and a privacy first design that does not store your code or context data. Backed by a large open source community and a curated set of benchmarked models through Zen, it gives developers a flexible, transparent alternative to a single-vendor coding assistant.

Fina is a flexible finance tracker that gives individuals, families, freelancers and small businesses one platform for both personal and business finance. It is built around modular financial tracking, meaning users organise money through profiles, categories, rules, templates and custom metrics that match the way they think about money rather than forcing everything into one fixed structure. The platform connects to bank accounts, categorises transactions automatically with AI, and presents the results through visual dashboards and AI-powered insights. Fina states that it is trusted by more than 20,000 active users and describes itself as the most flexible tracker in the world, built for clarity, control and real financial lives. Traditional budgeting and accounting tools tend to impose their own structure on users. Founders, in Fina's words, started a business, not an accounting department, and the Product Hunt listing positions Fina Business as a $20/mo alternative to QuickBooks that gives founders the essentials of business accounting without the complexity of traditional tools. On the personal side, Fina's testimonials describe users who left Mint, went back to spreadsheets, or found that other trackers did not convert currency values correctly across accounts. Fina's answer is a system that brings together the ease of spreadsheets with the power and flexibility of dashboards that are accessible to anyone, so people can keep an incredibly detailed view of their spending and a clear understanding of where their money goes. Getting started is described as taking minutes: get set up in minutes, stay in control forever. Users securely link all their bank accounts so their finances stay updated automatically with no manual work required. Fina then uses AI to instantly categorise transactions, while still letting users customise categories and rules when needed. Visual dashboards and AI insights sit on top of that data to help users budget smarter, understand spending and spot opportunities to grow. Fina describes this combination of secure data connections, smart rules and AI-powered insights as a way to reduce manual work and help users understand their finances faster. This core loop of connect, categorise automatically and review visually is the foundation on which the more advanced capabilities are built. Flexibility is the centre of the product. Multiple financial profiles let users manage personal, family and business finances separately, each with its own setup, while still viewing everything together when needed through a combined overview. Custom organisation lets users create categories, rules and structures that match how they think about money, not how an app expects them to. Templates and snapshots provide reusable building blocks. Because these elements are modular, users can assemble their own tracking system and reuse it across different dashboards. Fina's headline for this section is modular financial tracking and the most flexible tracker for all your financial needs, summarised on the site as a tracker that is unique as you. Advanced metrics and analysis let users build custom metrics using formulas, so they can track what actually matters to them beyond totals and balances, and those metrics can be reused across dashboards. Budgeting and planning support multiple budgets at once, each with custom time ranges, so planning is not locked into rigid frameworks. Under automation and AI, Fina lists secure data connections, smart rules and AI-powered insights. For sharing and collaboration, users can work with partners or clients through shared access, pages and reports while keeping separate logins, so clarity and control are preserved. Together these features let a single workspace serve very different financial workflows without forcing users to compromise on how their money is structured. Fina Business is the tier built for small businesses and freelancers who want to run invoicing and bookkeeping in the same place. It brings bookkeeping, invoice automation, customer records and real-time business cashflow into one workspace for operators who need clarity without extra accounting overhead. Automated invoice management lets users create, send, track and reconcile invoices from the same place they manage business finances. Double-entry bookkeeping keeps business records organised with bookkeeping workflows built around debits and credits. Connecting Stripe lets clients pay quickly and keeps incoming payments tied to their invoices so they get paid faster. Quick finance statements, including balance sheet and P&L reports, give a fast read on performance, while the business cashflow view shows live invoice operations such as open invoices, cash collected, amounts due this week and a 30-day cashflow forecast. Fina's approach is modular rather than prescriptive. Data enters the system through secure bank connections; AI categorises it instantly and rules refine it; profiles separate personal, family and business contexts; templates and snapshots make configurations reusable; custom metrics and formula-driven analysis turn transactions into insight; and dashboards present everything in one place. On the business side, the same workspace handles invoices, Stripe payments, customer accounts and double-entry bookkeeping. Because each piece can be combined and reused, the system adapts to the user's own mental model of money. The site also links to API / MCP documentation, templates, categories, a glossary and a changelog, alongside comparison pages for other finance tools. The stated outcome is clarity and control. Fina promises to create clarity across all your finances with a system that adapts to you, and to let users get set up in minutes and stay in control forever. Testimonials on the site describe going from never tracking finances to checking Fina all the time and feeling much more in control of money; one user says tracking money helped them go from $260,000 in debt to millionaire in 10 years; another describes having an incredibly detailed view of spending and a clear understanding of where their money goes. Reviewers also highlight simplicity, versatility and an intuitive experience, with one long-term user saying Fina is the best financial management tool they have used. Concrete use cases described on the site include managing personal, family and business money through separate profiles with a combined overview; building budgets and financial plans across custom time ranges; creating custom categories, rules and metrics for spending analysis; and collaborating with a partner or client through shared pages and reports with separate logins. For businesses and freelancers, use cases include creating and sending invoices, tracking and reconciling them, managing customer accounts, accepting Stripe payments tied to invoices, producing balance sheets and P&L statements, and monitoring business cashflow with open invoices, cash collected and amounts due. The testimonials add scenarios such as replacing Mint, moving away from spreadsheets, and handling multiple currencies across accounts. Fina is aimed at people who care about clarity: individuals and families managing personal finance, professionals and advisors, and founders, small businesses and freelancers who need lightweight business accounting. Product Hunt lists it under Business, Accounting and Budgeting, and the listing calls it the flexible $20/mo QuickBooks alternative for founders. Integrations explicitly mentioned include bank connections, Stripe for payments and Google Sheets for transactions, plus links to API / MCP documentation. Pricing has three tiers, all with a free trial available. Free is $0 for personal finance clarity and includes transactions in Google Sheets, one personal finance portfolio, fast manual entry, smart AI categorisation and unlimited custom templates. Premium is $8.25/m billed yearly ($12/m billed monthly) for advanced setups and multiple profiles, adding unlimited bank connections, multiple custom portfolios, multiple currencies and partner collaboration. Business is $20/m billed yearly ($25/m billed monthly) for founders and professional use, including all Premium features plus automated invoice management, Stripe payments, customer account tracking, double-entry bookkeeping, and balance sheet and P&L reports. Fina also offers a referral perk: invite 3 friends and get Premium free for life. Fina's primary value proposition is flexibility with clarity: one modular platform where personal, family and business finances can be tracked, automated, analysed and shared without being forced into a rigid structure. With AI categorisation, custom metrics, multiple profiles and budgets, invoicing, Stripe payments and double-entry bookkeeping, it covers both personal finance tracking and the essentials of business accounting, starting free and scaling to $20/mo for founders who want clarity without extra accounting overhead.

Takes for Mac is a free, open-source Mac app for making talking-head videos. You read your script from a teleprompter that scrolls as you speak, record a few takes and pick the best one. Claude Code, running on your own Claude plan, then storyboards the video, cuts the edit, produces the vertical version and writes the posts. The whole loop — Storyboard, Record, Post — lives in one place, so going from an idea to a posted video does not mean jumping between a scripting tool, a camera app, an editor and a scheduling tool. It is made for the person in front of the camera rather than for someone who wants to spend the evening in a timeline. The problem Takes is built around is the editing tax on talking-head video. The demo on the site opens with a line many creators recognise: "Every video I post takes me four hours to edit." Most of those hours are not creative. They go on hunting silences, trimming false starts, lining up captions, exporting a second vertical cut, then rewriting the same message for LinkedIn, an X thread, YouTube, Shorts, Reels and TikTok. Takes aims to move that work onto an AI agent that already understands the footage, while you stay focused on the part only you can do: being on camera and saying something worth watching. Storyboard is the first step. Claude writes the script and splits it into shots, drawing a sketch for each one. Every shot shows its line, how to film it and a Record button, so instead of reading a wall of text you look at seven or so frames that carry the video from a hook through the main body to the end. The built-in chat is a Claude Code chat that sits next to your takes. It reads the same folders the app writes, so it already knows the script, the best take and the posts. You can ask it in plain words — for example, "Cut my best take into a vertical short. Keep it tight, no silences" — and it works through the steps, reporting what it changed. The same chat can also draft the posts when the edit is done. Record is where the camera work happens. A teleprompter sits beside the camera and follows your voice, so you never lose your place and never look away from the lens. Each shot offers a few opening lines to choose from, so you can start strong instead of settling for the first thing you wrote. You record as often as you need and mark the best take; the app keeps every one in a visual list. Takes records the camera and the screen at the same time, producing two files that are recorded in sync, which means you can show a screen recording or a product demo inside the same talking-head video. Post is the publishing step. Takes shows a post for each platform the way it will actually look there — LinkedIn, an X thread, YouTube, and vertical formats for TikTok, Reels and Shorts. Line breaks, the "more" cut-off, the thumbnail and the caption all sit where the platform puts them, so you review the real thing rather than a text box. You can try other versions and then schedule the post. Styles control the look of every edit — captions, titles and colours — so the visual identity stays consistent across videos without you rebuilding it each time. Feedback is what makes the edits improve over time. You point at the frame and say what is wrong: pick the moment, mark the area, type the note. Takes fixes it in a new version and replies with what changed. The rule and the parts you liked are saved to your style, and the next video starts from there. The site shows the effect with a chart of notes per video dropping from six to three to one, and a style panel listing rules such as "Cut every silence longer than 0.4 s", "Calm music, well under the voice" and "Captions sit low, never on a face". Underneath, Takes deliberately avoids lock-in. Your library is plain folders: the app records into ~/Movies/Takes, one folder per project, one per video, with Markdown scripts, .mov takes and an edits folder. Claude Code works on the same files, and when a file changes the app shows it at once. Nothing lives in a cloud you cannot open, and you back the folders up the way you back up anything else. Claude Code can be driven from the chat panel inside the app, or from any compatible agent. On first launch Takes adds the takes MCP server to each agent it finds on your Mac — Claude Code, Codex, Cursor, Gemini CLI, Antigravity, opencode, pi and Claude Desktop — and Claude Code also gets two skills, so you can ask any of them to "storyboard my next Takes video" or "cut my last take". The stated benefits are time and cost, not features. Edits happen on work the agent can do, so you write fewer notes each time and the next video arrives closer to finished. Because the AI runs on your own Claude plan through Claude Code, there is no API key and no extra bill — Takes never sees a bill or an API key, and it adds no cost on top of the plan you already pay for. Scripts and posts keep every version, so anything can be undone. The app has no account and sends no usage data, and the code is public on GitHub, with an 85-line install script you can read before running it. Typical use looks like a weekly posting rhythm. A founder wants a LinkedIn post and a short: they storyboard a seven-shot video, read off the teleprompter, mark the best take, ask for a vertical cut with silence removal, then ask for the posts and schedule them. A creator publishing to YouTube and Shorts films once with camera and screen in sync and gets both a 16:9 edit and a 9:16 short from the same take — the site's own demo shows ai-week-v1 at 16:9 and ai-week-short-v1 at 9:16 cut from the same footage. Someone who already uses Codex or Cursor can drive the same library from that agent instead of the built-in chat. Takes is aimed at people who talk to a camera and would rather not edit: founders, creators, marketers and anyone posting regularly to LinkedIn, X, YouTube, Shorts, Reels and TikTok. It is a Mac app and needs Apple silicon, macOS 15 or later, and a paid Claude plan (Pro or Max) for Claude Code. The makers stress it is not only for technical users: you paste one line into Terminal once, and after that you work in the app, reading off the teleprompter, picking the best take and asking the chat in plain words. Installation can also be done by downloading the .dmg. Takes is free and the code is public, made by Marvin Aziz, with new versions shipping several times a week and an iPhone app in the repo that you build yourself with your own Apple developer team. Takes for Mac takes the repetitive parts of talking-head video — silences, false starts, vertical re-cuts, per-platform captions — and hands them to Claude Code, while keeping the teleprompter, the takes and the posts in one plain-folder library on your own Mac. You talk; Takes does the rest.

Agent Craft is a team social media system that turns a company's employees into a coordinated content engine. Instead of asking marketers to write posts for a single company page, Agent Craft has each team member answer a daily prompt in a mobile app with a short voice note or video of about three minutes. The product then handles the formatting, narration, logo and URL placement, hashtags, classification, analysis and adaptation to each platform's best practices, and publishes the results across eight social platforms from each person's own account. A single web dashboard at app.agentcraft.ai collects everything for review, so nothing goes out unseen. Agent Craft is built on a simple observation stated on its site: people follow people, not logos. The same message delivered from a real team member's account earns roughly seven times the engagement of a company page post, because customers get to know the humans behind the business and trust follows. A company page today is one account with one voice — a logo avatar and a marketing voice — which the site describes as a 1× baseline. Real numbers published by Agent Craft from its own three-person team show the gap in practice: over the 30 days to September 13, 2026, exposure reached 234.1K, up 183% on 82.7K in the previous 30 days; engagements hit 13.3K, up 377% on 2.8K, including 10.4K likes, 1.8K comments, 854 shares and 225 saves; the engagement rate reached 5.7%, up 2.3 points from 3.4%; and 285 posts were published, up 115 on 170, across 26 active days at 11.0 posts a day with a longest gap of two days. The starting point is the Agent Craft mobile app. Every team member receives one prompt a day — a question about their work, a customer, or the week's news — and the prompts are tied to your campaigns so the whole team stays on message. The site gives an example prompt: "What's one thing a customer taught you this week?" The person answers by talking rather than typing, the way they would explain it to a colleague, with no scripts and no editing. A recording takes about three minutes, after which it flows automatically to the dashboard for review. Agent Craft states that team members do not need to be marketers: if someone can talk for three minutes about their work, they can use the product, which was built for teams where the people with the most expertise have the least time to write. Once a recording lands, Agent Craft runs the "Craft" step. The system handles formatting, narration, the placement of your logo and URL, hashtags, classification, analysis, and adaptation to each platform's best practices, producing platform-native versions of the content in the speaker's own voice. Agent Craft describes the flow as three steps: record a short voice note or video in the app about a customer story, a lesson or a take on the news; let Craft handle formatting, narration, brand assets, hashtags and platform-native versions; then review and publish through a single queue — one approval covering eight platforms. The eight platforms named across the site are LinkedIn, TikTok, YouTube, Instagram, Facebook, X (Twitter), Threads and Bluesky. All team content flows through one unified web dashboard where it is approved, edited and kept on brand before it posts. Three capabilities are called out. Approve means one queue for the whole team, so nothing ships unseen. Edit means any post can be refined before it goes out, per platform. Stay on brand means templates put your visuals, logo and URL on every post. Because the queue is shared, managers always know what is going out, from everyone, everywhere. The summary the product uses for the workflow is "3 min a day, 8 platforms, 1–1,000 teammates, 1 review queue." Agent Craft's methodology is captured in its "math" section: 7× engagement × 8 different platforms × number of team members = 100× the engagement you currently get from social media. The site breaks that down step by step — a company page today is one account with one voice at 1×; one teammate's account, because people follow people, performs at 7×; distributing that across eight platforms where each post is adapted natively reaches 56×; and putting the whole team to work in unison takes it to 100×. Coordinated campaigns are what hold it together: a promotion, a customer win or industry news goes out across the team in the same week, with each post delivered in that individual's own voice, while custom templates close every post with brand visuals, logo and URL. The outcomes Agent Craft publishes are engagement, reach, follower growth and qualified pipeline. Case studies on the site include Dr. Dominick Gadaleta, a facial plastic surgeon, with +644% and 73.3K impressions in 30 days, a 6% engagement rate that the site calls twice the industry standard, and 13.9% follower growth (+2,561 in 30 days). Mindset AI, a B2B AI SaaS company, recorded +296% and 148.9K impressions in 30 days, a 5.4% engagement rate described as almost twice industry standard, and 1.5% follower growth (+611 in 30 days). Juan Mouton, a fractional CMO, saw 41.3K views in 15 days, a 2.2% engagement rate and 2 new clients in 15 days. Agent Craft's own accounts reported 326.4K impressions in 90 days, reach +812% and a 5.1% engagement rate, and the site notes that many of its earliest users started brand-new accounts and began posting immediately on sign-up. On the lead side, Agent Craft moves "from engagement to pipeline." Hundreds of thousands of engagements generate hundreds of comments and DMs, and Craft reads every one across every teammate and all eight platforms. It captures each comment and DM, automatically qualifies buying intent and separates it from noise while keeping the context of what the person engaged with, and pushes qualified leads to your CRM where the sales team already works. The site illustrates this with an example inbox: a comment from a 40-person sales team asking how onboarding works, a DM from someone looking to replace their agency by Q1, and more than 412 further engagements in a week, with the buyer-intent messages surfaced as hot leads. The stated goal is that reps focus on the conversations that matter instead of sorting through everything manually. Agent Craft is designed for teams that want many voices rather than one, and the dashboard supports from a team of 1 to a team of 1,000. Published examples include a solo facial plastic surgeon, a B2B AI SaaS company, a fractional CMO, and a startup posting around a Product Hunt launch. Pricing is $95/month for the first teammate and $75/month for each additional teammate, and the plan is stated to include everything: unlimited recordings, 8 platforms, custom brand templates, analytics and dedicated onboarding. A 30-day free trial is available with no card needed, and teams can also book a 20-minute demo in which Agent Craft maps the first week of coordinated posts. Comment and DM data is pushed to the CRM the sales team already uses, and the mobile app handles recording while the web dashboard handles review and publishing. Agent Craft's core promise is that three minutes a day per person, multiplied across a whole team and eight platforms, produces engagement and leads that a single company page cannot. It removes the two biggest obstacles to consistent social posting — deciding what to say and doing the production work — by prompting each teammate for a spoken answer and handling everything from narration and brand templates to platform-native formatting, review and publishing. The result is coordination without scripts, brand consistency without bottlenecks, and a flow of qualified conversations moving from social engagement into the CRM.

nestd.ai is a property intelligence platform that turns listing photos into structured property features and then into professional, room-by-room listing descriptions. You can paste an Airbnb or Booking.com URL or upload interior, exterior, and amenity photos, and the platform analyzes each room, extracts visible materials, finishes, and appliances, and generates description copy that matches what is actually there. The pitch is simple: photos in, structured features out, then professional descriptions. It is built for landlords, Airbnb and Booking.com hosts, real estate agents, and agencies or catalog operators who need listing copy without starting from a blank page. Writing listing descriptions is slow and repetitive. As nestd.ai describes it, hosts and agents can spend 45 minutes staring at a blank page for every listing, or end up with descriptions that all sound the same because they run out of ways to say "spacious." Generic one-shot AI prompts often produce generic fluff that does not match the photos, missing room-level detail. Meanwhile, on the guest and buyer side, catalog filters cannot answer a request like "a quiet place with a real kitchen." nestd.ai addresses both problems by turning photos into a structured feature graph that descriptions — and natural-language search — can be written from. The first feature group is import and upload. You can paste your Airbnb or Booking.com listing URL and nestd.ai pulls the title, room details, location when available, and photos, which skips the blank form and gets you to analysis faster; you must own or manage the listing. Alternatively, you can upload your own photos — interior, exterior, and amenity shots — and smartphone photos work perfectly, so professional photography is not required. The system then groups the uploads into rooms, so a set of twelve listing photos becomes an organized set of spaces rather than an unstructured dump. Once photos are in, nestd.ai builds a structured feature analysis. It identifies each space (kitchen, bedroom, living room, and so on) and extracts materials, finishes, appliances, and style, along with condition signals. When you add a location, it also researches nearby amenities, transit and accessibility signals, schools, neighborhood vibe, and local characteristics. The platform then suggests a listing name, property type, and room counts from the photos, plus a location when EXIF GPS is available, and you can review and edit anything before continuing. This review step means the structured analysis reflects your property accurately before any copy is generated. From that analysis, nestd.ai generates descriptions. You choose the style (marketing or social), length, language, and target audience such as families, couples, digital nomads, professionals, or a custom audience, and the AI adapts the wording so it speaks to the right guests or buyers. Every property gets deep analysis: room-by-room identification of kitchens, bedrooms, and bathrooms; feature extraction of materials, appliances, and finishes; style classification such as modern, traditional, or luxury; quality assessment of condition and renovation level; location intelligence covering nearby amenities, accessibility, and local character; and target audience selection. The same structured features also power natural-language search on Enterprise, so guests and buyers can find properties by how they actually think rather than by checkbox filters. nestd.ai's approach differs from generic one-shot prompts because it looks at each room, not just the first photo. The workflow runs in four steps: upload your photos or import a listing; the AI builds your feature analysis; you confirm suggested details; then you generate descriptions from the analysis. You can review the complete property breakdown in your dashboard — rooms identified, features extracted, style classification, and location insights — before generating copy. The outcome is descriptions that mention the marble countertops in the kitchen and the barn doors in the bedroom because those details were actually extracted from the images. No prompt writing is required to get good results: you upload photos and a location, and the platform analyzes rooms, features, style, and neighborhood context automatically, with the option to adjust style, tone, and length and add an additional prompt if needed. The benefits are speed, specificity, and consistency. Landlords can generate descriptions for multiple units in minutes instead of hours, and can see the complete analysis before committing. Hosts get compelling descriptions that can help get more bookings without hiring a copywriter. Agents get consistent, high-quality descriptions for every property they represent, speeding up the listing process. Users can regenerate descriptions as many times as they want and adjust style, tone, or length until it is right, and can tailor the copy to different guests or buyers. Because the analysis highlights what makes each property unique, descriptions avoid sounding like every other AI description. As the site puts it, every description written by hand is an hour that could be spent on showings, client calls, or closing deals. Concrete use cases appear throughout the product. A luxury villa example uses vaulted ceilings, a fireplace, a kitchen island, and a barn-door primary suite as extracted features turned into a social-style description, with neighborhood context such as wildlife refuges and local restaurants. A coastal Cabarete apartment for digital nomads turns an open plan, private balcony, and resort pool into marketing copy. A compact Warsaw apartment uses a galley kitchen, accent wall, and built-in wardrobe. Beyond these examples, hosts and agents import listings and generate copy, and agencies and catalog operators use the same photo analysis to power natural-language search across a catalog. Demo mode lets users search example properties with natural language even without uploading photos. nestd.ai is designed for landlords, Airbnb and Booking.com hosts, real estate agents, and agencies or catalog operators. The main integrations are Airbnb and Booking.com listing import, and an Enterprise path for embeddable natural-language search for catalogs and platforms. The product runs on the web at app.nestd.ai. There is no credit card required to start: new users get free credits with no expiration, and instant demo access is available. In short, nestd.ai turns photos into property intelligence. By replacing manual feature forms and blank prompts with a structured, room-by-room analysis of what is actually in the photos — plus location context and audience targeting — it helps anyone listing a property produce accurate, specific descriptions faster.

EmbeddingGemma 2 is an open, lightweight multimodal embedding model from Google DeepMind. It natively maps combinations of text, code, images, audio, and video into a unified embedding space, so a single query can search across all of them. The model is intended for developers building on-device applications: apps that organize, search, and connect information directly on consumer hardware. Released under the commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, making it optimal for on-device inference, and its model weights are available on Hugging Face and Kaggle. The original EmbeddingGemma was introduced to give developers a lightweight option for high-quality text embeddings. That release was used to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines, and it surpassed 20 million downloads as the developer community's response blew past expectations. EmbeddingGemma 2 addresses the next step: content is no longer only text. Users have voice memos, image libraries, video archives, and code repositories, and cloud embedding services can add latency and raise privacy concerns because data has to leave the device. EmbeddingGemma 2 unifies those modalities in a shared space so developers can build cross-modal search and retrieval that works entirely offline. EmbeddingGemma 2 is described as the most capable model for on-device multimodal embeddings. It is built from the same technology as Gemini Embedding models and is positioned as best-in-class for its size, achieving leading scores among sub-1B multimodal embedders on benchmarks such as MTEB (Massive Text Embedding Benchmark) Code and MAEB (Massive Audio Embedding Benchmark). According to Google, it matches or outperforms many larger models across text, vision, and audio tasks, and across image, video, documents, and audio it sets a new standard in quality-per-parameter for sub-1B models, even outperforming some specialist models more than twice its size. It also matches the strong multilingual text performance of the original EmbeddingGemma while improving code performance significantly: the MTEB Code score rises from 68.76 to 78.68, a 9.92-point improvement. That makes the model well suited to local codebase indexing, semantic code search, and coding agent retrieval. Modular by design, EmbeddingGemma 2 requires as little as 270M parameters for text-only workloads, with optional vision (170M) and audio (300M) encoders available for full multimodal support. Developers can therefore scale the deployed footprint to the workload instead of always shipping the largest configuration. Storage efficiency is handled through Matryoshka Representation Learning (MRL): output vectors can be dynamically truncated from 768 dimensions down to 512, 256, or 128 dimensions, which provides up to 6x storage reduction for local vector databases and reduces memory usage. For apps that keep embeddings on the device, this directly affects how much media and content can be indexed and searched locally. The model is optimized for on-device performance and runs efficiently within tight resource constraints. With quantization, on a Google Pixel 11 Pro, EmbeddingGemma 2 requires as little as approximately 191MB of active RAM for text-only weights and roughly 567MB for the full multimodal model. It also features an 8K token context window, four times larger than EmbeddingGemma 1, which allows it to process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations of those inputs directly on local hardware. That extended context lets a single embedding cover a substantial piece of real content, such as a recorded clip, a set of images, or a sequence of video frames, rather than only a short text snippet. Overall, EmbeddingGemma 2 brings robust embedding capabilities directly to edge hardware. Generating embeddings locally helps ensure data privacy, reduces pipeline latency, and empowers developers to build cross-modal search and retrieval that works entirely offline. Because the model is built on the Gemma 4 architecture and shares its text tokenizer and audio encoder, developers can run EmbeddingGemma 2 together with generative models such as Gemma 4 in a unified pipeline with a lower combined total memory footprint. That pairing enables on-device RAG pipelines that understand complex multimodal data, for example using EmbeddingGemma 2 for local file retrieval and Gemma 4 for contextual reasoning. The benefits described by Google follow directly from running embeddings on the device: data privacy, since content does not need to be sent to a cloud service; reduced pipeline latency; and offline operation, so search and retrieval keep working without a network connection. The modular parameter counts and MRL truncation reduce memory and storage requirements, letting developers index more content within a device's constraints. The quality gains in code, vision, and audio mean cross-modal search is practical at this size, and the extended context window means embeddings can represent longer stretches of audio, images, or video rather than only short text. Google demonstrates several concrete use cases. In Google AI Edge Gallery's Instant Media Search, users find the top matches in a media library using text or an image, ranked by semantic similarity. Video Moments Finder locates specific moments in video using text or audio queries. The Google AI Edge Foresight app pairs EmbeddingGemma 2 for local file retrieval with Gemma 4 for contextual reasoning. Developers can also create real-time decision engines that leverage multimodal context for classification, routing, and predictive capabilities via the MediaPipe Decision Task API. Because of the code improvement, the model is well suited for local codebase indexing, semantic code search, and coding agent retrieval. The launch example also describes finding a specific video clip from a voice memo, or searching through hours of audio recordings based on a text query, all handled by a single natively multimodal model. EmbeddingGemma 2 is aimed at developers building on-device and edge applications, including those working on search, retrieval, and RAG experiences. Getting started is supported by model weights on Hugging Face and Kaggle, with Gemini Enterprise Agent Platform Model Garden availability coming soon, and LiteRT Community on Hugging Face for models optimized for on-device use. For on-device deployment, developers can use Google AI Edge MediaPipe for turnkey embedding, retrieval, and decision tasks, or LiteRT for custom model integration; browser builds are possible with transformers.js or WebGPU. The model can be served with transformers, sentence-transformers, MLX, vLLM, llama.cpp, SGLang, Ollama, and LMStudio, embedding vectors can be stored with Qdrant, and Unsloth provides guidance on fine-tuning. Google also points to a developer guide, documentation, and specific inference and fine-tuning guides. The model is released under the commercially permissive Apache 2.0 license. EmbeddingGemma 2 continues the EmbeddingGemma line by moving lightweight embeddings beyond text. It unifies text, code, images, video, and audio in one shared embedding space, stays small enough to run on-device, and stays flexible through modular encoders and truncatable vectors. For developers who need privacy-first, low-latency search and retrieval that works offline, it provides a single multimodal model for cross-modal search, on-device RAG, and real-time decision tasks.

Ancher is an AI knowledge workspace that turns everything you read, watch, and save into work you can actually use. It is built for people who read, watch, and save for a living — knowledge workers, founders and indie hackers, investors, researchers and academics, creators, product, growth and marketing teams, and consultants. Instead of starting from a blank page, Ancher starts with everything you consume: you capture articles, videos, PDFs, social posts, podcasts, screenshots, and ideas, and the product connects that knowledge, answers questions across it, and helps you produce research briefs, reports, presentations, and content in your own voice. The promise is simple and repeated throughout the site: remember everything, create anything. The problem Ancher addresses is one that anyone who consumes a lot of content knows well. The products people use today start in the wrong place. As Ancher's own comparison puts it, Notion is built for documents and collaboration — it starts with a blank page, and you write it down. Obsidian is built for markdown and local knowledge — it starts after you collect, and you build it yourself. Both are excellent at what they do, but neither begins with the raw material of your thinking. Meanwhile, the material itself is slipping away: articles, videos, social posts, and podcasts disappear or become impossible to find again, and the exact part you remember from a rough description is often unrecoverable. Ancher's answer is to begin from consumption rather than from a blank document, so that everything you encounter is already connected by the time you need to create something from it. Capture is the first stage of the loop. Ancher lets you drop in URLs, PDFs, podcasts, videos, and screenshots — anything you encounter — and it accepts them from the browser, Slack, Telegram, or email, so you can save from wherever you already are. There is no tagging, no folders, and no organizing; the design intent is explicitly to let you capture and move on. The platform also saves directly from every feed, grabbing posts, threads, and videos straight from X, LinkedIn, YouTube, Reddit, Instagram, and more, plus any link, PDF, or screenshot you come across. Because saving is frictionless, the habit sticks, and the archive grows without the maintenance overhead that usually kills knowledge systems. Connect is where the stored material becomes more than a pile of links. Ancher automatically links every new piece of content to what you have saved before, surfacing patterns, contradictions, and evolving themes on their own. The company describes the effect as compounding: the more you feed it, the sharper it gets, and your knowledge compounds silently in the background. Alongside that automatic linking, every source is worked into a clean, skimmable note with key points, takeaways, and keyframes pulled straight from videos — no formatting required from you. A contribution heatmap visualizes your saving activity, turning the growth of your second brain into a habit you can actually see. Retrieval is handled by an agent rather than a search box alone. You can ask your knowledge anything across everything you have saved, and the answers are grounded in your own sources rather than the open web — which matters when accuracy and provenance are the point. Ancher also lets you find the exact part of a source from a rough description, so a half-remembered clip or paragraph is still reachable. Collections turn this into working context: you group related notes into a collection and hand that collection to the agent, so creation draws from exactly the sources you chose. That combination of grounded answers and scoped context is what separates the workspace from a generic chatbot. Create is the payoff. Ancher produces real deliverables rather than chat replies: memos, newsletters, board decks, and research briefs. Everything it generates is grounded in what you have actually consumed rather than generic AI guesswork, and it is written the way you write — the site's framing is that your editor will not know Ancher helped. Output can include charts, diagrams, and illustrations generated to match the deliverable, and the same knowledge base supports posts alongside longer documents. The company's own activity numbers describe over 1.1 million creations shipped, covering notes, briefs, posts, decks, and documents, which gives a sense of the range of formats the workspace is expected to produce. The overall method follows one loop: save, recall, create. Consumption enters the system without friction, the system connects and structures it automatically, and the agent retrieves from it with your sources as the ground truth before generating output in your voice. Ancher positions this as the new way to think, learn, and create, and it works with ChatGPT and Claude, so it sits alongside the AI tools people already use rather than replacing them. The critical difference from note-taking apps is the starting point: Ancher starts with everything you consume, rather than starting from an empty page you must fill in yourself. The stated benefit is that your saved knowledge becomes more useful every time you add to it. Because new material is linked to old material automatically, the archive does not decay into an unfindable backlog; it sharpens. Users gain recall of the exact fragment they remember from a rough description, answers that trace back to their own sources, and deliverables that reflect genuine reading and watching rather than generic model output. Framed in the site's own terms, this is described as an unfair advantage: you consume hours of content every day, and Ancher turns that consumption into work you can ship. Ancher publishes dedicated workflows for six audiences. Founders and indie hackers turn everything they track into their next move. Investors build conviction from everything they have read. Researchers and academics turn scattered sources into a brief they can trust. Creators create from everything they have already consumed. Product, growth and marketing teams keep every insight where they can use it. Consultants show up with the work already assembled. The same save-recall-create loop is reshaped to each of these jobs, which is why the platform frames itself around workflows rather than a single generic feature set. Ancher is available as a web app for saving, searching, and creating from any browser with nothing to install, as a mobile app for saving links, screenshots, and ideas from a phone with syncing everywhere, and as a browser extension for one-click capture of any page, video, or post. Pricing starts free: the Free plan is always free and includes 100 credits per month, all core features, and community support. Digest costs $5 per month, or $59.99 billed yearly, and adds 5,000 credits per month plus email support. Pro costs $15 per month, or $179.99 billed yearly, and adds 20,000 credits per month, priority support, and early access to new features. Yearly billing saves up to 20 percent. On security, Ancher states that data is protected with the same encryption banks use, that notes, files, and conversations are never used to train AI, that you can sign out of any device in a single tap and erase everything you have stored, and that Google sign-in uses only your name, email, and profile to create, secure, and open your account. Ancher's core proposition is that the content you already consume should be the raw material for the work you produce. By removing the organizing burden from capture, connecting new material to old automatically, grounding answers in your own sources, and generating deliverables in your own voice, it aims to make a knowledge base that compounds rather than a bookmark list that rots. The tagline the company uses captures it: turn everything you read and save into work you can ship.

SquadRoom is a workspace where a squad of specialist AI agents works for you. According to the site, they are "AI employees that finish the work": you write the Task, you read the Report, and everything in between is the squad's job. You build the roster, put Tasks on a board, and the agents do the work, hand off to each other, and report back with proof. When you come back, the work is finished and a report shows every step and every source. The product is aimed at people who want work completed rather than a conversation, and the site advises writing a Task "the way you'd brief a new hire." A live demo with a sample company loaded lets you explore the real product, and there is no signup required for the demo. The problem SquadRoom addresses is the turn-by-turn nature of ordinary AI chat. As the FAQ puts it, you don't prompt it turn by turn: you put a Task on the board and one agent owns it, then it wakes up, reads its brief, does the work, and every step lands in Activity, Docs and a final Report. Work is therefore not something you have to supervise continuously. The site stresses visibility with the phrase "Nothing happens in the dark," and the product is designed so that agents only stop to ask you when a decision really needs you. Product Hunt notes that nothing goes out without your OK. Instead of chat logs that fade away, each Task produces a receipt showing what got done, how it got done, and the exact versions delivered. The first core capability is the Task board and routing. One line on the board gets one owner straight away, and nothing runs without an owner. Ava, the Chief of Staff, reads every new Task and hands it to the specialist whose job it is. A Task can be an Epic, with child Tasks appearing as specialists pick up stages, and the board tracks states such as Todo, In Progress, Blocked and Done, along with priority levels including high, medium and low. Every move is visible in an Activity feed as it happens, so a Task such as "Research and rank 15 integration partners" or "Turn three customer wins into proof briefs" can be followed step by step from assignment to completion. The second capability is the way agents ask for help. When an agent gets stuck it raises a Ticket: one to three plain questions, with what they found and a recommended answer, and only that one Task waits. A demo Ticket, for example, asks which verified partner batch should be contacted manually first and offers two options, one of them marked as recommended. You can reply in the Ticket inbox or in Chat, and the Task picks back up on its own. This keeps decisions with the human while letting the rest of the squad continue working, so a single question does not stall the whole board. The third capability is Reports and Docs. Every Task ends in a Report describing what got done, how it got done, and the exact versions delivered, which the site describes as "a receipt, not a chat log." Outputs live in Docs, a shared knowledge base where every save is a new version: nothing gets overwritten, any Doc can be rolled back without losing history, and any Doc can be shared with a link. Reference material can be uploaded for the squad to use, and everything they deliver lands there too. A partner outreach shortlist Doc, for instance, shows multiple versions with contact names, statuses such as verified or parked, and a note explaining why ten partners were parked rather than deleted. The squad itself follows a clear structure: one lead routes, specialists own the work. In the demo workspace the roster includes Ava as Chief of Staff, Scout in Market Research, Rowan in Partnerships, Quinn in Operations and QA, and Mira in Content Strategy. Specialists pitch in on each other's Tasks, review each other's work, and the lead groups related Tasks into Epics. Because Tasks can be blocked on dependencies, the board shows clearly when one agent is waiting on another's output, for example drafts blocked until a research shortlist is ready. The overall methodology is captured in four steps the site lays out: you assign, they work, they ask only when stuck, and you get a Report. In detail, one line on the board gets one owner immediately; the right specialists pitch in and every step shows up in Activity as it happens; a short question with findings and a recommended answer pauses only that one Task; and finally you receive a Report covering what got done, how, and the exact versions delivered. Scheduled Jobs and Playbooks let recurring work run on its own, and the Calendar view shows what the squad will run today, such as a partner signal scan at 09:00 or a pipeline receipt check at 16:30. A distinctive part of the approach is the bring-your-own-key model. You connect a ChatGPT account or a key from OpenAI, Anthropic, Google, Azure OpenAI or OpenRouter, give each agent the model that fits, and pay the provider directly with no markup from SquadRoom. The demo shows Ava on GPT-5.5, Scout on Gemini Pro, Rowan on Claude Sonnet, Quinn on GPT-5.5 and Mira on Claude Opus. The site frames this as "Your key. Your bill. No markup," and a sample month shows the SquadRoom platform fee billed by SquadRoom while model usage stays on your own provider bills. There is also a usage view showing what each API key has spent and credits consumed by day. Integration coverage is broad: 24 integrations and any MCP server. Named integrations include Gmail, Google Calendar, Notion, Slack, GitHub, Linear, HubSpot, Stripe, Google Analytics, Google Search Console, Google Drive, Google Sheets, Google Docs, Google Slides, Outlook, YouTube, Reddit, LinkedIn, Trello, GitLab, Semrush, Microsoft Clarity, Segment and PostHog. Any MCP server can be added by pasting its URL, and agents pick up every tool it exposes. Chat also works over Telegram so briefs, replies and Tickets reach your phone. Each connection has a scope such as Read, Write or Full, destructive actions need a confirmation in Chat, and tool permissions can be set per agent. Research is built in rather than bolted on. Agents have web search, full page reads and seven social platforms available with nothing to set up, covering Web, LinkedIn, X, Reddit, YouTube, Instagram, Facebook and TikTok. Findings land in a Doc and the Report cites each source, so a weekly signal sweep or market research Task produces a traceable document rather than a loose summary. For users, the benefit is finished work with evidence attached. Because every Task has an owner, every step appears in Activity, and every Task ends in a Report, you can see how an outcome was reached and which versions were delivered. Decisions stay with you: agents only stop when a decision really needs you, and nothing goes out without your OK. Work continues across the squad even while one Task waits on your answer, and customers are not contacted unless you approve it, as the proof brief example makes explicit. Concrete use cases shown in the demo include preparing a verified partner outreach sprint, where Scout scores 15 companies and recommends five, Quinn verifies contacts against two sources, and Rowan drafts five personalized partner emails; turning three customer wins into proof briefs with every claim tied to an approved source; reconciling CRM owners and lifecycle stages across reviewed records; running scheduled checks such as partner signal scans and pipeline receipt checks; and ongoing market research that ranks expansion-ready customer accounts and lands findings in versioned Docs. SquadRoom is delivered as a web workspace. Pricing is one flat plan for all the agents: the current early-access price is $49 per month, down from $99, with a 7-day money-back guarantee and the ability to cancel any time, and you keep the early-access price for as long as you stay subscribed. The plan lists unlimited AI agents, Tasks, Tickets and Reports, versioned Docs with share links, scheduled Jobs and Playbooks, Chat including Telegram, bring your own AI key, per-agent tool permissions, and 1,000 research credits a month. Model usage is paid directly to your chosen provider. A live demo with a sample company is available to try without signing up. In short, SquadRoom turns a prompt-by-prompt AI assistant into a squad of specialist AI employees that take a Task from assignment to a finished, documented result. You write the Task, the lead agent routes it, specialists do the work and hand off as needed, and you read a Report that shows every step, every source and the exact versions delivered, with your own AI key and a single flat platform fee.

Understory is a Mac notes app for people who take notes in order to write something. It is built for students, researchers and bloggers, and for anyone carrying a research paper, an essay, a blog post, a video script or even a book from the first note to the finished draft. The app is described as part outliner and part notes app: you collect your research, shape it into an outline, write the draft, and then publish the finished piece, all inside one application and without copying anything from one app to another. The problem Understory addresses is the gap between taking notes and writing. Notes tend to be taken in one place while the writing happens in another, so material has to be moved across before it can be used. Understory closes that gap by treating notes as the raw material of a draft rather than as a separate archive. It also distinguishes itself from a conventional outliner: an outliner is good for shaping ideas into a hierarchy, but every item in it is a bullet, whereas in Understory every item is a whole note written as regular text, and the same note can appear in several outlines, or in none at all. The first step in the workflow is collecting. Understory is designed for taking notes as you read, chapter by chapter: a note is just text, and you do not have to come up with a title or a filename before you start writing. You keep each quote or idea as a short note filed under the book and the chapter it came from, so the source of every thought is preserved in the structure of the notebook rather than in a separate record. Notes can also be linked to one another; the app's own material shows reading notes filed chapter by chapter, with one note linking to a related note taken from another book, so ideas that cross between sources stay connected without being merged into a single block of text. The second step is outlining, where the argument is built out of the notes already taken. You add a note for each main point and then put the notes you have already collected underneath the points they support, moving things around until the structure works. Because every item in the outline is a full note rather than a bullet, the same note can sit in more than one place, so an idea can belong to every subject it touches. That means a single piece of research can support several arguments at once without being duplicated or rewritten, and it can also sit in no outline at all until it is needed. The third step is writing the draft. Each point is drafted in its own note, with the research you gathered listed right beneath it in the sidebar, so the supporting notes stay visible while the section is being written rather than sitting in a separate window or document. Selecting the main points lets you read the whole piece from beginning to end as one continuous draft, which gives you a way to check how the sections read together before anything is published. The app shows sections being drafted in Markdown, with their supporting notes listed alongside. Publishing turns the outline into a document that can be shared. The finished piece is published as a PDF, Rich Text, Markdown or a website, in the order you arranged it, so the structure built during outlining carries straight through to the output. The same project can therefore become a printable PDF, an editable Rich Text or Markdown file, or a web page without being rebuilt. The published result follows the arrangement of the outline rather than the order in which the notes were originally taken. The app's approach to storage is part of how it works. Notes are kept in a plain folder of Markdown files on your Mac, which Understory calls a Project Notebook, or in your own iCloud account as a Personal Notebook synced between your Macs. In both cases there is no Understory account to create, and nothing of yours passes through the developer's hands. Because the notes live as Markdown files in an ordinary folder, they remain in a form that is not tied to an account on someone else's service. For the writer, the benefit is that research and drafting live in the same place. Nothing has to be copied from one app to another between the first note and the finished draft, so quotes and ideas stay attached to the books and chapters they came from while they are being shaped into an argument. Because the same note can serve several outlines, an idea does not have to be rewritten to fit each piece it appears in. And because the published document follows the arrangement of the outline, the structure worked out during outlining becomes the structure of the final output. Use cases follow the writing the app was designed for. A student writing an essay or a research paper can take notes from each book and chapter, add a note for each main point, and move existing notes underneath the points they support until the structure works. A researcher working through a long book can file quotes and ideas chapter by chapter and link related notes from other books. A blogger can turn reading notes into a post by arranging them into an outline and drafting each section with the research beside it. The same workflow is described for a video script or a whole book, and the finished piece can be published as a PDF, Rich Text, Markdown or a website. Because one note can sit in more than one outline, a writer working on several subjects at once can reuse the same idea in every piece it touches. Understory is aimed at students, researchers and bloggers, and more broadly at anyone who takes notes in order to write something. It is a Mac app, downloaded from the Mac App Store: there is no iPhone or iPad version yet, and the app is Mac only for now. Pricing is not a subscription, as one purchase makes it yours. It is free to try first, with every feature working from the first launch, publishing included; the only limit is on how many notes a notebook holds, and there is enough room to find out whether it works for you. A one-time payment removes that limit, and the pricing page has the details. Support is available by email, and there is a Discord community for talking about the app. Understory's value proposition is straightforward: it is a notes app for writing, not just a notes app for storing. It carries a research paper, an essay, a blog post, a video script or a book from the first note to the finished draft in one place, letting you collect notes as you read, arrange them into an outline, draft each section with your research beside it, and publish in the order you arranged. Its tagline, notes that tell the tale, captures the intent: the notes are not an archive, they are the path to a finished piece of writing.

Kudos is a free financial companion and browser extension that turns everyday spending into bigger earnings. According to its website, Kudos automatically maximizes your card offers and benefits, lowers your bills, and keeps finding new ways to save. It is built for people who hold one or more credit cards and want more value from the purchases they are already making. Instead of simply presenting charts, Kudos surfaces useful actions: it auto-activates card-linked offers, recommends the best card to use at checkout and in stores, tracks card benefits before they expire, and uses AI to negotiate bills and cancel subscriptions. Members keep 100% of the savings. The product is described as working at more than two million stores and supporting more than 3,000 credit cards, with a membership base of over 500,000 people. The problem Kudos addresses is money that quietly slips away. As the product's own material explains, most people do not know that their credit cards come with personalized offers they have to turn on by hand, and many card benefits go unused before they expire. Forgotten subscriptions keep charging month after month, and recurring bills such as internet or cellphone plans are often higher than they need to be. Kudos frames its mission around recovering that value, telling users to "take back +$3,000 in missed benefits every year" and stating that the average member earns over $3,000 per year with Kudos Premium. Most money apps, the company argues, show you charts; Kudos instead does the work of finding and recovering that value on your behalf, so saving does not depend on constant manual checking. One of the core feature groups is card-linked offer activation and rewards stacking. Kudos auto-activates card offers across issuers including Amex, Chase, BofA, Wells Fargo, and Citi, describing these as worth $750+ a year, and reminds you when to use them. Product Hunt metadata puts the figure at about $800/yr in offers in early testing. The free browser extension then works at the point of purchase: it picks the best card, autofills card details, applies offers, and stacks cash back automatically at checkout. According to the FAQ, when you visit an issuer portal such as Amex, Chase, or Citi with Kudos installed, it automatically activates hundreds of offers across all your cards with no manual clicking. At checkout, Kudos shows you active offers and recommends the card that maximizes your savings, factoring in your card's rewards rate, activated offers, and Boost Cash Back. Kudos Boost Cash Back is available at 15,000 partner stores, and Premium members earn 1.25x more Boost at those stores. A second major feature group is AI-powered bill negotiation and subscription cancellation. Kudos AI cancels unused subscriptions, negotiates your bills, and handles the calls to save you money. Kudos states that its AI calls providers to lower your bills, quoting $216/yr per negotiation, and cancels subscriptions you forgot, quoting $247/yr each. Premium members can tap "Cancel for Me," and Kudos handles the entire cancellation process — phone calls, live chat, and emails — while you get real-time status updates until it is done. Kudos also identifies and helps you negotiate bills that you might be overpaying for. The website's comparison table notes that unlike Rocket Money, which it says takes up to 10 days and up to 50% of savings, Kudos lowers your bills and cancels subscriptions with no cut taken. A member testimonial describes the AI calling a cellphone company within 12 hours of subscribing and reducing a bill from $420 to $315 a month with no plan changes. A third group of features centers on benefits tracking, spending insight, and card guidance. Kudos tracks every benefit across all your cards and makes sure you use them before they expire, backed by a promise to help members take back more than $3,000 in missed benefits every year. Kudos analyzes your transaction history and proactively finds personalized ways to earn more rewards and save money, and Premium unlocks more advanced AI insights tailored to your spending plus two years of transaction history to spot patterns and trends. A location-based Cheatsheet shows the best card to use wherever you are shopping, so you never miss a reward in-store. The free plan also includes personalized card discovery tools — Dream Wallet, Card Explore, Test Drive, and Approval Odds — and a member review notes that the Test Drive feature helped them select the right card. Finally, "Ask your wallet anything" lets Kudos AI connect the dots across your cards, bills, and spending to answer questions, uncover hidden opportunities, and take action to save you money. The overall workflow is described in four steps. First, Activate: download Kudos and connect your cards securely. Second, Connect: Kudos learns how you spend to find ways for you to earn and save. Third, Automate: activate offers, negotiate bills, and use the best card. Fourth, Earn & Save: watch your savings pile up and keep more of what you earn. Card connections are powered by Plaid, and the company emphasizes that with Plaid you choose what is shared, with whom, and for how long. Security is positioned as a priority: Kudos says it uses Zero-Knowledge Architecture so sensitive data is encrypted on your device and invisible to anyone, including Kudos; it has passed rigorous independent audits to achieve SOC 2 (Type 1) compliance; and data is protected with 256-bit encryption, the same standard trusted by banks worldwide. For users, the stated outcomes are earnings and savings with minimal effort. Kudos says the average member earns over $3,000 per year with Kudos Premium, and that Premium members on average earn an additional $754/year and save $324/year. The product currently advertises 500,000+ members, a 4.8-star rating, and 10,000+ reviews. Testimonials on the site describe auto-activated offers that actually get used, a personal finance experience "without the busywork" that surfaces useful actions instead of just charts, and negotiated savings that more than pay for a lifetime membership. One reviewer reports that the AI got their bill reduced by $50 more than the entire subscription cost within 12 hours, while another notes that Kudos brings everything into one place and saves time by removing the need for constant checking. Concrete use cases follow directly from these features. When shopping online at any of the two million-plus supported stores, the free browser extension chooses the best card for that merchant, autofills card details, applies activated offers, and stacks cash back at checkout. When shopping in person, the Cheatsheet tells you which card to use based on your location so you do not miss an in-store reward. Before a benefit expires, the Automated Benefits Tracker flags partially redeemed credits and prompts you to use them. For recurring expenses, Kudos detects subscriptions across your connected cards, helps you decide what is worth keeping, and handles cancellations end to end, while also negotiating bills you may be overpaying on. If you are considering a new card, the free discovery tools including Dream Wallet, Card Explore, Test Drive, and Approval Odds help you choose one that fits your setup. And when you want a straight answer, you can ask Kudos AI about your cards, bills, and spending. The target audience is people who use credit cards — especially those holding multiple cards or unsure which card fits their setup — and who want to capture rewards, offers, and benefits without tracking them manually. Kudos is delivered as a browser extension and a mobile app, with Product Hunt topics listing Android and Chrome Extensions alongside Fintech, Artificial Intelligence, and Personal Finance. It connects cards through Plaid and supports more than 3,000 credit cards, with offers automated for issuers including Amex, Chase, BofA, Wells Fargo, and Citi. Pricing follows a freemium model: the free plan includes seamless checkout with best card recommendations and autofill at 2M+ stores, Boost Cash Back at 15,000 stores, all rewards, credits, and benefits in one place, card discovery tools, and the Cheatsheet. Kudos Premium is sold as a $190 lifetime deal (listed as $240 total, with $50 off for a limited time) or a $141.99 yearly subscription, and includes automated benefits tracking, AI negotiations to lower bills, auto-activate card-linked offers, 1.25x more Boost at 15,000 partner stores, a 500 Boost welcome bonus for lifetime buyers, two years of transaction history and dashboards, welcome bonus tracking with smart reminders, and early access to new autopilot features. The company positions Kudos against Rocket Money, CardPointers, Rakuten, and Credit Karma, and its coverage includes Yahoo Finance, Bloomberg, Nasdaq, and The Motley Fool. In short, Kudos takes the manual work out of credit card rewards. It auto-activates offers you would otherwise have to turn on by hand, tells you the best card to use online and in stores, tracks benefits before they expire, and uses AI to negotiate bills and cancel subscriptions — all while letting you keep 100% of the savings. For cardholders who want the money they are already leaving on the table, Kudos is positioned as a set-up-once, earn-forever financial companion.

Codemind is a build service that a coding agent calls directly. Instead of writing code itself, your agent hands Codemind a task — a title, the acceptance criteria and the stack — and Codemind writes the code, tests it against those criteria and returns the files. A build that passes is counted as one verified build; a build that fails returns an error code and no charge. It is built for developers who already work inside coding agents such as Claude Code, Cursor, Codex and OpenCode, and who want the writing, testing and fixing to happen somewhere other than their own session and plan. Everything is designed so the agent's plan goes on planning and review, while Codemind handles the build. Coding agents typically operate in a write, run and fix loop. Every attempt, including the ones that fail, consumes tokens or plan quota, and the trial and error fills up the agent's context. While the agent is busy running that loop, it cannot move on to the next task. Codemind addresses this by moving the writing, testing and fixing to its own servers, off your plan. Rather than paying for every attempt whether the code works or not, you pay for verified builds only: a build that passes tests written from your acceptance criteria. Codemind states that it has not measured how total cost compares with an agent writing the same code itself, and that it will publish the method when available. The workflow has three steps. First, your agent sends a task containing a title, the acceptance criteria and the stack; if the criteria are too vague to test, Codemind says so before it starts. Second, Codemind writes the code and tests it against your criteria before anything comes back. Third, the files return to your agent, which writes them into your project. A build that passes counts as one verified build. The design intent is that you stay in your coding session while the writing, testing and fixing happen on Codemind's side, off your plan. Every build comes back with its result. You see whether it passed and what you were charged. A passing build produces a build receipt — for example, a task such as checking the webhook signature on POST /events in a TypeScript stack, sent from Claude Code over MCP — showing that the code was written, that it was tested against your criteria, how many files were returned and what you paid. A build that fails returns an error code and no charge; code that fails its tests does not reach your agent, so your agent can retry or do the task itself. Builds inside your free allowance cost nothing either way. Pricing is a flat rate charged only for code that works, with no seats and no tokens to count. The Free plan costs $0 and requires no card: it includes 100 verified builds in your first month, then 50 every month, with up to 2 builds at once and 20 an hour. Pay as you go costs $0.10 per verified build for builds beyond the free allowance, drawn from a prepaid balance you top up — although paid top-ups are coming soon and are not available yet. A Custom plan, by contact, offers a negotiated volume rate, reserved capacity and support. A Pro plan is coming soon at a price to be announced, adding Swarm and higher limits of up to 10 builds at once; Ship is also coming soon. If you use up the free allowance, new builds are refused with a clear message until your next month starts, and nothing is charged unless you have added a paid balance. Your free allowance counts verified builds only. Setup is designed to be run by the agent itself. A button copies a one-line prompt — read and follow the setup steps at codemindhq.dev/agent-onboarding/SKILL.md — that you paste into your coding agent. The agent adds the Codemind server to your host and asks you to reload it, and a free account is created when it connects. You then restart your agent and delegate your first task. No script runs and no key is stored, though if you restart your host, Codemind starts a new free account. You can also sign up yourself in the dashboard. If you already have a key, you can add the server by hand with a claude mcp add command that keeps your key out of the config file. Codemind's MCP endpoint is https://api.codemindhq.dev/mcp and it needs no API key; agents are instructed to call get_usage_guide before their first build_feature. The stated benefits fall into productivity and performance. On productivity, a coding model normally works through one task at a time, whereas Codemind builds run side by side on its servers — 2 at once on Free, more with Pro — so your agent can send a task and move on to the next one instead of sitting in a write, run and fix loop. Because the writing and testing happen off your plan, your tokens go on planning and review, letting your agent get more done on the same plan. On performance, a build is marked verified, and charged, only after it passes tests written from your acceptance criteria; code that fails its tests does not reach your agent; and the trial and error happens on Codemind's side, so it does not fill up your agent's context. The result is code that has already been tested coming back to your agent. Codemind works over MCP or REST, and the same builds are available from your agent or from your own code. It currently builds JavaScript and TypeScript, including Cloudflare Workers, Node and React. You can send a task to create new code, or change files you already have by sending the current file with the task and getting the complete modified file back. Your agent can also ask for a code review or a test run on its own. Beyond a single build, two larger modes are planned: Swarm, where your agent defines a set of tasks and a swarm builds them in parallel, verifies each one and tells you once when the whole set is done; and Ship, where you hand over a spec and the target repositories and Codemind splits it into tasks, builds them and delivers the result. Both are coming soon, Swarm with Pro. You can also estimate your savings through the analyze page linked from the site. Codemind is aimed at developers who use coding agents and want to delegate implementation work without leaving their session. It supports Claude Code, Cursor (tested through its agent CLI), Codex (CLI 0.160.1 or later; 0.27.0 has no remote MCP) and OpenCode, with Cline coming soon. It does not replace your coding agent: your agent still plans the work and edits your project, while Codemind takes the tasks it delegates and returns code that has already been tested. Languages other than JavaScript and TypeScript are not supported yet. Codemind keeps what it needs to run your builds and show you their results, with details on what it stores, for how long, and how to ask for export or deletion on the Privacy page. Taken together, Codemind's value proposition is straightforward: your agent's plan goes on planning and review, while a separate build service writes, tests and fixes the code and returns only what has passed. You are billed per verified build — $0.10 after a free allowance, with failed builds costing nothing — and you start with 100 verified builds in your first month without a card. Delegate your first task and see what comes back.

Manifold is a hosted MCP server that gives your AI agent live marketing data across every channel you grow on. Rather than opening another dashboard, you connect Manifold to the AI app you already use — Claude, ChatGPT, Cursor, Claude Code, Codex, Gemini CLI, Grok, OpenCode or any other client that supports remote MCP servers — and ask questions in plain words. Your agent picks the right tools and brings back answers built on real data: Google rankings and backlinks, what AI assistants say about a brand, social posts and videos, Reddit threads, competitors' live ads, and companies and people with verified work emails. It is aimed at marketers, founders and growth teams, and it is free to start for every new workspace. Marketing data is usually spread thin. Rankings live in one tool, AI answer visibility in another, creator research somewhere else, competitor ads in an ad library, and buyer intent across LinkedIn, X and Reddit. Pulling that together means a pile of tabs, several logins and API keys for services like Reddit, LinkedIn or a search data provider. Manifold collapses it into one hosted connection: your agent signs in once and uses Manifold's own provider accounts, so there is no Reddit API app, no LinkedIn login and no DataForSEO deposit to manage. The result is a single place where an agent can look up live marketing data whenever it needs to, and answer with real numbers rather than guesses. SEO is the first data group. Your agent can see every keyword you rank for, who outranks you and why, and what to fix on your site. A typical question — which keywords do we rank 5 to 15 for that could reach page one? — returns terms sitting just below the first page. A sample answer might tell you to put a phrase in a page title because you already rank seventh for it, to answer a question directly on the page because an AI assistant gets it wrong, or to link to that page from your best-ranking posts. Because the data is fetched live from search providers, the recommendations reflect current rankings rather than a stale export. AI answers, sometimes called AEO, covers how assistants describe your brand. Your agent can ask ChatGPT, Claude, Gemini, Perplexity and Google's AI Overviews any prompt, on demand, and report which brands each answer names, in what position, and which pages it cites. You can check whether AI assistants recommend you for the questions your buyers ask, see who they name instead, and review which pages they cite. You pay per prompt and engine rather than a monthly fee for tracked prompts, and a free check shows whether your site lets AI crawlers read it — a prerequisite for appearing in AI answers at all. Social research finds the creators and videos already winning your niche. Your agent can locate the TikTok, YouTube and Instagram creators your buyers watch, along with their audience and their top videos with transcripts — for example, which sales creators on TikTok and YouTube get the most views and what their top videos are. Instagram, Facebook, LinkedIn and X are covered too. Ad libraries sit alongside this: you can pull competitors' live ads on Meta and Google, see what each one says, and check how long it has run, so you can write better ads by seeing what already works. Reddit and lead research connect that data to action. Your agent can find every thread that asks for what you make, read the replies and draft your answer — for instance, threads this month asking for a CRM, or Reddit threads asking for recommendations. On the people side, Manifold spots posts on LinkedIn and X about the problem you solve and returns each author's verified work email, so you can reach buyers who are looking right now. The agent writes from this data too: blog posts, Reddit replies, ad copy and outreach emails are drafted in the chat with real numbers behind them, and you check the drafts and use them where you want. Manifold is the data your agent works from — not a scheduler or an autoposter. Under the hood, Manifold is a remote MCP server. You connect your agent once — in Claude, add the URL as a custom connector and sign in; in Claude Code, run claude mcp add --transport http manifold followed by the URL — and every client uses one URL, https://mcp.manifoldmcp.com/mcp, and one credit balance. Tools carry names such as seo_get_ranked_keywords, aeo_run_ai_answers, reddit_search_posts and linkedin_search_posts, and your agent picks the ones it needs for your question. Manifold fetches data live from specialist providers when your agent asks: search, keyword, backlink and AI answer data come from DataForSEO, while Reddit, TikTok, Instagram, YouTube, Facebook, LinkedIn, X and the ad libraries come from the platforms' public data. Results are cached briefly — most for a day to a week — so a repeat question comes back fast and costs less. Answers you can act on are the point. Instead of raw exports, the agent returns specifics: what to fix on a page, who to reach out to, which threads to join. From there it can take the next steps for you, drafting the content or the outreach in the same conversation. You get real numbers rather than estimates, one balance and one URL across every client instead of a collection of keys and logins, and cost control built in — every call is paid from a single credit balance, each tool states its price before it runs, and your agent can cap what a call spends. Concrete workflows run across the product. In SEO, teams ask which keywords they rank 5 to 15 for that could reach page one. In AI visibility, they ask whether they are recommended for a query like "best CRM for startups". In social, they ask which sales creators on TikTok and YouTube get the most views and what their top videos are. In prospecting, they ask who posted about leaving their CRM this week. In competitive research, they ask what a competitor is running on Meta this week. And in community marketing, they ask for threads posted this month asking for a CRM. Manifold is for marketers, founders and growth teams doing SEO, AEO, social, ads or lead research, and it works with Claude, ChatGPT, Cursor, Claude Code, Codex, Gemini CLI, Grok, OpenCode and any other client that supports remote MCP servers. Clients without OAuth use a Manifold API key instead. Pricing is easy to start: every new workspace gets 250 free credits, then a Pro plan from $20 a month for 2,500 credits, or Max at a lower rate per credit, with top-ups from $10 between renewals. Manifold is a marketing data MCP server founded in London in September 2026 by Jesse Sibley, and it is not related to Manifold Markets. Manifold MCP turns the AI assistant you already use into a marketing research and growth partner. One connection, one credit balance, and live data from Google, AI answers, Reddit, social platforms, ad libraries and lead sources — so your agent can answer in plain language, then help act on what it finds.

Invited is an AI reminder scanner that turns any document containing a date into a smart reminder. Bills, cheques, renewals, invitations and appointments are all fair game: you snap a photo, pick a screenshot or PDF, or share a document from another app, and Invited's AI finds every date on it so you can be reminded before it matters. It runs on iPhone and Android, requires no signup, and is made for anyone who wants a simple, reliable way to stay ahead of due dates, renewals and events. The trouble with dates is that they arrive on paper, in PDFs, in screenshots and inside apps, not in a single tidy calendar. A bill carries a due date and a late-fee date; an insurance policy has a renewal date; a wedding invitation lists five separate functions across several days; a boarding pass has a departure time and a gate. Most reminder apps can only capture one date, so people end up typing entries by hand, copying amounts and times, and still missing the moment that mattered. Invited exists to close that gap: instead of asking you to remember, it scans the document and does the work of finding the dates for you. Invited accepts almost any input that contains a date. You can take a photo with your camera, pick an image or screenshot from your library, choose a PDF or document from your files, or share something straight into Invited from any other app. That means the same workflow handles an electricity bill photographed on your kitchen table, a screenshot of a subscription confirmation, a PDF insurance schedule, or a boarding pass shared from your email. Because sharing is supported from any app, you do not need to export or retype anything — the document goes in as it is and the AI takes it from there. The standout capability is that one document can produce many reminders. Where most tools find a single date, Invited finds all of them. A Gujarati wedding invitation with five functions — Mehendi, Haldi, Sangeet, the wedding ceremony and the reception — becomes five separate reminders, each with its own time and place. A BrightGrid electricity bill can produce both a pay-bill reminder for the due date and a separate late-fee reminder for the later penalty date. This matters because the same paperwork often contains several deadlines that sit days or weeks apart, and catching all of them is the difference between a small task and an avoidable fee. Invited reads 80+ languages, so a document does not need to be in English to be understood. The site highlights English, Gujarati, Hindi, Arabic, Chinese, Spanish, Portuguese, Japanese and French among the supported languages. It also understands nine calendar systems: Gregorian, Vikram Samvat, Hijri, Hebrew, Chinese Lunar, Persian, Thai Buddhist, Ethiopian and Japanese era. That means a date written as Kartik Sud 5, 1 Shawwal, 1 Tishrei, the first day of the first month, 1 Farvardin, year 2569, 1 Meskerem or Reiwa 8 is converted into a real date in your own calendar, rather than being lost because it does not look like the format you are used to. You control when you hear about each reminder. Invited lets you choose a lead time of 1 hour, 6 hours, 12 hours, 24 hours or 2 days before the date. You can set a default once and then change it for any individual reminder, so a flight can warn you a day ahead while a bill nudges you 24 hours before it is due. The product notes that reminders can be set from 1 hour to 2 days before, giving you a simple range that covers both errands and events. Invited is deliberately minimal and honest about how it handles your information. There is no signup and no account — you open the app and scan. To find the dates, the document you scan is sent securely to an AI service that reads it, while your reminder list itself is stored on your device rather than in the cloud. You stay in control by reviewing every date before saving, and the app warns that AI can make mistakes with dates, times and payment locations, so you check the extracted details first. You can edit or delete reminders at any time, and unlimited reminders can also be created by hand if you prefer. The overall flow is described as three taps with zero typing. First, snap or share: take a photo, pick a screenshot or PDF, or share from any app. Second, the AI reads it, extracting dates, times, places and amounts in 80+ languages — and you check them before saving. Third, you get reminded anywhere from 1 hour to 2 days before, at the lead time you chose. Behind the scenes the app moves through a small pipeline (load, read, AI, done) and then presents the events it found for your review, with each event showing its title, date, time, place, amount and reminder offset. The benefit is straightforward. Instead of acting as a second memory, Invited removes the need to remember at all: you scan once and the reminders appear before the deadline. Bills are paid before late fees, cheques are deposited before they run out of time, insurance renewals are handled before cover ends, free trials are cancelled before you are charged, and invitations and appointments are not missed. Because everything stays on your phone and needs no account, using it is fast and low-friction — a single scan can create several reminders at once, and the reminders carry context such as the amount owed, the place, or the gate number. Invited is used across a wide range of everyday documents. Bills and utilities are scanned for due dates and late-fee dates. Cheques are scanned so you deposit them before they expire. Insurance and renewals are scanned so you act well before cover ends. Subscriptions are scanned for free-trial and renewal dates, before you are charged. Invitations — weddings, birthdays, parties and conferences — are scanned so every function on the card gets its own reminder. Appointments such as doctor and dentist visits are captured with their time and place. Travel tickets and boarding passes are scanned for departure and gate times. Warranty cards are scanned so you claim before the warranty quietly runs out. The site also lists rent, streaming subscriptions, credit card statements, passport renewals, gas bills, license renewals and school events among the things people scan. Invited is aimed at anyone who deals with dated paperwork — households paying bills, families juggling multiple invitations and appointments, travellers tracking flights and tickets, and anyone who wants a no-account reminder tool that works with documents they already have. It is available on iPhone and Android, in 80+ languages. Pricing is freemium: the app is free to download and includes 3 free AI scans, plus unlimited reminders created by hand; Invited Pro unlocks unlimited AI scans and more, with the current price shown in the App Store and Google Play. At its core, Invited is a reminder app that reads for you. It takes the documents that already carry your deadlines — bills, cheques, policies, invitations, tickets, warranties and more — and converts every date it finds into a reminder you will actually see, in your language and your calendar, with no signup and no typing.

ngram is an AI video generator that creates perfect AI videos for every team, covering onboarding, training, demos and explainers from a prompt or a screen recording. Its Product Hunt listing describes ngram 2.0 as a tool for creating explainers, screencasts, and AI avatar videos by turning ideas, documents, and recordings into narrated videos. The site presents it for sales teams, marketing teams, support teams, HR teams, product teams, founders, explainers and every team in between. The core promise is that you describe the video you want in plain words, ngram plans and builds it, and the finished video stays editable so you can then make it perfect. The problem ngram addresses is the gap between having something worth explaining and having a video that actually explains it. The site draws the contrast directly: Loom is useful for quick screen recordings but does not help with structure or polish, while Descript requires editing skills. ngram instead starts from your intent — who the video is for and what it should achieve — and builds a complete video with structure, visuals, and polish. The customer stories suggest why that matters. The Owelet team says, “I can finally show people what Owelet does instead of just describing it.” The Wolvio Solutions team reports that “what used to take weeks can now be done in a day” and cites 80% less production time, while the Quillr team went from producing 3 to 7 reels a week. A G2 reviewer notes ngram makes it easier to create and refine instructional content. ngram can begin from whatever material a team already has. The most direct starting point is a prompt: you describe your video idea, audience, and tone, and that description is treated as the whole brief. The site's own example brief asks for a 30-second launch film for Inkpolish, specifying the opening moment, the ranking drop, the fix, and a request to use the brand kit. Projects can also start from a screen recording or an uploaded video, which ngram analyzes to identify key moments and build clear product walkthroughs. Other inputs named on the site include documents and PDFs, screenshots, photos and references, your README, and even your website — for example, one prompt asks ngram to turn a website into a 30-second explainer for facility managers. The homepage is explicit that assets are optional: you can start with just a description, and ngram will write the script, plan the storyboard, and generate visuals to match. Before anything renders, ngram plans the story. After you describe the video, ngram outlines every scene and waits for your approval — the site's example plan lists five scenes with individual durations covering the publish moment, the ranking drop, the score and fix, the prompt, and the publish decision. Once approved, you watch it build: narration, soundtrack, visuals and animation come together while you watch, and the site describes ngram recording the narration, composing the soundtrack, generating visuals, building the animation and checking the result. The site also states that ngram plans the story structure before generating anything and uses your brand kit for consistency, a deliberate approach designed so videos look intentionally produced rather than obviously automated. Every video stays editable, which is where the “then make it perfect” half of the promise lives. You can edit any scene by chatting: you point at the frame you want changed and say what you want, and ngram makes a new version. In the site's example, a change pinned to scene two — “Get to this drop sooner. The intro runs long.” — caused ngram to shorten scene one from 4s to 2s so the drop lands sooner, producing version two. Iteration works in plain language after the video is done too, with requests like “make the intro shorter” or “swap the second scene for a screen recording.” You can also take the controls yourself: captions, music and scene edits are a click away, and captions can be set to styles such as Pop Light from the Edit video menu. Deeper control is available through the timeline editor, which offers frame-level precision, plus the ability to swap scenes, edit text, change styles and regenerate sections. ngram supports narrated, animated and avatar-led videos. You can create animated explainers or add an AI avatar presenter, and the site lists AI presenters and custom voices, cinematic AI footage, and the ability to swap the voice or the language. Captions are added automatically and can be matched to your brand colors. A brand kit is set up once, and every video then uses your identity — colors, fonts, logo, intro/outro sequences — for consistent branding across your entire team. Output is flexible: finished videos export as MP4, and short ones can also be downloaded as a GIF. You can create in 16:9 (landscape), 9:16 (vertical), or 1:1 (square) when the selected models support it, and you can download the finished video or share a link to it, or embed it. The overall method is a seven-step flow the site lays out in order: describe it, approve the plan, watch it build, preview the video, point at a change, take the controls, and download. The preview stage plays the finished video beside its storyboard, scene by scene, so you can see how each beat maps to the plan. This structure is what the site presents as the core difference from other tools — ngram starts from your intent and builds a complete video with structure, visuals, and polish, rather than handing you a raw recording or expecting editing expertise. It also means the video is planned before it is generated, so story decisions come before rendering, and revisions afterward are targeted rather than wholesale. The stated outcomes for users are speed, clarity and iteration without starting over. Teams report moving from weeks to a day, 80% less production time, and higher output like 3 to 7 reels a week. The site frames the benefit as being able to show rather than describe — “I can finally show people what Owelet does instead of just describing it.” Because you review the script and storyboard before anything renders and can iterate anytime afterwards, changes do not require rebuilding the video. ngram also reports a G2 rating of 4.6 out of 5 from 62 reviews, and offers a free start with no credit card. Use cases map onto teams. For sales and revenue, ngram pitches sending the demo and skipping the call, with pitch explainers built from screenshots. For marketing, it generates industry explainers from screenshots, promo videos with an AI presenter from a PDF or doc, and product launch films from photos and references. For customer success and support, it makes customer onboarding videos from screenshots and customer education from a PDF or doc. For HR, People and L&D, it produces compliance training and messages with an AI presenter from photos and references — including non-English training, such as a safety video in Vietnamese about hand hygiene compliance in hospitals. For product and engineering, it creates stakeholder demos from screen recordings and developer launch teasers from your README. For founders and leadership, it builds board update videos from reports and all-hands announcements from a prompt. For explainers and education, it turns a website into an explainer or builds technical explainers from photos and references. ngram is aimed at teams rather than individual editors. The homepage explicitly names sales, marketing, support, HR, product, founder, and explainer/education audiences, and the example library spans brands such as Slack, Headspace, Revolut, Robinhood and Cernel, alongside companies listed as customers including HubSpot, Deel, Zapier, ITV, Veeva, DP World, Genpact and SafetyCulture. Pricing is described as “Free to start · No credit card.” The marketing site is built on Next.js. The site is also clear that the product is not limited to marketing videos: teams use ngram for product demos, customer onboarding, internal updates, training videos, sales enablement, changelogs and investor pitches — any message that lands better as video. In short, ngram's value proposition is that anyone on a team can describe the video they need — or bring a prompt, a screen recording, a document, or a website — approve a plan, and end up with a narrated, branded, polished video they can refine by chatting or by hand. It removes the structure and production burden while keeping the finished video fully editable.

SalesRep is an all-in-one omnichannel customer communication platform built around a single, AI-powered team inbox. It unifies every customer channel, CRM and AI provider in one place so teams can capture leads, automate conversations, broadcast campaigns and close deals faster from a single platform. The product also acts as a virtual sales rep that sells for you 24/7: it pulls live prices and stock from Shopify, WooCommerce, PrestaShop, Meta Catalog or your own database and API, answers customers on WhatsApp, Instagram, Messenger, email, SMS and other channels, and sends product cards and quotes on the fly in any channel. SalesRep follows up automatically until the deal closes, allows human takeover at any time, and works in more than 30 languages that are auto-detected. It is used by more than 1,000 businesses worldwide. As chats and calls multiply, old inboxes and workflows break. Teams that sell and support customers across WhatsApp, Instagram, Messenger, email, SMS, voice and social media end up juggling multiple platforms, losing context and leaving conversations unanswered. SalesRep addresses this fragmentation by giving every customer one reliable record and every conversation one thread, no matter which channel it came from. Instead of silos, the platform offers a unified conversation history, automatic contact merging and deduplication, real-time sync with your CRM, and smart routing based on channel and context. The stated goal is to help businesses manage the entire customer journey in one place, even at high volume — a framework SalesRep calls Conversation-Led Growth. At the centre of SalesRep is the Omnichannel Team Inbox. It unifies contacts from voice calls, email, WhatsApp, TikTok, Facebook Messenger, Instagram, Telegram, SMS, WeChat, MS Teams, Slack, iMessage, Google Business and more, including Asterisk. Every conversation appears in a single thread and every customer in one place, so agents no longer switch between apps or maintain separate inboxes. The inbox includes unified conversation history across all channels, automatic contact merging and deduplication, real-time sync with your CRM (or SalesRep's own CRM), and smart routing based on channel and context. SalesRep states that chat channels deliver 92%+ open rates, and that messaging channels support product catalogs and in-chat payments. The inbox also supports team and custom inboxes, live chat and popup widgets, a knowledge base and canned responses, mobile app access with 2FA, and AI call and chat summaries. SalesRep's AI Agents are AI that sells, supports and scales. Ready-made or custom agents qualify leads, answer FAQs, recommend products, book appointments, route enquiries and handle support requests around the clock, across every messaging channel. The site reports that 75% of chats are handled entirely by AI and 80% of conversations are resolved by AI. Agents perform multimodal processing — text, images, audio and files — so they can send documents and images and process screenshots, audio and files for full context understanding. AI sales automation sends product offers with photos, prices and live stock in real time, answers voice messages with its own voice replies, auto-detects each customer's language and replies in it, generates and sends quotes instantly without human intervention, follows up automatically on every quote until it closes, and hands the conversation to your team whenever needed. Additional AI Agent capabilities round out the offering. Guardrails define what AI Agents say and do so they stay on-brand and compliant, and humans can take over active conversations instantly. A RAG-powered knowledge base lets agents pull verified answers from knowledge sources such as PDFs, doc files, catalogs, images and websites, continuously updated for accuracy. Voice AI provides natural, human-like speech. Agent specialization lets you orchestrate teams of micro-agents focused on tasks like retrieval, routing and escalation for precision results at scale. Teams can start quickly with ready-made AI Agent templates or build a fully customized agent tailored to their business needs and workflows. Agents also collect data, update lifecycle stages, resolve support issues, recommend products, share images and trigger workflows automatically. Beyond the inbox and agents, SalesRep provides AI Workflows and Broadcast campaigns. Workflows let teams build powerful automations with a visual drag-and-drop builder to automate repetitive tasks, sync data across platforms and keep the team focused on closing deals rather than manual busywork, with automatic lead routing and assignment, real-time CRM sync and custom field mapping. Broadcast lets you send targeted messages across WhatsApp, SMS, Facebook, Instagram, Telegram and Email in one click, sharing promotions, product updates and newsletters directly to the channels your customers use most. Broadcast features include multi-channel campaigns, audience segmentation and targeting, delivery and engagement analytics, and automated opt-in/opt-out management. SalesRep connects to HubSpot, Salesforce, Jira, Zapier and 40+ tools, plus Shopify, WooCommerce and PrestaShop. SalesRep's overall approach is organised around its Conversation-Led Growth framework, which is designed to help businesses manage the entire customer journey across every channel in one place, even at high volume. The platform describes three pillars. The first is the Omnichannel Team Inbox, which unifies contacts from voice, email, WhatsApp, TikTok, Facebook Messenger, Instagram and more into one thread per customer. The second is AI Agents, which answer FAQs, qualify leads, route enquiries, book appointments and handle support requests around the clock. The third is Automate & Integrate, which connects SalesRep to HubSpot, Salesforce, Jira, Zapier and 40+ tools to automate workflows, sync data and eliminate manual busywork. A published sample conversation shows an AI agent greeting a customer, quoting an IP phone with a reference, price and live stock, preparing a quote for four units, sending a PDF quote by WhatsApp and email, and scheduling an automatic follow-up. The site publishes a range of outcome metrics. SalesRep reports 42.5x average ROI, 500K+ conversations per month, 99.9% uptime and 1,000+ businesses across 30+ countries. It also cites a 60% faster sales cycle, an 81% conversion rate with AI Agents and 3x more leads from ads. Customer stories describe AirCharter.com cutting its sales cycle by 60% with the omnichannel inbox for private jet charter bookings, Maroc Ventes eCommerce handling support and sales across WhatsApp, email and live chat from one inbox with 3x faster quotes, Sport Center reaching an 81% conversion rate with AI Agents on SalesRep, and Sip.ma tripling leads and streamlining customer communication. The stated promise is to transform customer conversations into revenue. SalesRep is marketed as built for a range of business types, each with a concrete workflow. Clinics and healthcare use it for bookings, confirmations, reminders and patient follow-ups right inside WhatsApp. Sales teams use it to capture, qualify and close leads from ads and chats in one visual pipeline. E-commerce and retail teams use it for order updates, abandoned-cart recovery and product campaigns that convert. Salons and local services use it for appointment scheduling, deposits and no-show reduction on autopilot. Customer support teams use one shared omnichannel inbox, with AI resolving the routine majority. Real estate and agencies route property and project inquiries to the right agent in seconds. A separate workflow example shows quote requests for hardware being received via WhatsApp, assigned to an agent, tracked through lifecycle stages such as Proposal, Warm Lead and Purchased, and answered with a quote attached. SalesRep targets businesses that sell and support customers through messaging apps — sales teams, support teams, e-commerce and retail operations, clinics, salons and local services, and real estate agencies — and reports being used by 1,000+ businesses worldwide. Integrations named in the content include HubSpot, Salesforce, Jira and Zapier, plus Shopify, WooCommerce and PrestaShop, and the product states it connects to 40+ tools; it also supports Zapier, Make and n8n, API access and webhooks, and Meta & TikTok Ads and Catalog. Pricing starts at $39/mo for the Starter plan with 3 users included and +$12/seat, then $20 per extra 1,000 conversations that never expire, with conversation-per-month tiers of 1000, 2500, 5000 and 10000+. The Growth plan is $79/mo with 5 users, the Advanced plan $139/mo with 10 users, and Enterprise $249/mo with 20 users. A 21-day free trial requires no credit card. SalesRep emphasises safety and onboarding. It uses the official WhatsApp Business API and enforces Meta's messaging policies — opt-ins, approved templates and sending limits — so your number stays safe, and the team helps resolve any issues with Meta. The WhatsApp Business API requires its own dedicated number, which can be new or migrated, and SalesRep guides users through every step. Teams can migrate contacts, templates and automations from other tools or from the WhatsApp Business app without interrupting ongoing conversations. The free trial lasts 21 days with no credit card required, including help connecting channels and launching first automations, with evaluation extensions available on request. Most teams launch within a few days, and guided onboarding is free on every plan. The platform is available in 30 languages and AI agents converse naturally in Arabic (including Darija), French, English, Spanish and more, with a tone you control. AI agents do not replace teams; they take over repetitive work such as answering common questions, qualifying leads and routing conversations, handing anything complex to the right human agent with full conversation context. As a summary, SalesRep combines an omnichannel team inbox, AI sales agents, workflow automation and broadcast marketing into one platform for businesses that sell and support customers across messaging channels. Its central value proposition is that a virtual sales rep can pull live prices and stock, answer in the customer's own language, generate and send quotes, follow up until the deal closes, and hand off to a human whenever needed — turning everyday customer conversations into revenue while reducing response times and manual workload.

OpenScreen is a free, open-source screen recorder and video editor for Windows, macOS and Linux that takes a raw screen capture and turns it into a finished demo. Its stated purpose is that a raw capture goes in and a finished demo comes out, in the category Screen Studio defined. It is aimed at anyone who records their screen — a founder demonstrating a product, a developer walking through a tool, a support person showing a workflow — and wants the recording to look finished without learning a separate editing suite. Both halves of the job live in one application: the recorder captures the screen, and the editor places zooms, restyles the cursor, dresses the frame, and cuts the video, including cuts described in writing and handled by an AI agent over MCP. Screen recordings usually look unfinished on the first take. The cursor is small, the frame is a bare rectangle of desktop, and the moment that matters — a button being clicked — scrolls past at normal speed. The usual fix is to re-shoot, or to move the file into an editing program and rebuild the framing by hand. OpenScreen's approach is to make the recording look good before you touch anything: a finished frame with wallpaper, padding, shadow, rounded corners and a little motion blur is applied by default, and zooms are planned from where you clicked. It is MIT licensed, with no watermark and no account, and it continues the original OpenScreen project, which its creator archived after v1.5.0. Recording is done with the operating system, not around it. You pick a window or a display, and OpenScreen uses the capture path the system itself provides: macOS goes through ScreenCaptureKit, Windows through Windows Graphics Capture, and Linux through PipeWire and the ScreenCast portal. System audio is captured without an extra driver, and Windows uses Media Foundation for webcam capture. The important technical detail is that the pointer is recorded as data rather than burned into the pixels, which is the only reason the cursor can be restyled later instead of being committed at record time. On macOS, cursor shape and clicks become available once Accessibility is granted. The editor is built around a small number of things you will actually do. For style, you can swap the background to an image, a colour or a gradient behind your recording with no re-shoot, set the background animation to none, Drift, Aurora or Waves, and apply background blur. For effects, padding, motion blur, shadow and roundness each composite live, so you frame it your way. For the cursor, size, smoothing, motion blur and click bounce are all adjustable, because every move should read on screen. Five cursor packs ship with the app — Studio Ink, Prism Glow, Pop Coral, Pixel Candy and Star Sprout — and each can be drawn as a 3D object with a shadow. Zoom placement is the headline editing behaviour. Zooms from your clicks means each take is zoomed where you clicked, by a fixed rule, with no AI and no network call, and it can be turned off in Record mode. On top of that sits a 3D camera: each zoom can stay flat, turn the screen left or right, or orbit it while following the zoom's focus, and depth of field softens the far side to add depth. Around the screen you can place a frame — a window, laptop, phone or screen, in light or dark — over an animated background. On the timeline, zooms, speed ramps, trims and comments each land as a pill, so one click places every zoom and the edit stays visible rather than hidden in menus. The transcript is a second editing surface. You edit video like text: delete a word or a silence and the cut lands on the timeline, and nothing is destructive, so the original material stays intact. Captions lean on the same idea. Transcription runs on your machine because whisper.cpp ships with the app; the model downloads once on first use, and after that it works with the network off across 100 languages. The audio never leaves the laptop, and what comes back is editable text — set the typeface, the size, the colour and the position, then burn it into the render. Webcam work is handled with layouts and segmentation. There are four layouts: picture in picture, dual frame, vertical stack, or no webcam at all. Full Camera gives a stretch of the timeline to the camera, so it grows to full screen and then eases back. Blur, cut out or replace is driven by a segmentation model that separates you from the room on your own computer, and nothing is uploaded. The same take can be framed for any aspect ratio — 16:9, 9:16, 1:1, 4:3, 4:5, 16:10 or 10:16. Blur regions, smooth or mosaic, as a rectangle, an oval or a freehand shape, can be drawn over an email, a key or a password before the clip is shared. Then there is the agent. You describe the edit and the agent makes it. The wizard places zooms by watching where your cursor went; the agent goes further by reading the actual transcript and the actual timeline, so it answers with timecodes you can go and check, telling you which spans it will cut and how much that saves. Every edit it makes is an ordinary undoable one, it needs a provider key you supply, and it is off by default — nothing runs until you connect one. Export writes the file on your machine, which is where the local-first promise is easiest to see. Output is MP4 from 720p up to source resolution, at 24, 30 or 60 frames per second, in H.264, or a GIF. The encode runs on your machine and counts frames while it does, so you can see progress rather than wait behind a queue. There is no account, no watermark, and the file is on disk when the bar fills. The outcome for users is a demo that looks deliberate without a second tool. Nothing is uploaded: recording, transcription and rendering all happen on your machine, and your video never leaves it. Text leaves only when you ask — the chat panel and caption translation each use a key you supply, and transcription downloads its 264 MB Whisper model once, on first run. Every feature ships free for personal and commercial use, with no paywalls, no premium tier and no usage caps. Because transcript cuts and agent edits are non-destructive, you can experiment without risking the take. The concrete workflows are the ones the product was built around. You record a product demo and let the zooms fall where you clicked, as in the walkthrough showing a soil sensor being paired in the Fern garden app: no app store, no account, the probe talks to the laptop, and the whole thing took forty seconds — with the dead air still to be trimmed. You trim that dead air by deleting words and silences in the transcript instead of scrubbing a timeline. You cut a vertical or square version of the same take for a social post without recording again. You mask a key, an email or a password with a blur region before sharing a support clip. You let the agent read the transcript and propose which spans to cut, with timecodes you can verify. And you burn subtitles in whatever language the audience needs. OpenScreen runs on Windows, macOS and Linux from one source tree with native capture on each. Distribution is broad for a free tool: a Microsoft Store listing, a .dmg, a .exe, a .deb, a .rpm, a .pacman, an AppImage and a Nix flake, with a signed and notarized macOS build — the Windows .exe is not code-signed, so SmartScreen warns before it runs. The interface is available in 15 languages: English, Arabic, Czech, German, Spanish, French, Italian, Japanese, Korean, Russian, Turkish, Vietnamese, Brazilian Portuguese, Simplified Chinese and Traditional Chinese. The tech stack leans on the platform: ScreenCaptureKit, Windows Graphics Capture, PipeWire, Media Foundation and whisper.cpp for local transcription, with H.264 encoding on the way out. The takeaway is straightforward. OpenScreen is a free, open-source screen recorder and video editor that makes the first take look finished, keeps the whole pipeline on your machine, and leaves the tedious part of editing — placing zooms, trimming dead air, cutting spans — to rules, transcripts and, if you want it, an AI agent you connect with your own key. No watermark, no account, MIT licensed, and every edit reversible.

FloorGen is a free floor plan maker that runs in the browser and combines 2D drafting, 3D walkthroughs, video export and code checking in one place. You can describe the space you have in mind and let FloorGen AI draw an editable plan, or draw the plan yourself wall by wall. It is built for homeowners and renters, interior designers and architects, real estate professionals and builders, and people who want an AI assistant to edit their drawings. Every wall, door and sofa in a plan is editable, and every plan is already a 3D model, so you can orbit it, open it as a dollhouse, or walk through it at eye level before anything is built. The problem it addresses is the gap between wanting to change a space and being able to show anyone what that change looks like. Measuring a room, testing where the sofa fits, planning a kitchen or a backyard ADU usually means sketching on paper, guessing at scale, or learning heavyweight drafting software with a licence and an install. FloorGen lets you measure a room, draw it to the inch or the millimetre, and try the sofa in three places before you lift it. There is nothing to install and no account required to draw: plans save in your browser until you choose to sign in. Plans can start from scratch or from existing work. Walls snap and rooms close as you draw, or you can import a photo or PDF of a plan, set the scale by clicking two points a known distance apart, and accept or skip the walls FloorGen suggests. That tracing workflow is semi-automatic, so you check each wall and draw over the image where it misses one. Ready-made plans include houses, ADUs and rooms, such as a 3-bed ranch of about 2,000 ft², a 4-bed two-story of about 2,240 ft², an 800 ft² backyard ADU, a garage-to-studio ADU, an L-shaped kitchen, a 5 × 8 full bath and a 12 × 12 bedroom, each drawn to scale, furnished, and openable in the editor without an account. Furnishing and finishes are handled on the same drawing. About 3,000 models in 14 categories cover kitchens and bathroom fixtures through to cars, workbenches and garage shelving, and each face of a wall can take its own paint while floors take marble, ceramic or wood. Because every plan is already a 3D model, you can orbit it, view it as a dollhouse or walk through at eye level, then record an MP4 walkthrough in 16:9, 9:16 or 1:1 up to 1440p with captions. A view-only HTML file lets anyone open the result without installing anything. Drawings and exports are produced at scale. Plans, elevations and sections sit on sheets from Letter and A4 up to ARCH D and A1, with your client, address and revision in the title block. PDF plan sets include a title block, schedules and a 3D view; PNG images go up to 4,500 px; SVG gives layered vector output; DXF (R12) carries a layer per object type for AutoCAD and other CAD tools and also imports walls, doors and windows as editable objects; glTF exports a 3D model; and MP4 or WebM delivers the walkthrough video. FloorGen states clearly that there is no DWG or IFC export and no photoreal rendering. Code checking runs as you edit. You pick a profile, including IRC (US), NBC (Canada), OBC (Ontario), the UK Approved Documents, DIN (Germany), CDMX (Mexico City) or a generic set, and each issue names the room, the rule and what it needs, with a numbered marker on the plan that you can mark as handled. The checks cover habitable room size and width (IRC: at least 70 ft² and 7′ across), stairs including risers, treads, width, headroom, handrails and guards with IRC limits of 7¾″ risers, 10″ treads and 6′-8″ headroom, net and gross area per room and floor in ft² or m², and schedules for doors, windows, finishes and furniture with unit rates for a budget. FloorGen describes itself as a design aid, not a permit approval, and advises confirming with your building department. AI is available in two forms with the same tools and the same undo. Sign in and press Ctrl+I on any plan to use the built-in FloorGen AI, typing what you want or dropping in a photo of a sketch; every change appears as a step with its own Undo. Alternatively, connect the assistant you already use, including Claude, ChatGPT, Cursor, Claude Code, Claude Desktop, claude.ai, VS Code with Copilot, Windsurf, Codex CLI, Gemini CLI or Cline, over MCP. The MCP server exposes 150 tools, 15 resources and 12 prompts that read and change the real drawing rather than a picture of it, so areas and checks stay correct. Keys are scoped to read, edit or full access per project with an expiry, while dry runs, a restorable trash and a checkpoint before big changes add safety, and deleted projects can be restored for 30 days. Everything works on one drawing, which is the core of the approach: change a wall and the areas, the 3D model, the code checks and the printed sheets follow. The workflow runs from drawing or tracing, to furnishing and checking, to walking the plan in 3D, to exporting or printing a plan set. Metric and imperial units are both supported, with feet and inches to 1/16″ or metric to 1 mm, and areas shown in ft², m² or sq yd. For homeowners and renters that means trying a layout before moving anything, planning a kitchen, bathroom or backyard ADU, and handing a contractor a drawing to scale instead of a sketch. For interior designers and architects it means furnishing from the model library, giving walls individual finishes, presenting in 3D or on video, and producing elevations, sections and print sets. For real estate and builders it means tracing plans from a photo or PDF, getting room areas and a budget for paint, flooring, tile, doors and windows, and sending a view-only HTML file or a walkthrough video. Concrete workflows described on the site include tracing a plan from a photo or PDF and setting the scale with two points, furnishing a bedroom or kitchen and reviewing numbered issues room by room, walking a four-bed two-story template in 3D, and printing an 800 ft² ADU plan. The AI workflow is equally concrete: ask an assistant to widen the kitchen by 2 ft, add a window over the sink, then check the rooms against the IRC, and watch each change land as an undoable step. There is also a direction for education, for teaching how plans work. Target users are listed explicitly: homeowners and renters, interior designers and architects, real estate and builders, AI users, and education. It runs in the browser on phones, tablets and desktops with nothing to install, though a bigger screen is easier for detailed drafting. Pricing is $0: the editor in metric or imperial, furnished 3D with walk-through and video, code checks and schedules, PDF, PNG, DXF and glTF exports, and the AI assistant connection are all free, with no trial, no card and no watermark. Signing in, also free, is what unlocks exports and adds cloud sync and the AI chat in the editor. FloorGen's value proposition is straightforward: a free, browser-based way to draw or describe a floor plan, see it furnished in 3D, check it against building code guidance and export a scaled drawing set, all without installing software, paying for a licence, or creating an account just to start.

FocusWrite is a calm, local-first writing app built around the act of writing itself. According to the product's own description, it combines deep focus modes, ambient sounds, optional cloud sync, and PDF export in a single editor. The application presents a minimal writing surface centered on a single draft, shown in the interface as "Untitled 1," with a live word count and reading-time estimate displayed beneath it. A saved indicator confirms that work is stored, and a sidebar can be opened or closed depending on how much space the writer wants to give the page. FocusWrite is positioned as "your words, your space, no distractions," making it clear that the product is intended for anyone who wants to write without the clutter and interference of a heavier productivity suite. Most writing tools have grown into complex workspaces. They bundle collaboration, project management, formatting panels, comment threads, and notification streams into the same window where a person is supposed to concentrate. The result is a surface that constantly competes for attention. FocusWrite addresses that problem directly by naming itself around focus. Its stated purpose is a calm writing environment, and its feature set is deliberately limited to the things that support sustained attention rather than breaking it: a distraction-free editor, adjustable focus depth, ambient sound, and quiet local storage. The phrase "local-first" in its description signals a stance on where writing lives - on the writer's own device by default, with cloud sync offered as an option rather than a requirement. For people who write for a living or write to think, that framing matters, because it treats the draft as private, immediate, and always available rather than something that depends on a server round trip before it feels real. Focus modes are the centerpiece of the product. The interface exposes three settings - Off, Standard, and Deep - which let the writer choose how much of the environment remains visible while working. At one end, the Off setting leaves the app in its normal state, with the surrounding interface available. The Standard and Deep settings progressively narrow the experience toward the text alone, which is useful when the goal is a long stretch of uninterrupted drafting rather than quick editing or review. Because the modes are presented as a simple three-position control inside the editor, switching depth does not require navigating preferences or reloading the document; the writer can move from a more open workspace into a quieter one and back again within the same session. That matters for real writing rhythms, where attention demands change from minute to minute: a person might want the full picture while planning a section and then drop into Deep focus once the words start to flow. Ambient sounds are included as part of the same calm environment. Rather than requiring a separate app or browser tab, background audio sits alongside the writing surface, so a writer can settle into a steady soundscape without leaving the document. The local-first design complements this: drafts are kept on the device, and the app indicates the state of cloud connectivity directly in the interface with a "Cloud offline" status, making it explicit when work is being held locally rather than synced. Cloud sync itself is described as optional, meaning writers can use FocusWrite entirely offline and enable syncing only if they want their work available elsewhere. In practice, that combination - sound for atmosphere, local storage for reliability, and an optional cloud layer - means the editor keeps working the same way whether or not a network is present, which is the core promise of a local-first application. The writing surface itself stays deliberately plain. FocusWrite displays a document title, a live word count, and a reading-time estimate that appears beneath the text, shown in the interface in formats like "0 words | 1 min read." Those two numbers give a writer an immediate sense of scope - useful when drafting to a target length or judging whether a piece is long enough to be worth trimming. A saved indicator reassures the writer that changes have been committed. A find function, bound to the familiar Command-F shortcut, allows quick searching within the document without leaving the editor. An open sidebar can be toggled on or off. Export is available from the toolbar, and PDF export is named specifically in the product description, giving writers a way to turn a draft into a fixed, shareable document. A brief "Finishing setup" state appears when the app is preparing to run for the first time. FocusWrite's approach is cumulative rather than clever. It does not try to reinvent the text editor; it removes things. The product's identity is built from three decisions that reinforce one another: keep everything local so writing is instant and private, make concentration adjustable through Off, Standard, and Deep focus modes rather than a single all-or-nothing fullscreen toggle, and add only the light atmospheric touches - ambient sound - that help a writer stay in place. Optional cloud sync means the app never forces a trade-off between the reliability of local storage and the convenience of access from another machine; the writer decides. The result is a tool whose methodology is essentially restraint: a calm surface, a few well-chosen controls, and no mechanism by which the software can demand attention away from the sentence being written. The benefits described for users follow directly from those design choices. Writing in FocusWrite is calm by default: the interface is quiet, the focus setting can be deepened when concentration wavers, and ambient sound supplies background texture without a separate player. Because the app is local-first, drafts remain available and responsive without depending on a connection, and the explicit cloud status removes the ambiguity that usually surrounds offline editing - a writer can see whether work is synced or held locally. Word count and reading time give immediate feedback on progress and length. PDF export provides a finished artifact for sharing or archiving. Taken together, these outcomes point to the same goal the product states outright: your words, your space, no distractions. A few workflows follow naturally from what the app provides. A writer drafting a long piece can open a document, switch to Deep focus, and work through a session with ambient sound running and nothing else on screen, checking the live word count as a progress gauge. Someone who needs to work without a connection - on a train, in a place with poor signal, or simply offline by preference - can keep writing locally, with "Cloud offline" shown in the interface, and sync later if cloud sync is enabled. A writer finishing a draft can use Command-F to search the document for a term or a passage that needs revision, and then use Export to produce a PDF for sending to an editor or reader. A shorter session works the same way at the Standard focus level, with the sidebar open while writing. FocusWrite is listed on Product Hunt as a Web App, and its topics there are Web App, Productivity, and Writing. Product Hunt metadata shows it has received 115 votes and 14 comments at the time of listing. The application runs in the browser at focuswrite.app and does not require installation, which fits its local-first model: a writer opens a page and starts typing. Pricing and plan details are not stated in the available content, and no specific integrations or technology stack are named beyond the optional cloud sync layer that supports storing work across devices. FocusWrite's primary value proposition is straightforward: a calm, local-first writing environment where focus can be dialled up or down, where the draft lives on your own device unless you choose to sync it, and where the only outputs that matter are your words and, when you are ready, a PDF. It is a writing app that treats attention as the scarce resource and builds everything else around protecting it.

HireKey is a recruiter-informed AI job-search co-pilot designed to help people apply strategically and interview with confidence. It brings the entire job search into a single workspace: assessing how well a candidate fits a role, tailoring a resume to a specific job description, preparing answers out loud with an interactive AI voice coach, drafting cover letters, researching companies, and tracking every application and next step. HireKey is built for professionals applying to competitive roles, including engineers, product managers, designers, data professionals, marketers, sales professionals, and operators. Its stated purpose is to help job seekers see their next move more clearly — to understand their fit, apply with purpose, practice the conversation, and keep moving forward instead of sending applications and hearing nothing back. Much of the frustration HireKey addresses is captured in its own framing: sending applications, hearing nothing, and not knowing where to focus. The platform is informed by the other side of the hiring process, drawing on ten years of tech recruiting experience at Microsoft, Amazon, and Stripe. HireKey explicitly warns that a high ATS score may not lead to interviews and that a score or recommendation cannot predict an employer's decision, so the product takes a different angle. Instead of promising a prediction, it aims to explain which gaps need clearer communication and which need actual experience. That distinction shapes every feature — from role-fit analysis to resume tailoring — and gives candidates a clearer reason to apply, improve, or move on. Role-fit analysis compares a resume with a specific job description. It separates the experience a candidate can genuinely support from the wording of the resume itself, showing where the evidence is strong, what needs a clearer explanation, and where there may be a genuine gap. HireKey assesses role fit and resume impact separately and explains the evidence behind each assessment, so users can understand their strengths and gaps and decide where to focus their effort. A companion free tool, the recruiter resume review, checks a resume on its own for positioning, evidence, scan visibility, and seniority signals; it requires no account, and the free checker processes the resume on the user's device. The distinction matters: the free checker looks at the resume alone, while role-fit analysis in HireKey compares documented experience with a specific job description. Resume tailoring turns vague bullets into a clearer account of what a candidate actually did, bringing the experience that matters for the role to the front. Users review original and tailored wording side by side, so they can see what changed and why each revision clarifies their contribution and impact. HireKey keeps the candidate in control: the final say over every change stays with the user, and tailoring is intended to make real experience clearer and more relevant rather than to invent qualifications, results, or responsibilities. It should not turn a contributing role into ownership the candidate did not have, and every revision is meant to be reviewed before use. Cover letters follow the same principle — a draft grounded in the user's background and the specific job, connected to the opportunity and given the user's own voice before it is sent. Interview preparation centers on an interactive AI voice coach. Candidates work through their answers in a live audio conversation on their own schedule, choosing questions for a recruiter screen or a hiring-manager conversation, or adding their own. The coach asks follow-up questions based on the answers given, so practice feels like a real conversation rather than a script; it also provides feedback during practice and a debrief on what to improve. For those who prefer preparing on paper, HireKey can save role-specific questions and answer guidance as a PDF. Company research briefs build context before an interview, turning what the candidate learns into more specific questions and a clearer reason for their interest. Together these features aim to give job seekers a clearer story for their application and better context for the next conversation. The job application tracker keeps every opportunity in one place, from the first application to the final interview. Users can see their whole pipeline in either a table or a Kanban view, with stages covering Not Applied, Applied, Phone Screen, Interview, Offer, Rejected, and Withdrawn, and keep stages, deadlines, notes, and follow-ups together rather than scattered across spreadsheets. Each opportunity stays connected to the preparation that supports it. Overall, HireKey describes a simple loop: find your fit, apply, practice, and keep moving. Role-fit analysis informs the decision to apply, resume tailoring and cover letters shape the application, the AI voice coach and company research briefs prepare the conversation, and the tracker keeps the whole search moving forward with a visible next step. The benefits HireKey claims are visibility and confidence at each stage of the search. Candidates know their fit before they apply rather than guessing; they can tailor a resume to the role so their contribution and impact are easier to see; they can practice out loud and receive real-time interview feedback before the interview counts; and they can track every application and next step without another spreadsheet. Because assessments are explained with evidence, users can separate a genuine experience gap from a communication gap and decide where to spend their effort — apply, improve, or move on. The result the site describes is a search with a clearer plan: understanding fit, applying with purpose, practicing the conversation, and keeping momentum instead of sending applications into silence. Concrete scenarios map closely to that four-step workflow. A candidate finds a job posting they are excited about and runs role-fit analysis to compare their resume against the job description, seeing where the evidence is thin. If the role is worth pursuing, they use resume tailoring to review original and tailored bullets side by side and confirm what each rewrite clarifies, then draft a cover letter connected to that opportunity. Before a recruiter screen, they practice out loud with the AI voice coach in recruiter-screen mode, receive feedback and a debrief, and save role-specific questions and answer guidance as a PDF. Company research briefs prepare them to ask more specific questions. Throughout, the tracker shows which applications sit at the Not Applied, Applied, Phone Screen, Interview, Offer, Rejected, or Withdrawn stage, and what follow-up comes next. HireKey is built for professionals applying to competitive roles, including engineers, product managers, designers, data professionals, marketers, sales professionals, and operators. It is available on the web, and users can start with a free plan. Free costs $0 per month and includes 5 resume tailoring sessions, 5 interview practice sessions, 2 live voice coaching sessions, 5 cover letters, 5 company research briefs, and unlimited job application tracking in the dashboard. Premium costs $39 per month for candidates actively applying, interviewing, and preparing for competitive opportunities, and includes 50 resume tailoring sessions, 50 interview practice sessions, 10 live voice coaching sessions, unlimited cover letters, unlimited company research briefs, unlimited job application tracking, plus Pipeline Intelligence and Application Insights. Premium can be selected after sign-in and onboarding under Settings > Billing, and additional credits are available: 5 voice sessions for $9.99 and 15 resume tailors for $9.99. The free checker and the full app have different data workflows; the free checker processes the resume on the device, while the app uses the information provided to deliver its features. For job seekers tired of sending applications and hearing nothing, HireKey's value proposition is a clearer plan informed by a recruiter's perspective: understand your fit, apply with purpose, practice the conversation out loud, and keep moving forward. It does not promise to predict an employer's decision; it aims to make the experience a candidate already has easier to see, explain, and prove — and to keep every opportunity connected to the preparation behind it.

kōdlo is an AI-native sales workspace for high-ticket B2B teams. It finds prospects that fit your ICP, tells you how to warm each one up, and writes the message, the offer, and the deck from your own documents. Instead of stopping at a list, the workspace carries a deal through the full cycle: ICP interview, lead sourcing, list enrichment, deep research, cited brief, personalized outreach, pipeline update, campaign content, and closed deal. Your team tracks clients, contacts, and tasks in the same place, so nothing gets lost between steps. An agent sits on every page, and a shared memory layer means the research, outreach, and CRM capabilities never start from zero context. The product positions itself as the AI workspace for sales, built around one source of truth. Most sales tooling solves one slice of the cycle. As the site itself puts it, other sales tools hand you a list and stop. The gap is everything that comes after: understanding whether a prospect genuinely fits, finding the public signals that show how to approach them, writing outreach that sounds like you, and keeping the pipeline current while you do it. When each of those steps lives in a different tool, context fragments — the reasoning behind a lead, the sources behind a claim, and the history of a relationship all get scattered. kōdlo addresses this by holding your ICP, your contacts, your campaigns, and every brief the agent has written in one place, so prospects arrive with their signals and sources attached and stay that way. kōdlo's outbound loop starts with the ICP Builder, an interview-style setup where the agent builds your ideal customer profile with you. From there the Lead Discovery agent hunts for leads that match it, and each lead is backed by real signals rather than a guess. Those signals are drawn from public record sources the site lists explicitly: press releases, patent filings, job postings, trademark registries, conference programs, public funding rounds, open repositories, public LinkedIn pages, podcast transcripts, tender notices, Companies House, and SEC EDGAR. Every signal is cited to its source, so you can check for yourself that the fit is real. A list enrichment step sits between sourcing and research, filling out the records that discovery produces before the agent goes deeper on them. Deep research turns every account into a cited brief. The agent plans the question, runs the searches, cross-checks what it finds, and cites every source — on screen, step by step, while it happens. That visible trace (ask, plan, search, cross-check, cite) is what lets you see exactly where the agent went wrong when it is wrong. Research draws only on public sources, and every record carries the links it came from. The site notes that the record is live and re-indexed hourly, and that the lawful basis for processing is legitimate interest, documented. The result is a brief you can verify rather than a summary you have to trust, which matters a great deal when the account in question is a high-ticket deal where a wrong assumption is expensive. Outreach goes out personalized over LinkedIn or email and is drafted in your tone of voice. What makes it specific is the Knowledge Hub: you upload product catalogues and other documents, the hub parses them with OCR, and the agent turns them into personalized messages, offers, deep searches, and decks. The Knowledge Hub holds up to 1,000 pages on the Starter edition and up to 10,000 pages on Pro. On the closing side, pitch decks and proposals generate from your own template, so a deal that advances or closes turns into campaign content that keeps the account warm. Documents and attachments that arrive through the inbound lane are parsed and made queryable in the same workspace. The workspace pairs tools and agents on one shared context. On the tool side sit the capabilities your team works with every day: outreach, web intelligence, the Knowledge Hub, and CRM operations. On the agent side sit deep research, lead discovery, content and slide-deck generation, and scheduled automation jobs. Everything sits on a single memory layer made up of short-term memory, long-term session-wise memory, and user profiling memory, so no capability and no agent ever starts from zero context. The CRM and contact management keep the pipeline current, holding companies as an entity layer, people in each role, and the industry they move in. A ticket system handles inquiries, replies are grounded in Knowledge Hub citations and the memory of past threads, and Telegram and Google Chat connections deliver follow-ups and digests. Pro adds long-term session memory and agent learning. The stated outcome is continuity: each step feeds the next because they all run on the same data. Open a contact six weeks later, ask "what's next?", and the agent answers from the record it built — grounded in their public profile, their company news, and your own history with them. On the data side, the promise is that you pay for signal rather than seats: no per-contact charges and no data add-ons. Your data stays in your workspace, under GDPR, CCPA, and global privacy standards, with sourcing the site describes as GDPR-clean. Two workflows are described in detail on the site. In the outbound loop, you define the play with an ICP block, the Lead Discovery agent discovers leads, deep research qualifies prospects with cited briefs, and personalized LinkedIn and email outreach goes out in your tone of voice; the flow ends in a close-and-retain stage with pitch decks, proposals, and scheduled check-ins, and wins and losses feed back to refine the ICP. In the inbound lane, incoming inquiries become contacts and tickets with attachments parsed and queryable, every reply is grounded in Knowledge Hub citations and past threads, follow-ups and digests reach you in Telegram and Google Chat, and pitch decks and proposals generate from your own template. Closing and retaining run in the same workspace as the hunt. kōdlo is aimed at high-ticket B2B sales work: solo reps ready to work a real pipeline, small teams sharing one workspace, and organizations whose needs do not fit a standard plan. Pricing runs across three editions. A free tier offers a seven-day trial with one seat, the agent-native workspace, ICP Builder and Prospect Finder, integrated web and deep research, CRM and contact management, Knowledge Hub with OCR, cited deep research briefs, Telegram and Google Chat connection, and the ticket system. Starter is $49 per month plus VAT (about $43 per month on quarterly billing, a 15% saving) for one seat and Knowledge Hub OCR up to 1,000 pages. Pro is $199 per month plus VAT (about $169 per month quarterly) for up to three seats, workspace collaboration features, 4x month limits, long-term session memory, agent learning, and OCR up to 10,000 pages. An Enterprise edition adds custom AI agents built around your workflow, custom integrations, SSO and advanced security, SLA, DPA and procurement support, dedicated onboarding, and an optional bring-your-own-API-key option. Claude Desktop, ChatGPT Work, or your code agent can connect with kōdlo MCP to scale and orchestrate contexts. kōdlo's core proposition is an end-to-end sales workspace with unified context: one record that finds the prospect and answers the inquiry that arrives without one, one memory that every tool and agent shares, and one place where the ICP, the briefs, the outreach, and the pipeline live together. For high-ticket B2B teams, that means the research behind a lead is cited and checkable, the outreach is written from your own documents and in your voice, and nothing gets lost between the steps of the cycle.

Kooler is a free cocktail recipe app for people who like making drinks at home. You tell it what is in your bar — the bottles and ingredients you already have — and it shows you the drinks you can make right now, drawn from a library of more than 100 curated recipes covering classics, modern cocktails and Brazilian drinks. Every recipe is presented with clear measurements, a step-by-step method and a consistent photo. Kooler runs on the web and on iOS, and it is free to use: no ads and no paywall. The problem Kooler addresses is the gap between a shelf of bottles and a drink in your hand. People who mix drinks at home typically own a set of spirits, liqueurs and mixers, but it is not obvious which combinations of them actually add up to a good cocktail. The product states the premise directly: classic and Brazilian cocktail recipes with measurements and step-by-step method — tell us what's in your bar and see what you can make. Inverting the usual way of browsing recipes, from what do I feel like drinking to what can I make with what I already have, is what turns a recipe collection into something you can use on the spot instead of a list of drinks that require a shopping trip. My Bar is the heart of the app. You add the bottles and ingredients you have, and Kooler then shows you every drink you can make right now. This removes guesswork from the moment you decide to mix something: instead of reading through recipes and discovering halfway down the ingredient list that you are missing a component, you start from what is physically available to you. Because the bar inventory is the starting point, the drinks surfaced are ones you can actually pour without leaving the house, which is exactly the promise in the app's tagline: find cocktails you can make with what's in your bar. Behind My Bar sits a curated recipe library of more than 100 drinks, organised so that there is something for every level of effort and taste. The Classics selection includes well-known standards such as the Old Fashioned, Dry Martini, Negroni, Espresso Martini, Cosmopolitan, Margarita, Manhattan, Mojito, Aperol Spritz and Daiquiri. A dedicated Brazilian section covers drinks such as Caipirinha, Rabo de Galo, Macunaíma, Caju Amigo, Caipiroska, Batida de Coco, Passion Fruit Batida, Quentão, Clericot, Bombeirinho, Leite de Onça and Rabo de Galo Paulista, alongside Brazilian Frozen Limeade and Sakerinha. Further collections include Just in for recently added drinks, To impress for more involved serves such as the Penicillin, Mai Tai, French 75, Sidecar, Boulevardier, Pisco Sour, Clover Club, Alexander, Aviation and Brandy Crusta, Sours such as the Whiskey Sour, Tom Collins, Virgin Mojito, Amaretto Sour, Gimlet, Tommy's Margarita, Fitzgerald and Gin Fizz, and Built in the glass for simple drinks mixed directly in the serving glass, such as Gin and Tonic, Mimosa, Dark 'n' Stormy, Shirley Temple, Fernet and Coke, Passion Fruit Tonic, Rusty Nail, Black Russian, Garibaldi and Campari Soda. An A–Z index lists the full catalogue alphabetically, from Adonis and Algonquin through to White Russian and Zombie, so a specific drink can be found quickly by name. Each recipe is built around clarity. Kooler gives clear measurements for every ingredient and a step-by-step method that walks through how the drink is made, rather than leaving the reader to interpret a loose ingredient list. Recipes are accompanied by consistent photos, so the drink you are aiming for looks the same across the catalogue and you can see what the finished serve should resemble. These three elements — measurements, method and photography — are the reason the app describes itself as a source of classic and Brazilian cocktail recipes with measurements and step-by-step method. The library is also organised as browsable collections rather than one flat list. Collections such as Classics, Brazilian, Just in, To impress, Sours and Built in the glass group drinks by theme and by how they are made, which makes it easier to move from a general intention — something simple, something sour, something to serve to guests — to a specific recipe. The Just in collection highlights recent additions, and a From the community section surfaces drinks submitted by users, including entries such as Honey Deuce, Carajillo and Espresso Tonic with Orange and Spices. On the home page these collections sit alongside a For you feed, and each collection links through to a see all page for the complete list. Beyond recipes, Kooler adds a social layer through check-ins. You can rate a drink you had at a bar and share it, which extends the app beyond your own kitchen and lets you record what you thought of a cocktail you tried out. Signing in — with Apple or Google — creates an account that lets you save your favourite drinks, interact with the community and submit your own best recipes. That account layer is what turns a recipe reference into something personal, where the drinks you like and the drinks you have created accumulate over time. Kooler's overall approach is bar-first and curated. Instead of an open-ended database of every cocktail imaginable, it presents a defined set of over 100 curated recipes and pairs it with an inventory you control. The two halves work together: the inventory answers the question of what is possible tonight, and the curated library answers the question of what is worth making. Collections then give a second route into the same catalogue, by style, by occasion or by method, so the app can be used either by typing in what you own or by browsing by mood. The practical result is a recipe app that does not get in the way. There are no ads and there is no paywall, so the entire recipe library and the My Bar matching are available without interruption or subscription. It is available on the web at kooler.me and as an app on the App Store for iOS, meaning the same account and collection can be reached from a laptop in the kitchen or a phone at the bar. The site is also offered with a Portuguese (Brazil) locale alongside English, reflecting the Brazilian cocktails that are a distinct part of the catalogue. Use cases follow directly from how the app is built. Someone with a partly stocked home bar opens My Bar, enters their bottles and ingredients, and sees a list of drinks they can make immediately. Someone curious about Brazilian drinking culture browses the Brazilian collection and works through Caipirinha, Rabo de Galo or Macunaíma. Someone hosting looks at To impress for a more involved serve such as a Mai Tai or Clover Club, or at Built in the glass when they want something quick like a Gin and Tonic or Mimosa. Someone at a bar uses check-ins to rate a drink and share it. Someone with their own variation submits it to the community, and someone who simply wants to remember what they liked saves favourites to their account. Kooler is aimed at people who like making drinks at home — from those who keep a basic set of bottles and want to know what those bottles can produce, to drinkers interested in classic cocktails and in Brazilian recipes in particular. Access is through the web at kooler.me or the iOS app, with an optional free account created via Apple or Google sign-in that unlocks favourites, community interaction and recipe submission. There is no paid tier described: the product is free, with no ads and no paywall. The takeaway is that Kooler solves a narrow, everyday question well: what can I make with what I have? By connecting a My Bar inventory to more than 100 curated classic, modern and Brazilian recipes, each with clear measurements, a step-by-step method and consistent photos, it removes the shopping-list problem from home bartending and lets you pour something good tonight.

Ayven describes itself as the AI super coordinator that carries and drives your team's agenda. It works on Slack and Microsoft Teams: she reads what is due, asks the one person who can move it, hands off what she cannot do herself, and follows every piece through to Done, with a ticket changing only on its owner's yes. In practice that means Ayven observes the work, cuts it into pieces the team can take one at a time, puts an owner and a date on each, and files them in Jira or YouTrack. From there she runs, tracks and talks about the work for weeks — asking whoever knows, waiting, chasing silence and moving work when someone is away. Your team answers in Slack and the board moves. Ayven is aimed at the people who own outcomes for a team: the leads, executives and founders whose initiatives stall because no single person is explicitly holding the next step. Every open question in new territory has no obvious owner, and the ones nobody owns are the ones that stall. That is the problem Ayven is built around. A ticket that gates a launch passes its due date and quietly sits there. A question asked at six dies in the thread until tomorrow, and nobody counts the day it cost. Three days with no next step is a stall. When you are finding out, what was true on Monday is wrong by Wednesday, and someone has to ask around. Worse, the few people who can push into the unknown are the ceiling, and every new initiative waits on them. Teams that staff this kind of work hire a chief of staff at a $160,000 median base, which is the coordination burden Ayven is designed to carry instead. Ayven's first job is driving and delegating the work itself. She observes what is happening, cuts it into pieces your team can take one at a time, puts an owner and a due date on each, and files them into the board your team already uses, such as Jira, YouTrack or Asana. What she cannot do herself becomes a ticket with an owner and a due date, filed after you approve it, and she follows it until the board says Done. When work runs late, she keeps it moving: past its due date she asks the assignee on the ticket, then hands the work to a teammate who can carry it once you say yes. She also moves work when someone is away. Throughout, she tells whoever raised the work as it moves, so the person who reported a problem hears about it the moment the ticket changes. Ayven also asks the person who actually holds each answer. She sends the question to whoever can answer it — one person by direct message, or the channel when anyone can. Before she asks, she reads the relevant ticket or document, so she is checking rather than guessing. She takes 'no reply yet' as an answer, asks another person when one goes quiet, and tells the asker who answered. One example on the site shows her checking billing and finding that the annual discount is applied at invoice time rather than at checkout, so the demo bug is in checkout, not pricing — she found the answer in the code and asked no one. Another shows her checking competitor prices before putting a pricing decision to a person, because the call gates the pricing page. A third capability is that Ayven reads the tools you already run rather than asking you to move into a new one. She reads Slack threads before writing, reads the live Google Drive doc, and re-reads Notion pages before she acts. She reads a ticket's state and due date in Asana, watches a Jira ticket through to Done, reads the due date off a YouTrack ticket, and reads a GitHub issue's state and owner. She reads the live SharePoint file and can browse any Microsoft Teams channel on demand. She asks through Slack and Microsoft Teams, checks the ticket or the doc before she asks, and files or reassigns on the board only after you approve the exact change. Ayven is built to work over time and in the background, over months. A promised Thursday is a deadline she keeps, no ask is left waiting forever, and she follows up in step with the deadline. On the site she works to a morning clock at 08:00, tracks expiry windows, and comments on a ticket the day it passes its date. For a goal such as a launch by a given date, she reads the due date off the ticket, notes a blocker, and acts — commenting on the ticket the morning after it passes, emailing you if the launch projects past its date, and recomputing after every step. The stated outcome is that she warns you about a slip while you can still do something about it, rather than after the fact. The centre of Ayven's approach is that her messages and comments go out on their own, but a change to a ticket waits for your yes. She files work she cannot do as a ticket once you approve it, reassigns only on your yes, and is watched to Done without ever marking a ticket Done herself. Missions hold multi-week work: you give the outcome, she messages the people each step waits on, comments on tickets past their date, and files or changes a ticket after you approve the exact change. A reply on the ticket or a move on the board updates the mission automatically, so the record stays true without anyone re-explaining it. The benefits follow directly from that model. Because Ayven carries the asking, the following-up and the coordination between people, those things happen on their own instead of waiting on a human to remember them. Planning stops being the barrier, and the team ships its agenda faster. More gets done with the same team, because the few people who can push into the unknown are no longer the bottleneck for every unrelated step. Responsibility stays clear — every piece has an owner and a date — and you are told when a ticket you care about moves, and when something you own slips, with enough time left to act. Concrete scenarios appear throughout the site. An account executive reports that two prospects hit the checkout bug in demos; Ayven files it as a ticket to an engineer, due Thursday, watches it until the board shows Done, and tells the account executive as it moves. A senior executive asks for a summer team trip to be organised; Ayven asks four people one question each — dates, venue, headcount and budget — in direct messages. A finance, product and sales update becomes one assembled draft on Google Drive, filed on the requester's yes, with each team asked for its own numbers by a Thursday noon deadline. Late work is chased on the ticket, then handed to a teammate once approved, and a launch-gating ticket past its due date triggers a comment on the ticket the same day. Ayven runs on Slack and Microsoft Teams and integrates with Google Drive, Notion, SharePoint, Asana, Jira, YouTrack and GitHub, so it reads the tools a team already runs rather than replacing them. Its stated audience is teams that would otherwise staff this coordination work by hiring a chief of staff, whose median base is $160,000. Pricing starts with a free plan at $0 that includes 500 credits a month, every integration included, and no card required; a 15-minute demo can also be booked. There is no mention of a separate paid tier on the site, though the credit-based free plan implies usage is metered. Taken together, Ayven is a coordinator rather than a dashboard: it takes the outcome you give it, asks the one person who can move each step, files and tracks tickets to Done, and keeps every change behind your approval. Its value proposition is that the asking, the chasing and the coordination stop depending on a human remembering them — the board moves, the record stays true, and you hear about a slip while you can still do something about it.

Foley is a voice-first to-do list that turns spoken thoughts into structured tasks. Instead of typing a task, choosing a project, setting a date and adding a reminder, you say a sentence — or several things at once — and Foley works out the tasks, the dates and the project before anything lands. It is built for people who keep their work and their life in one place and want the admin of task management to disappear. The app is available on Mac, Windows, the web, iPhone, Android, Apple Watch and Wear OS watches, and the same list follows you across those surfaces. Its promise is simple: out of your head, into your day. Most task apps ask you to do the filing yourself. You have to think of the task, open the app, type it, decide where it belongs, pick a date, and set a reminder — often at the exact moment your hands are full or your attention is elsewhere. That friction is why lists go stale and why thoughts get lost between one meeting and the next. Foley approaches the problem from the other direction. The thought is the input, not the chore. You say what needs doing the way you would say it to a colleague, and the app handles the structure. When two tasks could fit what you said, or when it needs to know where before it goes looking, Foley asks a question rather than making a guess, so you stay in charge of the result. Capture is where everything begins. A desktop shortcut brings Foley to you without pulling you out of what you are doing, and on iPhone, Android and the watches the capture sheet or capture mark listens while you talk. You can say one thing or several — “Add two tasks: buy milk, and call the electrician” — and Foley works out the separate tasks, the dates and the project before anything lands. You review what it understood before the tasks are saved. On the wrist, Foley shows what it is doing: while the microphone is open the wordmark pulses and the clock counts up, so waiting never looks like something is broken, and you stop when you are done. Nothing asks you to confirm. Once tasks exist, Foley gives them a place. Folders keep related projects together and projects hold the work and its context, so work, life and everything in between can live in one system with clear boundaries — “Create a folder called Clients and move work into it.” For the parts of life that are not projects, Lists provide a simple checklist: shopping, packing, the little things to remember. You can add an item to the shopping list and tick things off. Task context goes further than a title. You can connect related work, keep notes and attachments with the task, and see the dependencies that explain what comes next, such as an invoice that is blocked by a board deck. Seeing what is holding things up is part of knowing what to do next. Foley is also a planner. The Daily desk brings tasks from across your projects together in one clear place, where you can open a task, change a detail, or check it off when it is done. Your day shows the shape of the day in a calendar, so when plans change you can move a task or change its time and keep the rest of the day in view — “Change Sam's pull request review to 4 PM.” You can ask Foley to help plan your day around the tasks already there, follow the plan back to the work, and adjust it as you go. On mobile, the Plan tab answers questions like “What should I work on first?” from what is actually on your list. Clarification is a deliberate feature: when two tasks could fit what you said, Foley asks which one you mean. The approach that ties all of this together is voice plus verification. Capture is conversational, and the result is confirmed rather than assumed: after a capture the app shows what it saved, with the project and the time already set, and then it says so out loud — one sentence, by the same rules on every device — while the words stay on the screen whether or not the voice plays. When the voice does not play, the watch says why, in one dim line. History keeps your original words beside what Foley changed, so you can review the result and undo a capture when you need to. And because the phone, the desk and the browser share one list once you are signed in, a change made in one place is a change made everywhere. On the wrist, a short answer is read back before it goes anywhere, because that is where recognition is least reliable. The outcome Foley aims for is a lighter head and a clearer day. Tasks do not need to be remembered, because capture is only a sentence away on whatever device is nearest. Tasks do not need to be re-typed, because the project and the date arrive with them. Plans do not need to be rebuilt from scratch, because Foley plans around the tasks already on the list and you can adjust from there. And because the app asks rather than guesses, the list stays trustworthy — you are not silently filing tasks into the wrong project or accepting a wrong date. The Timeline groups what is late, what is due today and what the week holds across every project, so the day is one glance instead of a search. Concrete scenarios run through the whole product. In the middle of work, you press a shortcut and say “Add two tasks: buy milk, and call the electrician,” review the two tasks Foley produced, and carry on. Walking out of a meeting, you capture on your phone and Foley files it with the project and time already set. When something changes, you say “Change Sam's pull request review to 4 PM” and the day rearranges. When you are not sure where to start, you ask “What should I work on first?” and Foley answers from your list — for example, renewing an overdue parking permit before posting a parcel due at 12:30. On the wrist, a question like “Which area, and how many people?” can be answered with a tap on one of the suggested chips or by voice. And for a household errand, “Add bread to the shopping list” is enough. Foley is aimed at anyone who keeps a personal and professional task list and wants to keep it without doing the filing — knowledge workers at a desk, people who capture thoughts on the move, and anyone who wants their tasks on a watch as well as a screen. It runs on Mac and Windows desktop apps, in the browser at my.foleyapp.com, on iPhone and Android, and on Apple Watch (watchOS 10 or later) and Wear OS watches (Wear OS 3 or later). Setup lets you connect your tools, choose your capture shortcut, and decide which projects AI may use as context — and you can pick Foley-managed AI or bring your own key. Desktop task management is free, with no sign-in needed to get started and a personal AI key for AI features; your AI provider charges for personal-key usage. Foley Pro is $8 per month (currently $4 for the first month), with hosted AI without a personal key and your account across devices on monthly or annual billing. Website pricing may differ from store prices; signing in is required for hosted AI, sync and sharing. Foley's value proposition is that the thought is the work. By starting with voice — on a desktop shortcut, a phone, a browser tab or a watch — and finishing with a task that already has its project, date and reminder attached, it removes the administrative step that makes most task lists fail. It asks when it is unsure instead of guessing, it keeps your original words so changes can be undone, and it keeps one list in sync across Mac, Windows, web, iPhone, Android and the watches. The result is exactly what the product promises: out of your head, into your day.

Skreno is an AI-powered screen recorder that runs entirely in your browser. It records your screen, captures screenshots and records meetings such as Zoom calls, then turns each recording into a shareable link that arrives with a transcript, an AI summary and comments placed on the exact second. Alongside recording, Skreno includes Studio, a recorder and editor that runs in the browser and is included with every plan, with exports up to 4K. It is built for individuals and teams who need to show something on screen rather than describe it in words. The problems Skreno addresses are the small frictions that pile up around explaining things visually. Installing an app just to hit record. Uploading a file, waiting, then pasting a link. Opening an editor just to draw an arrow. Cropping and labelling screenshots by hand. Chasing feedback across five threads. On engineering and support teams the friction is sharper still: a ticket says 'it works on my machine', a developer says 'I can't reproduce it without the error', and a hand-off arrives with no stack trace and no idea where to look. Support teams report that each ticket can take three replies before anyone can reproduce it, and video conversations tend to turn into long meetings when a short recording would do. Skreno's answer is to put recording, editing and sharing in one place in the browser, so the explanation travels with the evidence. Recording is the first layer. Skreno records right in the browser, so there is no app to install before hitting record. Screenshots are captured and labelled automatically, so each step in a sequence is grabbed and annotated rather than cropped and labelled by hand. Annotations can be drawn and marked up while you record, so an arrow or a highlight lands exactly where the point is being made instead of being added afterwards in a separate editor. Screen and camera can be recorded as separate tracks, which gives the editor freedom to choose the layout later. Sharing is immediate. The link is live the second you stop recording, so there is no upload-and-wait step between finishing and sending. Every recording arrives with a transcript, an AI summary and comments, and comments are placed on the exact second they refer to, which keeps discussion anchored to the moment in the video rather than scattered across threads. When typing three paragraphs would be slower than showing it, you can reply with a quick video instead. And when input is needed from other people, the feedback is collected right on the video. Studio is the editing layer, and it runs in the browser. It trims, captions, blurs and polishes, with exports up to 4K and no download required to do the work. A ten-second trim no longer means downloading the raw file first. Captions, blur and backgrounds live in one editor instead of being spread across one app for captions and another for backgrounds. Editors can pick a layout, cut silences, and add animated captions and titles. Because editing happens from the cloud, every edit updates the same link, so nobody has to re-upload the whole recording after a small change. For engineering work, Skreno attaches the technical evidence to the recording. A bug report can come with the console log attached, and a network panel shows three failed requests highlighted with the 500 error response body open, so the person fixing it can see what actually broke. That directly addresses the three familiar lines in a bug thread: 'it works on my machine', 'I can't reproduce it without the error' and 'no stack trace, no idea where to look'. Teams can also install Skreno on their own site with a Web SDK that needs one script tag, and an AI assistant can be connected so it searches your recordings over MCP and acts. Beyond screens, Skreno covers meetings and other people's recordings. The Meeting Notetaker records the call and writes the notes, so a meeting produces a transcript and a recap automatically, and transcripts and AI recaps are generated for recordings. For customer stories, you can send a link, the customer records, and you receive the testimonial. Request links work the same way for bug reports: anyone can record for you, no account needed. The way it all fits together is deliberately simple. Everything happens in the browser from a single starting point, with an optional Chrome extension and an SDK for your own site. Recording produces separate tracks, the cloud editor assembles them into a layout, and every edit updates one link. That link carries the video plus the transcript, AI summary, timestamped comments and, where relevant, the console and network logs. Nothing needs to be downloaded to trim, caption or export, and recordings stay attached to the work they belong to rather than living in a separate folder. The payoff is speed in the places where speed is scarce. Showing something takes minutes instead of meetings. A support ticket can be recorded by the customer directly, so the first reply is closer to a fix. A developer receives the error and the log together, which removes a round of guessing. Feedback lands on the second it concerns, so reviewers do not have to reconstruct context. Edits update the link that has already been shared, so nobody has to redistribute a new file after every change. Use cases span the teams that explain things on screen. Support teams send Request links so customers record the bug themselves, build a library of their ten most repeated tickets, and keep recordings attached to the ticket rather than in a separate folder. Engineering teams use dev logs and the Web SDK to capture what broke. Product and design teams mark up screens and collect feedback on the video. Meetings and calls get a notetaker that records the call and writes the notes. Marketing and adjacent teams collect customer stories by sending a link. Guides and tutorials are produced in the same editor used for everything else. Skreno is aimed at individuals and teams who work in a browser and need to explain, review or report something visual — support, engineering, product and design, meeting-heavy roles and anyone gathering testimonials. Pricing matches that reach: free is a real plan rather than a trial, with no card and no countdown, no watermark on any plan, and no account needed to try it. The full editor is free, Studio is included with every plan, and exports run up to 4K. Skreno's value proposition is the promise in its own line: record once, explain nothing. Record, edit and share in the browser, and let the transcript, AI summary, timestamped comments and technical logs do the explaining that usually costs another round of replies.

Hypervibe is a native macOS application that brings AI coding agents together on one infinite visual board. Instead of juggling terminal tabs and separate windows, you can run Claude Code, Codex, Gemini CLI and a catalog of other command-line agents side by side as nodes on a single canvas. It is built for developers who already work with agentic CLIs and want to see, organize and orchestrate many of them at once, on their own Mac, using the agent subscriptions they already pay for. macOS 14 or later on Apple Silicon is required. The starting point for Hypervibe is a simple observation: don't let a screen decide how many agents you run. Developers adopting AI coding agents quickly hit the limits of the terminal. tmux gives you panes, but as the Hypervibe comparison puts it, it does not tell you which agent is waiting for you, hand a task from one agent to another, or bring every conversation back when you reopen. Terminal tabs multiply, context is lost between sessions, and there is nowhere to plan next to the work itself. Hypervibe keeps the real terminal and adds the board around it: status per console, teams, loops, voice, and a place to plan. Each console on the board is a real terminal. Every one is a full PTY with a controlling terminal, so Ctrl-C, job control and full-screen TUIs behave exactly like iTerm. The agents run as the interactive CLIs you are already logged into, which means you use your own subscription rather than API keys, and nothing falls outside the plan you already pay for. Hypervibe runs the binaries already on your PATH, supports Claude Code, Codex, Gemini, Grok, opencode and a catalog of 34 CLIs, and also accepts any custom executable you add yourself: if it runs in a terminal, it can live in a node. Around those terminals, Hypervibe provides a set of studios and board tools. Live web tiles show a localhost preview with a real Web Inspector docked to the board. A code studio lets you browse the file tree, preview code with highlighting, and pin files as nodes on the board. A markdown studio offers an Obsidian-style vault with a file tree, autosaving editor and a force-directed link graph. A diagram studio turns diagrams of your repo into loose ink figures you can spill onto the board and export as PNG or .excalidraw. A document studio generates documents from the repo with a live preview and exports a real paginated PDF. Kanban, sticky notes, text and checklists sit beside your consoles so you can plan next to the work, and ink and markup let you draw directly on the board or mark up dropped images in place. A live git card shows your branches alongside a GitHub panel for commit, push and pull. Orchestration is where the board goes beyond a pretty desktop. You can line up a whole team in the composer and launch twelve consoles in one action. Consoles can be grouped into teams, with a mission map to keep track, and work can be handed from one agent to another. Loops know when to stop: you can trigger a console on a schedule, on idle or on output, give the loop a goal, and it retires itself once the goal is met. Voice control lets you direct the board by speaking; the voice planner turns spoken instructions into plans and the app speaks short replies back. State and continuity are handled with care. Every workspace is saved on each change, and sessions come back so consoles return exactly where they were, conversation included. A usage pill per CLI and token analytics by day and agent are built from your local session logs. Profiles with isolated homes let you run two accounts of the same CLI side by side. Project memory works at the per-repo and global level, and nothing reaches a console unless you push it. Under the hood, Hypervibe is local-first. State lives on your disk as versioned JSON and speech is transcribed on-device. Only three things talk to the servers: your sign-in, handled by Firebase Authentication; your planner commands with their board context, which the planner service sends to Groq to turn into a plan; and the text of the short replies the app speaks, which the gateway sends to Inworld to generate audio. Your microphone audio and your repositories never leave your Mac. You also have the last word: agents type, but you send. Nothing prepared for you is ever sent for you, a live console only dies by an explicit confirmed action, and each CLI's auto-approval flag is a global opt-in that is off by default. Getting started follows three steps. First, install your CLIs: log in to Claude Code, Codex, Gemini or whichever agents you use, the same way you do today, and Hypervibe runs the binaries already on your PATH. Second, open a repo by pointing Hypervibe at a folder, which becomes a board with workspaces you switch like tabs and presets that lay one out in a single tap. Third, spawn consoles by adding agent consoles as nodes and putting them to work, directing them by voice or keyboard, grouping them into teams, or leaving them on a loop. The outcomes follow from that structure. Because every console is a real terminal, developer workflows, keyboard shortcuts and full-screen tools keep working. Because each agent runs on your own subscription, there is no resold model access and no separate API key bill. Because the workspace is saved on every change, reopening Hypervibe brings back the exact arrangement and conversations you left. Because loops can watch for output, idle time or a schedule, agents can keep working without a human watching each step, and because their goals retire the loops, automated work stops on its own. Concrete scenarios fall directly out of the features described. A developer can run Claude Code, Codex and Gemini CLI side by side on the same repo to compare their output, using profiles with isolated homes to run two accounts of the same CLI at once. A larger effort can be laid out as a team of consoles on a mission map, with handoffs between agents and twelve consoles launched at once from the composer. While agents work, you can keep a live localhost preview open in a web tile with the Web Inspector docked beside it, check the git card and push from the GitHub panel, or generate a PDF document and a repo diagram from the same board. Loops handle recurring or conditional work: a console triggered on a schedule, on idle or on output, given a goal like a specific task to finish, retires itself when the goal is met. Usage analytics track token consumption by day and by agent from local session logs. Hypervibe is for one person working on their own Macs. It requires macOS 14 or later on Apple Silicon, and ships as a direct download rather than through the Mac App Store because real terminals need to spawn real processes that the App Store sandbox does not allow. The Pro plan costs USD 19 per month, billed monthly with taxes calculated at checkout, and can be cancelled anytime with a 14-day refund window on first purchases and annual renewals. Pro includes 10,000 credits every month for the voice planner and spoken replies, unlimited on-device voice dictation, 34 agent CLIs on one visual board, teams, handoffs and automated loops, the documents, diagrams and code studios, and all updates. Credits renew monthly, and if they run out, new voice planner requests and spoken replies pause until renewal while the board, consoles, voice dictation and local commands keep working. Agent subscriptions such as Claude, Codex or Gemini are paid separately to their providers, and Paddle acts as merchant of record. A Discord community run by the makers of Hypervibe is open for showing boards, reporting bugs and shaping what comes next. The takeaway is that Hypervibe turns a pile of terminal tabs into one workspace for AI coding agents. It keeps the real PTY terminals and your existing subscriptions, then adds the board, the teams, the loops, the studios and the voice control that make running many agents at once manageable, all on your Mac and with your repos never leaving it.

Maildun for Mac is an agentic email builder for macOS, released as a free beta. It is part of the Maildun family of email tools: the open source Maildun platform, described on the website as the full email marketing stack for campaigns, sequences and transactional email; Maildun for Mac, a native Mac application for designing emails; and Maildun Platform, a managed version listed as coming soon with the promise "We run it, you send." The home page summarises the whole offering as "Campaigns, sequences, and transactional email. Run it yourself, design on your Mac, or let us host it for you." The Mac app is built for people who want to produce on-brand emails either by hand, with an AI assistant, or over MCP, and it is free to download. Maildun is positioned as email marketing tooling "built in the open." The open source edition is described as the full email marketing stack that you can self-host, read the code of, and help shape, and the site notes it is free to self-host. Rather than pushing teams toward a single closed hosted product, Maildun offers three ways to run the same idea: hosting it yourself, designing on your Mac, or letting the vendor host it for you through the forthcoming Maildun Platform. This matters because email remains a core channel for campaigns, automated sequences and transactional messages, and teams often want control over where their sending stack and their design workflow live. The Mac app gives designers a native place to build those emails, while the open source project and the upcoming managed platform offer a path to actually sending them. Designing by hand is the foundation of Maildun for Mac. The app provides a drag-and-drop editor in which you build an email by adding elements such as a heading and body text, as shown in the product's editor screenshot with the prompt "Write something people want to read." The editor exposes three views — Design, Preview and Code — so you can lay out the email visually, check how it will render, and inspect or edit the underlying code. An "Apply brand" action sits alongside the assistant and an export control in the toolbar. The result is a hands-on workflow in which the person designing the email keeps direct control over layout and copy rather than relying purely on generated output. The AI assistant takes that hand-built design and edits it live. Maildun for Mac lets you connect Claude, ChatGPT, Gemini or Grok using your own API key, so the model you already use can work directly on the email you are building. Critically, the assistant's changes are presented as a proposal that you accept or reject, rather than being applied silently to your design. That review step keeps a human in the loop: you can see what the assistant wants to change, approve it if it improves the email, or discard it and keep your original version. Bringing your own API key also means the choice of model provider stays with you, and the app supports several of the major assistants by name. Beyond the in-app assistant, Maildun for Mac ships with a built-in MCP server. Turning it on allows developer-facing AI coding tools — Claude Code, Codex and Cursor are the ones named — to create and edit emails as well. MCP, the Model Context Protocol, is the mechanism through which those tools talk to the app, and having the server built in means there is nothing extra to install beyond enabling it. This opens a second, more technical route into email production: instead of pointing and clicking in the editor, a developer or power user can ask an agent inside their coding environment to generate or adjust an email, with the resulting changes landing in the Mac app's design. Maildun for Mac also adds organisational features around the design itself. Workspaces per client let an agency or freelancer keep each client's emails separate, so one client's work does not bleed into another's. Brand kits carry shadcn-style component styles, and the "Apply brand" action brings those styles to a design, which helps keep every email visually consistent with the brand it represents. For repeatable layouts, saved sections let you store blocks you use often and drop them into new emails rather than rebuilding them each time. Media can be stored on your own Cloudflare R2 or Amazon S3 bucket, giving you control over where images and other assets live. And when an email is finished, it can be published to maildun.com. The overall approach is summed up in the Product Hunt tagline: "Design on-brand emails by hand, with AI, or over MCP." Maildun for Mac deliberately supports three ways of working on the same design rather than forcing a single path. You can build everything yourself in the drag-and-drop editor; you can hand an existing design to Claude, ChatGPT, Gemini or Grok and let the assistant propose edits; or you can enable the built-in MCP server and let Claude Code, Codex or Cursor create and edit emails through an agent. Whichever route you take, the app remains the place where the email lives, and the assistant's edits always arrive as proposals you accept or reject. Underneath, the wider Maildun project is open source and positioned as something you can self-host, read the code of, and help shape. The stated benefits follow from that mix of control and assistance. Because the Mac app is free and the open source platform is free to self-host, teams can adopt Maildun without a licence fee, and because the project is built in the open they can read the code and help shape it. Designing by hand keeps full control over the output, while the AI assistant speeds up changes without removing the designer from the loop — every suggestion is accepted or rejected rather than applied automatically. Support for MCP means the same email work can be driven from the AI coding tools many developers already use. Brand kits and saved sections reduce repeated work and help maintain consistency across emails, and media storage on your own R2 or S3 bucket keeps assets under your control. Concrete uses described in the material include building a marketing email by hand in the editor — adding a heading, writing body copy, and checking the result in Preview or Code view. Teams running campaigns, automated sequences and transactional email can use the wider Maildun stack for sending, with the open source edition self-hosted or the managed Maildun Platform used once it is available. Agencies and freelancers can organise work by client using workspaces and apply each client's brand kit so their emails look on-brand. Designers who want a faster first draft can ask the AI assistant to edit the design and then accept or reject its proposals. Developers can enable the MCP server and create or edit emails from Claude Code, Codex or Cursor, and finished emails can be published to maildun.com. Maildun for Mac is aimed at people who design emails — in-house marketers and designers, and agencies or freelancers working across multiple clients — as well as developers who want to generate emails from their coding tools. It runs on macOS as a native application and is free. The wider Maildun project is open source and free to self-host, with source code available on GitHub and a managed Maildun Platform listed as coming soon. Named integrations and connections include Claude, ChatGPT, Gemini and Grok for the AI assistant; Claude Code, Codex and Cursor through the built-in MCP server; Cloudflare R2 and Amazon S3 for media storage; and publishing to maildun.com. The Mac app is labelled beta, and Maildun itself is labelled open source beta. Maildun for Mac packages an on-brand email builder, a bring-your-own-key AI assistant and a built-in MCP server into a single free Mac app, and connects it to an open source email marketing stack you can self-host. Its core promise is choice: design by hand, with AI, or over MCP, while keeping the final say over every change.

Cue is a free macOS app that turns your headphones into a focus switch. Put your AirPods in and your Mac turns on your Focus mode, blocks distracting sites and starts a timer in the notch; take them out and it is break time, with your sites coming back. It runs on macOS 14 (Sonoma) or later and the download is built for Apple Silicon, and it is made for anyone who wants to start and stop focusing without opening yet another app. Most focus tools wait for you to open them, pick a mode and start a session manually. Cue takes the opposite approach: it starts when you put your headphones in and ends when you take them out, so there is nothing to remember. The problem it addresses is the friction between deciding to focus and actually being in a focused state: the small habits of opening an app, choosing a timer length and remembering to stop are exactly the moments where distraction creeps back in. Because triggering focus is tied to a physical action you already perform, putting earbuds in, the switch happens automatically and consistently. Cue also notes that macOS has no public way to set a Focus mode, so it sets up its own shortcuts once and switches Focus for you with no more prompts. The core of Cue is the link between your ears and your Mac. When AirPods are detected in your ears, your Focus mode turns on, distracting sites are blocked and the timer starts. The blocker covers social, video and news lists plus any sites you add yourself; you can paste a comma-separated list and Cue splits it. Apps you list are asked to quit while you focus. Both the blocked sites and the quit apps come back when you take a break. Take your AirPods out and, after a 10-second grace period in case you are only adjusting a bud, Focus turns off and your sites return. The break ends after 15 minutes by default, and you can set it anywhere from 5 to 60 minutes in Settings. AirPods Pro, Max, 3 and 4 are the most precise because they report when they are in your ears, but any Bluetooth headphones work, with other headphones starting the session when they connect. Cue lives in the notch: a small dot and a timer sit next to the camera cutout, and you point at it to open it, tap to pin it, and hover to see your day. There is no Dock icon and no window in the way. On Macs without a cutout, such as the MacBook Air M1, iMac, Mac mini and Mac Studio, Cue draws a floating 190 x 32 pill instead, and the same pill floats at the top of the screen when you are plugged into an external monitor. The notch serves as a compact surface for your session: now playing appears with artwork and play, next and previous controls for whatever is playing, from Spotify to a YouTube tab. Notifications from any app land in the notch instead of the corner and tuck away by themselves. Volume and brightness show in the notch rather than as a big box mid-screen. You can give the notch your own colors by picking from presets such as Peach, Lilac, Mint, Sunset and Aurora, or using Mix, which blends three to five colors you choose into three gradient options so you keep the one you like. Battery information appears when you open your AirPods case, showing each bud's battery with one-tap connect. Small islands cover extras such as keep awake, a caps lock indicator and a nudge to unplug at full charge, and a single switch hides the notch from recordings. Two tiny mascot buddies hang under the notch and react to what you do. They run while you focus, nap on your break, dance when music plays and blush when you point at them. New in version 2.6, outside a focus session the buddies leave the notch and roam your whole screen, walking on windows, climbing their sides, hanging from the menu bar and jumping between them. You can pick one up and throw it or click it to cheer. A loop button sprays 20 to 30 buddies out of the notch, and the power button adds ten more per tap up to 60; tap loop again and they all go home. The skin you pick is worn on screen too, with seventeen outfits free from day one and eleven dance moves that change with each phrase and stay smooth on the beat. The buddies also react to your music, volume, notifications, AirPods and face. When they are on the notch they bob to the volume level, cover their ears when it is loud and squint when it is bright. Cue says two roaming buddies cost no more than the notch alone. Cue's approach is built on three steps and one workaround. First, you put your headphones in and focus starts: your Focus mode turns on, sites get blocked and the timer starts. Second, you take them out and a break starts: after the 10-second grace period Focus turns off and your sites come back, with the break ending after a configurable 5 to 60 minutes. Third, you see your day: today against your goal, your streak and the last seven days all appear in one place. Because macOS provides no public way to set a Focus mode, Cue sets up its own shortcuts once and then switches Focus for you with no more prompts. The app is also deliberately private: there are no accounts and no analytics, and your data stays on your Mac. It sends only feedback you write yourself, an optional update check that is off by default, and two anonymous one-time setup signals (opened, ready). It is written in Swift only and is described as light as a feather. The result is a focus routine that starts with a physical habit rather than a decision. You do not have to remember to open an app or start a timer, and you do not have to remember to stop either, because taking your earbuds out ends the session and returns your blocked sites and quit apps. The notch keeps session information, media controls, notifications and system levels out of the middle of your screen, so nothing is in the way while you work. The optional Face Unlock, for Macs without Touch ID, lets the buddies look at the camera while Cue scans your face and wave a welcome back once you are unlocked; Cue itself notes this is for convenience, not security, since a photo of you could open it. Cue also greets you by name and reminds you to drink water and rest, and you can wear any of the seventeen free skins on the notch. Use cases follow the rhythm of a working day. When you sit down with earbuds in, Cue quietly starts a focus session, blocks the social, video and news sites you listed, quits the apps you asked to quit and begins the timer in the notch. While you work, the notch carries what is playing, incoming notifications and your session timer, so the middle of the screen stays clear. If you need to check something, you can take one bud out; the 10-second grace period covers the case where you are only adjusting, so a quick adjustment does not end the session, and then you settle back in. At break time, sites and apps return for the length you configured. At the end of the day you can open the Today tab to see minutes against your daily goal, your streak, your longest session, sessions today and the last seven days. Cue also supports other headphones: any Bluetooth headphones work and Cue tells them apart, with AirPods starting when in your ears and other headphones when connected. Cue is aimed at Mac users who want to reduce friction around focusing, particularly people who already wear AirPods or other Bluetooth headphones while they work. It requires macOS 14 (Sonoma) or later and the download is built for Apple Silicon. It is free: there are no ads, no accounts and no paid tiers, and all seventeen buddy outfits are free from day one. Installation involves downloading the DMG, dragging Cue to Applications and completing a one-screen setup; because Cue is a free hobby app and is not notarized by Apple (which requires a paid developer account), macOS asks you to confirm once through Privacy & Security before it opens. During setup you click Start, enter your password once for the site blocking helper, allow Motion, and turn Cue on in Privacy & Security to Accessibility; Full Disk Access is optional and lets you pick your own Focus modes. The app is written in Swift, and the maker describes it as vibe-coded by one person and Claude. A feedback form accepts feature requests, feedback, bug reports and notes, and an optional email list announces new versions. The site is available in English and Vietnamese. In short, Cue turns a pair of headphones into the simplest possible focus switch for the Mac: in means focus, out means break, with site blocking, a notch timer, media and system controls, playful mascots and a private, account-free design. For anyone who wants focus to start on its own rather than from an app they have to remember to open, Cue makes the transition automatic and keeps everything it needs to show you in the notch.

History Monk is a replacement for your browser's built-in history page. Instead of scrolling through days of browsing history, you type a few letters and the page you want shows up. It is aimed at anyone who has remembered visiting a page but cannot find it again, and it handles the full life of that record: searching it, filtering it, exporting it and clearing out the clutter. According to the website, History Monk is searchable 80 milliseconds after it opens, it replaces the browser's history page in Chrome, Edge and Opera, and in Firefox you open it from the toolbar button or with Alt+Shift+H. Everything runs on your own computer. Browsers record everything you look at but give you very little control over that record. The default history view is a long chronological list that is painful to scan, and it accumulates search result pages, sign-in screens, shortened links and pages you clicked exactly once. Most people never clean it because cleaning it would take hours. The website frames the problem plainly: no more scrolling through days of browsing history. It also points out that browsers cannot restore deleted history, for any extension, which is why History Monk pairs bulk deletion with confirmation steps and a protected-sites list. Privacy is the second problem: browsing history is sensitive, so Search, Analytics and Cleanup are run locally. Search is the core of the product. You type part of a title or the name of a site and matching pages appear while you type, with results updating for every letter, no Enter key and no waiting for a page to load. Spelling mistakes are corrected from the words in your own browsing history, so typing "plannig" still finds the planning doc. Power filters narrow the list further: site:github.com restricts results to one site, -site:youtube.com excludes one, title:invoice matches the page title only, url:/pull/ matches the address only, after:yesterday and before:17:30 restrict by date and time of day, and visits:>10 finds pages opened more than ten times. A regular expression can be combined with any of these filters, sort:new orders by best match, newest or most visited, and selected: shows only the rows you ticked. The site counts more than eleven power filters. Terms can be mixed in any order, quotes match an exact phrase, and the site states that 100,000 pages can be searched with no lag. Recently closed keeps every tab and window you close, newest first, next to your browsing history. The last tab you closed sits on top, you can type a word to filter the closed tabs, and clicking one opens it again. In Chrome, the Devices view lists the tabs open on your other signed-in devices — phone, tablet and laptop — grouped by device and newest first, with the time each tab was last used, searchable by title or address and openable right there. The website notes this uses the Chrome sync you already have, needs no extra account, and that Firefox does not share other devices' tabs with extensions. Sites is where bulk deletion happens: tick one site or fifty, using shift-click for a range or Ctrl+A for every site, and press Delete to remove every page from those sites, including older visits you cannot see on screen. You can also right-click any page or link and choose "Delete all history of this site". Deleting a site always asks first, and sites on your protected list can never be removed. Export turns what you have found into a file. Search or filter first, then export just those results, or a whole Cleanup group. CSV opens in Excel and Google Sheets, JSON is ready for your own scripts, and the HTML file is a searchable, sortable page you can keep offline. Every export keeps the same five columns — title, URL, site, last visited and visit count — and the file is created in your browser and saved to Downloads, with nothing uploaded. By group is another way to organise history: each search and the pages you opened from it stay together in one card, covering Google, Bing, DuckDuckGo, YouTube and dozens more with no setup. A group card can reopen the whole search with "Open N pages", which brings every page back in a new tab group, or delete a search and its pages in two clicks, leaving pages you also visited at other times untouched. Cleanup sorts history into 24 groups of removable pages, such as search result pages, duplicate pages, sign-in and callback pages, links with tracking parameters, shortened links, pages visited once and never again, untitled or failed pages, AI chat conversations, cart and checkout pages and webmail and calendar pages. You can look inside a group before anything is deleted, every delete waits for a second click to confirm, protected sites are never touched, and there are one-click deletes for the last hour, day or week. Timeline shows your history one day at a time, newest first, starting a new session after 30 minutes of quiet and displaying the gap for how long you were away. Analytics adds a weekday-by-hour heatmap, a focus score, streaks, rising and fading sites and the terms you search for most, and clicking any cell opens those pages. A weekly digest card summarises the last seven days — pages, top sites, your busiest hour and late-night share — and can be copied as an image or text. Keyboard shortcuts such as Alt+Shift+H to open History Monk from any tab, / to jump to search, arrow keys to move, Enter to open and Ctrl+Enter to open in the background keep your hands on the keys. History Monk's approach is local-first. It reads your browsing history through the browser's history API and searches it inside the extension page, so Search, Analytics and Cleanup all run on your computer and your browsing history never leaves your device. There are no content scripts, so it never runs inside the pages you visit; it only does work while its history page is open, which is why the site says it will not slow down your browser. In Chrome and Edge, site icons come from the browser's icon cache, so showing results makes no web requests. There is no account to sign up for and no third-party trackers or analytics. The only network requests it makes are to verify a licence key and to look up the current price. Hidden sites can be kept out of search, Sites, Analytics and Cleanup without touching the underlying history, and protected sites can never be deleted from any view. The payoff is speed and control over a dataset you already own. Search is ready 80 ms after the page opens and refreshes on every keystroke, so finding a page takes seconds instead of minutes. Typo correction removes the need to remember exact titles, and the filter language turns a flat list into something you can interrogate by site, date, title, URL, visit count or pattern. Bulk delete clears a whole site's history in one action, and Cleanup removes large blocks of junk in a chosen time range while still showing you exactly what will go. Export gives you a portable, reusable record of any slice of your history. Timeline and Analytics show where your time actually goes, and because everything is local with no account, using the product does not create a new privacy problem of its own. Concrete workflows come straight from the product's own examples. You remember the Lisbon flight you looked at yesterday, type a couple of letters, and the page is back. You close a tab by mistake and reopen it from the top of Recently closed. You are shopping for a surprise, so you tick that site under Sites or right-click the page and delete all of its history in one go. You want a record of your pull request pages, so you search site:github.com pull and download CSV, JSON or HTML. Your history has filled up with search result pages, duplicate pages and sign-in screens, so Cleanup shows you the 24 groups, you review a group such as the 1,204 search result pages, and delete them after a confirming click. You start reading something on your phone, open the Devices view on your laptop, and pick it up there. Or you simply open the weekly digest to see pages, top sites, your busiest hour and your late-night share. History Monk is distributed as a browser extension for Chrome, Edge, Opera and Firefox 140 and later. In Chrome, Edge and Opera it replaces the built-in history page, so the History menu and its shortcut open History Monk; in Firefox you reach it from the toolbar button or with Alt+Shift+H. It searches as far back as your browser keeps history — Chrome keeps about 90 days — loading the most recent 100,000 pages in the range you pick, with the first 2,000 searchable before the rest finish loading. It is free for 7 days with no account, and after the trial it keeps working for 3 opens a day; a licence removes the limit and enables deleting from Cleanup. It ships with 28 themes drawn from official palettes, including Nord, Dracula, Catppuccin, Solarized and Rosé Pine, and eight languages: English, Español, Français, Deutsch, Português, 日本語, 简体中文 and हिन्दी. You can also set a 12- or 24-hour clock, date order, row density and text size. In short, History Monk takes the browser's least useful built-in page and turns it into a fast, private tool for finding, filtering, exporting and clearing your browsing history. It searches as you type and forgives typos, it recovers closed tabs and shows tabs from your other devices, it deletes a whole site's history in one action, and it groups the junk into reviewable cleanup categories. Nothing is uploaded, there is no account, and the only network requests are for licence verification and price lookup — which is exactly what a tool built on this much personal data should look like.

GitGlow is a local-first visual Git workbench for independent developers who want to know what their code and their agents will ship. It brings precise staging, clear commit history, conflict resolution, a checkout-aware terminal, coding-agent review, and release preparation into a single desktop workspace. Instead of switching between separate tools, a developer opens a clone, reviews the current diff file by file, stages precisely, and ships with confidence. GitGlow is available as desktop builds for macOS, Windows, and Linux, and the free tier works with a private local clone without requiring an account. The problem GitGlow addresses is that producing changes is easy, but verifying them is not — particularly when some changes come from AI coding agents. Developers need to inspect what actually changed, decide which parts belong in a commit, and remember what they have already reviewed. GitGlow's review confidence approach is explicit about this: mark files reviewed and run checks, and later edits bring files back to your attention while making earlier results stale. Agent context is also connected to the checkout, so observed and managed runs can carry repository, checkout, event, and attributed-file context into the same Changes workflow. Local private work remains useful on the free tier, covering precise staging, commits, branches, conflicts, history, blame, and reflog without a GitGlow account. Staging in GitGlow is deliberately granular: review files, hunks, or selected lines before composing a clean commit, so only what belongs is staged. This is available on the free tier for a local repository. Building on that, the Pro review confidence feature lets developers mark files reviewed and run an explicit check with timeout and cancellation, keeping results tied to the checkout that was tested. When a file is edited again later, it returns to attention and the previous verification becomes stale. Nearby, checkout-aware terminal tabs let developers run Git, tests, and installed agent CLIs beside the diff; one checkout is free, while concurrent checkouts are part of Pro. History in GitGlow is presented as topology rather than a flat list: explore real topology, compare revisions, and trace file or line history, with core history free and editing features in Pro. Developers work with branches, remotes, stashes, tags, and trusted worktrees — daily actions are free, while managed worktrees are Pro. Conflict resolution focuses on understanding the real operation: GitGlow explains the roles in merge, rebase, cherry-pick, and revert conflicts, and continuation and recovery are free. For people working across several clones, GitGlow keeps registrations, context, and workspaces without flattening local data, with one active repository free and concurrency in Pro; Pro also adds a selected-repository fetch that brings repositories up to date together with clear per-repository results. Agent work is treated as something to be reviewed rather than merely watched. GitGlow follows authentic provider and Git signals across live, observed, and managed runs, with basic observation free and managed controls in Pro. Observed sessions remain owned by their originating Desktop or CLI client: GitGlow monitors supported hook events and independent Git changes, but hides message, approval, and interrupt controls unless the provider exposes a supported channel, while runs launched by GitGlow are Managed and can expose those controls. An Activity Tape lets developers search and replay sanitized operational detail in context, with the latest Replay free and advanced retention in Pro. Through a packaged MCP sidecar, GitGlow gives agents bounded Git context by exposing read tools and reviewed native actions; reads are free and approved writes are Pro. Free MCP access is read-only, and every MCP-originated write waits for native approval in Agent Observatory while the desktop app is open. GitGlow's overall approach is local-first, with an explicit privacy boundary: local status, diffs, history, repository paths, previews, and audit records stay on the device. Optional GitHub, agent-provider, billing, and consented analytics connections have separate, explicit data boundaries. If GitHub is connected, GitGlow sends only the GitHub requests needed for installed repositories through the GitGlow API, and tokens never enter the desktop webview or MCP responses. For agent observability, an encrypted local flight recorder can retain sanitized provider-visible plans and commentary, commands, bounded output and tests, approvals, collaboration, relative paths, statistics, and safe text diff hunks. It excludes raw prompts, full transcripts or final replies, credentials, absolute paths, binary or oversized bodies, and contents or hunks from secret-prone files, and redaction and truncation are always disclosed. GitGlow states that it never exposes or stores hidden reasoning. Pro also extends the workspace beyond the local clone. A GitHub connection lets developers review authorized GitHub repositories, pull requests, issues, workflows, and checks. Release Workspace helps prepare a release you can review by bringing checks, merged work, notes, and tags into one guarded sequence; release publication is available only there, after a fresh guarded preview and explicit final confirmation. Insights explore the shape of the work — activity, commits, graphs, trends, complexity, work types, rhythm, sessions, and exports — with commits and activity free and advanced insights varying by plan. Indie Log turns evidence into a useful work log, written manually or produced as an analysis-only report pack built from selected sources, with manual logging free and managed reports in Pro. The stated benefits follow from these capabilities. Review confidence means knowing what you reviewed and what changed, so verification does not silently drift out of date. Fetching selected repositories together gives clear per-repository results when catching up across a workspace. Precise staging produces clean commits that contain only intended changes. Checkout-aware terminals keep tests and agent CLIs beside the diff they relate to. Agent observability makes agent work reviewable and ties it to the checkout, while bounded MCP context keeps agent access controlled through native approval. Moving between repositories safely preserves context and local data instead of flattening it. Concrete workflows start with onboarding: install GitGlow, open a clone, and create a first reviewed commit. From there, the daily Git loop covers reviewing changes, working with refs, resolving conflicts, and inspecting history. Developers who use coding agents connect supported clients, review events, and open the exact changed files, with repository, checkout, event, and attributed-file context carried into Changes. Developers preparing a ship can run the guarded release sequence in Release Workspace. They can also search the Activity Tape or replay sanitized operational detail when they need to return to the moment that matters, and produce a work log with Indie Log. The documentation additionally covers finding every control — exact button labels, shortcuts, availability, and recovery behavior. GitGlow describes itself as local Git for independent developers. It ships desktop builds for macOS, Windows, and Linux; GitGlow 2.0.5 has been manually checked on macOS, while Windows x64 and Linux x86_64/aarch64 packages are build-validated for version, architecture, payloads, sidecars, and checksums. Pricing is free for private local clones with no account needed, including one active repository or checkout, precise staging, commits, branches, sync, merge, conflict resolution, stashes, tags, topology history, compare, blame, reflog, basic observation, latest Replay, manual Indie Log, analytics, export, read-only MCP, and terminal tabs in the active checkout. Pro costs €2.99/month or €29/year and adds concurrent terminal sessions, review freshness, verification tied to checkout state, multi-repository fetch, guarded history editing, concurrent workspaces, managed agents, advanced Observatory review and Replays, GitHub integration, Release Workspace, managed Indie Log, and MCP writes. AI provider subscriptions and API usage are separate. Taken together, GitGlow is a local-first Git workbench that treats review as the central act of shipping software. It keeps everyday Git free and local, adds confidence and control through review marks, checkout-tied checks, concurrent workspaces, managed agents, GitHub integration, and release tooling in Pro, and gives coding agents bounded, approved access rather than open-ended control. For independent developers who want to know exactly what they reviewed and what will ship, the product's value proposition is simple: review every change and ship with confidence.

Baby Desk is a macOS app that turns your Mac into a full-screen playground for a baby or toddler while your work stays exactly where you left it. The product's own summary is blunt about its priorities: the point isn't the fun, it's the wall. With one Accessibility permission, Baby Desk intercepts input at the system level — before macOS or any other app can see it — so every keypress, click and scroll lands on the play screen instead of on your documents, windows or applications. It is built for parents and caregivers who work on a Mac at home and need a few safe minutes, not a separate device to hand over. A small child's hand on an open keyboard is unpredictable in the most expensive way. The website lists the shortcuts that routinely threaten an afternoon of work: ⌘Q quits the front app, ⌘W closes a window, ⌘Tab switches applications, ⌘Space opens Spotlight, ⇧⌘3 takes a screenshot, and ⌥⌘⎋ can trigger force quit. Media and volume keys, plus every click and scroll, are just as easy to trip. Baby Desk exists to make those inputs harmless: dangerous shortcuts are swallowed whole rather than merely covered up, which is why the product leads with safety rather than entertainment. Input interception happens at the system level. Baby Desk asks for a single Accessibility permission, and that permission is what lets it swallow keystrokes before macOS acts on them. Because interception happens ahead of the operating system and any other app, protection is not limited to one window or one application — it applies across the whole machine while the play screen is on. Multi-display setups are fully covered as well, so an external monitor becomes part of the playground instead of a gap in the wall. The permission is used only while the play screen is active, and a friendly onboarding walks users through granting it. On screen, Baby Desk offers three ways to be completely mesmerized. The first is Starlight Keyboard: every key pops a giant letter or emoji accompanied by its own xylophone note. Those notes are tuned to a pentatonic scale, so even chaotic mashing sounds lovely rather than jarring, and fast mashing earns fireworks as a reward. The second is Sparkle Drawing, where the mouse paints a glowing rainbow trail that lingers on screen. These are real drawings that stay long enough to be admired and then gently fade away, turning random pointer movement into something that looks intentional. The third mode is Balloon Pop. Balloons drift up the screen and any key or click pops them in a burst of confetti. The supply is endless, so the game never runs out and the giggles keep going for as long as the session lasts. Together, the three modes share one underlying idea: the keyboard and mouse still feel responsive and rewarding, but everything they produce stays inside the playground. Nothing typed here can reach a document, a draft or a chat window, and nothing drawn here can alter a file. Getting out of the playground is deliberately harder than getting in. A parent gate requires the secret exit shortcut ⌘⌥⇧L, or alternatively holding any screen corner for three seconds. From there, an optional second factor — Touch ID or a PIN — makes sure a lucky mash can never unlock the Mac by accident. A play timer can be set to 1, 3 or 5 minutes, after which a calm "that's all for today" screen ends the session. A volume cap and dim mode keep the stimulation gentle, and system volume itself is never touched. Baby Desk is honest by design about what it cannot do. No software can intercept the hardware power button, a forced shutdown, or some system trackpad gestures, and rather than pretending otherwise the app shows the exact protection level in force: Full protection when Accessibility has been granted, or a clearly-labeled partial mode without it. There are no surprises in either state, and the app still works in partial mode if the permission is refused. This transparency is presented as a feature in its own right — the product would rather show you what it covers than overstate its guarantees. The overall approach explains why Baby Desk is not on the Mac App Store. The App Store requires sandboxing, and a sandboxed app cannot intercept system-level input, which is the entire point of the product. It therefore ships directly, signed with a verified Apple Developer ID and notarized by Apple, the same way pro tools like keyboard remappers are distributed. The result is a single-purpose utility with a narrow, well-defined job: hold a system-wide wall for a bounded amount of time, then hand the Mac back untouched. Requirements are macOS 13 Ventura or later on Apple Silicon or Intel, with the interface available in English, Korean and Japanese, following your system language. The benefit is measured in ordinary parental currency: minutes. When playtime ends, your desktop comes back exactly as you left it — same windows, same focus, same volume. Input never reached other apps in the first place, so there is no cleanup, no lost draft, no relaunched application and no reopened tabs. The site frames the trade plainly: trade five minutes of chaos for five minutes of wonder. For a working parent, that means the Mac can be shared with a curious toddler without becoming a liability, and a 1, 3 or 5 minute timer gives the session a natural, calm ending. Typical scenarios follow directly from that design. A parent working from home can set the play timer and let a baby mash the keyboard while an important document stays open and untouched. Someone with a multi-display desk can extend the playground across an external monitor, so a child reaching toward the second screen finds the same protected surface. A one-minute session suits a quick distraction, while five minutes covers finishing an email or a call. In every case the wall is the product: the Mac remains a Mac underneath. Baby Desk is aimed at parents and caregivers who work on a Mac and need their machine to survive tiny hands, including users running multi-display setups and those who choose to run in clearly-labeled partial mode instead of granting Accessibility. The interface follows your system language and is available in English, Korean and Japanese. Pricing is a one-time purchase described as yours forever: $1.99 during the launch offer and $4.99 regularly, bought directly from the developer rather than through the App Store. A demo is available on the website to try before buying. Baby Desk's primary value proposition is narrow and clear: it is a wall, not a toy. By intercepting input at the system level with a single Accessibility permission, it swallows ⌘Q, ⌘W, ⌘Tab, Spotlight, screenshots, force quit, media keys and every click and scroll before macOS or any other app can react. Stars, xylophone notes, rainbow trails and confetti balloons keep little hands busy on every display, while a parent gate, optional Touch ID or PIN, a play timer, volume cap and dim mode keep the session controlled. When playtime ends, your work is exactly where you left it.

Onepin is an AI voice production agent that turns any script into voice that is ready to ship — model-matched, validated, and auto-fixed. It sits on top of the text-to-speech models teams already use, handling the models, the cleanup, the pronunciations, and the checks so that every line comes out production-ready. The site describes its role simply: text-to-speech creates voice, and Onepin decides what ships. It is built for teams shipping AI voiceover in several languages, including product videos, dubbing, and courses, and it is free to start with no credit card required. Onepin exists because of a specific gap in modern voice AI. AI voices sound human now, but they still guess names from the spelling, and nobody has time to listen to every line. Addresses, prices, dates, and abbreviations make every TTS model stumble, and mispronouncing a brand, a drug, or a name can make an entire line unusable — it is the first thing generic TTS gets wrong. One wrong name ruins the take. Onepin attacks these failures on both sides of synthesis so that teams producing multilingual voice work do not have to catch every error by ear or listen to each take twice. The first pillar of Onepin is access to every top text-to-speech model in one place. A script flows into 30+ models behind one account, routed line by line. Named providers shown on the site include ElevenLabs, OpenAI, Google, Microsoft, AWS, Cartesia, Murf AI, Deepgram, MiniMax, Inworld, Rime, Fish Audio, and Naver Clova, with more than twenty additional models listed. Because no model wins every language, Onepin benchmarks continuously and picks per run, so the model choice is made for each job instead of being fixed up front. That has a practical operational value: if a provider hikes prices, deprecates a capability, or shuts down, the pipeline does not move. Teams shipping at scale never have to marry one model again, which matters most when the output is going out to customers and legal teams alike. The pipeline then starts by cleaning the text before a single line is generated. Addresses, prices, dates, and abbreviations — described as the most common cause of misreads — are normalized in every language before synthesis, so the model pronounces the script the way it was meant. The site illustrates this with strings such as 3042 becoming thirty forty-two, St. resolved as either Saint or Street depending on context, and NW expanded to NorthWest. Because these cases are fixed before synthesis rather than after, roughly half of voice errors are removed up front, which means fewer regenerations, fewer review cycles, and far less manual correction for anyone producing voice at volume. Pronunciation is the next stage, and it is treated as the highest-stakes one. Onepin uses a 4M word dictionary that teaches how to say the hard words right, in any language. It covers brand and product names, complex medical and drug terms, and hard names in any language. The examples on the site span consumer brands and artists — AbbVie, Versace, Sade, Siobhán, Måneskin, Hermès, Givenchy, Björk, Saoirse, Stromae, Balenciaga, Rammstein, Röyksopp, Moët, Ng, Xóchitl, and Hoegaarden — alongside medical and scientific terms such as semaglutide, esomeprazole, hydroxychloroquine, esophagogastroduodenoscopy, and electroencephalography, each paired with a phonetic transcription. The point is that a single mispronounced name or drug term forces a redo of the whole line, so getting pronunciation right is the difference between a usable take and a wasted one. After generation comes validation. Every line comes back scored on four dimensions: naturalness, word accuracy, clarity, and pronunciation. The same bar is applied in every language, and lines are scored against your bar rather than the provider's, so one standard holds across an entire multilingual project. The site shows this working across locales — a US line, a Spanish line, a Japanese line, a Korean line, and a German line — each returning with its own score. That means a team can review a project by looking at scores instead of listening line by line, and can spot which specific lines need attention before anything ships. The final stage is automatic repair. Misses fix themselves: when a line is flagged, Onepin either regenerates the line or fixes just the word that missed. The example given is a line where Siobhan was misread — the system holds the gate on that line and corrects it, using the same model with new settings, without re-rendering the entire take. The stated outcome is that you always get the best take. Because only the failing word or line is touched, the rest of the approved audio is preserved, which keeps a review workflow from cascading into a full re-record. Overall the method is a single drop-in workflow: you drop in your script, and Onepin handles the models, the cleanup, the pronunciations, and the checks. The site frames it as a three-part flow on screen — select, generate, validate — backed by four pipeline steps: clean, pronounce, validate, and fix. The promise is a script that goes in and production-ready lines that come out, on the voices you already use, without a person having to listen to every line twice. The benefits extend beyond audio quality into how a team operates. Because output quality is scored rather than guessed at, review becomes a matter of checking scores and fixing flagged lines. Because model routing is abstracted, procurement risk is reduced. The site also addresses ownership directly: every output is assigned to you and cleared for commercial use, and Enterprise adds a negotiated IP indemnity, so the audio can legally ship. On the cost side, you buy one credit volume and allocate it across workspaces, reallocating anytime, with seats provided free — a structure designed for multiple teams drawing from a single pool. Use cases named on the site include product videos, dubbing, and courses for teams shipping AI voiceover in several languages, and the demo script panel offers verticals such as Product, L&D, Healthcare, Support, Travel, and Sports. The site also illustrates the kinds of lines where pronunciation decides the outcome: a gaming line — Slough off the poison before it spreads; an audiobook line about the minute inscription above Hawthorne's tomb; an advertising line about booking a stay in Yosemite before the summer rush; an e-learning line about the reign of Louis XIV; and a film line about subtlety never being his forte. Each depends on a name, a number, or a word that generic text-to-speech tends to get wrong. The product also states it is trusted by teams shipping voice at scale, listing HeyGen, Hyundai Motor Group, Sierra, Bland, and Johns Hopkins University. On integrations and commercial terms, Onepin connects to the voice providers it routes across, including ElevenLabs, OpenAI, Google, Microsoft, AWS, Cartesia, Murf AI, Deepgram, MiniMax, Inworld, Rime, Fish Audio, and Naver Clova, with more than twenty additional models reachable through the same account. Access starts free with no credit card required, and the pricing section positions Pro for when you are shipping, with a contact option for larger needs. Enterprise adds the negotiated IP indemnity mentioned above. In short, Onepin is the production layer between your script and shippable voice: it cleans the text, gets the names right, scores every line, and fixes the misses automatically across 30+ TTS models in any language. Its core value proposition is confidence — shipping AI voice you do not have to listen to twice.