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Productivity AI Tools
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GBrain is a team workspace built around a single shared AI memory that stays synced to every AI its members use. Rather than each person keeping private notes and separate account connections for the AI tools they rely on, the workspace holds one memory, one set of connected accounts and one set of skills that everyone on the team draws from. The memory is stored in files the team owns, and the workspace is described as the room a team works in, plus the server underneath it. GBrain is positioned as Garry Tan's AI memory, tools, and skills for any harness, aimed at teams who prompt AI together and want what one person tells an AI to be available to everyone else. Teams today work across several AI tools at once, and each one tends to remember only what was told to it, in its own silo. Notes written while working in one assistant stay invisible to the next one, and account access has to be wired up again and again. GBrain addresses this by putting memory and connected accounts in one place that every AI can reach. The stated example is simple: write a note in Claude Code and ChatGPT knows it. The same principle applies to accounts, where connecting Gmail once means Cursor can search it without a key sitting in a config file. Instead of memory being a feature scattered across separate products, it becomes something the workspace holds and the team shares. Memory is the first of the four parts of GBrain, described as what the workspace knows about the team and the work, held in files the team owns. Everything the workspace has learned is plain markdown in a folder that can be copied, and copying that folder takes the notes with you. Because the memory is made of readable markdown rather than a proprietary store, the team can read it, correct it directly, and carry it out of the product whenever they choose. The open source parts are free to run yourself, which means the team is not locked into the hosted service to keep access to what it has accumulated. This matters because the value in an AI workspace compounds over time: the longer a team uses it, the more context it holds, and the more important it becomes that this context belongs to the team rather than to a vendor. Tools make up the second part: the accounts the workspace reaches, and what each AI may do with them. Email, calendar and the web are connected once, at the workspace level, and then become available to the AIs the team uses. The stated benefit is that connecting Gmail once lets Cursor search it without a key in a config file, removing the repetitive setup work of granting each assistant its own credentials. Some of these tools are metered, such as web search and page crawling, and those are paid for from the usage credit included with the workspace. Keeping connections at the workspace level also means an administrator can express what each AI may do with a connected account, rather than leaving that decision to individual configs scattered across people's machines. Skills are the third part, described as the jobs the workspace knows how to run, on demand or on a schedule. Skills come already installed, so the workspace arrives with work it can already do rather than an empty shell the team has to build from scratch. The scheduled side is what GBrain calls work that runs while you sleep: jobs triggered on a cadence rather than in response to someone sitting at a keyboard. Onboarding and support come direct from the team behind GBrain, which is presented alongside the skills as part of the package. Taken together, memory, tools and skills are the raw material, and the workspace is where the team puts them to use. The workspace is the fourth part, and it is both a shared room for the team and the server underneath it. GBrain is multiplayer, meaning the whole team works in one workspace rather than in separate accounts, and an invitation to the rest of the team costs nothing extra. Members share the same conversation and the same memory, so context from one person's session is available to the others. On the model side, the workspace uses models from Anthropic and OpenAI and they can be switched at any time, and it can run on your own inference or on GBrain's. Getting started is a sign-in rather than a setup project: the workspace is described as running in about two minutes, with nothing to install. The benefits follow from that structure. A team gets one place where what it has learned lives, rather than a set of disconnected memories inside separate AI tools. Access to email, calendar and the web is granted once and reused by every AI, which removes the repeated key-in-a-config-file work. Scheduled skills mean recurring jobs happen without anyone remembering to start them. Because memory is markdown in a folder the team owns, leaving takes the notes with you, and the open source parts are free to run yourself. On cost, one price covers the workspace and everyone invited into it rather than being charged per person, and a monthly usage credit of $100 covers the AI models the workspace thinks with as well as metered tools, with the workspace telling you before you run out rather than after if you reach the limit. Concrete use cases follow from the parts. A developer writes a note while working in Claude Code, and a teammate using ChatGPT can rely on it without being told separately. Someone connects Gmail once in the workspace, and Cursor can search that mailbox with no key in a config file, so a coding assistant can draw on email context. A team member sets up scheduled work that runs while they sleep, so recurring jobs complete on their own cadence. Several people prompt in the same workspace and share one memory and one conversation, so the context of the team accumulates instead of fragmenting per person. And when a team leaves, they copy the markdown folder so the notes come with them and can be carried to whatever they use next. On pricing and plans, the Product Hunt launch offers the whole workspace at $99 for the first month, then $199 a month, billed monthly, and it can be stopped at any time from the workspace's own billing page. The $99 covers the workspace and everyone invited into it, so adding the rest of the team costs nothing extra, and nothing about the workspace changes when the first month ends. Each month includes $100 of usage credit for AI models and metered tools, and more can be bought from the billing page if heavy use exhausts it. The offer ends on September 29, after which the link stops selling that price, though a workspace started before then keeps the price it started on. GBrain's core promise is a single shared AI memory and one set of connected accounts and skills that every AI a team uses can reach, running in a workspace the whole team prompts together in. The memory belongs to the team as plain markdown files, the accounts are connected once instead of per tool, the work can be scheduled, and the whole thing starts with a sign-in rather than an installation.
ToneBird is an AI reply assistant for Mac and Windows that drafts replies inside the apps you already type in. It is built around three things the product states on its site: it understands your relationships, what you have already said, and where you left off. ToneBird brings in the person you are talking to, earlier conversations, and relevant facts so you can pick up where you left off, and it drafts the next reply in your voice in Gmail, Slack, and the other supported apps. You review the draft and send it yourself, using your app's own send button. ToneBird is positioned against the moment of being stuck on words: the site's headline promises that you don't have to be stuck on words again. The everyday problem it addresses is that a good reply depends on context that lives outside the reply box: what you promised someone last week, the price you agreed in a proposal, and whether this is the first time a customer has raised an issue or the second. Without that context, people either leave the conversation to build a giant prompt, explain the backstory, and bring the output back, or they keep postponing the reply. ToneBird is designed around keeping that context available so the assistant can help without requiring a full briefing every time. That context is relationship-specific: a client waiting on a deadline needs a different reply from a teammate checking in, and ToneBird adjusts the wording accordingly. The core of the product is relationship memory. While drafting, ToneBird recalls knowledge relevant to the person and the thread and shows it in the draft; the examples on the site include "David asked for a checkpoint" and "Sam's scope changed last quarter." It uses earlier conversations when drafting a reply, including the date you promised to confirm. It also uses facts from your files: when a client asks for the agreed price, ToneBird finds it in your connected proposal and includes it in the draft, and the site's example pulls $2,400 for the full package out of a client proposal PDF. Person cards hold the running context for each contact, such as the note that Sam's renewal follows last quarter's scope change and that he needs options he can get approved, and you can add a fact to a person manually. ToneBird is equally focused on keeping your voice intact. It adjusts tone for each person using your past conversations with that person, rather than producing one generic rewrite. The tone profile has sections for About Me, Style and Context, with sliders the site shows set to Directness 60, Formality 40 and Warmth 50, plus a written instruction such as "Keep replies concise." Saved presets appear as Internal, General, External and Casual, covering lines such as a separate quote, a renewal over coffee, a checkpoint at 3 PM, or a casual Thursday at 4. When ToneBird drafts, it offers three replies so you can compare them; you tweak the one you want and insert it. Tweak options include More Precise, Warmer and Add Humor. Using ToneBird follows three steps. First, open a reply and open ToneBird by clicking the orb or pressing Option twice on Mac or Ctrl twice on Windows. Second, pick a reply by clicking the arrows or using the left and right arrow keys to choose among the suggestions. Third, edit and insert: tweak if needed, insert the draft, then review and send. Insert only places the draft in your reply box; ToneBird does not send messages for you, and you use your app's own send button. Supported apps listed on the site include Gmail, Slack, WhatsApp, iMessage, Discord, WeChat, Lark, X, Reddit, LinkedIn, Instagram and Google Chat. ToneBird works beside readable reply fields, so support depends on the app and the field you are typing in, and some fields may not be readable. ToneBird also learns from the way you write. "Keep what you learn" lets you review facts and writing preferences from the conversation and choose what to save. A learned style such as "Clear dates first. Scope discussions second." can be applied from a message you wrote to David, and a learned fact such as "A checkpoint with David is due at 3 PM today" can be applied to your About Me context, with Apply and Dismiss options next to each. Because your projects, commitments and saved preferences are already available, you can skip the introduction and start the next conversation without re-establishing who you are talking to and what the relationship involves. Beyond single replies, ToneBird helps you track what the conversation still owes. "See what needs a reply" brings promised quotes, unanswered questions and dates to confirm into one follow-up list, organized as tasks from your replies with All, To do and Done views; the site shows 9 tasks, 5 to do and 4 done. Each task is attributed to the person and the app it came from, such as sending David the revised quote via Gmail, confirming the delivery date with Sam via Slack, sending Chen the tracking number via Gmail, or adding discussion points to the meeting notes via Lark. From reply to follow-up, ToneBird can also schedule meetings from your chats: once you have agreed on a time, it prepares the event in Apple Calendar or Google Calendar from the conversation, and you check the details and confirm. ToneBird is designed to travel across apps and languages. Reply suggestions are based on your conversation and appear right in the reply box, so you get help with wording without switching apps or moving the text somewhere else. The site lists English, Español, 中文 and 日本語 among its languages, and shows a Gmail example where ToneBird drafts a Spanish reply to a customer who cannot export a monthly report, apologizes for the repeat issue, commits to checking workspace permissions and sending findings and next steps before 4 PM, and provides an English translation alongside the draft. Privacy is framed as control over what is remembered. Your tone profile, person cards, learned corrections and usage counts live on your device, and you can export or delete them any time in Settings. Conversations can reach an AI provider: the model provider receives the context and instructions needed for your reply, and with a managed model ToneBird forwards the request, while with your own model key the writing requests go directly to that provider. There are four plan tiers. Free costs $0 and includes 15 uses every day and 30 contacts. Standard costs $9.90 per month, billed monthly, with 100 uses every day and 100 contacts. Pro costs $39.90 per month, billed monthly, with 1,000 uses every day and 1,000 contacts. Enterprise is custom pricing for your team, with custom daily usage and custom contacts. Annual billing saves about 20 percent, and the product is free to start. Concrete scenarios from the site show how this plays out. A manager messages in Slack that he needs the launch plan by Friday after already slipping once; ToneBird recalls that David asked for a checkpoint and drafts a reply committing to the core launch plan by Friday with a checkpoint at 3 PM today. A customer in WhatsApp says the renewal increase is higher than expected and he is not sure he can get it approved; ToneBird recalls that Sam's scope changed last quarter and drafts a reply proposing to work through the renewal over coffee near his office, which then becomes a calendar invite for Thursday at 4 PM near Sam's office. A customer writes in Spanish in Gmail that the monthly report export still fails for the second time; ToneBird drafts a Spanish reply and supplies an English translation. A client asks what price was agreed, and ToneBird pulls $2,400 for the full package from the connected client proposal. After a second nudge on a slipped deadline, ToneBird offers several ways to tell David that a firm date will be confirmed tomorrow, and a differently worded update for a teammate who just wants a quick answer. ToneBird runs as a desktop app with downloads for Mac and Windows. It is aimed at people who answer a lot of messages and need each reply to fit the person and the history behind it: people replying to managers, teammates and customers inside Slack, Gmail, WhatsApp and other chat and email apps they already use, including teams that need custom daily usage and contacts on the Enterprise plan. ToneBird's value proposition is narrow and specific. The product is about the reply, not the first draft of a document, and it is the relationship behind the reply that makes the draft useful. By recalling what you have already said, what you promised, and what lives in your connected files, and by adapting the wording to the person while preserving your voice, ToneBird reduces the friction that makes people postpone a response, and it keeps the final decision to send in your hands.
Solid gives AI agents their own computers, accounts, and budgets, then lets them take on a job from start to finish. You describe what you need in plain language, and the agents work out the steps, connect to the tools the job requires, build anything that is missing, and check the result before reporting back. The product is positioned for complex, long-running work that a person or a team hands over rather than supervises click by click. Instead of a personal assistant that lives inside a chat window, Solid is described as a system for work that continues after you close your laptop: the agents keep going and ping you when it is done. The site lists builders and operators at companies including Revolut, ElevenLabs, EY, British Airways, NVIDIA, Stanford, Berkeley, MIT, NYU, Swiggy, and the Government Digital Service among its users. The problem Solid targets is the distance between asking for something and actually having it done. A request in a chat tool typically ends with a suggestion, a draft, or a set of instructions that a person still has to carry out across several apps. Real jobs, however, involve signing up for services, connecting accounts, writing code, deploying it, testing it, and following up — often over hours or days. Solid's answer is to give the agents their own machines, their own accounts, and a budget, so they can perform those steps themselves. The website stresses that no prebuilt connector is required: agents can connect through an API, build a missing integration, or operate a website, desktop application, or phone app directly. That matters because the tools needed for a job are frequently ones that have never been added to an integration directory, and because the person delegating the work may not know how to use them either. Self-sufficiency is the first capability Solid highlights. The agents choose the tools they need and handle the setup themselves, even for software you have never used; the site's framing is that if a person can use it, they can too. They work from their own devices — Windows and macOS computers, Linux servers, iPhones, and Android phones — and their own Google and Apple accounts. Beyond devices and accounts, they can sign up for services and pay for them within the budget and approval rules you set. The illustration the site uses shows an agent controlling its computers and phones alongside account badges, a budget gauge, and a checked payment receipt. The practical effect is that you are not asked to prepare an environment, purchase the tooling, or configure integrations before work can begin; you provide the access that is required, choose the approval rules, and let the agent work out the rest. Solid describes its agents as self-healing. If a tool fails or their setup breaks, they can investigate the failure, make a repair, and check that the job runs again; when they genuinely need help, they explain what is blocking progress rather than stalling silently. This is paired with self-improvement: the next job starts with what they learned. The agents keep the fixes that worked and learn from your team's corrections, and those lessons change how they use tools and approach future jobs. The site illustrates this with an agent reusing a corrected pattern from a previous job as a drawing guide for the next one. In practice, this means corrections are not one-off patches that have to be repeated — feedback about how something should be done becomes part of how the agent operates on later, similar work. Self-scaling covers how Solid handles work that outgrows a single agent. The agents can create more Solid agents or bring in outside agents such as Codex and Claude Code, divide the work across whatever tools the job needs, coordinate the team, and return a single checked result. The site's illustration shows a Solid agent gathering results from other agents working across business tools and handing one verified outcome to a person. This matters for jobs that are too broad or too long for one worker: rather than a single agent attempting everything sequentially, the work can be split across agents and then reassembled into one deliverable. The example workflows Solid publishes follow the same pattern — a defined job with a numbered sequence of steps, ending in a result a person can review, approve, or share. Overall, Solid works as an always-on system rather than an interactive session. You bring the goal and the ground rules; the agents work out the steps, check the result, and report back. While a job runs, you can watch progress — the site's example shows an agent that has connected Gmail and HubSpot, researched on LinkedIn, built a dashboard, deployed it, and is verifying the data — but you do not have to be present. You close your laptop, and the agents keep working, messaging you when the result is ready or asking when they need a decision. You choose what the agents can access and which actions require approval; for example, they can research and draft freely while approval is required before sending a message, buying a service, or deploying a change. The agent is described as a meta-agent that can see and manage its own workspace within the access you give it, so you can ask what is running, why it is needed, or how much a job cost, including a breakdown of the AI usage, machines, and purchases used for the job. The stated benefits follow from that design. You are not managing every step of setup and troubleshooting, because the agents handle configuration, connections, and code on their own. You do not need to stay online or babysit a process, because the job continues without you and returns a finished result rather than an open question. Costs are visible and bounded: budgets and approval rules limit what agents can spend, and each job's consumption of AI, machines, and purchases can be broken down on request. Corrections persist across jobs, so instructions do not have to be repeated. When an agent remains blocked, it explains what needs your help, and the site notes you can also talk to a real person on the Solid team. For teams, the outcome is the ability to delegate more work without giving up control. Solid publishes example workflows that show what a finished job looks like, each starting from a first request and ending with a result you can review. In a sales demo workflow, an agent reads customer meeting notes, maps the buyer's workflow, builds the demo with sample data, hosts and tests the app, and returns the hosted link along with test results. In lead generation, an agent applies your targeting criteria, researches buying signals on LinkedIn, qualifies accounts and contacts, drafts outreach and waits for approval, then follows up, books qualified meetings, and updates the CRM. For AI product evaluation, an agent builds user scenarios and success criteria, runs after each release or change, simulates users completing key tasks, judges outcomes against expected behavior, and reports what passed, what failed, and why. A bug resolution agent investigates a production alert, reproduces the issue and assesses its impact, writes and tests a fix, opens a pull request for engineer approval, and verifies recovery after deployment. A support agent reads a stalled ticket, gathers the full customer history, finds the cause across systems, applies the fix within your policies, and confirms and records the resolution. Solid is aimed at individuals and teams with work they lack the time or expertise to do, and the site lists builders and operators at large companies, universities, and public sector organizations. On integrations, the position is that none are required in advance: agents can connect through an API, build a missing integration, or operate a website, desktop software, or phone app directly, using the access you approve. Pricing is subscription-based, with the full monthly payment becoming one balance for AI usage, machines, and purchases the agents make, with no extra platform fee. Starter is $40 per month for getting started with a focused task, a simple app, or a small workflow; Pro is $160 per month for regular work, active app building, and more room to test and iterate; Max is $640 per month for heavier workloads, larger apps, and several projects running at once. The trial lets you start with $20 on Solid. A Solid API lets you deploy always-on agents inside your product, and an enterprise offering adds access, budgets, policies, and approvals across a workspace, running on Solid Cloud, in your own VPC, or on-premises depending on your setup. The takeaway Solid offers is a shift from assisted work to delegated work. By giving agents their own computers, accounts, and budgets, and by letting them handle setup, repair, coordination, and verification, the product aims to let you hand over a goal — a sales demo, a researched lead list, an evaluation run, a production fix, or a stalled ticket — and receive a checked result without supervising the steps in between. You keep the ground rules, the approvals, and the spending limits; the agents keep working, and they ping you when it is done.
Fez is a desktop app for Mac where several AI agents work together as members of one workspace, and the room decides who takes what. Each agent is its own member with its own identity, its own model and its own skills, and you talk to them the way you would talk in any chat channel. Instead of you picking which assistant should handle a request, the room reads the message and decides who takes it, whether the work is done, and whether you even need to read the answer. Fez is built on nostr and on Jev, a judgment model built by TypeSafe, and it is released early as an MIT licensed app for Apple silicon Macs. The problem Fez is built around is management. Most agent apps give you one assistant; some give you several, and then you become the manager: you pick the agent, repeat the question, judge the answer, and call the next one. That overhead grows with every agent you add, and it is exactly the work you were hoping an agent would take off your hands. Fez moves that job into the room itself. Instead of you deciding who should handle a message, the room evaluates it first, and chat models run only when there is real work to do. A message that needs no answer costs nothing and produces no noise in the channel. That routing is handled by Jev, the judgment model built by TypeSafe. Every message goes to Jev, which does not write anything itself: it decides, with a calibrated probability, in under a second, for a fraction of a cent. The room reads the message and picks the agent, or nobody, as the site illustrates with a "Who takes it?" decision at 0.95. The published routing results report 96 of 97 messages routed to the right agent, a 184 ms median decision, and $0.002 for the whole run — one pass, three agents, frozen fixtures, which the site explicitly notes is not a universal guarantee. The practical consequence is that a cheap, fast decision happens before any expensive chat model is invoked. The room also closes the loop after an agent responds. "Is it done?" runs at 0.94: it checks the answer against what you asked and signs off, silently, so you are not left manually judging whether the agent actually finished the work. And "Does it need a reply?" runs at 0.08: a thanks gets a reaction, not a paragraph. When the room decides a message needs no reply, there is no turn and no cost. Together these three checks — who takes it, is it done, does it need a reply — are what the site calls the room doing the managing. Agents are real members of the workspace rather than tools you switch between. Each one has its own identity, model and skills, and the roster shown on the site includes @fez, @drift and @quill. @fez is the guide: docile and helpful, it knows its way around, and when you ask it anything it brings in the teammate the work belongs to. You mention @fez in a channel and it routes the request onward; it ships with the app, so there is a natural starting point on first launch. Because agents are referenced by mention in an ordinary channel, several of them can share one thread instead of each living in a separate assistant window. The overall approach is a single pass of judgment followed by chat models that only wake up for real work. You interact with the app through the channel: mention an agent with @, press Enter and it answers, and press Esc and the relay remembers. Identity is handled without accounts. Your identity is a keypair generated on first launch, and every agent has one too, so every message is signed by the key that posted it. Nobody issued the keys, so nobody can suspend them. Everything lives on a nostr relay rather than inside the app, and the site describes Fez as a window onto it — you can run a relay on your laptop or on a server. The release is available as a macOS Apple silicon build under the MIT license, with the source on GitHub. The benefits follow from that design. You stop acting as the dispatcher for your agents: no picking the agent, repeating the question, judging the answer, or calling the next one. Routing costs a fraction of a cent and takes under a second, so decisions are cheap and fast compared with running a chat model for every message. Unnecessary replies are suppressed, which keeps channels readable and avoids paying for turns that add nothing. Completion is verified against the original request, so threads close rather than drift. And because identity is a keypair on a relay you can host yourself, conversations are not tied to an account that someone else can suspend. Concrete scenarios come straight out of the material. The recorded demo shows two agents, two models, two keys and one thread running for seven minutes, live — a single conversation where more than one model does the work and the room keeps track of who takes which message. If you do not know which agent is right for a question, you mention @fez and it brings in the teammate the work belongs to. When someone says thanks, the room returns a reaction rather than a paragraph, so no turn is spent. When a piece of work appears finished, the room checks the answer against what you asked and signs off silently. And if you want the history to outlive the app, you run your own nostr relay and press Esc — the relay remembers. Fez is aimed at Mac users on Apple silicon who already work with AI agents and no longer want to be the manager of several of them. It suits people who want more than one model and more than one agent in a single conversation, who prefer a signed, account-free identity, and who value open source: Fez is MIT licensed with the repository on GitHub, and the app ships as a direct download. The site describes the release as early, and it notes that the routing numbers come from one pass over frozen fixtures rather than a universal guarantee. There is also a lightweight update list for new releases and what changed in them, described as occasional and nothing else. Pricing is not described beyond the free, MIT licensed download. In short, Fez turns a chat room into the manager of a team of agents. Several members, each with its own identity, model and skills, share one workspace; a fast, inexpensive judgment model decides who takes each message, whether the work is finished, and whether a reply is needed at all; and everything is signed and stored on a nostr relay you can run yourself. The value proposition is simple: you stop coordinating agents and start talking in the room.
Edyt is a lightweight desktop application for Mac and Windows that adds AI actions to any text field. Select text anywhere, press a shortcut, and the result appears right where your cursor is. It is designed for people who write in many different applications—email clients, browsers, notes apps, chats, and documents—and who want help without switching windows, copying text into a separate tool, or breaking their flow. Edyt's stated purpose is simple: bring AI right where you write. The company describes it as an AI text assistant that works inside any app with an editable text field, on both Mac and Windows, with Ubuntu listed as the next platform. The problem Edyt addresses is that most writing assistance requires leaving the place where the writing actually happens. You copy text, open another app or a browser tab, paste it in, get an answer, and then copy it back. That interruption is especially noticeable when the text cannot easily be selected at all—a scanned PDF, a screenshot, or a shared screen. Edyt's tagline puts it plainly: AI for text you can't even select. By living inside the apps people already use, and by offering a way to read text from images on the device itself, Edyt aims to remove the copy-paste round trip and let people get an answer, a correction, or a better phrasing without leaving their work. The Rewrite action polishes tone, grammar, and clarity in place. According to Edyt, Rewrite keeps your tone and follows your instructions, so it is not limited to correcting mistakes—it can also adjust how something sounds while preserving the writer's voice. Proofread takes a different approach: every issue is flagged in place and grouped by kind on the left, so the user can see what kind of problems were found. The arrow keys move between issues, and Enter fixes one; users can also fix them all at once. The result remains editable—after typing, a Re-check catches anything new—and Insert pastes the text back in, formatted, right where it came from. The demo also shows a readability measure such as easy-to-read word counts. Alternatives is for moments when the phrasing is uncertain. After selecting text and pressing the shortcut, Edyt suggests several ways to say it. A Show another option produces a genuinely new alternative, never a repeat, and the user can move between options with the arrow keys and press Enter to open Insert or Copy. Explain, meanwhile, is built for understanding. Selecting text and pressing the Explain shortcut opens a panel with definitions on the left and the explanation on the right. A follow-up question keeps the conversation going, so users can dig deeper into the same passage without starting over. The product demos show this with technical language such as a SOC 2 Type II attestation being explained in plain terms. Translate lets a user select a message and render it in another language. The demo shows a chat in Spanish being translated into English, and the reverse workflow: select a message, pick the language, and insert the result into the reply box before sending. Edyt also lets users ask how to adjust a translation if it is not quite right. Use Prompt is the customization layer. Pressing its shortcut runs your last prompt right away. A library on the left lets you pick another prompt, and personalized prompts remember how you like things phrased. Asked to rewrite a weekly sync draft, the demo produced progressively stronger versions, and the Insert action drops the result straight into the document without retyping. Saved prompts run on anything you select and follow your account to every machine. One of Edyt's distinctive capabilities is reading text that cannot be selected. If the text is in a PDF, a screenshot, or on a shared screen, the user can hold Option (Alt) and drag over it. Edyt reads it on the device, using Apple's built-in recognition on Mac and the built-in Windows OCR on Windows. The image never leaves the device. On Mac this uses the Screen Recording permission, and only to capture the area you drag; Windows asks for no extra permission. Once the text has been read, the same actions apply—Proofread, Alternatives, Explain, Translate, and Use Prompt—so even a scanned lease agreement can be explained in plain language. Edyt follows a three-step pattern. First, select your text: highlight anything in any app, such as email, docs, or chats, with no copying and no switching windows. Second, press a shortcut: Ctrl+Shift+Space brings up the toolbar, and you pick the action you want. Third, get your result in place: Edyt calls the AI and drops the result right where your cursor is. Each action also has its own shortcut, and every shortcut can be remapped. Users can write their own prompts and organize them in a library from the dashboard. On privacy, Edyt states that text is sent over an encrypted connection for processing and then discarded—never stored, logged, or used to train AI models. The benefits Edyt claims are straightforward. It removes the need to leave your app or break your flow, and it works across the applications people already write in. It can handle text that cannot be selected, which most writing assistants cannot. It aims to preserve the user's own tone rather than replace it, and it offers a free plan that Edyt says is enough for most people, with no credit card required. There is also a stated privacy benefit: the text is discarded after processing, and screen text is read on the device so the image never leaves the machine. The demos on Edyt's site sketch several concrete scenarios. One is fixing a grammar-heavy message before sending it to a client. Another is proofreading a personal essay, where errors are flagged in place and can be fixed one at a time or all at once. A third is replying to a Gmail message when the phrasing is uncertain, using Alternatives to find a better way to say it. A fourth is understanding jargon in a work email—the SOC 2 explanation—and asking follow-up questions. A fifth is translating a Spanish chat message and replying in the reader's own language. A sixth is rewriting a weekly sync announcement in a clearer, shorter style. Finally, holding Option and dragging over a scanned lease agreement lets the user read the automatic-renewal clause and ask what it actually means. Edyt is aimed at people who write in many apps and want AI help without switching tools—knowledge workers, writers, and anyone who sends email, chats, or documents on a Mac or Windows PC. It runs on macOS 12.3 (Monterey) or later, on both Apple Silicon and Intel, and on Windows 10 or later, 64-bit, currently as a beta. It is distributed directly from the website rather than the Mac App Store, because reading and typing text in other apps requires macOS's Accessibility API, which does not work inside the App Store sandbox; the app is signed and notarized by Apple. Edyt is free to download and free to use, with generous daily, weekly, and monthly usage on the free plan and no credit card required. Pro removes the limits for unlimited use. In short, Edyt is a desktop AI text assistant for Mac and Windows that puts rewrite, proofread, alternatives, explain, translate, and custom prompts inside any app. It keeps the work in place, reads text you cannot select, and treats privacy as part of the design. Free to download and free to use, it is built for people who want AI right where they write.
Hola AI is an AI voicemail app that picks up your missed calls, talks to whoever called, and sends you a clear summary in seconds. It is built for busy professionals who cannot answer every call but still need to know who called, why they called, and how important the call was. Rather than leaving callers with a beeping recorder, Hola AI answers like a human, understands the intent behind the call, and delivers actionable text instead of long audio. The product is presented as a smart voicemail replacement and also works as an AI call assistant that handles calls for you, so you stay reachable without picking up every call. Traditional voicemail is passive and robotic. As the site puts it, old voicemail records everything and filters nothing: it captures every cold pitch, robocall, and telemarketer, leaving you to guess who called until you listen through minutes of static audio. The familiar experience of a beep followed by silence and then a rushed message means missed context, wasted time, and lost opportunities. Hola AI positions itself as the opposite: proactive and human. It listens and talks like a human, filters the noise, understands intent, and sends only what is worth your attention. For people whose work depends on the phone, the difference between a raw recording and an understood message is the whole point, because the call itself stops being something you have to decode later. The core of the product is answering like a human. When a call is missed, Hola AI answers, talks to the caller, understands why they called, and makes them feel heard instead of ignored. Alongside that, automated spam filtering removes junk before it reaches you, so robocalls and cold pitches never take up space in your inbox or your attention. Because the assistant understands intent, it can capture next steps rather than just ambient sound. The site contrasts this directly with traditional voicemail in its comparison table: Hola AI talks like a human while traditional systems just record sound, and Hola AI filters junk automatically while traditional voicemail records spam too. After each answered call you receive an instant summary. Instead of long, unclear audio, Hola AI sends a concise summary of who called, why, and how important the call is, described by the company as actionable text rather than audio you have to replay. Summaries are delivered via app, SMS, or email, so you can receive them through the channel that fits how you work. Hola AI also records and transcribes conversations, which helps you understand why someone called and gives you the context you need before you call back. The comparison table summarises the difference in effort: with Hola AI you skim and act instantly, whereas with traditional voicemail you replay and guess. Hola AI can be personalised with multiple personalities, letting you choose how it sounds. The company gives examples of a sharp tone for work, a warm tone for friends, and a witty tone with spam. This customisable tone applies based on the caller, in contrast with the single robotic voice of traditional voicemail. Privacy is a stated priority: all recordings and transcripts are encrypted end-to-end and stored safely, and you have full control, with the ability to delete any call or wipe everything anytime from the app. The site also notes that Hola AI works in flight mode or when your phone is off, while traditional voicemail only works when your phone is on. Mechanically, Hola AI is straightforward to adopt. You do not need a new number. Your unanswered calls are simply forwarded to Hola AI, which takes over when you cannot pick up. From there the assistant answers the call, holds a conversation with the caller, captures the intent and next steps, filters spam, and produces the summary that is sent to you. The comparison table frames the overall approach as capturing call context such as intent and next steps, versus traditional voicemail which merely stores an audio message. The site summarises the workflow in its FAQ: Hola AI answers calls like a human even when your phone is off or in flight mode, talks to callers, understands why they called, filters spam, and sends a short summary via app, SMS, or email. The outcome for users is less time spent on the phone without becoming unreachable. Instead of replaying audio and guessing at importance, you skim a summary and act. Spam and robocalls stop consuming attention, urgent calls are surfaced with context, and callers feel heard rather than ignored. Because summaries arrive in seconds, users can triage calls and respond to the ones that matter first, with the context they need before calling back. The comparison table lists the differences users feel most: a human-like response, automatic spam filtering, short text summaries instead of long audio, customisable tone and personality, minimal effort, availability when the phone is off, and captured call context rather than a stored audio message. Use cases are illustrated by the company's own customer stories. Marcus Thorne, a legal partner, used to miss urgent calls when in court; now Hola AI handles the greeting and he receives a text summary instantly. Sarah Jenkins, a freelance creative, uses the witty personality with spam, letting it waste telemarketers' time while she focuses on her work. David Chen, a real estate agent, cannot talk when he is showing houses, so Hola AI asks the right questions and he can identify serious leads by reading the summary. More broadly, the stated scenario is anyone who cannot pick up, whether they are in a meeting, driving, on a flight, or simply unavailable. Hola AI is aimed at busy professionals who depend on the phone, including legal partners, freelancers, and real estate agents, and anyone who wants smart voicemail without changing their number. It runs as a mobile app, downloadable from the App Store and Google Play, with summaries also delivered by SMS and email. Pricing is subscription based: a limited-time 50% off offer gives new customers $4.99 per month for their first six months, after which the price is $9.99 per month, and you can cancel anytime; the site also notes that annual billing saves 17% more. The product is powered by ElevenLabs Grants. In short, Hola AI turns voicemail from a passive recorder into an active assistant: it answers missed calls in a human-like way, filters spam, captures intent, and delivers a short, actionable summary so you never miss an important call again.
Grok 4.7 is SpaceXAI's most powerful model for coding and knowledge work, and the company describes it as its most capable model for these tasks. According to the announcement, Grok 4.7 works longer on difficult tasks, checks its own work more carefully, and comes with SpaceXAI's best-calibrated safeguards to date. It is served at the same price and speed as Grok 4.6, which the company says makes it highly competitive in its class, and it is pitched as twice as fast at half the price of comparable models. Grok 4.7 is available today in Cursor and Grok Build, through the Grok API, and across third-party coding harnesses, model routers, and cloud platforms. The announcement frames Grok 4.7 around the demands of long, difficult work rather than short chat interactions. SpaceXAI states that the model uses a new, larger base model compared to Grok 4.6 and that it was trained with a longer reinforcement learning run on a harder mix of tasks, weighted toward problems that take many hours to complete. This focus matters because long-running coding and knowledge tasks place unusual pressure on a model: it must stay coherent across extended context, avoid drifting from the original objective, and catch its own mistakes before handing work back. The company reports that Grok 4.7 is better at verifying its own work and managing longer context, which directly targets those failure modes. On CursorBench 4.0, a benchmark that stresses longer-running coding tasks, SpaceXAI says Grok 4.7 sits at the frontier in price-performance, with a chart comparing it to Fable 5.1, Opus 5, GPT-5.6 Sol, and Sonnet 5 on benchmark score against average cost per task. Beyond raw size and training duration, SpaceXAI highlights a specific capability addition: Grok 4.7 was trained to natively understand the Grok Bot harness. The company states this makes the model better at conversational tasks and general knowledge work. In practical terms, a model that natively understands its harness can operate more naturally inside that environment, handling multi-step conversations and everyday knowledge tasks without the friction that comes from adapting an externally trained model to a new interface. That combination, a larger base model, a longer reinforcement learning run weighted toward multi-hour problems, improved self-verification, better long-context management, and native harness understanding, is the core of what distinguishes Grok 4.7 from its predecessor. SpaceXAI publishes a detailed benchmark table comparing Grok 4.7 against Grok 4.6, GPT-5.6 Sol, and Fable 5.1. Grok 4.7 scores 46.3% on CursorBench 4.0, 71.0% on DeepSWE v1.1 with a high-effort marker, 64.0% on EEBench, 1,657 on AA Briefcase v1.1, 37.6% on Terminal-Bench 4.0, 19.6% on the Harvey Legal Agent Benchmark, and 56.7% on HealthBench Professional. The company also reports a GDPval Elo score of 1,695 for Grok 4.7 at xhigh effort, against 1,735 for Fable 5.1 at max, 1,605 for Grok 4.6 at high, and 1,542 for GPT-6 Astra at max. These benchmarks span software engineering, multi-hour terminal work, multi-hour office work, electrical engineering, legal work, and clinical reasoning, which reflects the breadth of tasks the model is positioned to handle. On professional knowledge work, SpaceXAI states that Grok 4.7 is better at creating documents and presentations. In GDPval and AA Briefcase, the company explains, AI is asked to work on tasks done by professionals such as lawyers, nurses, and financial analysts. Grok 4.7 improves upon Grok 4.6 on both benchmarks and performs comparably to other frontier models. That means the model is not positioned only as a coding tool: it is also aimed at the document-heavy, multi-hour office work that these professions perform, where producing a usable deliverable matters more than producing a quick answer. The benchmark table supports this positioning, showing Grok 4.7's AA Briefcase score of 1,657 against 1,546 for Grok 4.6, 1,487 for GPT-5.6 Sol, and 1,678 for Fable 5.1. Safety and cybersecurity are treated as a first-class part of the release. SpaceXAI says Grok 4.7 was built with an entirely new safeguard stack and that it is the strongest model the company has tested on refusals and jailbreak resistance. In dual-use domains such as cybersecurity and biological work, the company reports that it leads on both utility for benign tasks and safe refusal on dangerous ones, topping LatchBio's biosafety benchmark at 62.4%. The company also states that Grok 4.7 balances strong cyber defense capabilities with low refusal rates for legitimate use, showing the highest safety on HackerBench v0.3, its benchmark for risky and malicious cyber tasks, allowing only 3.3% of risky dual-use prompts through while rarely blocking legitimate security work. In addition, SpaceXAI has started giving select cybersecurity partners invite-only access to Grok 4.7's red-team capabilities for defense research. The overall approach behind Grok 4.7 is a combination of scale, extended reinforcement learning, and deliberate alignment work rather than a single headline change. SpaceXAI describes a new, larger base model trained with a longer reinforcement learning run on a harder mix of tasks weighted toward problems that take many hours, which produces a model that verifies its own work and manages longer context more effectively. Native training on the Grok Bot harness adds conversational and general knowledge work strength, and an entirely new safeguard stack supplies refusal and jailbreak resistance alongside dual-use safety. The company then serves the result at the same price and speed as Grok 4.6, with an additional fast variant that runs at twice the output speed for twice the price. Each element reinforces the others: a stronger base model makes longer autonomous runs viable, and a stronger safeguard stack makes those runs safer to deploy. Pricing and availability are explicit. Grok 4.7 is priced starting at $2 per million input tokens and $6 per million output tokens. SpaceXAI also serves a fast variant with twice the output speed at twice the price. The model is available today in Cursor and in Grok Build, and it is also available through the Grok API, third-party coding harnesses, and model routers and cloud platforms. For developers who want to try it before committing, the company offers a free try in Grok Build, and it publishes a terminal install command: curl -fsSL https://x.ai/cli/install.sh | bash. API keys can be created from the console, and documentation is available at docs.x.ai. For users, the stated benefit is straightforward: frontier-class coding and knowledge work at a price-performance point that makes long-running tasks economically practical. Because Grok 4.7 is served at the same price and speed as Grok 4.6, teams that already use the previous model can adopt the new one without changing their cost structure, while gaining a larger base model, longer reinforcement learning, better self-verification, and stronger long-context handling. The improved document and presentation abilities extend that value beyond engineering into professional knowledge work, and the new safeguard stack gives organizations that operate in sensitive or dual-use domains a model that the company says rarely blocks legitimate security work while refusing dangerous requests. Concrete use cases are visible in the benchmarks SpaceXAI selected. Software engineering and longer-running coding tasks are covered by CursorBench 4.0 and DeepSWE v1.1. Multi-hour terminal work is covered by Terminal-Bench 4.0. Multi-hour office work, producing documents and presentations, is covered by AA Briefcase v1.1 and GDPval. Legal work is measured by the Harvey Legal Agent Benchmark, clinical reasoning by HealthBench Professional, and electrical engineering by EEBench. Cybersecurity is addressed both through the HackerBench v0.3 safety results and through invite-only red-team access for selected cybersecurity partners conducting defense research. Together these scenarios describe an agent that can be pointed at a long task, left to work, and expected to check its own output. The target audience follows from that positioning: software developers and engineering teams building in Cursor, Grok Build, or through the Grok API; organizations running third-party coding harnesses, model routers, and cloud platforms; and professionals whose multi-hour work involves documents, presentations, analysis, and research, such as those in legal, clinical, financial, and engineering roles. SpaceXAI also targets cybersecurity defenders, both through the model's balance of strong cyber defense capability with low refusal rates for legitimate use and through the invite-only red-team access it has begun granting to select partners. Grok 4.7's takeaway is a single proposition: SpaceXAI's most capable model for coding and knowledge work, delivered at the same price and speed as Grok 4.6 and described as twice as fast at half the price of comparable models. It pairs a larger base model and longer reinforcement learning on multi-hour tasks with improved self-verification, better long-context management, native Grok Bot harness understanding, and an entirely new safeguard stack that leads the company's testing on refusals and jailbreak resistance. Available in Cursor, Grok Build, the Grok API, third-party harnesses, model routers, and cloud platforms from $2 per million input tokens and $6 per million output tokens, it is built for teams that need long, difficult work completed reliably and affordably.
Googlebook is the first laptop designed for Gemini Intelligence, offered by Google with pricing starting from $899. It pairs premium hardware with Gemini-powered experiences that are built into the machine, and it syncs effortlessly with an Android phone. The laptop arrives ready from day one with a full suite of premium Gemini services and 5 TB of cloud storage. Its stated purpose is to remove the gap between the phone people carry and the laptop they work on, so that less time is spent transferring and more time is spent creating. Googlebook is sold alongside a range of accessories as part of a wider lineup. The problem it addresses is the stop-start rhythm of working across two devices. A task begun on a phone usually has to be picked up again manually on a laptop, screenshots have to be sent to yourself, and files have to be hunted down by cryptic filenames. Googlebook's page frames the payoff directly: users can jump between phone and laptop without skipping a beat, spending less time transferring and more time creating. It also frames the assistant problem in everyday terms, arguing that a laptop should meet you in the moment rather than waiting to be asked, whether that means surfacing a suggestion based on what is on screen or letting you talk to your device instead of typing at it. On the hardware side, Googlebook is described as crafted for performance. It is supercharged with powerful processors such as Intel Core Ultra and Snapdragon X Elite, starts from 16 GB of RAM and from 256 GB of storage, offers up to 14 hours of battery life, and weighs under 2.85 LBS. It is built with premium materials including aluminium, magnesium and carbon fiber. The Glowbar is described as functional, beautiful and uniquely Google, acting as an at-a-glance indicator of battery life and charging status, so users can read their power state without hunting for a number. A dedicated G key enables one-touch searches, putting answers always right at hand. Gemini Intelligence is the centre of the product. The pointer becomes a magic wand: wiggle it to activate Gemini and select anything on screen to ask, create and compare effortlessly. Rambler turns messy thoughts into tidy text by removing filler words like "um" and adding formatting to clean up a stream of consciousness, so users can talk naturally, review, and send. Proactive suggestions appear with your next move based on what is on your screen, surfacing exactly what you need when you need it. Smart file search lets users search files and photos by their actual content instead of cryptic filenames. Gemini Live turns "Hey Google" into a live conversation where you can talk, share your screen or brainstorm with zero typing required. And an agent in the Gemini app can close out to-dos even when the laptop is closed. Googlebook's connection to Android phones is handled through three named capabilities. Cast My Apps lets users open their phone's apps directly on Googlebook, so quick tasks can be handled without leaving the screen. Continue On lets a task started on an Android phone be finished on Googlebook, picking up exactly where you left off. Quick Access lets users find their phone's files directly from the laptop — for example, grabbing a mobile screenshot for an email with no transfers needed. Beyond the phone, Googlebook reaches millions of apps and tools through the Google Play Store, spanning categories the page groups as Best of Google, Creativity, Productivity and Streaming, from Google Drive, Gmail, Google Docs and Gemini Notebook to Adobe Photoshop, Canva, CapCut, Notion, Microsoft Copilot, Dropbox, Spotify, Netflix, Disney+ and more. Thousands of games are also available, from mobile hits like Clash Royale on Google Play to PC titles like ARC Raiders streamed via GeForce NOW. Security is positioned as a design principle rather than an add-on. Googlebook is designed with built-in protection that helps keep viruses, malware and hackers out, and a Titan C chip is described as keeping the device, passwords and personal data safe with dedicated security built into every laptop. Secure authentication lets users unlock the laptop with their face or fingerprint, and the machine instantly recognises them. Taken together, the approach is to weave the assistant into the hardware itself — the pointer, the keyboard's G key, the Glowbar, the security chip — so that intelligence is not a separate app you open but a layer that is always present. The benefits the page promises follow from that integration. Users spend less time transferring work between devices and more time creating. Search becomes faster because it works on content rather than filenames. Talking replaces typing for brainstorming, drafting and note capture. Suggestions arrive before you ask for them. Routine to-dos can be closed even when the laptop is shut. And the machine itself is built to carry: under 2.85 LBS with up to 14 hours of battery, made from premium materials. Buyers also receive extra perks: up to $300+ in apps, described as a curated collection available for a limited time. Concrete scenarios described on the page include picking up a session across devices, using apps from your phone on the laptop to handle a quick task without changing screens, and retrieving a mobile screenshot for an email through Quick Access. Creative work is represented by apps such as Adobe Photoshop, Lightroom, BandLab, Canva, CapCut and Luminar, while productivity workflows are represented by Notion, Notability, Microsoft Copilot, Adobe Acrobat, Dropbox and Fantastical. Streaming is handled by YouTube, YouTube Music, Spotify, Netflix, HBO Max and Disney+, while gaming spans mobile titles and streamed PC titles. Dictation with Rambler suits people who think out loud, and Gemini Live suits brainstorming or screen sharing in conversation. Googlebook starts at $899, with pre-orders available through the official site and a shop covering both laptops and accessories. The product is positioned around Android phone owners in particular — the tagline calls it the laptop your Android phone has been waiting for — and around people who want Gemini services available from the moment they open the box, since a full suite of premium Gemini services and 5 TB of cloud storage is included ready from day one. The page also points to a limited-time bundle of up to $300+ in apps. No subscription tiers are described beyond the included services. Googlebook's proposition is simple: premium laptop hardware plus Gemini Intelligence plus an Android phone in your pocket, working as one continuous surface. It is for people who want less transferring and more creating, and who would rather ask, talk and select than hunt and type.
Plane Agents are AI teammates that join your Plane workspace as members, so you can assign them work exactly like you would a human colleague. Rather than writing a one-off prompt, you give an Agent a job: you assign it a work item, mention it in a conversation, trigger it when work changes, or run it on a fixed or custom schedule. Plane Agents take on repetitive, context-heavy workflows 24/7—responding to changes, coordinating next steps, and acting across Plane and your connected tools. They are built for teams that plan and deliver work inside Plane and want recurring coordination, reporting, and triage work to happen continuously rather than in bursts. As the website puts it, the idea is to "give agents a job, not just a prompt." The work keeps coming, and much of it is the same kind of work over and over: collecting standup updates, chasing blockers, turning rough requests into structured specs, spotting slipping timelines, sorting incoming requests, and clustering customer feedback. This work is context-heavy because it depends on what is happening inside projects, what changed in a connected tool, and who is responsible for what. Plane Agents address that problem by living inside the workspace where the work actually happens. Instead of asking a person to gather context from Plane and from connected tools and then write an update, an Agent is given boundaries, a playbook, and the sources it needs, and then runs on that recurring work. The product positions these Agents as "your 24/7 teammates," starting with "the work that keeps coming back" across planning, delivery, reporting, and operations. Building an Agent starts with defining what it owns. You can start from a template or create an Agent from scratch, giving it a name, a clear responsibility, and the outcome it should work toward. This matters because an Agent with a defined ownership area can be trusted with a recurring job, rather than being used as a general-purpose prompt-and-response tool. An Agent that knows its responsibility and its target outcome can be handed work the same way a teammate is, and it can be evaluated against the outcome it was asked to produce. Next, you give the Agent a playbook. The playbook describes how the work should be done, what a good result looks like, and which boundaries it should follow. Within the playbook you add the right skills and choose the model behind the Agent. Because the playbook encodes expected output quality and boundaries, teams can standardize how recurring work is handled—for example, what a good standup summary or a structured specification should contain—without re-explaining the standard every time the work comes around. You then choose when the Agent starts. An Agent can be brought into work through an assignment or a mention. It can also start automatically when work items change—when work is created, updated, changes state, gets reassigned, or removed—or on a schedule, with filters that decide exactly when it should run. This means an Agent can respond to change in real time, or handle known recurring checkpoints such as reviews, reports, checks, and follow-ups. Finally, you connect the Agent's working context. You choose the Plane projects, trusted web sources, and connected tools the Agent can use, and decide which connected account it should work through. Context is what makes the output usable: an Agent that can see the relevant projects and trusted sources returns results grounded in the actual work instead of generic text. Plane ships ready-made Agents for recurring work, which teams then adapt to their own team, triggers, and context. The Standup Agent collects progress, blockers, and next steps from the team and turns them into a concise update that highlights what matters most; it works on Slack, Gmail, and Projects and uses progress tracking and team summarization skills. The Delivery Risk Agent monitors work for stalls, blockers, dependencies, and slipping timelines to help teams identify and address delivery risks early; it works on GitHub, Slack, and Projects and uses risk detection and dependency tracking skills. The Spec Agent turns rough ideas and requests into structured requirements, identifies gaps, and asks the right questions to make the scope clear; it works on Figma, Wiki, and Pages, with PRD writing and product clarity skills. The Request Triage Agent reviews incoming work, identifies duplicates, adds relevant context, and routes each request to the right team or workflow; it works on Slack, Projects, and Intake, using request analysis and duplicate detection skills. The Customer Feedback Agent groups customer feedback into meaningful themes, surfaces recurring insights, and connects them to relevant issues, projects, or ongoing work; it works on Slack, Projects, and Wiki, with feedback analysis and theme clustering skills. Agents show up when the work does. You can assign an Agent to a work item, in which case it receives the relevant context and starts working on the job. You can mention an Agent in a work item conversation to ask for information, to prepare something, or to take the next step. You can trigger an Agent from change, starting it when work is created, updated, changes state, gets reassigned, or removed. Agents can also use selected connected tools and trusted work sources to get context, and recurring work can be put on a schedule so that reviews, reports, checks, and follow-ups run automatically on a fixed or custom schedule. Whatever the trigger, the Agent acts with context and returns the result in Plane. Control and visibility are built around each Agent. You choose its reach—the Plane projects, trusted work sources, and connected tools available to it. You choose whose access it uses, giving connected tools access either through a person's account or through a service account managed by the team. And you can see where your credits go: the product tracks adoption, response and completion rate, average time to complete, and consumption by Agent. This combination lets teams set boundaries and follow every run—seeing when an Agent starts, what it returns, and when it needs input. The benefit for teams is that recurring, context-heavy work stops depending on someone remembering to do it. Standups get summarized, delivery risks get flagged early, rough ideas become structured requirements, incoming requests get routed, and customer feedback gets clustered into themes—without a person manually assembling context from multiple tools each time. Because Agents act with context and return results in Plane, the output lands where the team already works, and the work keeps moving even when nobody is actively watching it. Concrete workflows follow the ready-made Agents. A team can put the Standup Agent on a schedule to gather progress, blockers, and next steps and turn them into a concise update. A delivery-focused team can let the Delivery Risk Agent monitor for stalls, blockers, dependencies, and slipping timelines so risks are identified early. Product teams can mention the Spec Agent on a rough idea or request so it drafts structured requirements, identifies gaps, and asks the questions needed to clarify scope. Support or operations teams can have the Request Triage Agent review incoming work, catch duplicates, add context, and route each request to the right team or workflow. Teams collecting user input can use the Customer Feedback Agent to group feedback into themes and connect recurring insights to the issues, projects, or ongoing work already in flight. Scheduled runs cover reviews, reports, checks, and follow-ups on a fixed or custom schedule. Plane Agents are aimed at teams that run planning, delivery, reporting, and operations in Plane—the site notes that 50,000+ teams use Plane. Inside Plane, Agents can work on Projects, Pages, Wiki, and Intake; connected tools include Slack, Gmail, GitHub, and Figma, and Agents can also use trusted web sources for context. Access to connected tools can run through a person's account or through a team-managed service account, and adoption, response and completion rate, average time to complete, and consumption by Agent are all tracked. Agents run on the AI credits included in every paid plan, and you can get started free or book a demo. Plane also publishes compliance information covering GDPR, HIPAA, ISO 27001, and SOC 2, and offers downloads for Mac, Windows, iOS, and Android. In short, Plane Agents turn AI from a prompt you have to remember to write into a teammate you can assign work to. By defining ownership, playbooks, triggers, and context, and by keeping every run visible, they let teams hand off the repetitive, context-heavy work that keeps coming back—so the humans can focus on what actually needs them.
thestory.run is a web app that coaches employees to write their own LinkedIn posts. It is described as a writing coach for corporate influencers — the people who post under their own name at work. The coach asks one question at a time, helps you find the story inside a rough thought, and hands you a structure to write into rather than a draft to approve. It never writes the post and it never rewrites your words, so what you publish is what you wrote. You can start on your own and bring your team when you are ready. The product exists because its makers believe we stopped writing. Its site quotes the question 'Why would I write if AI can do it for me?' and answers with three stated problems. AI slop is real: 81% of long-form posts on LinkedIn are likely AI-written, and the personality is gone even when the AI 'has your voice.' Too busy to think: it is not that you have nothing to say, it is that nobody asked you the right question about it. People use AI wrong: let it write for you and you slowly lose your own voice and the ability to put a thought into words. The stated belief is that AI is here to support humans, not replace them, and that you should leave the edits to AI and be the superstar. The core of the product is a single coached writing session that takes you from a messy thought to a finished post. The demo walks through three steps: share a rough thought, talk it through, and write it yourself. You drop in raw thoughts — the example given is 'a lot of teams confuse doing a lot with progress' — and the coach asks about it one question at a time rather than producing text. In the sample conversation the coach replies, 'That's an interesting observation, I bet you've watched it play out somewhere specific. What did that look like?' The writer answers with what they actually noticed, and the coach reflects it back, uncovering the point the writer does not yet see. The same session is shown as plain text, so the exchange stays readable and the thinking stays yours. Once your thoughts are out, the coach lays out the arc that helps you tell the story: open on the moment, name what is really going on, then the pivot. In the example these become 'Open on the moment — the room where everyone says we're making progress, and your quiet but are you, really?', 'Name what's really going on — momentum as a hiding place,' and 'The pivot — defining a goal means saying no, and that's the scary decision teams avoid.' Then you write it yourself, and the coach turns editor. It marks what carries the post and what to try. Two examples are given: 'Keep · your spine — the whole post in six words. Let it stand,' and 'Move · your opener — sharper than the meeting scene. Open on it.' Moves are applied reversibly — the coach moves your opener to the top, and you can undo anytime. The post is labelled 100% you, 0% coach. Between the posts, thestory.run keeps the week moving so you never have to guess what to do next. A weekly to-do shows what is on at a glance — for example one post on leadership and shaping a team culture idea you saved. Daily reflections deliver a nudge from your coach in the form of a question, such as 'What have you changed your mind about in remote work?', with a link to answer it directly. A posting streak and an activity calendar track activity — the sample shows five weeks in a row — with achievement stickers such as one for posting on every topic, described as keeping you accountable without guilt. Drafts are surfaced with their topic and status, such as 'nearly there' or 'a few lines in', so you can finish them while they are hot. For teams, corporate influencing is presented as a team sport. Most people start alone, but it works when the whole company shows up on the same theme. Campaigns let an admin set a theme and a window — a hiring push, a launch, a conference — and the coach folds it into everyone's week as something to draw on; nobody gets handed a post to write. The team calendar shows who is posting, on what, and which days nobody has taken, so the team can spread out instead of everyone publishing on Tuesday. Wins let people share what landed, if and when they want to, and colleagues cheer; watching a colleague's post open a real door is what gets the next person writing. Crucially, your drafts, your ideas and your conversations with the coach stay yours — not your teammates', not your admin's, not your boss's — and that is enforced in the data layer rather than hidden in the interface. Teammates see coordination only: who is posting on which topic, on which day, and the wins a person chose to share. Cheat-mode is the one exception to the rule that thestory.run never writes for you. You activate it yourself on a piece you have already started, and the coach writes it from what you brought: your rough thought, your conversation, the lines you kept, the arc you agreed on, and a few of your own published posts so it can hear how you write. It spends one of a small monthly allowance — one cheat for every four posts in your plan, never more than three a month. The resulting post is marked permanently as coach-written and does not count toward your progress. Publishing stays under your control. thestory.run posts to LinkedIn only if you ask it to, on a post you have already written and read; it never writes the text and never publishes anything you have not pressed the button on. You can also publish by hand and simply tell the app that it happened. Once you have published a few posts, the coach can hear how you write and ask for more of it. The company is explicit that thestory.run is not an employee advocacy platform and not an AI ghostwriter. An employee advocacy platform hands employees a pre-written post to reshare, so every share sounds like the marketing department; thestory.run hands nobody any words, coaching each person to write their own post in their own voice, with teams coordinating through shared topics and a calendar instead of a content queue. thestory.run is free forever for teams of five people or fewer, with no credit card, no trial clock and no feature gates. From the sixth person it is pay as you go: 1 euro per coaching session plus tax, and nothing else. People, channels and every feature are included either way — you only ever pay for coaching. Prices are in euros, and at checkout you can pay in your own currency instead in over 150 countries; Stripe does the conversion at its own rate, which carries a small currency fee, and paying in euros is always an option if you would rather avoid it. The product is aimed at people who post under their own name at work, sometimes called corporate influencers: founders, salespeople, recruiters, engineers and experts. It is operated by Swat.io GmbH in Vienna, Austria, under the GDPR and the Austrian Data Protection Act, and runs in the European Union, with the privacy policy and the data processing agreement published on the site. Swat.io is the social media management tool behind it — fifteen years, fifty people, no investors — used by more than 18,000 people and the team behind social media for 1,700+ brands, with one calendar for everything that goes out and one inbox for everything that comes back. Corporate influencing runs in there too, next to the company's own posts rather than in a separate tool; thestory.run is the other half, the coach that helps a person find and write the post in the first place. thestory.run exists to put the writer back in charge. Rather than generating posts that a human then edits, it asks questions, marks the strong lines you already wrote and offers a structure to write into, so the finished post sounds like you because you wrote it. Free for teams of five or fewer and priced per coaching session beyond that, it combines a coached writing session, a weekly rhythm of to-dos and reflections, and lightweight team coordination — campaigns, a team calendar and optional wins — while keeping every draft and conversation private to the writer. Its primary promise is simple: less AI slop, more people who write authentically.