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Research AI Tools
Discover and compare the best research AI tools and software. Browse 55+ curated tools with reviews and rankings.
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Ghostlaunch is a seven-day validation service for startup ideas that have not been built yet. A founder describes the idea in a few sentences, and Ghostlaunch asks a handful of questions to get the details right. From there the service builds a landing page written and designed to convert, complete with the founder's pricing, fast on any phone, and then puts real people in front of it. Every button on that page opens a 'coming soon' note that asks for an email. Ghostlaunch finds people on Reddit, X and LinkedIn who are already asking for a fix to the problem the idea solves, reaches out to each one about what they asked, and tracks every visit, scroll, click and email for seven days. On day 7 the test closes and a full report arrives: who came, how far they read, which plan they reached for, whether to build it, and every lead, ready to contact. The product sums the division of labour up as 'five minutes of yours, seven days of ours.' Ghostlaunch exists because, in its own argument, the usual ways to validate an idea share the same flaw: nobody has anything at stake. Asking AI means the model has read everything about your market but has never wanted to buy anything. Asking friends means they will tell you what you want to hear, because saying yes costs them nothing. Surveys produce hypothetical answers to hypothetical questions, and 'I would pay for that' is free to say. Feedback swaps are coloured by the other person's own launch. Market research tells you the market exists, not that anyone wants yours; Ghostlaunch calls it homework, not proof. Building first is expensive too. Ghostlaunch's own tally runs to 300 hours for the MVP, 40 hours designing it and writing the page, $600 for domain, hosting and tools, 20 hours of launch posts, DMs and cold emails, $500 of ads for first visitors, and 30 hours refreshing the dashboard for signups, plus endless second-guessing: 390+ hours and $1,100 to find out nobody wanted it. The only test Ghostlaunch treats as valid is putting a real page in front of people who are already looking for a fix, and watching what they actually do. The first half of the service is the page itself. Ghostlaunch writes and designs a landing page made to convert, includes the founder's pricing, and keeps it fast on any phone. Every button opens a short 'coming soon' note that asks for an email and sometimes one follow-up question, so nobody is charged and nothing is sold. The page is editable before the test starts: a founder can change any text or rewrite a section with one click. Ghostlaunch publishes examples of what these pages look like, built from the idea underneath each one, for products that do not exist yet: Paylark, a tool for freelance designers that automatically chases late invoices with polite, escalating reminders; Migalo, an app for neighbourhood bakeries that alerts locals over WhatsApp when fresh bread comes out and lets them reserve before it sells out; Nibblo, an app for parents of picky eaters that suggests tiny, realistic next foods from a photo; Strainly, a Slack bot for remote engineering managers that spots quiet overload; and Slotpup, a booking tool for independent dog groomers that fills last-minute cancellations from a waitlist. A gallery of 44 more examples is linked from the site. The second half is distribution and measurement. Ghostlaunch does the outreach itself: it finds people on Reddit, X and LinkedIn already asking about the problem the idea solves, keeps only the ones who clearly want a fix, and reaches out to each one about what they asked. Founders do not post, DM or ask anyone. Ghostlaunch addresses the obvious objection directly, stating that it only reaches people who are already asking about the problem, one conversation at a time, with no mass posting and no bought lists. That is what makes the traffic meaningful rather than a vanity number: the visitors arriving at the page are people who had already described the problem in public before the page existed. Measurement is continuous and public to the founder through a live dashboard. The site shows the feed filling up during a test on day 3 of 7: a visitor from a Reddit DM reached Pricing, clicked 'Get Team' on the Team plan, opened the waitlist and joined it; a visitor from a Reddit comment reached the FAQ and opened a specific question; a visitor from X reached the footer and scrolled 100%; a visitor from LinkedIn reached How it works. Every visit, scroll, click and email is tracked in that dashboard whenever the founder looks. At the end of the seven days, Ghostlaunch closes the test and writes the report: the verdict, the numbers behind it, what visitors told us, and what to change if you run it again. A published sample report for an idea called Shiftswap returned a 'Promising' verdict with 140 visitors, 8 emails, 6% of visitors leaving an email and 34% of button clicks going to a paid plan, plus a recommendation to build the smallest version, starting with the restaurants that left an email. If almost nobody shows up, the verdict says 'not enough data yet' below 100 visitors, together with how many more visitors it would take; Ghostlaunch states it never stretches a small sample into a conclusion. The overall method is deliberately linear, and Ghostlaunch frames it as sending the ghost first: watch the idea go out before the code does. The founder describes the idea, Ghostlaunch builds the page, Ghostlaunch finds the high-intent leads, Ghostlaunch measures everything, and on day 7 it delivers the verdict. The pitch throughout is to pay for answers rather than for code you will throw away, and the closing line is 'Don't build it yet. Ghostlaunch it.' After day 7 the page and the data stay in the founder's dashboard rather than disappearing, so the test can be revisited, relaunched or rewritten later on a subscription plan. Because the whole cycle is short and the output is a report rather than a product, the benefits are stated in terms of decisions rather than features. A founder gets a verdict grounded in what visitors did, not in what they said, along with the underlying numbers: how many came, how far they read, and which plan they reached for. The emails collected are the founder's to keep, stored with the plan each person picked and what they told Ghostlaunch, visible in the dashboard as they arrive, included in the day-7 report and exportable to CSV at any time. The report also records what visitors said and what to change on a second run, so a negative result still leaves something actionable. And the cost of being wrong drops from the estimated 390+ hours and $1,100 of building first to seven days and the price of a test. Concrete workflows follow directly from the published examples. A founder with a Slack bot idea for remote engineering managers can see whether managers click through to a paid plan before writing any bot. A maker with a booking tool for dog groomers, a reminder tool for freelance designers, a feeding app for parents of picky eaters or a WhatsApp reservation app for bakeries can put the pitch in front of people already complaining about those problems. Founders who are unsure what to build can start from Ghostlaunch's idea library, which it built by reading 1,452 posts where people asked for help and turning what they keep asking for into 50 ideas, each with the posts behind it, grouped into AI business ideas, app ideas, online business ideas, SaaS ideas and small business ideas. Founders working through a notebook of ideas can queue several tests rather than running one. Ghostlaunch is aimed at founders who have an idea but have not built it, including those testing in batches. Pricing reflects that: One test costs $49 once and includes the page built and hosted, 7 days of outreach to high-intent leads, every email collected yours to keep, the live dashboard and the day-7 report. Founder is $99 per month and is marked as recommended for a notebook full of ideas, with 3 tests a month, the ability to rewrite any section and relaunch any page, and everything in One test. Studio is $249 per month for testing in batches, with 10 tests a month, everything in Founder, and priority for its tests. Separately, the team notes that Ghostlaunch itself is for ideas you have not built yet, and points founders with a live product to ReplyHey, which finds people on Reddit, X and LinkedIn asking for what you sell, writes the reply and posts it from your account every day. In short, Ghostlaunch replaces encouragement with evidence. One described idea becomes a converting page, targeted outreach to people already asking for a fix, seven days of click-level measurement and a written verdict with every lead attached, so the decision to build rests on what strangers did rather than on what friends politely said.
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OpenRadView is a free, open-source desktop viewer for opening and exploring your own medical images on your computer. It is designed for CT, MRI and X-ray files, and it opens DICOM, NIfTI-1/2 and ZIP archives locally. The site describes it as a personal research project for education, not a clinically validated diagnostic tool. It is built for Apple Silicon and also runs on Linux, with Mac, Windows and Linux builds available from GitHub. No account is required, and the app does not upload scans or save study history. The product addresses a practical privacy problem: scan CDs and ZIP files often contain DICOM images that are awkward to inspect without sending them to a third-party service. OpenRadView keeps imports local, reads archives without extraction, and processes images on the computer. The site states there is no telemetry, analytics, account or scan upload, and no study history is saved. This matters for people who want to look at scans they own without creating an account or handing files to a cloud viewer. The app displays images without interpreting them; the site repeatedly says it is not for diagnosis and that a qualified clinician should interpret scans. File support is broad within the limits stated on the site. OpenRadView opens grayscale DICOM datasets including native, JPEG, RLE, JPEG-LS, JPEG 2000 and deflated data, plus 8/16-bit RGB/YBR and palette DICOM and native packed 8-bit YBR422. It reads NIfTI-1/2 as .nii, .nii.gz or paired .hdr/.img files, including gzip. Time points and vector/tensor components can appear as separate volumes, and complex data can be viewed as magnitude, phase, real or imaginary. Embedded RGB ICC profiles are converted to sRGB. Files, folders and ZIP archives including ZIP64 are supported, with up to 32 million samples per frame. The site lists unsupported formats such as RGB/RGBA or extended-precision NIfTI, legacy Analyze and alpha/enhanced DICOM palettes. For 2D images, the viewer provides window and level controls for grayscale contrast, with presets or manual window/level adjustment, plus pan, zoom and invert for supported color images. A reset restores the initial display. The site also mentions image information, scalar and RGB histograms, and pointer pixel values, so users can inspect display settings and pixel data. Series can be filtered by modality, series number or time/component label, and source images can play at a chosen speed; slow decoding reduces speed instead of skipping images. Users can export a local PNG and view settings for the completed pane without app overlays or labels, although identifying text may remain in the pixels. Linked MPR is a central viewing mode. It builds axial, coronal and sagittal planes from scalar or color volumes with consistent geometry, and one crosshair selects the same point in all three views. Users can move a plane with the Images control or arrow keys, which helps show how slices relate across planes. Oblique planes let users tilt a plane through a supported scalar or color volume and then step along it. The site notes that MPR, oblique and 3D views need consistent geometry, and that missing or invalid NIfTI geometry stays unlabeled in the 2D source grid. Values on resampled planes can differ from source images. 3D and maximum intensity projection views are labeled experimental. Scalar data has Source range and CT presets, while color data has brightness-based opacity and independent RGB maxima. Users can drag to orbit a projection of the same scan. Measurements include distance and angle where physical geometry is known, and rectangle statistics report scalar values or separate RGB channel codes. The site cautions that RGB maxima can combine colors from different depths, RGB codes are not physical tissue measurements, and volume views have a 512 MiB budget plus GPU limits. The decoded cache is bounded at 128 MiB, with codec and GPU allocations using additional memory. OpenRadView has no AI of its own and gives no opinion. Its optional agent feature is an MCP connection, so an agent the user already uses, on their own subscription or a local model, can see a view the user allows. MCP support is required. The workflow is explicit: a local MCP-capable client requests access, the app shows the series, tools and risks, and the user allows one series or reviewed views. Access can be revoked, changed to another series, or ended by quitting, but copies already sent cannot be recalled. Replies come from the agent, not OpenRadView, and the site says they are experimental AI commentary that can be wrong. Privacy controls are described in detail. All processing happens on the user's computer, with no telemetry, analytics, account or scan upload, and no study history saved. Automatic GitHub update checks are enabled by default and can be turned off on first launch or in Privacy → Updates. Checks send no scan data, but GitHub sees the IP address, app version and requested platform/architecture. Agent access only happens when the user approves a named session and its series or views and tools. A connected agent may send that data to its provider, and logs, retention and training depend on that provider's terms and settings. The stated benefits are educational and practical. The app is good for looking at scans you own on your own computer, seeing how contrast settings change an image, and understanding how slices form planes and a volume. Three public example scans ship with the app so users can try every view before opening their own files. It is explicitly not for diagnosis, screening, treatment or emergencies, not a replacement for a doctor or second opinion, and not for deciding whether to seek care. If urgent help is needed, the site says to contact local emergency services. Concrete use cases follow from those features. Someone with a scan CD or ZIP can open the files or folder locally and inspect the series without extraction. A user can load the bundled chest CT demo and move a linked crosshair through axial, coronal and sagittal views. On the chest X-ray demo, a user can drag to change window and level and then reset the display. A learner can tilt an oblique plane or orbit an experimental 3D/MIP projection. A user with an MCP-capable client can allow one reviewed series for their own AI agent, then revoke access. A user can export a PNG of the completed pane for their own records. The target user is someone who has their own CT, MRI or X-ray files and wants a local, account-free way to view them, plus learners who want to understand planes, volumes and contrast settings. The site stresses education and personal research rather than clinical use. Integrations are limited to optional local MCP clients such as Claude, Codex, Cursor and Grok on a subscription, or local models through LM Studio and Ollama paired with an MCP-capable client. Any client that supports local MCP servers works. Pricing is free and open source under GPL-3.0-or-later, with no account and optional update checks. OpenRadView's primary value is a free, local, open-source way to open and explore personal medical images without uploading them or creating an account. It combines DICOM and NIfTI support, linked MPR, 2D window/level, experimental 3D/MIP and measurements, with an optional MCP bridge to a user's own agent. It is explicitly educational, not diagnostic, and should be paired with a qualified clinician for interpretation.
Tapetide is an AI-first stock research platform built for Indian equities. Instead of clicking through a dozen screens, you ask TapeAi in plain English (for example: "compare HDFC Bank and ICICI Bank on ROE and valuation") and it pulls the numbers, reads the filings and lays out the answer with charts and tables.What you get:Every NSE and BSE stock (8,000+): price history since 2000, 12 years of financial statements, quarterly results, shareholding patterns, key ratios, technical indicators, analyst ratings and forecastsA 326-ratio stock screener with AND/OR filters and live technical signalsMarket dashboards: FII/DII flows since 2014, IPOs, bulk and block deals, F&O ban list, MTF, market valuation, mutual fund holdings, heatmapsParsed corporate filings: annual reports, concall transcripts, credit ratings, promoter pledgesTapetide MCP server: one remote URL (https://mcp.tapetide.com/mcp) gives Claude, ChatGPT, Cursor, VS Code, Windsurf, Codex, Gemini CLI and other MCP clients 55 tools for Indian market data. Google sign-in or API token, nothing to install.Free plan forever (50 MCP tool calls/day, screener, FII/DII data). Paid plans start at ₹299/month with a 7-day free trial.
The Brains is a directory of MCP servers that turn well-known founders, CEOs, investors and other public thinkers into searchable knowledge sources for AI assistants. Each brain is built from that person's public talks, essays, interviews and posts, and can be added to Claude, Cursor, ChatGPT or Codex. The site describes itself as the smartest brains on earth, in an MCP, and its stated purpose is straightforward: when you ask an AI assistant a question about pricing, fundraising, launching or growth, the answer should come back with a citation pointing to the talk, essay or post it was drawn from. The directory currently lists 39 brains covering 8,154 cited sources, spanning indie hackers, AI builders, CEOs, investors, marketers, operators, health experts and thinkers. Business and startup advice is abundant but scattered. A founder who wants to know how Pieter Levels prices a product, what Paul Graham says about fundraising, or how Ryan Hoover launched Product Hunt would otherwise have to search across podcasts, newsletters, essays and social posts, then hope the assistant remembers the source correctly. The Brains gathers those public materials into a single searchable unit per person and exposes it as an MCP server that plugs into the assistant the user already works in. That moves research into the tool instead of across a dozen browser tabs. The citation requirement matters because advice from public figures is easy to misattribute, and every answer is tied back to a source link so the user can read the original material and judge it in context. The core unit of the product is one MCP server per brain. Founders, CEOs and investors each become a searchable server built from their public output, and users add a brain to Claude, Cursor, ChatGPT or Codex to query it directly. The Product Hunt description notes that users can work with one URL per brain or a single URL for all of them, so the product supports both focused research on a single person and broader browsing across the whole roster. The directory also shows install counts, which give a practical signal of which brains other users reach for most: Alex Hormozi leads the listing with 85 installs, followed by Naval Ravikant with 31 and Marc Lou with 12 at the time of writing. Every answer cites its source. The site's sample questions show the intended format in practice: how do I know if my offer is underpriced, and how fast should I raise it, for Alex Hormozi; how do I come up with a startup idea worth working on, for Paul Graham; how do I validate a B2B SaaS idea before I build anything, for Vic Giurgiu. In each case the answer is expected to quote the talk, essay or post it came from with a link back to that material. That makes a brain useful both as a fast research shortcut and as a way to trace an idea to its original wording, which matters when a founder wants to quote or act on the advice. Discovery happens through the directory page, which is organized around several filters. Visitors can browse all 39 brains or narrow to indie hackers, AI builders, CEOs, investors, marketers, operators, health or thinkers. A search box lets users search founders or topics, with the site suggesting a query like pricing as an example. Results can be sorted by most installed, most sources, recently updated or alphabetically. Each entry surfaces the person's role and a short line of context, plus the number of talks and essays and posts behind the brain, an install count, and how recently it was updated, so users can judge depth and freshness before adding a server. The roster illustrates the range of the approach. It includes indie hackers and solo founders such as Marc Lou, Pieter Levels, Jack Friks, Alex West, Nevo David and Tibo Louis-Lucas; investors and fund figures such as Naval Ravikant, Jason Calacanis and Rob Walling; CEOs and operators such as Elon Musk, Sam Altman, Guillermo Rauch, Alexandr Wang and Jason Cohen; marketers and writers such as Justin Welsh, Ruben Hassid, Corey Haines, Shaan Puri, Lenny Rachitsky and Ryan Hoover; health-focused figures such as Andrew Huberman and Bryan Johnson; and even Socrates as a thinker. Source depth varies widely, from brains backed by a handful of talks and essays to Jason Cohen's 221 talks and essays and 300 posts. Overall, the product works as a layer between public knowledge and AI assistants. Public material is collected per person, packaged as an MCP server, and made reachable from Claude, Cursor, ChatGPT or Codex through a URL. The directory acts as the front door, with search, filters, sorting and per-brain statistics that help users pick the right source. The site also invites visitors to request a founder, CEO or investor for future builds, and to claim a brain if they are listed so they can verify it, fix anything that is wrong and add sources. Updates are visible on each card, with most brains showing recent refresh dates. The benefit is grounded answers rather than generic ones. Instead of asking an assistant what a well-known founder thinks and getting a plausible paraphrase, users get a response that quotes the underlying talk, essay or post and links to it. That is useful for founders making pricing, positioning, hiring or fundraising decisions, and for anyone who wants to check the original reasoning. Because the brains are exposed as MCP servers, the same source can be reused across different assistants without rebuilding the research each time, and the single-URL option makes the entire roster available in one connection. Use cases follow the sample questions on the site. A founder can ask how to tell whether an offer is underpriced and how quickly to raise prices, drawing on Alex Hormozi's material. A first-time builder can ask how to come up with a startup idea worth working on and get Paul Graham's essays as the cited basis. Someone preparing to launch can ask how Ryan Hoover approached building an audience before a product. A SaaS founder can ask how to validate an idea from Tibo Louis-Lucas or Vic Giurgiu, how to handle flattening growth from Jason Cohen, or how to set up Claude Code skills to run SaaS marketing from Corey Haines. Others use the health brains for questions about morning and evening routines for better sleep, or the marketer brains for LinkedIn growth and cold outreach replies. The audience is broad but specific in intent: founders, indie hackers, solo founders, operators, marketers, investors and anyone who wants cited answers from experienced public figures rather than unsourced AI output. The product is free and open source under the MIT license according to the Product Hunt listing. The site notes that the brains are unofficial and not affiliated with the people represented. The primary interface is the web directory, with the actual brains delivered as MCP servers that connect to AI assistants, and the roster continues to grow through requests. The Brains makes expert public material usable inside AI assistants without losing the trail back to the source. By packaging each founder, CEO or investor as a searchable MCP server and requiring every answer to cite the talk, essay or post behind it, it turns scattered advice into something a founder can query, verify and act on.
Exa 3.0 is the largest improvement to search quality that Exa has ever released, delivered as an update to its search API for AI agents. It is built for developers and teams who need to feed AI agents richer, more useful data from the web at a lower cost, and it is available today through the API. The release bundles three core changes: a larger embedding model trained with more compute and data than any previous Exa model, an index expanded with billions of new documents across useful data types, and Dynamic Highlights, which are now generally available. According to the announcement, Exa 3.0 ranks first on every benchmark the team tested, from public evaluations to coding, finance, and go-to-market intelligence. The context behind the release is the rapid growth of AI agents and personal AI assistants that depend on web retrieval to answer questions and complete tasks. Exa states that it has seen an explosion in usage from personal AI assistants, which is why it prioritized expanding its index and data partnerships to serve those use cases. At the same time, AI applications are cost-sensitive: sending full page text to a model consumes large numbers of tokens, and running heavy retrieval for every task adds up. Exa 3.0 addresses both sides of that trade-off at once, pairing broader and more accurate retrieval with a highlight model that dramatically cuts the amount of text passed downstream, plus a new lower-cost search tier for applications where budget is the binding constraint. The result, per the announcement, is a new Pareto frontier for search quality versus cost. The first pillar of the update is the retrieval stack itself. Exa trained a larger embedding model with more compute and data than any previous model, and states that every search type benefits from the improvement. Alongside the model work, the company added billions of documents to its index across data types such as products, local businesses, flights, events, sports, weather, and more. Together these changes produced gains on evaluations spanning coding, finance, deep research, and company intelligence. Because the improvements live in the shared retrieval stack rather than in a single product surface, developers do not need to switch endpoints or rebuild integrations to take advantage of them. The second pillar is Dynamic Highlights, which was previously released as a research preview and is now generally available at no additional charge. Instead of extracting highlights from each page individually, the new model selects tokens across all results, giving more tokens to the best pages, cutting repeated information, and using 95% fewer tokens than full page text. Exa reports that this approach improves answer quality while reducing token consumption, and that improvements to model efficiency have made it substantially faster and cheaper to run. For teams building agents, this means the retrieval layer returns a more compact, ranked selection of relevant text rather than long page dumps, which lowers both cost and context pressure downstream. The third pillar is the expanded index. Exa runs its own crawler and an independent index, tracking over 2 trillion URLs, serving 100 billion pages, and growing around what its customers search for. Exa 3.0 now includes 1.27B ecommerce listings with prices and availability, 211M+ local businesses with hours and ratings, real-time news, sports, weather, and market data, plus flights, local events, transit, and podcasts. This new data adds to coverage customers already rely on, including people, companies, code, and publications. The announcement highlights concrete search examples such as business hours and ratings for places, product prices and availability, game scores, and forecasts, illustrating the kinds of structured, real-world facts agents can now retrieve. Exa 3.0 also introduces Exa Lite, a new search type priced at $4 per 1,000 requests and built for cost-sensitive applications. Lite runs on the same index as Exa Auto. For higher quality, Exa Auto uses more compute across the retrieval stack, including candidate generation, highlights, and ranking, and is priced at $7 per 1,000 requests. Exa positions the two tiers as complementary: Lite offers strong search quality at a lower price, while Auto trades more compute for the highest quality. Measured as agentic search score versus cost per task, Exa 3.0 Lite scores 79 at about $0.045 per task and Auto scores 80.5 at about $0.065, which Exa describes as setting the quality-and-cost frontier among the tested providers. Overall, the product works through a retrieval pipeline that Exa operates end to end: its own crawler feeds an independent index, queries are handled by an embedding model in the retrieval stack, and candidate generation, highlighting, and ranking are applied with differing amounts of compute depending on the search type chosen. Dynamic Highlights then operates across the full result set rather than page by page, deciding how many tokens each result deserves. Developers access all of this through the API, configure which search type they want based on their quality and cost requirements, and get back ranked results with compact highlights and access to the expanded data sources at no additional charge. The stated outcomes center on benchmark performance. Exa 3.0 scores 91.8% on BrowseComp, 88.2% on DSQA, 56.1% on WideSearch, 42.0% on ATLAS, 75.5% on WebCode, 68.0% on FinSearchComp, 15.4 on Company FindAll, and 7.6 on People FindAll, and the announcement states that Exa 3.0 leads Perplexity, Brave, and Parallel on all eight benchmarks. For users, the practical benefits described are higher search quality for agent tasks, richer and more current data from an expanded index, and substantially lower token usage per query, all of which feed into better answers at a lower operating cost. Use cases named in the announcement include coding, finance, deep research, go-to-market intelligence, and company intelligence, which are the areas covered by the benchmark evaluations. The expanded index points to additional scenarios: product search with prices and availability, local business search with hours and ratings, real-time news, sports, weather, and market data lookups, and queries over flights, local events, transit, and podcasts. Exa also explicitly calls out personal AI assistants as a fast-growing usage category driving the index expansion, and cost-sensitive applications as the target for the Lite tier. Availability is immediate: Exa 3.0 is available today through the API, including Exa Lite, upgraded highlights, and the expanded index. Exa Lite costs $4 per 1,000 requests, Exa Auto is $7 per 1,000 requests, and the improved highlights and expanded data sources are included at no additional charge. Developers can get started in the API dashboard or read the documentation, and Exa hosted a live Exa 3.0 Q&A with its Head of Product and Head of R&D on Wednesday, October 21 at 1pm PT. In summary, Exa 3.0 is a search-quality release for AI agents that combines a stronger embedding model, a substantially larger and more structured index, and token-efficient Dynamic Highlights, while adding Exa Lite at $4 per 1,000 requests alongside Exa Auto at $7. Its core value proposition is straightforward: richer data and better search results for agents, delivered at a lower cost per task.
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.
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.
Odyssey-3 is a foundation world model that generates embodied environments from a prompt and predicts in real time how those environments change as a person or an agent takes actions or introduces events, using previous observations and the latest inputs. It is described as a learned dynamical system, implemented as an autoregressive diffusion transformer, that predicts how objects move and interact through space and how situations evolve over time. The model learns representations of physics, dynamics, and cause-and-effect from a broad dataset of visual observations, and developers use that knowledge both to simulate environments and to train policies for different physical systems. The research preview is available now, and physical AI developers who want to build with Odyssey-3 are invited to get in touch for access. The team behind Odyssey-3 comes from a decade spent building driverless cars, where predicting the world was essential to determining what a car should do next. The founders started Odyssey to pursue that idea far beyond the roads and to build a general-purpose technology that could bring learned world knowledge to all machines and tasks. Physical accuracy has been made a central focus of the research, on the reasoning that a foundation model for physical intelligence must learn to predict how the world actually behaves. In the team's own framing, with Odyssey-3 that idea is now being realized. Odyssey-3 generates embodied environments in real time and predicts how they change as a person or agent takes actions or introduces events. You can move through the environment or introduce an event during generation and observe how the model responds. The current preview provides first-person and third-person navigation alongside independent camera movement, giving different ways to interact with and inspect the model's predictions. The stated design goal was to enable dynamic, open-ended interactions with an environment that responds as the user acts. Odyssey-3 Pro sets a new state of the art on Physics-IQ Verified's video-to-video benchmark, achieving 66.1, the highest reported score. Physics-IQ, a benchmark from Anates Labs and DeepMind, tests physical behavior across fluid dynamics, optics, solid mechanics, magnetism, and thermodynamics by asking models to continue videos of real physical experiments and comparing their predictions with what actually happened. Odyssey-3 Pro also scores 54.7 in image-to-video. Reported video-to-video scores are 51.8 with base prompts, 61.6 with prompt enhancement, and 64.4 best-of-8 for the Odyssey-3 480p series, and 63.4 with prompt enhancement and 66.1 best-of-8 for Odyssey-3 Pro 720p. On image-to-video, Odyssey-3 records 41.0 with base prompts, 48.8 with prompt enhancement, and 52.8 best-of-8, while Odyssey-3 Pro records 50.0 with prompt enhancement and 54.7 best-of-8. Odyssey-3 also improves the measured tradeoff between physical accuracy and generation cost, making it possible to generate more simulations within the same compute budget. Resolutions are 832x480 for Odyssey-3 and 1280x720 for Pro. WorldMark measures control-following, visual quality, and world memory. In Odyssey's evaluation, using the benchmark's own captions and the mean of its 13 reported metric scores, Odyssey-3 ranks first in first-person stylized environments with 77.2, third-person real environments with 79.0, and third-person stylized environments with 76.3, and places third in first-person real environments with 80.6. The company notes that these results measure specific properties of generated worlds, and that applying the model to a physical system also requires evaluating the behaviors that matter for that machine and its tasks. Odyssey-3's learned world knowledge can be applied to different systems by training an action decoder or policy on paired observations and actions. These learned components translate that knowledge into the controls required by a particular machine, letting developers adapt the foundation model to a new body or task. With only tens of hours of robot demonstrations, Odyssey-3 completed manipulation tasks such as "Pour the cereal into the bowl" and "Close the screwbox" and showed recovery behaviors absent from those demonstrations, including reorienting a gripper after a missed grasp and retrieving a dropped object in an unusual position. Flexion has built humanoid control policies on Odyssey-3; the resulting policies exceeded the performance of the tested VLA baselines under environmental changes and continued to perform tasks under lighting changes that caused those baselines to fail. Odyssey-3 was also adapted to drive a car on real roads in India, training a driving policy on just 20 hours of driving data while keeping the Odyssey-3 backbone frozen. The policy uses the model's visual representations to predict waypoints ahead of the car, allowing it to drive in closed loop, and was demonstrated with instructions such as "Take the first roundabout exit" and "Drive along the road". Separately, Odyssey-3 was adapted to generate observations for particular sensor arrangements: in an early experiment using the front three cameras of an autonomous-driving dataset, an Odyssey-3 training checkpoint produced driving sequences with three camera views generated together after just 100 training steps. Odyssey-3 can also help train agents. An agent is an AI system that pursues a goal by observing its surroundings, choosing actions, and using what happens to decide what to do next. A world model can provide the environment in which those decisions are made, giving a way to study how an agent responds to changing conditions and whether it can complete a task inside a world whose behavior is learned. In Odyssey's task-completion demonstration, an agent receives a natural-language goal and pursues it inside Odyssey-3, observing the generated world as it works toward the task. Odyssey-3's training data combines internet video with time-localized, schema-verified event annotations, gameplay recordings with time-aligned keyboard and mouse inputs, and simulated rigid-body interactions with captions and metadata. Together these sources connect diverse observations with descriptions of what happens and, where available, the actions that produced it. The model is built as a multi-step video diffusion transformer, using temporally resolved prompts and controls to guide how the world unfolds. It is then extended autoregressively through teacher forcing and causal masking, training it to continue from preceding observations and predict future states conditioned on action inputs. Finally, a post-training pipeline combines distribution-matching and adversarial distillation to produce a distilled variant of Odyssey-3, a few-step model capable of real-time interaction. Odyssey believes world models will power increasingly capable physical AI, generate environments in which other intelligences can train, and enable new kinds of human experiences. Developers building robots, humanoids, self-driving cars, drones, or any other autonomous system are invited to explore how foundation world models can accelerate their work. The research preview lets people prompt an environment, act within it, and see how the world model responds, while API access is available by getting in touch with the team. In summary, Odyssey-3 is presented as the company's most powerful foundation world model, combining real-time, prompt-driven environment generation with strong physical accuracy results on Physics-IQ Verified and WorldMark, and with demonstrated adaptation to robot arms, humanoids, vehicles, multi-sensor data generation, and agent training.