Open Source AI Employees

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Fable 5 scored 99.19% on WebVoyager a few months ago, a be A benchmark is not your logged-in admin panel. Browser use agents are no longer demos. I believe this is a big tipping point. I have been a web developer for most of my career and building with AI agents for nearly four years. Last October I wrote that the missing piece was browser agents reliable enough to leave alone. So I published this open source repo, so everyone can have high quality AI employees. They are, for the most part, agent harness agnostic and can work on any of them. An AI Employee is a folder of plain text routines that covers one business role: SEO, social, sales, support, web dev, ads, launch work, and a chief of staff that reads the other employees' logs. Eight roles, 60 routines, MIT. They run on your own machine through the agent harness you already use, and they drive your own signed-in browser. Two of the big things that make my agents better: - Recursive self improvement reinforcement - Custom trained recipes to get meaningful work done in browser There's also some other neat features and more planned. Like only-when-needed push notifications for Claude Code and other harnesses that support that. I'm also building in community recipe and ecosystem contribution. Agents may discover workflows or tricks for high level tasks and contribute them to all other agent operators. They run a lot of my own business every day now. One example: full meta ad campaigns, content marketing, SEO, backlink building, community management, app development and management. I recommend you start with one that can take on the work you don't want to do. Or replace yourself. Happy to answer any questions.

Visit product View on Hacker NewsSep 21, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, model

98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

32%32% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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