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Cerebellum – Open-Source Browser Control with Claude 3.5 Computer Use

Hacker News

Cerebellum – Open-Source Browser Control with Claude 3.5 Computer Use

Hi HN! I was mesmerized by the Claude Computer Use reveal last week and was specifically impressed by how well it navigated websites. This motivated me to create Cerebellum, a library that lets an LLM take control of a browser. Here is a demo of Cerebellum in action, performing the goal “Find a USB C to C cable that is 10 feet long and add it to cart” on amazon.com: https://youtu.be/xaZbuaWtVkA?si=Tq9lE6BXv9wjZ-qC Currently, it uses Claude 3.5 Sonnet’s newly released computer use ability, but the ultimate goal is to crowdsource a high quality set of browser sessions to train an open source local model. Checkout the MIT licensed repo on github ( https://github.com/theredsix/cerebellum ) or install the library from npm ( https://www.npmjs.com/package/cerebellum-ai ) Looking for feedback from the HN community, especially on: What browser tasks would you use an LLM to complete? Thanks again for taking a look!

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Actual performance

42points
17comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, computer · Missing: mac, agents, macos
95%95% 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
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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
14%14% 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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