Attempt to create browser-use in Chrome extension

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Inspired by Playwright-MCP, Browser-Use and Langgraph. My goal is to create Manus like chrome extension. Eventually, this should be able to use the in built browser models instead of LLM APIs to make it completely free. I believe the browser coming from Perplexity will have these capabilities built in. So i thought why not make chrome more powerful just by extension :-) Playwright seems to be bit challenging to put in browser, so i repurposed the playwright mcp to work with puppeteer so that it can work in chrome extension. And i tried using langgraph instead of vanilla langchain. Published the chrome extension here - https://chromewebstore.google.com/detail/auto-browse/ngnikmg... Github repo : https://github.com/auto-browse/auto-browse-agent Looking for feedback. I know there are others out there which have similar features.

Visit product View on Hacker NewsMay 20, 2025

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

94%94% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

50%50% 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: google · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

40%40% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

18%18% 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.

Correct prediction on native model

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