I created an open source autoscaling AI browser agent

Hacker News

Hey HN, I'm working on a browser automation system via natural language and API calls. Now you might ask? How is this different than the already existing ones like Vercel browser, browser use, stage hand. e.t.c? Well none, NONE handles scaling and identity / proxy management for you, you'd have have to use the cloud service, and of course none written in RUST(the best PL ever). This is excellently useful for companies that wants to keep this parts of their system internal for privacy / cost reason. Rusty-Browser does. You just plugin your service account / AMI key (currently supports GCP, AWS) and in less than 30 seconds you are done with the setup. Now your limitation is how many servers your accounts can spawn. Noticed I said account, it's cloud agnostic and can run on multiple cloud accounts. You need 2000 browsers running simultaneously for whatever purpose, web bots, trading bots, web scraping e.t.c. RustyBrowser has you. The beautiful part is it downscales and upscales based on your demand. Once you are done. auto descales to the min servers you've set. Aside: 1. It also supports local usage 2. We don't dump raw html into the LLM, that's a waste, we leverage the Visual Tree of webpages to tell the LLM what to do, and to save on tokens we use a diff change on that tree so it knows exactly what changes made rather than redumping. 3. Also supports screenshots, however we prefer using it for the task evaluator rather than the instructor. 4. We are purposefully engineering it to work with cheaper, faster models like gpt-5.4-nano which works excellently well in filling out complex forms per our tests. it's here: https://github.com/dashn9/rusty-browser Other projects inspired for Rusty are: (Serverless management and deployment)[Flux: https://github.com/dashn9/serverless-flux ] (Browser automation, BiDi and CDP)[Rustenium: https://github.com/dashn9/rustenium ] It took me over three months to build the wonderful projects. Please star the repo if you think it has some value, would encourage me to take it seriously and work more on it. All contributions and support are appreciated. Thanks guys!!!

Visit product View on Hacker NewsApr 21, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, models · 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.

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

94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: month, trading · Missing: mobile apps, ios, personal

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

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

30%30% 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

16%16% 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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