We

We're Building a Desktop App for Browser Agents

Hacker News

We're Building a Desktop App for Browser Agents

What's up HN! This is Jared and Art. We met on HN and started building together. Over the last few months we've been thinking a lot about how AI agents are going to impact the future. We want agents to be something that's actually useful for normal people as well as the 10x'ers. This lead us to building Meha over the last few months, our first swing at our vision! We saw OpenAI release Operators then we said f*k it let's post. Meha is a desktop app that uses your Chrome browser to execute tasks in the background. It controls your installed Chrome browser and uses LLMs with playwright to plan and execute actions to accomplish your task. You get to see each planning step the bot is doing and have access to its long term memory. Meha also uses its own file system and can export files for download. Another thing we've been focused on in multi-agent workflows and Meha can run many bots at the same time. One of the reasons why we can ship this for free in the mean time is because of how cheap the agents are. But we are planning to have a Pro version for power users. We prefer not to raise since we're against VC funding. We have been influenced by a lot of concepts in probabilistic robotics and RL to develop a fairly robust 'agentic' framework. As well as an algorithm for efficiently converting/compressing large html pages into a semantic format. If you're interested we will open source this asap in an SDK (will work with all OpenAI API spec LLMs and with llama.cpp) let us know. We're currently in beta and working on figuring out what this product will become and super stoked! Let us know what you think. To get access to Meha we have links on our discord to download (Both MacOS and Windows is available). Please give us all the feedback/criticism (even if you hate AI). Link to Meha: https://meha.ai

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Actual performance

6points
5comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: cursor, claude, model
97%97% 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: started, efficiently · Missing: supports, reddit linkedin, podcasting
93%93% 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, llama, io · Missing: https docs, excited, just released
66%66% 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, users · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient, users · Missing: plus, platform, intuitive
20%20% 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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