AI Operator on Your Mac

Hacker News

Hey HN, I built an AI-powered UI for macOS that automates tasks on both macOS and in browsers. I didn’t want to pay $200 for OpenAI’s operator, so I made my own using browser-use and macOS-use. It handles simple tasks like scraping data, filling out forms, and using macOS apps. It's not great at working with files yet, but I’m planning to improve that with custom tools for the LLM. The goal is to create a desktop “pet” that takes care of boring stuff—like drafting documents and filling out forms. Check it out and let me know what you think!

Visit product View on Hacker NewsFeb 14, 2025

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Actual performance

6points

2comments

Did not reach leaderboard

Launch Intel predictions

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

99%99% 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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% 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: apps · Missing: mobile apps, ios, personal

36%36% 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

36%36% 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.

Correct prediction on native model

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