Hunch – A local MCP that lets your LLM use your Mac in the background

Hacker News

Hey HN, like the title says, I built a local MCP that lets your LLM drive your Mac in the background, without stealing your focus. I benchmarked Hunch against Peekaboo (OpenClaw's computer use) and Cua Drivers, and out of the 54 MacOS local tasks, Hunch completed 53 while costing $6.03. Peekaboo completed 41 tasks and used $31.03, and Cua Driver completed 31 tasks and used $37.42. More details on these tasks and methodologies are in the repo I have attached. I think background computer use tools are very useful, and can act like personal assistants that use your laptop as the same time as you. But a lot of the tools out there are very token intensive and takes a lot of time (also unreliable). Most are also screenshot based and more focused on applications with virtual machines, so cannot operate at the same time as you or on the same laptop. These were the reasons I created Hunch. I have also written some technical blogs about the problems I faced while building Hunch, which can be found on Hunch's website (tryhunch.ca). I am also working on a Swift app that sits on the menu bar powered by the Hunch SDK (hunch-sdk), so that you have instant access to an LLM powered by Hunch. It would be really appreciated if you guys can give Hunch a try! I would love to hear your thoughts and feedback. Hunch can be installed through Homebrew or Pip, and takes only one line to set up (more detailed instructions are in the repo and website). Thanks again!

Visit product View on Hacker NewsJul 29, 2026

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

98%98% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

80%80% predicted probability of success on Indie Hackers, 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

41%41% 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.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

10%10% 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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