Opair, a coding harness that eschews autonomy

Hacker News

Obviously everyone is writing their own coding harness these days, so I'll cut straight to the points of difference in mine: * There's no tool that grants shell access. Instead there's a bunch of development-specific tools, scoped to restricted functionality. * Reads and writes are gated by access control. Project files can be read without prompting, git-ignored files and dotfiles can't. * The shipped MCP integrations are restricted to read-only access. There are no tools for git add and commit. * Perhaps the biggest difference is separate driver and navigator modes. In driver mode, Opair works like other harnesses but with less autonomy for the agent. In navigator mode, the agent has no access to writable tools at all and instead monitors the project for changes you make in your regular editor. The idea is to imitate the relationship with a human pair programmer. Why on earth would anyone build this? Because I want less autonomy, not more. I don't want to read a massive diff at the end of an implementation push, I want to be involved all the way through. Opair is my attempt to make the human/agent relationship more like pair programming and less like code review. I wrote more about this in a philosophy doc: https://www.opairdev.org/philosophy

Visit product View on Hacker NewsSep 9, 2026

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

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

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Hacker NewsStrong engagement from HN community · Strong signals: code review, ide, io · Missing: https docs, excited, just released

68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

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