I

I build a tool to encourage before reviewing code, review intents

Hacker News

I build a tool to encourage before reviewing code, review intents

In the past, I’ve encountered agents repeatedly falling into the same old pitfalls while writing code—pitfalls that might be difficult to include in the spec documentation. These days, developers rarely review code line by line, but when agents do review it, they often focus only on code quality. Additionally, in some cases, agents from different developers make changes to the same product logic (not just within the same code file), but issues often aren’t discovered until the branch merge phase, requiring rework. To solve these problems, I created Mainline. Mainline uses CLI, skills, and coding agent hooks to store the intent that humans express through agents in Git. Before editing, agents can read historical intents, decisions, and risks; after making changes, they can record the rationale, trade-offs, and review notes. You can also export a static Hub for others to view historical intents, risks, and hotspots Repo: https://github.com/mainline-org/mainline How to use: curl -fsSL https://raw.githubusercontent.com/mainline-org/mainline/main... | bash mainline doctor --setup mainline init --actor-name "alice" I’d like to know if you think the granularity of intent records is useful. Does reviewing intent before a code review actually reduce the reviewer’s burden?

Visit product View on Hacker NewsMay 23, 2026

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

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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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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.

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