I made a heatmap diff viewer for code reviews

Hacker News

0github.com is a pull request viewer that color-codes every diff line/token by how much human attention it probably needs. Unlike PR-review bots, we try to flag not just by "is it a bug?" but by "is it worth a second look?" (examples: hard-coded secret, weird crypto mode, gnarly logic, ugly code). To try it, replace github.com with 0github.com in any pull-request URL. Under the hood, we split the PR into individual files, and for each file, we ask an LLM to annotate each line with a data structure that we parse into a colored heatmap. Examples: https://0github.com/manaflow-ai/cmux/pull/666 https://0github.com/stack-auth/stack-auth/pull/988 https://0github.com/tinygrad/tinygrad/pull/12995 https://0github.com/simonw/datasette/pull/2548 Notice how all the example links have a 0 prepended before github.com. This navigates you to our custom diff viewer where we handle the same URL path parameters as github.com. Darker yellows indicate that an area might require more investigation. Hover on the highlights to see the LLM's explanation. There's also a slider on the top left to adjust the "should review" threshold. Repo (MIT license): https://github.com/manaflow-ai/cmux

Visit product View on Hacker NewsOct 30, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: tiny, code · Missing: mac, agents, macos

68%68% 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: para · Missing: supports, reddit linkedin, podcasting

54%54% 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: code review, ide, io · 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.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · 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: para · Missing: mobile apps, ios, personal

27%27% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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