A tool that lets you reorder the changes in GitHub PRs

Hacker News

A few friends and I noticed that many PRs are easier/quicker to review if the changes are reviewed in some order other than alphabetical by filename (which is the default in Github). So, we'd often write our PR descriptions to include a suggested review order: "review file xyz.py first, then abc.js, then foo.rs". We did this often enough that we built a chrome extension that lets us reorder diffs in the Github PR UI rather than needing to type out the suggested order in the description. Anecdotally, we've noticed that using this tool to reordering diffs makes it much easier for reviewers to review PRs, especially when they are large/complicated, which is sometimes unavoidable. Narratives (what we're calling the extension) is open source and stores no data on remotely, so it should be safe to use on private repos. If your team writes/reviews lots of code - maybe give this a try. We would love to get your feedback, and PRs are welcome Chrome Web Store: https://chromewebstore.google.com/detail/narratives/ffcilgbo... Source: https://github.com/Naqu6/narratives-extension

Visit product View on Hacker NewsMar 23, 2024

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

71%71% 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.

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.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

40%40% 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: open source, io · Missing: https docs, excited, just released

33%33% 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: google · Missing: mobile apps, ios, personal

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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.

Incorrect prediction on native model

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