Backprop – Smarter Pull Requests

Hacker News

Hey everyone! Every reviewer loves smaller pull requests, but they can be hard to do practically. We created a Github integration that adds the following features - Commit checkpoints (i.e. stacked reviews) - Autogenerated tabs (based on dependency graphs) - A central pull request inbox (highlights which PRs need your attention) Commit checkpoints helps authors stack smaller changes within a single pull request without having to manage tons of branch offshoots. It works by using the common “[x/n]” prefix notation (where x is the stack number). This is the workflow: - Author makes some commits to a branch - Author is ready to set a review checkpoint, they leave a commit message prefixed with “[1/n]” - Author continues to make commits, and when ready for the next review checkpoint, leaves a commit message prefixed with “[2/n]” - etc Our UI collapses the in-between commits and makes it easy to see the diff between checkpoints. There’s also a feature to easily walkthrough them sequentially. Lots of things are still WIP, looking for feedback to know what to prioritize next, thanks!

Visit product View on Hacker NewsJan 12, 2023

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

84%84% 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 HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

47%47% 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

39%39% 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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