PR

PR Guard – A GitHub Action to ensure authors understand their PRs

Hacker News

PR Guard – A GitHub Action to ensure authors understand their PRs

PR Guard is a tool designed to assist reviewers in dealing with the increasing number of PRs as a result of AI assisted programming. AI assisted programming isn't inherently bad, but it does allow contributions from people who may not understand what exactly they are contributing. PR Guard aims to stop this. It works by: - Passing the diff of a PR to an LLM - The LLM returns 3 questions which the author must answer - The LLM then reviews the answers and decides whether or not they show the author understands their code The point is to relieve some pressure on reviewers AND to enable users of AI assisted programming to learn in a new and engaging way.

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos
78%78% 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.
TrustMRRLess likely to generate early MRR · Strong signals: answers, users, way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
43%43% 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: ide, 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, users · Missing: plus, platform, intuitive
21%21% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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