PullMaster – Recommends code reviewers from your repo history

Hacker News

I've been a developer for 20+ years and reviewer selection has been a recurring problem at every company I've worked at. Either you're a CODEOWNER getting spammed on every PR, or you're in Slack trying to find someone who actually knows the code you changed. CODEOWNERS is too coarse — it maps paths to people, but doesn't account for who's available, who reviewed this author before, or who actually touched these files recently. I built PullMaster to fix this. It's a GitHub App that analyzes your repo's actual history and recommends the best reviewer for each PR. It adapts to the risk level of each change, so critical PRs surface experienced reviewers while routine ones get distributed across the team. Install the GitHub App and comment `@pullmaster-ai suggest` on a PR to get a recommendation with an explanation, or `@pullmaster-ai assign` to also request the review automatically. No configuration needed — it learns from your repo as soon as it's installed. It's free. I'd use it at my day job but being in a heavily regulated industry without SOC 2 makes that a non-starter, so I'm looking for early users and feedback. Happy to answer questions about how it works. https://www.pullmaster.ai

Visit product View on Hacker NewsFeb 26, 2026

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Launch Intel predictions

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

70%70% 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 · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: code review, io · Missing: https docs, excited, just released

34%34% 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: soon, users · Missing: plus, platform, intuitive

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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.

Correct prediction on native model

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