Open-Source Stacked PRs on GitHub

Hacker News

Hi HN! I’m Ankit, co-founder of Aviator, a developer productivity platform. Today, we’re excited to introduce our open-source Stacked PRs CLI, designed to streamline your GitHub workflow by helping you stack smaller changes for faster review cycles. Why use stacked PRs CLI: - Seamlessly works with your existing GitHub setup. - Smaller changes make reviews faster and unblocks developers. - Easy to navigate, review and update stack tree. - Rebase the dependent PR without conflicts - Reorder, split, and merge the commits - Stack-aware merges - It’s free to use! Read the docs: https://docs.aviator.co/aviator-cli Short demo: https://www.youtube.com/watch?v=xSopIrkf2KM About Aviator: We are a team of ex-Google and Airbnb engineers building tools to enhance the entire software development lifecycle across code reviews, merging and deploying the code. Along with Stacked PRs CLI, Aviator offers: - MergeQueue - A highly customizable merge queue, perfect for monorepos - AttentionSet - inspired by Gerrit, tracks whose attention is needed for each PR - Releases - A unified dashboard for deployments, cherry-picks, rollbacks, and smart changelogs. We’d love to hear your thoughts and feedback!

Visit product View on Hacker NewsAug 26, 2024

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

88%88% 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.

Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, reviews · Missing: plus, intuitive, host

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal

36%36% 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: introduce, smart · 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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