Ellipsis – Automatic pull request reviews

Hacker News

TLDR; Ellipsis is a GitHub app that automatically reviews, summarizes, and answers questions about pull requests. Hi HN, @hunterbrooks and @nbrad here, cofounders at Ellipsis. We’re on a mission to build an AI software engineer. So far, we’ve built the automatic PR review functionality we’re sharing with you today. Whenever a PR is opened, Ellipsis will automatically: - append a summary of changes to the PR body, - perform a code review, checking for best practices like DRY, descriptive variable names, etc. - check for violations of any of the custom rules you’ve provided The most common question we get is “does it work?”, so we’ve added a 7 day free trial. Here are some examples from popular open source repositories within the past week: - https://github.com/hyperdxio/hyperdx/pull/326 - https://github.com/relari-ai/continuous-eval/pull/43 - https://github.com/jxnl/instructor/pull/467 - https://github.com/ion-design/numi/pull/1 You can also tag @ellipsis-dev in a comment and ask it for a re-review or to answer a question about the PR. You’ll get a new review for every commit that’s pushed to the PR branch. Our docs ( https://docs.ellipsis.dev/review ) have more info. Today, 17 companies, including PromptLayer and Warp (W23) are using Ellipsis. We’re working on the code generation component (ex: tag Ellipsis to fix bugs), but that’s still in a public beta. Ellipsis doesn’t store or train models on your code. Ask: Try it out, it takes 2 clicks to install at https://www.ellipsis.dev . Feedback? team@ellipsis.dev

Visit product View on Hacker NewsFeb 27, 2024

Share card

Actual performance

18points

11comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, models · 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: open source, code review, ide · Missing: https docs, excited, just released

80%80% 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 · Strong signals: including · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

20%20% 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

Similar products

Automated Pull Request Reviews45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automated Pull Request Reviews

Hacker News3

Improve your pull request quality with automatic linting53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Improve your pull request quality with automatic linting

Hacker News5

Leverage ChatGPT in the Automation of Pull Request Reviews29%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Leverage ChatGPT in the Automation of Pull Request Reviews

Hacker News1

CodeMouse40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI reviews every pull request in minutes — so nothing waits on a human

AppSumo1

MergeGuard39%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AI-powered pull request reviews for GitHub.

Indie Hackerscommitment-full-time

Pull Review – assign pull request reviewers intelligently42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Pull Review – assign pull request reviewers intelligently

Hacker News2

Automated Pull Request Reviews on Your Behalf45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automated Pull Request Reviews on Your Behalf

Hacker News1

[GitHub] Mark Pull Request as depending on another48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

[GitHub] Mark Pull Request as depending on another

Hacker News5

Delegate Your Pull Request Reviews to Automation with ChatGPT34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Delegate Your Pull Request Reviews to Automation with ChatGPT

Hacker News3

Softagram59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Smooth Pull Request Reviews for Developers, Automated

Indie Hackers7$15,000/moproductivity

Browse all products Analyze your launch