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A chrome extension to help with code review on GitHub

Hacker News

A chrome extension to help with code review on GitHub

This tool is for finding common mistakes in code that can get to code review without being picked up or lines containing debug or profanity. It works off a single json file using regex and simple pattern matching The extension comes with a simple example json file example included but this can (and should be) edited to match your own code problems. You can edit it in the 'Files changed' window of your pr A basic readme is available here: https://github.com/whatever555/git_helper#github-code-review-helper and the extension can be found here: https://chrome.google.com/webstore/detail/github-helper-code-review/aboioehhpmdocefcoofhpogffkdmdmga?hl=en thanks for checking it out

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: google, single, using · Missing: mac, agents, macos
59%59% 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 · Strong signals: mistakes · 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 · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, 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
40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
38%38% 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 · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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