A coding coach to help you write better code faster

Hacker News

Hey HN! I’m Julien, the founder of Code Inspector, a platform that helps developers and managers produce better code and reduce technical debt. We would love to get some feedback from the Hacker News community. Our platform inspects code, looks for defects (security, vulnerability, design, performance, lack of documentation), automates code reviews and reports on team activity. You can customize violation alerts to reduce false positives. We currently support GitHub, Bitbucket and Gitlab. I’d love to hear your thoughts on what you would expect from such a platform (what you like, dislike) and what could help developers produce better code. Thanks for reading thus far and hope to hear from the HN community. - Code Inspector: https://www.code-inspector.com/ - GitHub App: https://github.com/marketplace/code-inspector - Bitbucket App: https://marketplace.atlassian.com/apps/1222117/code-inspector

Visit product View on Hacker NewsFeb 23, 2021

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, activity · Missing: mac, agents, macos

77%77% 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

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

Hacker NewsStrong engagement from HN community · Strong signals: code review, hacker news, io · Missing: https docs, excited, just released

58%58% 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: platform, reviews · Missing: plus, intuitive, host

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

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