CodeClarity – an open source source code analysis platform

Hacker News

Hoot-hoot everyone! Big news! CodeClarity is officially launching to the public! We are incredibly excited to share our open-source platform designed to enhance the security of your software development process. CodeClarity analyzes your JavaScript dependencies, letting you: - List all installed dependencies - Review software licenses - Identify vulnerable dependencies & associated risks We're also building a powerful patching plugin to automatically update your dependencies and generate the least vulnerable dependency tree possible. And this is just the beginning – we don’t intend to support JavaScript only, and plan to add more languages soon! We're now officially looking for your feedback! Help us make CodeClarity the best it can be! Try the platform here: https://platform.codeclarity.io CodeClarity is open source, join the parliament! https://github.com/orgs/CodeClarityCE https://www.codeclarity.io https://www.producthunt.com/posts/codeclarity

Visit product View on Hacker NewsApr 30, 2025

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

62%62% predicted probability of success on Indie Hackers, 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, open source, ide · Missing: https docs, just released, exist

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews

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

Product HuntUnlikely to reach the leaderboard · Strong signals: new, code, open · Missing: mac, agents, macos

43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · 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

10%10% 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.

Incorrect prediction on native model

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