NpmStars by BasicUtils – Compare NPM downloads and GitHub stars

Hacker News

Hi HN! I built NpmStars by BasicUtils to solve a simple but important problem: how can you quickly understand the popularity of an npm package? npmtrends shows downloads. star-history shows GitHub stars. But I always wanted one tool that combined both — in a fast, clean, and no-login experience. So I built NpmStars: - Compare npm downloads and GitHub stars in a single view - Daily, weekly, monthly download trends - GitHub star growth over time - Side-by-side visualizations - Sharable URLs with query preserved - Built with Next.js, React, and Recharts I hope this helps devs, maintainers, and OSS researchers make smarter decisions when evaluating packages. Would love feedback, suggestions, and ideas to improve it! Try it out: https://basicutils.com/npmstars

Visit product View on Hacker NewsMay 22, 2025

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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.

best fitHighest predicted score across all platforms for this description.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: visual, single · Missing: mac, agents, macos

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · 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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