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Dotenv-Diff – Recent Improvements

Hacker News

Dotenv-Diff – Recent Improvements

Hi HN, I posted dotenv-diff here a couple of weeks ago. Since then, I’ve made several improvements based on feedback and real-world usage. The goal remains simple: statically audit how environment variables are actually used in a JS/TS codebase. Repo: https://github.com/Chrilleweb/dotenv-diff Docs: https://dotenv-diff-docs.vercel.app/ npm: https://www.npmjs.com/package/dotenv-diff Feedback always welcome :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
63%63% 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 NewsMay not resonate with HN audience · Missing: https docs, excited, just released
45%45% 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: way · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
27%27% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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