Pl

Plugin for TypeScript to Restrict Properties

Hacker News

Plugin for TypeScript to Restrict Properties

I created a plugin to enforce strict handling of TypeScript! "eslint-plugin-no-excess-property" was developed to prohibit excess properties in TypeScript. In small-scale product development, I felt that TypeScript's flexibility was excessive, so I wanted to restrict it. There is a sample repository in the Readme, so please give it a try. I'd love to hear your feedback. Document: https://github.com/tfs-tada/eslint-plugin-no-excess-property...

Visit product View on Hacker NewsJun 19, 2024

Actual performance

1points

Did not reach leaderboard

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Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

43%43% 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.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

16%16% 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 · 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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