Foxp – a TypeScript type checker enabling pseudo dependent types

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Hello. I’ve created a TypeScript package that enables dependent-type–like checks by implementing a scripting language called CionLisp entirely at the type level. By extracting types from runtime values, it enables type validations not natively supported by TypeScript. Using this, I’ve implemented several features, including: - Division-by-zero detection - Length-indexed vectors (similar to Vect n in idris) - Safe accessors (similar to Lenses) Additionally, anything that can be expressed on CionLisp and resolved at type level—such as email validation via regex—can potentially be type-checked as well. Please note that this is still in an alpha stage, so use with caution. Thank you for your interest.

Visit product View on Hacker NewsJun 27, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: email, using · Missing: mac, agents, macos

71%71% 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 · Strong signals: created, including · 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: io, including · Missing: https docs, excited, just released

57%57% 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 · Missing: mobile apps, ios, personal

48%48% 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

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

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

13%13% 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.

Incorrect prediction on native model

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