Semantic Primitives- TypeScript types that understand natural language

Hacker News

Weekend project exploring what TypeScript looks like when small, fast LLMs are cheap enough to embed at the type level. // Parse natural language await SemanticNumber.from("about a dozen") // 12 await SemanticDate.from("next Monday") // 2025-02-03 await SemanticBoolean.from("yeah I guess so") // true, confidence: 0.7 // Classify and validate with plain English await message.classify(['question', 'complaint', 'feedback', 'request']) await input.validate(["must be valid email", "must not contain profanity"]) // Context-aware operations SemanticNumber.from(150).isReasonable("human age") // { reasonable: false, explanation: "Human age rarely exceeds 120" } // Same error, different audiences error.explain("end-user") // "We couldn't connect. Check your internet." error.explain("developer") // "ECONNREFUSED on port 5432..." 15+ types: boolean, number, string, array, date, error, URL, promise, map, set, etc. Alpha—feedback welcome on what feels useful vs gimmicky. https://github.com/elicollinson/semantic-primitives

Visit product View on Hacker NewsJan 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, context · 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 · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

56%56% 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

37%37% 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

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

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

18%18% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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