Gen-ts-type – Code Generate TS Type from JSON data with collapsed field

Hacker News

I built a tool to generate TypeScript types from JSON/JS data, initially to tackle the deeply nested arrays in for a few JSON RPC API (e.g., array[0].content.section.contents[0].item.contents[0] could be a aItem or bItem). Some existing tools generate named union types at every level, which can get messy. This tool automatically converts fields to optional at the right depth, making the types cleaner and easier to use. Features: - Web UI: Paste JSON, get TypeScript types instantly - CLI or importable in TypeScript projects - Handles complex types (nested objects, arrays, etc.) - Supports optional fields and union types where needed Try the Web UI: https://gen-ts-type.surge.sh Also published on github and npm as gen-ts-type Feedback welcome!

Visit product View on Hacker NewsMay 21, 2025

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

76%76% 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: supports · Missing: reddit linkedin, podcasting, created

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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: arr · Missing: mrr, revenue, profit

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

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

Correct prediction on native model

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