Schema-first codegen for SQL, Rust, Go, Python and TS (single binary)

Hacker News

AstrolaDB (alab) is a schema-first code generator for polyglot systems. Define your schema once and generate: - SQL migrations (PostgreSQL, SQLite) - Language-native types (Rust, Go, Python, TypeScript) - API contracts (GraphQL, OpenAPI) It's build-time only, with zero runtime dependencies. All outputs are explicit artifacts you can commit. The schema is the single source of truth across languages and APIs. Links: - Repo: https://github.com/hlop3z/astroladb - Docs: https://hlop3z.github.io/astroladb/ - Comparison: https://hlop3z.github.io/astroladb/comparison/

Visit product View on Hacker NewsJan 29, 2026

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

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

nativeThis product was originally launched on this platform.

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

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

31%31% 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

26%26% 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

24%24% 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

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

16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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