SchemQl light TS library for SQL with type safety and schema validation

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SchemQl is a lightweight TypeScript library that enhances your SQL workflow by combining raw SQL with targeted type safety and schema validation. It simplifies SQL usage in TypeScript by offering two main features: - Robust Query Validation: Ensures the integrity of your query parameters and results through powerful schema validation, reducing runtime errors and data inconsistencies. - Selective SQL Typing: Leverages TypeScript to provide real-time autocomplete and validation for specific parts of your SQL queries, targeting literal string parameters for tables, columns, parameters, and selections. SchemQl is designed to complement your existing SQL practices, not replace them. It allows you to write raw SQL while benefiting from enhanced safety for specific query elements. Key characteristics include: - Database Agnostic: Works with any database management system (DBMS) that has a compatible JavaScript/TypeScript client library, allowing you to fully leverage your database-specific features. - SQL-First approach: Provides the freedom to write complex SQL queries while offering targeted type safety for literal string parameters. - Lightweight: Focused on core features and intends to remain so. - Targeted Type Safety: Offers TypeScript support for enhanced developer experience with literal string parameters, balancing flexibility and safety. SchemQl is ideal for developers who appreciate the power of raw SQL but want added security and convenience through schema validation and targeted TypeScript integration for specific query elements. Zod inside. An alternative to Kysely, Drizzle & co. WDYT?

Visit product View on Hacker NewsOct 15, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, compatible · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

55%55% 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 · Strong signals: para · Missing: mobile apps, ios, personal

30%30% 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 · Missing: arr, mrr, revenue

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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