GoSMig – minimal, type-safe SQL migrations written in Go

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I built GoSMig for my own projects and open-sourced it in case it helps others. It’s a tiny generic library (no external deps except golang.org/x/term) for writing SQL migrations in Go with compile-time checks. It supports transactional and non-transactional migrations, rollback, status, version, and a small CLI handler so you can ship your own migration binary. Why another migrator? - Minimal API, no DSL or file layout to learn - Type-safe via Go generics - Works with database/sql and sqlx out of the box - Should work with any db library (or wrapper) that implements some generic interfaces (see the "Core Types" section here https://github.com/padurean/gosmig?tab=readme-ov-file#core-t... ) - Tested with PostgreSQL, should work with any SQL RDBMS (MySQL, SQLite, MS SQL Server, ...) Repo: https://github.com/padurean/gosmig Docs & examples: README + examples branch https://github.com/padurean/gosmig/tree/examples Would love feedback: ergonomics, missing guardrails, API rough edges, and real-world gotchas, etc.

Visit product View on Hacker NewsOct 21, 2025

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

60%60% 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

53%53% 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 · Missing: https docs, excited, just released

52%52% 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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

22%22% 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

15%15% 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

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

Correct prediction on native model

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