Go-respec – Generate OpenAPI specs from Go code, no annotations needed

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Hey HN, I built go-respec to solve a long-standing annoyance with OpenAPI tooling in Go — the overwhelming reliance on magic comments and boilerplate. If you’ve used swaggo, oapi-codegen, or similar tools, you probably know the pain. go-respec takes a different approach: - No annotations - No code generation - No wrappers It statically analyzes your Go source and infers a full OpenAPI v3 spec — routes, request/response bodies, parameters, middleware, even security — and gives you clean override options when needed. It’s framework-agnostic (works with chi, gin, echo, etc.) and doesn’t care what architecture you use. You can get a working spec in seconds: go install github.com/Zachacious/go-respec/cmd/respec@latest respec . -o openapi.yaml I built this for my own projects after being frustrated with the existing ecosystem, but it’s already proven useful beyond just my use case. Would love your thoughts, critiques, or feature requests. Repo: https://github.com/Zachacious/go-respec Docs: https://github.com/Zachacious/go-respec#readme

Visit product View on Hacker NewsJul 24, 2025

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

69%69% 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: para · Missing: supports, reddit linkedin, podcasting

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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