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Fuego, a Go framework generating OpenAPI documentation from code

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Fuego, a Go framework generating OpenAPI documentation from code

I started this project because none of Chi, Echo or Gin were capable of generating the OpenAPI specification from source code. Relying on comments and explicit generation creates errors and noise when communicating with the frontend team. After a while, I started doing frontend directly in Go with HTMX & Templ/Gomponents, so I decided to adapt the framework for this use case too! You can find the project here: https://github.com/go-fuego/fuego Fuego's main features are: Automatic generation of OpenAPI documentation from code Fully net/http compatible Simplified serialization Validation (go-playground/validator + custom method) Centralized error handling I know most gophers are net/http purists, but I hope you'll like it! Don't hesitate to open issues, as some features may be missing.

Visit productView on Hacker NewsFeb 19, 2024
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · 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: started, compatible · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% 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
44%44% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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