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Huma 2.0, it's like FastAPI for Go built on OpenAPI 3.1 and JSON Schema

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Huma 2.0, it's like FastAPI for Go built on OpenAPI 3.1 and JSON Schema

I've released Huma 2.0, which is a rewrite I've been working on for the past year based on Go 1.20 and newer. It is updated for OpenAPI 3.1 & JSON Schema 2020-12 and works similar to FastAPI in that you define structs and API operations and then Huma generates the OpenAPI and documentation for you, which you can then use to generate CLIs, SDKs, mocks, etc. This new version makes it easier to adopt as it now supports many routers, incremental adoption, and more extensibility including full access to the OpenAPI when needed. It's also much faster than v1 and makes significantly fewer dynamic memory allocations. If you are new to Go or coming from Node.js/Python/etc then there is also a more basic step-by-step tutorial you can follow at < https://huma.rocks/tutorial/installation/ >. Check it out and let me know what you think!

Visit productView on Hacker NewsDec 8, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
65%65% 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, including · Missing: reddit linkedin, podcasting, created
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io, including · Missing: https docs, excited, just released
49%49% 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
36%36% 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
33%33% 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
19%19% 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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