Starlite, the little ASGI framework that can

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Hi there, I'd like to introduce you to Starlite - a new python ASGI API framework built on top of pydantic and Starlette. You can find the repo for the framework here: https://github.com/Goldziher/starlite The documentation here: https://goldziher.github.io/starlite/ I wrote an article introducing it, which you can read here: https://itnext.io/introducing-starlite-3928adaa19ae This article gives an overview of the framework and explains "why create a new framework?" using the same ingredients as FastAPI, and also the relation to FastAPI with some criticisms. I'd be happy to discuss and answer any questions you might have.

Visit product View on Hacker NewsJan 4, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, plain · Missing: mac, agents, macos

63%63% 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.

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.

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

44%44% 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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

27%27% predicted probability of success on Indie Hackers, 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 · Strong signals: introduce · Missing: web3, chat, crypto

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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