Fa

FastAPI like controllers for Gin based Go apps

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FastAPI like controllers for Gin based Go apps

Motivation: I should be able to write following handler / controller similar to FastAPI by just defining SpecificRequest and SpecificResponse i.e. get request body type as argument and return response type along with error: func myHandler(ctx context.Context, request SpecificRequest) (*SpecificResponse, error) {} Exposing simple CRUD APIs is as simple as following - Ensure your model implements crud.Model, you can embed pgdb.BaseModel to get default implementation for few methods. - Define your request and response types and have them implement crud.Request and crud.Response interfaces. - Add routes specifying your request, response and model types

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Actual performance

1points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, context · Missing: mac, agents, macos
88%88% 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 · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, 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
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% 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
14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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