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Building your first ASGI framework – step-by-step lessons

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Building your first ASGI framework – step-by-step lessons

I am writing a series of lessons on building an ASGI framework from scratch. The goal is to develop a deeper understand of how frameworks like FastAPI and Starlette work. A strong motivation for doing this is because - I have been using AI to write code lately. I prompt, I get code, it works. But somewhere along the way I see I stopped caring about what is actually happening. So, this is an attempt to think beyond prompts and build deeper mental models of things we use in our day to day lives. I am not sure about the usefulness of this but I believe there are good lessons to be learnt doing this. The series works more as a follow along where each lesson builds on the previous one. By the end, you will have built something similar to Starlette - and actually understand how it works. - https://github.com/grandimam/papuli/blob/main/docs/01-founda... - https://github.com/grandimam/papuli/blob/main/docs/02-hellow...

Visit productView on Hacker NewsMar 27, 2026
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos
86%86% 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
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
49%49% 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
48%48% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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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