Is this 2015?

Python async doesn’t suck. Python async is awesome.

But there’s giant red flags up if you’re trying to do async with Django, which is built as synchronous code.

Async django is not for the faint of heart, but it's definitely possible in 2025.

I recently wrote about issues debugging this stack[1], but now I feel very comfortable operating async-first.

[1] https://blendingbits.io/p/i-used-claude-code-to-debug-a-nigh...

We did the same for our app as well. I wrote a little library to make it as simple as FastAPI to generate swagger specs - you can try it out - https://github.com/sleeksky-dev/alt-swagger .

This kinda resonates with me. I've been using Python for over a decade and the only async method I trusted was gevent, and since I moved to Go after 2016, I never want use Python async in production level project, even I came back to see how it goes almost every year.

But since Python's LLM ecosystem is so well, I really appreciate the courage it takes to migrate to Node when writing a RAG system. I've tried similar things recently, working on a document analyzing project using React Router as the full-stack framework, while put some ETL related work on the Python side, use inngest to bridge Node and Python services. In this way, I got the benefit of Node for LLM chat, while stil able to facilitate Python's SOTA ETL libraries.

So basically you just rewrote boilerplate code with complexity of "hello world" and deploy scripts. Without any dependencies, data migrations, real user data and downtime SLA. And after that you had time to write quite long article.

What honest reaction you expect from readers?

Posts inviting language/frameworks flame wars are clickbaits... and i fell for it, again.

Who is the audience for a post like this? Presumably HN, since the author invoked PG.

But who is "we rewrote our stack on week 1 due to hypothetical scaling issues" supposed to impress? Not software professionals. Not savvy investors. Potential junior hires?

> Python async sucks

I always find this line of thought strange. It's as if the entire team hinges their technical decision on a single framework, when in reality it's relatively easy to overcome this level of difficulties. This reminds me of the Uber blunder - the same engineer/team switched Uber's database from MySQL to Postgres and then from Postgres to MySQL a few years later, both times claiming that the replaced DB "does not scale" or "sucks". In reality, though, both systems can work very well, and truth be told, Uber's scale was not large enough for either db to show the difference.

Do yourself a favor and use Elixir. Elixir has almost the same top libraries from Python you need to work with AI. As a matter of fact, the Elixir versions are far less fragile and more reliable in production use cases. I documented my journey of writing an AI app using Elixir and listed out the top libraries you can use, especially if you're coming from Python:

https://medium.com/creativefoundry/i-tried-to-build-an-ai-pr...