learn-x-by-doing-y VS fastapi

This list is enough, but after you get the basics from one of the above, do a project from https://aquadzn.github.io/learn-x-by-doing-y/

Some other knowledge-graph type projects for comparison:

> Metacademy - "Package Manager for Knowledge" - https://metacademy.org/

> MathLingua - language for easily creating a collection of mathematical knowledge, including definitions, theorems, axioms, and conjectures, in a format designed to be easy and fun to read and write. - https://www.mathlingua.org/

> Learn X in Y minutes - https://learnxinyminutes.com/

> Learn X by doing Y - https://aquadzn.github.io/learn-x-by-doing-y/

Many people are also starting to use the bidirectional-link style of note-taking to create their own knowledge graphs. I'm curious to see what sort of tools will emerge in the future to help people share the graphs they've created.

You see, I really needed something to do. I had been doing a few web related projects on the side and that was something I didn't want to do any more, at least for a while. So I looked into doing something "closer to the metal", something much lower level than sending requests back and forth to a web server. So I quickly fired up Learn X by doing Y and searched for something interesting, eventually ending up on Building your own Lisp (We all have a Lisp phase, it was just my turn).

Search is good, but if you're like me and would like to just see the list of available projects, it's here

https://github.com/aquadzn/learn-x-by-doing-y/blob/main/proj...

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.