mkdocs-material VS fastapi

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Material Mk-Docs by Martin Donath works well if you prefer python.

https://squidfunk.github.io/mkdocs-material/

I'm an 'outsider', but from from the outside the Material For MkDocs Project looks like a very well managed open source project.

Martin Donath's project uses a 'sponsorware' release strategy to generate donations.

From my vantage point it seems to be working pretty well.

I just recently went through this decision process. My aim is to write code and math oriented posts so I need good support for nice syntax highlighting (at least colored) and mathjax (preferable) or katex. Substack is the most popular newsletter platform but fails at these two criteria. I love how math and syntax highlighting (plus numerous other features) work in MkDocs Material, which recently added a Blog plugin.

I wanted to combine the best of both: Substack as an amazing email social network, and MkDocs Material’s awesome look. So I’ve gone with using Substack as the core platform which I use to manage subscribers, and use it to post either math/code-free posts or a short teasers pointing to my main blog site on MkDocs Material when I need to show math/code

https://squidfunk.github.io/mkdocs-material/

I've been migrating content to MKDocs (Material) over the last few months, so feel fairly qualified on this subject. It's somewhat limited in terms of navigation, but can probably handle 400-500 pages; you can see how navigation works in the link. Otherwise, it can handle most, if not all, the tasks you've listed.

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.