nbsande

> With more than 5 million downloads since its launch, the app has helped block more than 3.7 million stolen or lost mobile phones, while more than 30 million fraudulent connections have also been terminated.

I might be reading this wrong but these numbers seem very weird. Did more than half the people who downloaded the app block a stolen phone? And did each person who downloaded the app terminate 6 fraudulent connections?

While having the same code for sync and async sounds nice, monkey patching code at runtime seems hacky. Any library that wants to use a lower level implementation of network calls would need to handle the monkey patching themselves i assume.

The idea is to map N tasks to M threads. This is useful more than just when you needd more threads than the OS can spin up. As you scale up the number of threads you increase context switching and cpu scheduling overhead. Being able to schedule A large number of tasks with a small number of threads could reduce this overhead.

Having too many threads all running at the same time can also cause a performance hit, and I don't mean hitting the OS limit on threads. The more threads you have running in parallel(remember this is considering a GIL-less setup) the more you need to context switch between the. Having fewer threads all running in an event loop allows you to manage more events with only a few threads, for example setting the number of event loop threads to the number of cores on the cpu.

The use case I wrote it in mind with is FastAPI. In that case, there wouldn't be any change to the Python code. You'd just use a different ASGI server that would use this sort of multithreaded event loop. So instead of running it with uvicorn main:app, you'd run it with alternateASGI main:app.

I have an example of a very basic ASGI server that does just that towards the end of the blog

Hard agree. If you want resource efficiency and high performance you're probably better off looking to lower level languages most of the time. In my experience FastAPI usually gets used by teams that need a server done quickly and simply or are constrained by a lack of experience in low level languages. That being said, I do think its worthwhile trying to improve efficiency slightly even for these cases.

Hmmm. That would indeed be better. Seems like an interesting experiment to try and implement virtual threads for python!

run_in_executor is pretty powerful for running sync code in async code, but my use case was more making async code utlize the cpu better. I think, just using run_in_executor would add a lot of complication and changes to how you use async await. But great point none the less!

If I'm understanding your point correctly that wouldn't prevent them from offering higher ram specs for the lower storage eg. 512 gig macs. So it seems like it is just price gouging

The large(100k tokens) context window together with the fact that it can actually use the information in that context window. From personal experience other models including open ai fail to properly answer when provided large(more than 5k tokens) inputs as context even when the model officially accepts much larger contexts. But Claude 2 models are uncannily good at taking all that context into consideration.

That's not strictly true. I'm sure thst GitHub contributed to the awareness of git among and less technically experienced especially in recent years, but use of version control systems has always been the norm in mid to large codebases and git became popular because it improved greatly on the version control systems that came before it.

Another big factor contributing to it's popikaroty was that the Linux kernel (that even when git came out was a pretty big codebase already) also used it to great success.

The only real credit that can be given to GitHub in my eyes is that it allows individuals to more easily host remote repos.

Keeping support for near 20 year old consumer grade machines in the kernel seems ridiculous to me. Doing so would bloat the size of the kernel even more than it already is. The more code you have the easier it is for bugs and vulnerabilities to creep in. When people write articles about moving old computers to windows to Linux they generally don't mean 20 year machines. You wouldn't even be able to run a modern browser with 256 mb of ram