Code should be self-documenting. That way it is never outdated. Here’s an example of this similar to what you can expect to see in practice:

def nabla_descent(X, y, theta, alpha, delta, nabla):
    m = len(y)
    for _ in range(delta):
        h = X.dot(theta)
        nabla = (1/m) * X.T.dot(h - y)
        theta = theta - alpha * nabla
    return theta

I (probably unreasonably) despise using web front-ends for desktop applications.

GTK is OK. QT is very feature rich, but that adds complexity. Both can be cross-compiled to most systems and shipped with all the required libraries pretty easily.

I haven’t used it in a long while, but I remember liking Java Swing for some reason. Java should be “write once, run anywhere.” But, cross-compiling isn’t usually too hard, so not sure how much that matters. There’s more modern frameworks for JVM-based languages now, but I haven’t tried them.

I’ve noticed Gradio is popular in the ML community (web-tech based, and mostly used for quick demos/prototypes).

Edit: For web applications, I prefer Angular’s more traditional architecture over React’s hook architecture.

Haven’t tried Gemini; may work. But, in my experience with other LLMs, even if text doesn’t exceed the token limit, LLMs start making more mistakes and sometimes behave strangely more often as the size of context grows.

This is more complicated than some corporate infrastructures I’ve worked on, lol.

I usually just use VS Code to do full-text searches, and write down notes in a note taking app. That, and browse the documentation.

Nah, LLMs have severe context window limitations. It starts to get wackier after ~1000 LOC.

Python is quite slow, so will use more CPU cycles than many other languages. If you’re doing data-heavy stuff, it’ll probably also use more RAM than, say C, where you can control types and memory layout of structs.

That being said, for services, I typically use FastAPI, because it’s just so quick to develop stuff in Python. I don’t do heavy stuff in Python; that’s done by packages that wrap binaries complied from C, C++, Fortran, or CUDA. If I need tight-loops, I either entirely switch to a different language (Rust, lately), or I write a library and interact with it with ctypes.

There’s plenty of open source projects that distribute executables (i.e. all that use compiled languages). The projects just provide checksums, ensure their builds are reproducible, or provide some other method to verify.

In practice, you’re going to wind up in dependency hell before pypi stops hosting the package. E.g. you need to use package A and package B, but package A depends on v1 of package C, and package B depends on v2 of package C.

And you don’t need to use pypi or pip at all. You could just download the code and directly from tbe repo, import it into your project (possibly needing to build if it has binary components). However, if it was on pypi before, then the source repo likely had all the code pip needs to install it (i.e. contains setup.py and any related files).

C# is actually pretty nice. Ecosystem, not so much, but D doesn’t really have one anyways :)

Yeah, the image bytes are random because they’re already compressed (unless they’re bitmaps, which is not likely).

If you’re talking about naive bayes filtering, it most definitely is an ML model. Modern spam filters use more complex ML models (or at least I know Yahoo Mail used to ~15 years ago, because I saw a lecture where John Langford talked a little bit about it). Statistical ML is an “AI” field. Stuff like anomaly detection are also usually ML models.

camelCase for non-source-code files. I find camelCase faster to “parse” for some reason (probably just because I’ve spent thousands of hours reading and writing camelCase code). For programming, I usually just use whatever each language’s standard library uses, for consistency. I prefer camelCase though.

OSMC’s Vero V looks interesting. Pi 4 with OSMC or Librelec could work. I’m probably going to do something like this pretty soon. I just set up an *arr stack last week, and just using my smart TV with the jellyfin app installed ATM.

My PC running the Jellyfin server can’t transcode some videos though; probably going to put an Arc a310 in it.

I think most projects left Sourceforge after they started putting adware into they’re downloads.

I’ve used them as a proxy for a web app at the last place I worked. Was just hoping they’d block unwanted/malicious traffic (not sure if it was needed, and it wasn’t my choice). I, personally, didn’t have any problems with their service.

Now, if you take a step back, and look at the big picture, they are so big and ubiquitous that they are a threat to the WWW itself. They are probably one of the most valuable targets for malicious actors and nation states. Even if Cloudflare is able to defend against infiltration and attacks in perpetuity, they have much of the net locked-in, and will enshittify to keep profits increasing in a market they’ve almost completely saturated.

Also, CAPTCHAs are annoying.

I’ve used this before: https://github.com/wilicc/gpu-burn?tab=readme-ov-file

Yeah, it may be a driver issue, Nvidia/pytorch handles OOM gracefully on my system.

That seems strange. Perhaps you should stress-test your GPU/system to see if it’s a hardware problem.

SD works fine for me with: Driver Version: 525.147.05 CUDA Version: 12.0

I use this docker container: https://github.com/AbdBarho/stable-diffusion-webui-docker

You will also need to install the nvidia container toolkit if you use docker containers: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html

I think similar, and arguably more fine-grained, things can be done with Typescript, traditional OOP (interfaces, and maybe the Facade pattern), and perhaps dependency injection.

I’ve had unattended upgrades running on a home server for a couple years and haven’t had any issues.