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 That way it is never outdated.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.

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.

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

Both times I’ve received ChipDrops, the loads were an entire dump truck; ~20 cubic yards. I just used a wheelbarrow, a many-tined pitchfork, and a garden rake to make multiple large mulched beds, and a small pile in my back yard. I now have multiple large mulched beds, use it to cover food scraps in my compost bin, and use some in my vegetable beds/paths. It’s about a full day’s work to handle it all. I think ChipDrop also allows people to notify other users you’re giving some away if you can’t use it all, or you could try something like Craigslist.

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.

I’ve heard high velocity rounds (such as rifle rounds) send a kind of shockwave through your body. Dunno if it’s true or not.

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

I’ve been using last.fm for, I guess, decades now. Looking at what my “neighbors” are listening to is the most helpful.

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

Yeah, torrents usually run 100-300KiB/s. I guess not too bad for smaller files. About an hour or three per GB.

I mean, you can be sued for anything, but it will get thrown out. Like, I guess the MPAA could offer a movie for download, then try to sue the first hop they upload a chunk to, but that really doesn’t make any sense (because they offered it for download in the first place). Furthermore, the first hop(s) aren’t the people that are using the file, and they can’t even read it. If people could successfully sue nodes, then ISPs and postal services could be sued for anything that passes through their networks.

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.