Happens when you’re not proud of what you’re contributing to. Probably most workers, tbh.

I don’t think federation has to be an obstacle for non-tech people. They don’t really have to know about it, and it can be something they learn about later. I really don’t know if federation stops people from trying it out. Don’t people think, “I don’t know what instance to join, so I’m not going to choose any?”

Personally, having no algorithm for your home feed is what I don’t like about it. Everything is chronological. Some people I follow post many times a day, some post once per month, some post stuff I’m extremely interested in sporadically, followed by a sea of random posts. Hashtag search and follow is also less useful because there’s no option for an algo.

The UI seems fine to me. I guess I’m not picky about UIs. The one nitpick I have is on mobile, tapping an image will just full-screen the image instead of opening the thread.

Idk. Maybe it’s because I learned OOP first that it makes more sense to me; but OOP is a good way to break down complex problems and encapsulate them into easily understable modules. Languages like Java almost force everyone on the project to use similar paradigms and styles, so it’s easier for everyone to understand the code base. Whenever I’ve worked on large non-OOP projects, it was a hard-to-maintain mess. I’ve never worked on projects such as the Linux kernel, and I’m hoping it’s not an unmaintainable mess, so I’m pretty sure it’s possible to not use OOP on large projects and still be maintainable. I am curious if they still use OOP concepts, even though they are not using strictly OOP.

I also like procedural python for quick small scripts. And although Rust isn’t strictly OOP, it obviously borrows heavily from it. Haskell is neat, but I haven’t used it enough to be proficient or develop good sense of application architecture.

I’ve done production work in C, but still used largely OOP concepts; and the code looks much different than code I’ve seen that was written before C++ was popular.

Yeah, I agree. I do use Flatpaks, Snaps, and Appimages sometimes if I can’t find a suitable deb repo/package. Flatpak is the best out of the three because they do try to avoid too much duplication through runtimes. I also use Docker quite a bit, which has similar issues (and benefits).

I assume the “kill it” comment was a little tongue-in-cheek. On small SBCs, like a Pi, or old hardware, it could be a problem. I’ve seen people with flatpaks taking up 30GB of space, which is significant. I’m not sure how much RAM it wastes. I assume running 6 different applications that have loaded 6 different versions of Qt libraries would also use significantly more RAM than just loading the system’s shared Qt libraries once.

Wastes RAM and disk space (compared to package-manager installed applications) by storing more libraries on disk and loading them into RAM rather than just using the libraries already installed on the distro. It’s probably better than Snap and Appimage though.

I think some apps allow it, not sure though. You can also move to an instance defederated from .ml if that instance is more aligned with what you’d like to see. You can also just subscribe to communities and not browse “All.”

Well, yeah, the spectrum certainly exists, but almost all of us fall hard to one side or the other.

I’ve heard it described as a bimodal distribution. Bisexuality seems pretty common though. Statistics say it’s more common for women to identify as bisexual than lesbian. The majority of women I’ve dated have been bisexual, but with a preference for men. Most non-straight men I’ve known have been fully homosexual. Personally, I’m straight. I’ve always had a hard time trying to figure out what a conventionally attractive man was (mostly to make myself more attractive), and still sometimes get surprised when someone describes a particular man as attractive. I find most women physically attractive, at least in certain ways (though I have certain preferences, of course). Personality seems to be what makes or breaks the attraction for me.

I have. My bank did a chargeback like they would if it was a credit card. I was told it would’ve been a lot harder to get my money back if my PIN was used. But, I’ve only seen that option available for in-person purchaees.

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 :)

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.