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.

Meh, startups and businesses are capitalist organizations, and I think the idea of patents is questionable outside capitalism, so these wouldn’t really be a good metrics. I’d guess the richest countries “innovate” the most because they can support more risky endeavors. The U.S. is the capitalist imperial core, so it probably innovates the most. Other capitalist nations like Haiti, probably not so much.

The best measure of innovation would probably be something like scientific publications. China wins by raw numbers, Vatican City wins per-capita (???).

Yann LeCun would probably be a better source. He does actual research (unlike Altman), and I’ve never seen him over-hype or fear monger (unlike Altman).

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 That way it is never outdated.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.

Production AI is highly tuned by training data selection and human feedback. Every model has its own style that many people helped tune. In the open model world there are thousands of different models targeting various styles. Waifu Diffusion and GPT-4chan, for example.

I think you have your janitor example backwards. Spending my time revolutionizing energy productions sounds much more enjoyable than sweeping floors. Same with designing an effective floor sweeping robot.

AI are people, my friend. /s

But, really, I think people should be able to run algorithms on whatever data they want. It’s whether the output is sufficiently different or “transformative” that matters (and other laws like using people’s likeness). Otherwise, I think the laws will get complex and nonsensical once you start adding special cases for “AI.” And I’d bet if new laws are written, they’d be written by lobbiests to further erode the threat of competition (from free software, for instance).

The search engine LLMs suck. I’m guessing they use very small models to save compute. ChatGPT 4o and Claude 3.5 are much better.

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

Donation, patronage, gift economy, mutual aid, or whatever you want to call it is fine by me. People can pirate a lot of proprietary software as well, yet people still pay.

Yet, people still pay for it.

The problem is that HP writes drivers and software for those things for Windows, but not for Linux, so Linux depends on random people to write software for those things for free (which often involves complex reverse-engineering). With Linux you need to make sure you use widely-used hardware that someone has already written support for (this is mostly applicable to laptops and peripherals, which often use custom non-standard hardware). There may be a way to fix your problems, but you’ll have to search forums or issue trackers for the solutions, and they’re probably pretty involved to get working correctly. The router crashing thing is probably just a coincidence though, or the laptop is using a feature that’s broken on your router.

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.