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.

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).

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).

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 :)

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

I think I’ve seen calculations that we could explore every star in the galaxy with self-replicating probes in something like a million years; and other civilizations could do the same.

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.

This is good, IMO. People don’t have to smoke as much, so less damage is done to their lungs. Vapes, edibles, and concentrates that are not combusted are probably even less damaging.

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.