As someone who owns several RISC-V devices the primary thing preventing usable (low end) RISC-V laptops is the GPUs. Most RISC-V silicon has Imagination GPUs, and the current state of the drivers there is “proprietary drivers stuck on an old LTS kernel.”

If someone makes an RVA23 compliant chip with open mainstreamable drivers and a BXS-4-64 GPU (or, better yet, somehow manages to license a GPU from Intel or AMD for it), that’ll be a cash cow.

For fun.

Another win for Linux!

I don’t care if they’re selling computers to fascist psychos.

I do care that they’re using their soapbox to promote those fascist psychos.

This is essentially Google moving to do what I always thought was Apple’s malicious compliance on the DMA, but which European courts seem to have accepted as just fine. I’m pretty miffed at Google for sinking to Apple’s level on this.

Honestly? Get a large monitor and a sound bar.

If I were in the market for a new monitor and I could get an 8k monitor for under $1000 I’d consider it, but right now if one of my monitors broke I’d just be getting another 4k to replace it. The price isn’t worth it for me to have high DPI.

For TV my only justification for my 4k TV is that it was free.

I initially read that as “stop using VPNs to watch child porn, ministers told” and was expecting a very different article.

Integer storage in spreadsheets… There are a ridiculous number of ways to store any integer, and I don’t just mean because you could theoretically store 1 and 00000001 and they’d be interpreted as the same thing.

Given that part of my job is evaluating applicants’ ability to do the job, and given that LLMs are very good at answering the sort of questions many people ask in interviews, AI is making my job significantly harder.

If someone could make a prompt that actually made an LLM write good code, I wouldn’t have nearly as much of an issue.

I would agree, except that every piece of it is significantly more complex than it needs to be. ODF is considerably simpler in part because it makes use of other pre-existing standards for things like dates and times. OOXML redefines so many of those things, and in many cases Microsoft Office’s implementation isn’t actually compatible with their own standard.

I’m grateful to Microsoft for Windows 11 providing me a bunch of free machines to stick in my basement and put Linux on.

My experience with Apple has been more like

I credit Apple in many ways for their choice to design their business in a way that their profit motive often aligns with their users’ interests.

Their app store model for iOS is one of the strongest examples of them not doing that though.

Yeah, Steam is pretty much a monopoly. But I haven’t seen what I’d call monopolistic practices from them. It’s just that everyone else appears to fall flat on their faces when trying to make a competing product.

I’m less mad at Steam and Google because there are clear, simple ways to avoid their cuts.

I have no basis to say whether they’re providing a service worth the 30% charge. I’m also less mad at Steam than at Google because they’re being less shady about trying to push people into their store too.

The only reason my last machine didn’t get more than 10 years worth of in-place upgrades was because I decommissioned it as a desktop and turned it into a server, so I wiped it at that point.

Because despite all the people telling me I’m wrong, Kubuntu is still by far the best distro I’ve ever used. Rock solid, super fast, and continues to improve.

I think a better analogy would be that you’re tuning your bike for better performance because the trade-offs of switching to a car are worse than keeping the bike.

It’s all about trade-offs. Here are a few reasons why one might care about performance in their Python code:

1. Performance is often more tied to the code than to the interpreter - an O(n³) algorithm in blazing fast C won’t necessarily perform any better than an O(nlogn) algorithm in Python.
2. Just because this particular Python code isn’t particularly performance constrained doesn’t mean you’re okay with it taking twice as long.
3. Rewriting a large code base can be very expensive and error-prone. Converting small, very performance-sensitive parts of the code to a compiled language while keeping the bulk of the business logic in Python is often a much better value proposition.

These are also performance benefits one can get essentially for free with linter rules.

Anecdotally: in my final year of university I took a computational physics class. Many of my classmates wrote their simulations in C or C++. I would rotate between Matlab, Octave and Python. During one of our labs where we wrote particle simulations, I wrote and ran Octave and Python simulations in the time it took my classmates to write their C/C++ versions, and the two fastest simulations in the class were my Octave and Python ones, respectively. (The professor’s own sim came in third place). The overhead my classmates had dealing with poorly optimised code that caused constant cache misses was far greater than the interpreter overhead in my code (though at the time I don’t think I could have explained why their code was so slow compared to mine).