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There were pretty specific rumors about IPOs happening early this year, up to apparent insider bets on prediction platforms. Don't you think the apparent delays in IPOs could have something to do with the open weights/open source competition? The monetary conditions (interest rates,...) were better early in the year, arguably predictably so, so what would be your alternative explanation why they chose to postpone?

I hope the unassociated previously registered x.domain.name subdomains will be blocked somehow? This sounds super scary.

CachyOS is probably the new hip one (Arch-based). The new "I use x, btw" would be nixOS. Arguably, the distro matters a lot less if you use containers or flatpaks for most things (which I'd recommend). So there's nothing standing against using just boring Debian stable these days, or Fedora if you want to be more up to date. If you like fancy UI, check out the COSMIC desktop environment, should work on all mentioned.

I didn't click anything, the sound just started playing while there was a loading animation

Fyi, autoplays sound. Generally an unacceptable UI decision imho.

> The theory of computer science has become a form of mathematics that doesn’t concern itself with computers

Perfectly summarizes the post, which I'd qualify as a rant (the author's main gripe seems to be that it's a galactic algorithm that assumes exact complex multiplication). The theory of computer science literally started with a mathematical model by Alan Turing that has nothing to do with computers (ones that we could physically build, anyway; and even if we brush aside the infinite tape, it's just architecturally completely different from any computers we've ever built for practical purposes).

If you don't like mathematics without direct applicability, you do you, but it's a legitimate mathematical undertaking and cheap way to rant against this AI drop. Do the work and show us if there's a mistake in the argument, missing attribution or whatever actual problems with the paper, but spare us your sour grapes.

Yes, I don't remember what it was, probably something with file sync, but I remember I kept a WSL1 instance for some stuff for a long time. Rust tooling worked great in WSL2, as long as you stay inside the VM, can confirm.

Try WinBoat, it's different technology and works really well. Haven't tested with Office specifically though.

Those machines generally come locked down, so you need permission to install stuff like WSL. I had such a machine and asked if they could unlock WSL, and their answer was no, because Security was busy enough keeping the Windows installs reasonably secure. Which makes sense tbf. Would be surprised if that was much different in other places.

My memory is a bit foggy, but the original WSL promised a much more ambitious integration with Windows, sort of container-like. It had a lot of rough edges when I first tried it, but they promised they'd work on it. That, together with their promise that W10 would be the 'last' Windows and they'd switch to a more OSX-style rolling release model was what pushed me to adopt Windows in ~2016. In the first years of Nadella it really felt like MS would listen to developers. Within less than two years iirc they'd given up on the WSL ambition, with WSL2 being a mere VM with noticeably worse performance for some of my workloads, and completely reneged on their W10 promise, even vowing to make my newly purchased hardware obsolete.

In summary, I really couldn't care less what improvements they make to WSL, and neither should anyone else, imho. If you want Linux, for the love of god just use Linux. Emulation works great the other way round by now.

Sure, or let's say, no one else who was doing it before was noticed at this level. But hype doesn't automaically equal a paradigm shift. In this case, it's going back to what used to be one of the most common ML applications until not so long ago. If anything it feels like going old school (and I really mean that in a good way).

Paradigm-shifting? A classifier? It's great and it's a cool use of Transformers, but AI really didn't only start with LLMs. We used to have an own term for AI models that can handle text instead of just numbers, now a model that outputs numbers instead of text is considered paradigm-shifting.

The "island economy" can be an interesting thought exercise for getting a feel for economic concepts, from money and banking all the way real estate and taxes.

Imagine the first guy arriving on the island claiming the whole land to be his, and any successive arrivers have to rent space from him in perpetuity, at rates he can decide. Not great, but not immediately obvious what a minimally-invasive improvement would look like. Now add a tax that forces the first guy to pay for every acre of land he claims. This incentivizes him to give up some of the space he's not actually using. Even in the most "rational agents in a frictionless vacuum" type of free-market macroeconomic model this is going to lead to a more efficient outcome, even more so in a more Keynesian-style model.

Cool idea! Do you happen to have benchmarks for Strix Halo (AMD Ryzen AI Max+ 395)? I take it that Qwen3.8-Flash-Next is not supported?

And a more general question: does your engine detect and optimize for custom setups like multiple (possibly different) GPUs, eGPUs,...? Because if all you have is a stock major system like a Mac or DGX Spark, that's all you're going to care about, and there are a lot of highly optimized single-hardware engines out there that will be hard to beat in the long run. Something that automatically adapts to custom systems that don't have their own subreddits could really fill a gap.

LLMs are like lossy compression of ~all of written text ever produced, with useful recall. To the extent that the corpus contains labelled examples of the given classification task, it's not unreasonable to think that we'll be able to build a decoder for that, just like we already have a useful decoder for next-token prediction. Extend to image classification the same way we already have multimodal LLMs.