Lwerewolf

Flash is runnable in various gpu + cpu offload configurations. Not saying that 27b is bad (far from it, especially if they get n-grams in it with... hopefully today's release), saying that flash is runnable at decent speeds on way less than you'd imagine.

As for opus... I'm the weirdo that refuses to get an ant sub.

Mac user from May this year (maxed out mbp16, you can guess the use case). The dock sometimes doesn't want to unhide, the window manager sometimes thinks it's still there when trying to maximize something (not "fullscreen"), the taskbar (or whatever the thing at the top is called) sometimes blacks out completely when switching workspaces (clicking on it kinda fixes it - not sure if clicking _anywhere_ on it fixes it), still no shortcuts for stage manager, alt(cmd)-tab is still practically useless for me, workspace animations are still always there, etc, etc, etc.

Basically it's anything but "just works". It's still an extremely nice general purpose machine.

Oh, forgot. Spotlight takes over a second to autocomplete sometimes. Everything but apps is disabled from the settings menu for it, and I exclusively use it to launch (or bring back in focus, cause... "alt-tab") applications. Super annoying.

I mean, you do own your work in a way... it's not so different.

>Instead, think like an owner.

Exactly. LLMs make it, I'd say, _possible_, to own way more parts of the code than before, and understand even more.

This one is 2x m5 max, so ~1.2TB/sec.

There are resident systems, and they're of exactly the type listed in the article (i.e. water heaters). Residential... area heating pumps, so to say, are moving towards propane (afaik). I've been looking into getting a propane monoblock air-to-water pump, with an antifreeze loop towards an indoors heat exchanger / water tank.

I came to this relatively late (2013-ish, windows kernel development, scouring OSDev, etc) so I thought that was always the right name for them. Prior experience was mostly... higher-level langs.

As others have mentioned, nothing's stopping any other major provider from offering it. Given its popularity, you can guess how that'll develop. So, overall, irrelevant.

A regular yearly replay is in order for me, too. Already done with SC2... just SC1/WC3/DoW(2)/RA2(mental/flippedmissions/etc)/RA3(uprising)/HOMM3/etc/etc/etc. Ah well.

The model weights are MIT licensed.

Welcome to the club. If you're _really_ competitive in cs2, I'd swap out to a 9800x3d setup, but it's still a maybe. Very little reason to upgrade right now other than to run LLMs.

You need latency for token parallelism, not bandwidth. Hence actual RDMA that bypasses the software TCP stack (ROCe or whatever).

You just need social stigma on bad tires. It's the single most important thing on your vehicle, for literally all the reasons out there.

I literally had Fable tell me that I've checked out a nonexistent upstream commit of LMDB - two days ago. Sol called its BS, of course.

I mean, a big RAG setup with a smart LLM ought to do it, right? Or even just locally provided knowledge database - given the size of LLMs, what's a clone of wikipedia and whatever else you'd need?

Never been easier to make one.

Poolside's stuff (US) is pretty good.

You don't have to. Chatgpt-sol-high already does that for me. Could be the extra instructions or the base system prompt or whatever, point is - it already does it.

The MI350p exists and should run a decent quant (say, the ~96GB antirez mix) well, but you can get two rtx pro 6000s for one of these, or 8x (actually more) r9700 + probably the gear to run them, etc.

Otherwise, you can probably buy one of these second hand from somewhere (SXM A100s are available that way) and run it in an adapter board.

Almost reads like state policy-induced FOMO.

Just tried the preview on my little test codebase and a "check this out and tell me what you think" prompt used over double the tokens of the previous iteration, but it was a lot more eager as well. Kind of reminds me of the new laguna (s 2.1).

I've had it run to ~400k when debugging "obscure" (to it) sequences. Wouldn't recommend more.

Might wanna try it now, seems to have been largely fixed. Check huggingface threads and reddit. Works for me, very memory hungry and PP speed drops off a cliff around 200k context (~30tok/s decode and ~40tok/s pp - like... hope it isn't debugging with big logs), but it brings a fresh perspective alongside ds4-flash. The more, the merrier - I prefer keen eyeballs more than speed anyways.

...they'd messed something up in that quant, apparently fixed promptly. ds4 now supports it as well and it's... well, context-limited (<=250k on a 128gb machine) but it positively flies on an m5 max - the 60tok/s decode / ~500tok/s prefill is real.

Tool calling in pi completely broke with the updated q4 gguf (spinquant-less). Guessing it'll take some time.

Right:

https://huggingface.co/poolside/Laguna-S-2.1-FP8/discussions...

Wait time.

Well, just ran said gguf on the GeneralsX codebase with a pretty open-ended "Explain this codebase to me, and the general game loop." prompt, and...

  Let me also look at the GameLogic::update() to see the rest of the update flow, especially the object update loop.

  Actually, I think I have enough information now. Let me also check the GameLogic::UPDATE to understand the full update flow.

...repeating forever.

Since they mentioned that they're working on new quants, guess I'll wait. From earlier tests on work stuff, it's definitely capable.

Deleted earlier, didn't see you post, pasting here:

/* Just started testing with the gguf (with gpu offload, m5 max 128gb), q4_k_m, running seemingly well. Speed is initially slightly faster than antirez/ds4 - decode tok/s in the 30s, prefill ~400-ish. Expected, given the slightly smaller size. Looks to be working fine, but too early to tell. Definitely likes to "think". */

Anyways, guessing that discord might be focusing on the nvfp4 stuff. I've noticed spelling mistakes in the thinking traces, tool calls have been fine so far.

This: https://github.com/Blaizzy/mlx-lm/tree/pc/add-lg ...and this is what I should probably wait for (not sure why it's in vlm): https://github.com/Blaizzy/mlx-vlm/tree/pc/laguna-s-nvfp4 ...or perhaps I should've just used the gguf with the provided llama.cpp instead of trying to run the nvfp4-mlx from the get go, but where's the chaos in that :)

Running deepseek flash on something locally now, this will have to wait a bit. I still stand by my initial quick assessment - looks capable. Some people on r/localllama also reported loops. We'll see in ~10 hours. Hopefully I haven't terribly mislead people.

nvfp4 mlx, literally barebones pi.

edit: on bigger tests, got it to loop pretty easily unfortunately, probably local settings.