brrrrrm

6 ms·

1.
Show HN: Serverside.chat(github.com)
1 points by brrrrrm 2d ago|0 comments
2.
by brrrrrm 6d ago
this is basically the only thing pre-training teams work on in labs. compute efficiency is the metric, the assumption that scaling = intelligence is considered a given.
3.
by brrrrrm 6d ago
they say they're looking at base models, so I think it's fairly compared as written.
4.
by brrrrrm 15d ago
perhaps its unfair to say this in hindsight, but it's a fairly straightforward application of little's law that's been around for some time https://arxiv.org/html/2401.09670v2
5.
by brrrrrm 15d ago
this is a nice and concise writeup. what's striking to me is that these techniques really have not changed in /years/. sure, precision has become slightly lower, spec decoding acceptance has gotten slightly better and the c
6.
by brrrrrm 1mo ago
this is Qwen3.8 max, right? https://qwen.ai/blog?id=qwen3.8
7.
by brrrrrm 1mo ago
are these all uniform quantization? or mixed and matched by layer (can't tell from the naming scheme)
8.
by brrrrrm 1mo ago
this is cool but like, are we just vibe coding NAND burners at this point? these decode times don't really tell the whole story, because prefill becomes the bottleneck. half an hour to process 10k tokens on an M5 seems... not great
9.
by brrrrrm 2mo ago
there's wifi ah, which runs on 900mhz band and has the same 30dbm limitation I've used it with some raspberry pis to create hi-fidelity walkie talkies it's quite pleasant.
10.
by brrrrrm 2mo ago
Models are increasingly showing their ability to extrapolate into the human unexplored (math proofs being the most apparent). What gives you confidence the absurdity of life is uniquely difficult for models to source?
11.
by brrrrrm 2mo ago
> you are basically looking at a whole system prompt just describing the new language whats wrong with this? You may be over-indexing on the need for large quantities of examples. These days self-play through RL is far more effective and
12.
by brrrrrm 3mo ago
it's very much an in-domain term for folks in machine learning. heavily used when pipeline parallelism caught on in training https://alband.github.io/doc_view/pipeline.html
13.
by brrrrrm 4mo ago
it has paddle shifters - what are those for?
14.
by brrrrrm 4mo ago
what's MRT?
15.
by brrrrrm 5mo ago
same can be said for a lot of things tho. e.g. nature used to be fun but then we discovered it all :’( I miss when ships literally sailed into the unknown and found surprising and novel things like hot peppers and pineapples
16.
by brrrrrm 5mo ago
I agree fully. Hyundai has a mockup that starts to get there (different era, but same concept) called the N vision 74[1], but I doubt we'll see it in market anytime soon. The unfortunate reality IIUC is that modern cars (electric veh
17.
by brrrrrm 5mo ago
meta.ai in instant mode gets it first try too (I think?) ``` 2x + y = \operatorname{eml}\Big(1,\; \operatorname{eml}\big(\operatorname{eml}(1,\; \operatorname{eml}(\operatorname{eml}(1,\; \operatorname{eml}(\operatorname{eml}(L_2 + L_x, 1),
18.
by brrrrrm 5mo ago
you're right, this is actually correctly placed! I was confusing the orientation. I live right around there and recognize the M&T bank in the photo on the left, so it can't be down by 9th
19.
by brrrrrm 5mo ago
I checked 3 spots I'm familiar with and 1 is wrong https://www.oldnyc.org/#707133f-a this is supposed to be here https://www.oldnyc.org/#702487f-a also, if folks are interested in these old depictions
20.
How to Fail as an Organization in 2026(jott.live)
2 points by brrrrrm 8mo ago|0 comments
21.
Internal Combustion Engine Acoustic Synthesis(jott.live)
2 points by brrrrrm 9mo ago|0 comments
22.
by brrrrrm 9mo ago
looks cool! one bit of feedback: make your demo gif get to the point faster. either practice typing a bit quicker or speed it up 2x for the typing section
23.
Show HN: Binfer, an experimental LLM inference engine in TypeScript and CUDA(github.com)
1 points by brrrrrm 9mo ago|0 comments
24.
by brrrrrm 10mo ago
on Bun's website, the runtime section features HTTP, networking, storage -- all are very web-focused. any plans to start expanding into native ML support? (e.g. GPUs, RDMA-type networking, cluster management, NFS)
25.
Five Times Faster(jott.live)
2 points by brrrrrm 10mo ago|0 comments
26.
Bitwise Consistent On-Policy Reinforcement Learning with VLLM and TorchTitan(blog.vllm.ai)
1 points by brrrrrm 10mo ago|0 comments
27.
by brrrrrm 10mo ago
we've discovered some kind of differentiable computer[1] and as with all computers, people have their own interests and hobbies they use them for. but unlike computers, everyone pitches their interest or hobby as being the only one t
28.
by brrrrrm 10mo ago
a recent wave of interest in bitwise equivalent execution had a lot of kernels this level get pumped out. new attention mechanisms also often need new kernels to run at any reasonable rate theres definitely a breed of frontend-only ML dev t
29.
Should we apply old-school multi-core scheduling to GPUs?(jott.live)
4 points by brrrrrm 11mo ago|0 comments
30.
Show HN: GT: experimental multiplexed distributed tensor framework(github.com)
4 points by brrrrrm 11mo ago|0 comments

More ›