411–420 of 424 posts
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So which shovels companies are the ones to watch for burnt in silicon models ?
$CBRS - Cerebras Systems There is other in the space, Groq and Sambanova are both private companies attempting to develop their own technology.
Being less expensive than Anthropic isn't impressive.
Model segmentation & distillation like this that asks the consumers to pick exactly which version of the algorithm will solve their problem is evidence for lack of intelligence instead of its presence.
You really really don’t need to pick. Just use Sol on high. That’s my daily driver and I don’t touch the model picker at all. Now, if cost is your concern, then that’s a problem in all of computing. Hence why I’m sending you short plain text messages using an iPhone with a many-core CPU and gigabytes of RAM.
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You're right of course, LLMs provide a partial, unsound oracle. The "halting problem is unsolvable" argument relies on the oracle not being able to output "not sure". But adding that option admits trivial oracles, like ones which output "not sure" for everything, so some are better than others. The "real world" use most people have for halting oracles is as part of software safety, where if the checker outputs "not s…
Even if we had an answer to the halting problem, from a practical point of view, it's solving the wrong problem anyway. There's no point in distinguishing a function that would run forever from one that would exit after a century. They are both too slow. Knowing that functions terminate is important for proof languages like Lean, where you often want to prove things without running the code at all. You're proving tha…
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I think this is an unsolved problem. The most interesting thing I saw here is the Recursive Language Models paper. https://arxiv.org/abs/2512.24601 There's also a great write up here by the author: https://alexzhang13.github.io/blog/2025/rlm/
I used this architecture for a while. The problem I have with it is that starting from one agent and fanning out keeps things mostly aligned with that single reasoning trajectory, even as you get a few layers into the stack. Every recursive invocation is a product of the caller's current state. Diversity doesn't really occur on its own unless the environment (tool calling) is complex/chaotic. RLM might be more useful…
Research tasks benefit the most from this. Because such work benefits from having a large number of agents working on a problem in parallel (i.e. crawling the web), and the model size becomes less important past a certain minimum.
I don't know about other categories of work, like programming. I imagine looking for bugs or security issues would benefit from it.
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If you optimize program A and manage to wring out a 1% improvement, and I optimize program B and improve performance by 20%, you can see the problem with trying to infer anything from those two numbers. Edit: searching for the story now, further bolstering the point is that was 1% in training time [1], and the openAI claim is 20% in end to end inference cost . This is a bad comparison. [1] https://deepmind.google/blo…
> This is a bad comparison. How so? First, kernel writing (or ML engineering more broadly) is a highly specialised task. Not everyone can do it. It shows that models are getting better and better at (easily verifiable) hard tasks. And you can "hire" that expertise much easier than you can hire the equivalent meatbags. And more importantly you can "fire" them as soon as the task is done. And then hire them 3 months la…
No it doesn't. You have no idea how optimised the underlying kernels already were. If a kernel was already optimal, it doesn't matter if you bring a brain the size of a planet, you're not going improve it.
> Second, 20% gains in inference today gives better end results (i.e. lower overall cost) than 1% in training 2 years ago
Your claim (at least in implication) was some kind of trend here. It doesn't matter which one was better to optimise because it "gives better end results" if you're trying to make a trend line out of two unrelated things.
I'm looking for more efficient ways to pack items into packaging. You're looking for more efficient ways to dispatch workers. I report how much faster I can get a shipping container loaded. You report how much less it costs to to get powerlines fixed during a storm.
It's a bad comparison.
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You might need to reread that mate.
> Revenue: $3.7 billion > Cost of Revenue: $2.65 billion That's how standard accounting rules for public companies would measure it.
> 2025 — OpenAI Had $13.07 Billion In Revenue, $34 Billion In Costs and Expenses, and $20.92 Billion In Losses, with a net loss attributable to the company of $38.53 Billion