261–270 of 295 posts
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What kind of bot would say `less guardrails` instead of `fewer guardrails`? I guess someone could instruct an LLM to deliberately make mistakes, but isn't that too paranoid?
We’re reaching transvestigation levels of people trying to spot AI text everywhere they look
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We’re reaching transvestigation levels of people trying to spot AI text everywhere they look
Hmm, this sounds like something a bot would say to prevent being caught.
I've been waiting so long for something amazing to come out of the OpenAI and Cerebras collaboration. > In our evaluations, GPT-5.6 Sol on Ultrafast mode answered all 2,500 HLE questions in 11 hours and 11 minutes. Claude Fable 5 needed 78 hours and 27 minutes, more than three days of continuous compute, to arrive at the same conclusions. In other words, Ultrafast worked through the frontier of human knowledge in a s…
I discovered yesterday that the “amazing thing that comes out of OpenAI” is Sol, due to its token efficiency. Dollar for tokens, Sol and Fable are the same price. However, Sol uses (literally: in testing) around 10-100x less output tokens compared to Fable for the same task. We run our frontier models nearly 24/7, so switching to Sol will save us around $500 per day. And, due to less guardrails, Sol also performed be…
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By "advisor model pattern" you mean https://claude.com/blog/the-advisor-strategy ? How do you implement that outside of claude code?
the easier way to do this, with any harness, is to use an expensive main agent that is told to delegate all code reading, writing, exploration, research etc to weaker subagents to conserve tokens. It's an inversion of the pattern but the resulting split is the same
I've been waiting so long for something amazing to come out of the OpenAI and Cerebras collaboration. > In our evaluations, GPT-5.6 Sol on Ultrafast mode answered all 2,500 HLE questions in 11 hours and 11 minutes. Claude Fable 5 needed 78 hours and 27 minutes, more than three days of continuous compute, to arrive at the same conclusions. In other words, Ultrafast worked through the frontier of human knowledge in a s…
I'm finding Luna suprisingly adequate for my work. I slept on it due to the benchmarks, but it's very fast and even on low reasoning I'm finding it more than adequate for "menial" work. (The speed is crucial for "interactive" work -- if a model is fast enough it goes from "async" to "real time", subjectively, which is a huge difference.) In fact, I'd say it's overqualified for the kind of work I'm doing, because it s…
Faster & cheaper tokens = more reasoning capability and more reasoning = better problem solving as far as I have seen.
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the easier way to do this, with any harness, is to use an expensive main agent that is told to delegate all code reading, writing, exploration, research etc to weaker subagents to conserve tokens. It's an inversion of the pattern but the resulting split is the same
Yeah, that one is easy, but I was thinking of the other way around, where the weak model calls stronger one.
`You may use a Fable subagent to answer questions, solve problems, and provide an adversarial review of your ideas and code`
You can use a similar pattern in most any harness, and you can tell them to use other harnesses. In claude you can write `Use codex cli to have Sol56 Xhigh provide an adversarial review to your plan before presenting it to me` or `Use opencode cli with GLM 5.3 to verify all code review findings before presenting` or whatever you're doing, as long as those other tools are setup and ready to be called.
IMO: This isn't useful as a token saving pattern in my experience with agentic engineering, but it is useful as a quality-enhancer.
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Sol medium/high planner orchestrating -> Luna xhigh subagents doing implementation ...has been REALLY good for me. Even on xhigh, Luna is crazy cheap. Subjectively I'd say it's way better than Sonnet at a fraction of the cost. Luna xhigh can do some decently challenging things on its own, but when orchestrated by a model that is actually good like Sol, I am finding it very very nice.
How are you doing orchestration - using sol for plan mode in codex? Or some other pattern/harness?
I feel like I could be doing a lot better somehow. Regardless though Luna (xhigh specifically) is super good/cheap/fast for a lot of things
what about you
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Shall the better model still have the upper hand or will the raw speed compensate? At 14,000 tokens/sec there's just so much ridiculous stuff that might be possible. Let's assume that this POC proves they can take the next step, and can eventually etch a capable ~27B model into silicon. Let's call it Fred. Ralph loops automatically get real real interesting again. 200x the iteration speed. This is such a clear win I…
At 14,000t/s that's effectively a motor cortex for an android, you no longer need to train the robot to walk, it has a general idea for how to walk (baked into the 1b model), and then just corrects based on sensor input, in real time.
I was thinking about it the other day actually. How our brains evolved structure. I imagine it was purely just down to evolution adding/clustering additional cells around the areas where additional cells were needed. And after long enough a natural brain architecture emerged.
Makes me wonder if we're on the right track with transformer architecture/attention but if it'd be more effective on a larger scale, like MoE with a billion "experts".