It is generally frowned upon to have LLMs precisely regurgitate part of their training set, but it is an interesting question how you could use LLM training to nearly losslesly compress a huge corpus like the entirety of the Internet Archive. The Hutter Prize is for perfect
LLMS
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Switching to Hermes LLM from Nous Research
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We switched over to using Hermes from @NousResearch
. Talked about that on the space last week. Here: https://
x.com/Scobleizer/sta
tus/2042009419094106350?s=20
… Well worth listening to. -
GPT-5.5 delayed, Opus 4.7 release expected tomorrow
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No GPT-5.5 tomorrow, fingers crossed however that Opus 4.7 will be released.
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Jagged Intelligence and the Implications for Frontier Model Prompting
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AI model that wins gold at the Math Olympiad can't read a clock. Stanford has a name for this: "jagged intelligence." And their 2026 AI Index proves it changes everything about how you should prompt. Frontier models now score above PhD-level on science benchmarks and dominate
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Cognitive Architecture Performance: Local vs Cloud Models
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The problem is that the cognitive architecture by @blevlabs writes and analyzes way better than any other model. So running locally would make the site way worse. And believe me I'm playing with all of them (Just interviewed NousResearch's CTO for just that reason last week).
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Efficient RL Training for LLMs with Experience Replay
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"Efficient RL Training for LLMs with Experience Replay" LLM RL post-training is still run in an almost fully on-policy regime where you generate rollouts, take one update, and discard. This paper argues that when rollout generation is expensive, strict on-policy training is
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Multi-Agent Orchestration Demo with Coordinator
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It’s very important to understand that this petition is signed by more than 700,000 real, uniquely identified individuals with their official and unique access certified by the official technologies provided by the Government. It’s not some bullshit petition that 700 people
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Data Protection Critical Infrastructure for AI and ML Systems
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Commvalut Says … @Commvault @thomabravo Firms that protect, recover, and operationally stay resilient with data-intensive systems are not peripheral players. They are part of the foundational infrastructure stack behind ML, LLM, cloud computing, and enterprise scale AI.
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SambaNova Intel deliver 200+ tokens per second premium inference
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A single prompt triggers reasoning, tool calls, and validation—not just token generation. That’s why raw throughput isn’t enough. Deliver premium inference at 200+ t/s while maintaining the flexibility AI agents demand. Read the blog: https://
sambanova.ai/blog/sambanova
-and-intel-blog?utm_source=x&utm_medium=organic&utm_content=blog-announcement
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Cost per token: Real AI infrastructure economics beyond raw FLOPS
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Compute cost = what enterprises pay for AI infrastructure (cloud or on-prem) FLOPS per dollar = raw compute per dollar But raw compute ≠ real-world output. Cost per token = the all-in cost to generate each delivered token (AI output)