Why LLMs rarely payoff—and what I have been saying literally for 7 years—confirmed yet again: LLMs can’t handle the truth. (Nor apparently can my critics, who keep saying I am “always wrong”, when I have been saying keeps being confirmed, over and over again.)
LLMS
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Self-supervised learning cuts annotation costs, speeds up AI model deployment.
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Self-supervised learning reduces annotation costs by generating labels from raw data, limiting manual effort. As models scale across business units, pretraining on unlabeled datasets shortens fine-tuning cycles and frees skilled teams for higher-value work. Microblog @antgrasso
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Opinion on DeepSeek R1, o1, and suspicious o3 benchmarks
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My personal vibes based opinion on this gap – at DeepSeek R1 level I believe this was real – o1 and r1 were not that far apart. From o3 onwards, I think there's something fishy going on with the benchmarks. Open models are not bad and certainly getting better, but the utility
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Claude Code’s “Don’t Stop” Mode: Set Goals, Walk Away, Pay the Bill
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Anthropic just gave Claude Code a "don't stop until it's done" mode.
— God of Prompt (@godofprompt) 30 mai 2026
/goal lets you set a completion condition, walk away, and Claude keeps iterating across turns until the condition is met. Hours. Sometimes days.
The capability is real. So is the bill.
Every turn reprocesses… https://t.co/bGVCrv6YXRAnthropic just gave Claude Code a "don't stop until it's done" mode. /goal lets you set a completion condition, walk away, and Claude keeps iterating across turns until the condition is met. Hours. Sometimes days. The capability is real. So is the bill. Every turn reprocesses
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Hermes Agent Control Room: Manage AI Teams Seamlessly, Eliminating Chaos
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You can now run a Hermes agent team from one control room. Running multiple AI agents usually turns into chaos. Credentials scattered everywhere, no clear ownership, nobody knows what runs where. Hermes Agent Control Room is a public template that fixes this. It treats
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The transformer inside an LLM: two blocks repeated
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In our AI engineering workshops, one question comes up over and over. What's actually inside an LLM? The answer is one word. 𝗔 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿. And it's much simpler than the name makes it sound. A transformer is just two blocks, repeated many times. 𝗕𝗹𝗼𝗰𝗸 𝟭
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Use Codex instead of ChatGPT for better responses
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You need to ask codex, not chatgpt if you want good responses.
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Explain vs. Describe: Understanding the Nuance in AI Concepts
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"Explain" vs. "Describe" These feel identical. They're not. "Explain RAG to me" gets you retrieval mechanics, why chunks are embedded, how context windows are populated, and where the architecture breaks. "Describe RAG to me" gets you a surface-level overview. What it looks
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GPT 5.5 boosts prompt duration and confidence with new features
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With GPT 5.5, /goal, autoreview and crabbox my prompts moved from ~30-60min to often 4-10h tasks and my confidence that it’s ready is much much higher. Yielding agents is a skill.
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Too dangerous to release: Mythos sparks restricted AI era debate
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Too dangerous to release: is Mythos the start of the restricted-#AI era?
by Chris Stokel-Walker @Nature Learn more: https://
bit.ly/3RN4zRM #GenerativeAI #ArtificialIntelligence #MachineLearning #ML