If you’re not inside, you’re outside #AGIClub
@ceobillionaire
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Mixture-of-Recursions: Dynamic Recursive Depths for Adaptive Token Computation
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Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation Bae et al.: https://
arxiv.org/abs/2507.10524 #ArtificialIntelligence #AIAgents #LLM -

Inside or Outside: The AGI Club Divide
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"If you’re not inside, you’re outside." #AGIClub #AGIFirst
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The AI Layoff Trap: Impact on Jobs and Workforce
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The AI Layoff Trap Brett Hemenway Falk, Gerry Tsoukalas : https://
arxiv.org/abs/2603.20617 #ArtificialIntelligence #AIAgents #Jobs -

Hassabis vs LeCun: Major AI Researchers Clash on LLM Future
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The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world.
— Milk Road AI (@MilkRoadAI) 12 avril 2026
Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language… pic.twitter.com/qmrLXNEqXUThe CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.
→ View original post on X — @ceobillionaire, 2026-04-12 09:02 UTC
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Hermes Agent: Nine Months to Overnight Success
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Hermes Agent is an overnight success nine months in the making
→ View original post on X — @ceobillionaire, 2026-04-12 07:40 UTC
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ASI Achievement Reached Internally
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ASI has been achieved internally tbh
→ View original post on X — @ceobillionaire, 2026-04-12 04:12 UTC
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ASI Health Assistant Will Diagnose Any Disease Within 3 Years
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within 3 years your ASI health assistant will be able to detect and diagnosis any disease
→ View original post on X — @ceobillionaire, 2026-04-12 02:21 UTC
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David Silver RL Lecture 4: Model-Free Prediction
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David Silver RL Lecture 4: Model-Free Prediction
A core RL question: how do we evaluate a policy without knowing the environment model? Monte Carlo: learn from full returns after complete episodes.
Temporal Difference: learn earlier from reward + bootstrapped next-state value. My note:
https://ickma2311.github.io/ML/RL/david-silver-lecture-4-model-free-prediction.html [Translated from EN to English]→ View original post on X — @ceobillionaire, 2026-04-12 01:18 UTC
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Recursive Self-Improvement Coming to the Claw Soon
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Recursive self-improvement is coming to the claw soon. 👁️🦞👁️💅
→ View original post on X — @ceobillionaire, 2026-04-12 00:28 UTC
