One of the most jarring things about current AI is its lack of introspection ability and metacognition. It doesn't know what it doesn't know, how it knows, or how it could find out. It's a one-way system.
SYSTEMS
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Survey on Multi-Agent Systems from Classical to LLM-Based Paradigms
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// Survey on Multi-Agent Systems // The paper traces the landscape from classical paradigms (consensus, distributed control, swarm intelligence, cooperative learning) to foundation-model-enabled MAS (LLM-based planning, role specialization, task decomposition, multi-modal
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Multi-Agent AI System Architecture for Digital Workforce
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Having a 20-agent system is a million times more powerful than 100 agents working in silos. Last night, I stayed up way too late fixing and improving my digital workforce for my AI Agent Mastermind program but here are a few of the improvements I made – the hub and spoke
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Neurosymbolic Architecture with Deterministic Fact Validation
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Our neurosymbolic + ontology + deterministic architecture is almost the opposite design philosophy to the one in the quoted post: – Retrieval is not trusted by default.
– Every retrieved fact must pass deterministic validation.
– Facts are not accepted because they are -
M4 Morphobot: Revolutionary Multi-Mode Robot Technology Platform
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M4 Morphobot: One #Robot, Multiple Modes, Endless Possibilities
— Ronald van Loon (@Ronald_vanLoon) 21 avril 2026
via @IlirAliu_#Robotics #Tech #Technology #EmergingTech #TechForGood pic.twitter.com/Kb3deF7ZaBM4 Morphobot: One #Robot, Multiple Modes, Endless Possibilities
via @IlirAliu_ #Robotics #Tech #Technology #EmergingTech #TechForGood -
Enterprise resilience: beyond backup to actual recovery capability
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Most enterprises have backup systems, disaster recovery plans, and security controls. When disruption hits, that’s not enough – because none of those answer the question that actually matters: Can we actually come back, for each critical service, right now?
— Kirk Borne (@KirkDBorne) 21 avril 2026
That gap is what… https://t.co/D4CXpEzTT3Most enterprises have backup systems, disaster recovery plans, and security controls. When disruption hits, that’s not enough – because none of those answer the question that actually matters: Can we actually come back, for each critical service, right now? That gap is what
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GPU Data Centers Energy Demands Accelerate to Megawatt Scale
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An Intelligence factory is ultimately an engineering challenge. And the scale is accelerating.
— Nina Schick (@NinaDSchick) 21 avril 2026
A normal data center rack draws about 8 kilowatts of power. A GPU rack draws around 200. Soon it will be 600. The next generation is approaching a megawatt.
That is the energy demand… pic.twitter.com/u7dumsjCrmAn Intelligence factory is ultimately an engineering challenge. And the scale is accelerating. A normal data center rack draws about 8 kilowatts of power. A GPU rack draws around 200. Soon it will be 600. The next generation is approaching a megawatt. That is the energy demand
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Agentic AI Enables Self-Optimizing Autonomous Intelligent Agents
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Automation executes. Autonomy reasons. 🧠
— NVIDIA AI (@NVIDIAAI) 20 avril 2026
Using Agentic AI, operators are now deploying intelligent agents that can self-optimize, self-heal, and self-configure.
➡️ https://t.co/A1bcxck9I2 pic.twitter.com/241Oc1hiM8Automation executes. Autonomy reasons. Using Agentic AI, operators are now deploying intelligent agents that can self-optimize, self-heal, and self-configure. https://
nvda.ws/4vEuuKS -
Level 4 Autonomous Driving Already Here for Some Users
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You will only be right for a few more weeks. And I never look at the road anymore. So for me it's already Level 4.
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CPU Optimization for Agentic AI Systems: Xeon 6 and RDU Stack
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In agentic systems, CPUs do two things: orchestrate inference and run everything around it: LLVM compilation, vector DB queries, tool calls.
— SambaNova (@SambaNovaAI) 20 avril 2026
Faster execution at each step = shorter agent loop. That's why Xeon 6 + RDU is the full stack, not just the accelerator.
🔗… pic.twitter.com/GUfTThjp7SIn agentic systems, CPUs do two things: orchestrate inference and run everything around it: LLVM compilation, vector DB queries, tool calls. Faster execution at each step = shorter agent loop. That's why Xeon 6 + RDU is the full stack, not just the accelerator.