The agentic layer closes the loop entirely, pushing fact-based optimizations without waiting for human intervention.
AI
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Four Layers of Industrial AI: From Prediction to Automation
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Industrial AI operates across four layers: predictive forecasts what's coming, prescriptive recommends actions, generative exposes insights in natural language, agentic pushes optimizations directly to control systems. #industrialai #manufacturing
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AI Performance as System-Level Enterprise Challenge
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The enterprise takeaway is simple: AI performance is now a system-level challenge. The winners will optimize chips, memory, interconnects, software, and architecture together. Less latency means faster intelligence.
Less movement means lower cost.
Less waste means AI that can -
LogicFolding: 3D Chip Architecture for AI Inference Optimization
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One concept that makes this practical is LogicFolding. Traditional chips spread logic across a flat surface. LogicFolding brings related logic closer together by moving toward more 3D structures. Less distance means less delay. And in AI workloads, small delays compound fast.
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Tau Scaling Law: Beyond Chip Size in AI Systems
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This is the idea behind Tau Scaling Law, also known among peers as Her’s Law. Instead of asking only, “How small can the chip get?” We now have to ask: → Where is time being lost?
→ Where is data waiting?
→ Where are signals traveling too far?
→ Where is the system -
Proactive Agents: LLM Efficiency in Wake Triggers
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Do proactive agents really need an LLM to decide when to wake? The default proactive agent calls an LLM on every event just to decide whether to wake up. That is a lot of expensive inference spent on a yes or no. New research from Microsoft and Purdue asks whether the trigger
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Spatial Reasoning Benchmarks for AI Video Models
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Larus went ham with this one! Love the synced highlighting on the camera path, something I wanted to try myself.
— Bilawal Sidhu (@bilawalsidhu) 29 mai 2026
Makes me think these could end up as spatial reasoning benchmarks for ai video models, esp in cities with existing 3d data as ground truth. pic.twitter.com/x8BBPPuOEuLarus went ham with this one! Love the synced highlighting on the camera path, something I wanted to try myself. Makes me think these could end up as spatial reasoning benchmarks for ai video models, esp in cities with existing 3d data as ground truth.
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Wayve Labs Launches Frontier Physical AI Research Lab
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Introducing Wayve Labs: @wayve_ai
's frontier research lab for physical AI. Wayve Labs is where we'll pursue pioneering research in world models, spatial intelligence, and much, much more. Thanks to @ryajetha for the great write-up -
Teaching Robots Complex Tasks Without Extensive Training
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Teaching #Robots to do complex tasks WITHOUT spending hours training them
— Ronald van Loon (@Ronald_vanLoon) 29 mai 2026
via @IlirAliu_
#Robotics #AI #ArtificialIntelligence #MachineLearning #MI #Tech pic.twitter.com/QsA3VhFyOITeaching #Robots to do complex tasks WITHOUT spending hours training them
via @IlirAliu_ #Robotics #AI #ArtificialIntelligence #MachineLearning #MI #Tech -
Claude Dynamic Workflows: Parallel Subagents for Task Automation
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Claude's new Dynamic Workflows is amazing!
— AlphaSignal AI (@AlphaSignalAI) 29 mai 2026
1. Set model to Opus 4.8
2. Reasoning effort to /ultracode
It can spawn hundreds of subagents working in parallel to take on massive tasks. https://t.co/xa3mA4ewqF pic.twitter.com/frh9lov5wfClaude's new Dynamic Workflows is amazing! 1. Set model to Opus 4.8
2. Reasoning effort to /ultracode It can spawn hundreds of subagents working in parallel to take on massive tasks.