The agentic layer closes the loop entirely, pushing fact-based optimizations without waiting for human intervention.
MACHINE LEARNING
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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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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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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 -
Reconstructing Software Engineering for AI-Driven Coding
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Reconstructing software engineering around AI is going to take work (even as the ability of AI to code increases at a rapid rate). Organizations are ideally spending tokens for two things:
1) building stuff
2) experiments to figure out best practices (which involves failure) -
Use Claude to Interview Requirements for an AI Project
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5. Interview For anything ambiguous, don't write the full spec yourself. Make Claude extract it: "I want to build [rough idea]. Before we start, interview me. Ask me one question at a time about requirements, constraints, edge cases, and what done looks like. Keep asking until
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Prompt: Grill the AI to defend code changes
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2. The Grill After Claude says it's done, don't trust it. Make it defend the work: "Grill me on these changes. Diff your work against main, find every assumption you made, every edge case you didn't handle, and every place this could break in production. Don't open a PR until
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Anthropic’s Claude Opus 4.8 and 7 Claude Code Prompts
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Claude Opus 4.8 just dropped. Anthropic says it's 4x less likely to let flawed code slip through than 4.7.
— God of Prompt (@godofprompt) 29 mai 2026
But the model is only half of it. The other half is how you prompt it.
Here are 7 Claude Code prompts the best builders already run on repeat. Steal all 7: 👇 pic.twitter.com/bBX5KXnmCMClaude Opus 4.8 just dropped. Anthropic says it's 4x less likely to let flawed code slip through than 4.7. But the model is only half of it. The other half is how you prompt it. Here are 7 Claude Code prompts the best builders already run on repeat. Steal all 7: