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Last one of the day I was in NVIDIA's Rubin room at Hot Chips. Same loop as this morning. An agent is 20 to 50 inferences, not one. Then they said no one uses one chip. A 100 MW factory. 2 zettaflops of 4-bit inference. 11 petabytes of memory. 800 petabytes a second. I called this the Agentic CPU. This is the GPU half. Token revenue, not a faster chip. They said 30x versus Blackwell Ultra, on real silicon. The slide is 2x versus GB300 until you push interactivity

The first time a robot attempted "Wine Bottle in Bowl," its success rate was 15%. Then it crossed a threshold — and jumped to 95%. Not because the model was retrained. Because the code-based harness governing its execution had evolved itself. Today's VLA (Vision-Language-Action) models run open-loop. When a robot fails, there's no real-time correction — the feedback loop closes only after the episode ends, when a human reviews logs and manually adjusts parameters. That cycle is slow. It doesn't scale. Minor physical disturbances cascade into failures the system can't catch in the moment. Zetta ζ solves this across three timescales. During execution, high-frequency runtime critics monitor trajectory deviations and trigger recovery interventions before failures compound (action level). Failed rollouts are clustered by failure signatures, diagnosed through six causal layers, and the harness is minimally repaired (episode level). Only skills that generalize across held-out environments pass through the validation gate and are permanently committed to skill memory (iteration level). Base model weights stay frozen throughout. Only the code-based harness evolves. LIBERO-Pro: 34.5% → 90.8%. RoboCasa: 73.6% → 93.6%. Inference speed: 11.1× faster than the RPent baseline. Throughput: 1.7 → 35.1 episodes/min (20.6×). Learned skills transferred zero-shot across PnP-Sink, PnP-Cabinet, and PnP-Toaster tasks. Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence #EmbodiedAI# #Robotics#

History in the making for the Bitcoin network! 📉⛏️ Bitcoin mining difficulty has dropped 14% from its 2026 peak, marking only the second year-over-year decline in Bitcoin's 17-year history. The latest KuCoin blog breaks down why this isn't just another temporary cycle, but a massive structural transition: 📊 A Rare YoY Decline: Difficulty has contracted from its historic peak of 155.97T down to ~126.23T as 7-day average hashrate dropped from 1 Zetta-hash (1,000 EH/s) to ~868 EH/s. ⚡ Post-Halving Margin Compression: Hashprice plummeted to record lows of $27.66/PH/s/day in June, making older ASIC models (like the S19 and M30 series) unviable and forcing unplug events. 🤖 The Structural AI Migration: Unplugged power isn't coming back soon—public miners (Riot, MARA, Core Scientific) are actively pivoting gigawatt-scale infrastructure to multi-year enterprise AI and HPC contracts. ⚖️ Relief for Efficient Miners: The automated difficulty reduction is working exactly as designed—stabilizing margins for next-gen operators by giving them a larger share of daily block rewards. Is this difficulty drop a sign of miner capitulation or a masterclass in automated protocol design? Read the full analysis here: