Daily Edition · Page 3

THE DAILY INDEX

Technology, AI, software, security, crypto and business

GENERAL

TLDR Hardware

AGILITY'S DIGIT V5 TARGETS UNFENCED HUMAN-ROBOT COLLABORATION AND 20-HOUR SHIFTS

Agility Robotics says Digit v5 will work alongside humans without traditional safety fencing, using upgraded perception to detect people and cycloidal actuators to react while maintaining balance and payload control. Better battery life and fast charging target 20-hour operating days, while an ISO flange will support standard industrial end-effectors and eventually autonomous tool swapping. Early customer deployments are planned for December.

DYNA-2 ESTABLISHES A MILLION-HOUR SCALING LAW FOR ROBOT LEARNING

Dyna-2 is a video-diffusion world-action model pre-trained on more than 1 million hours of egocentric human-manipulation video, with no robot data included during pre-training. Dyna reports that prediction performance improved monotonically across a 1,000-to-1-million-hour training ladder, including zero-shot evaluation on 39 robot tasks from embodiments excluded from training. After limited robot post-training, normalized physical performance across 14 tasks rose from 20% to 53% as human-video pre-training scaled, with the largest model leading on nine tasks. Roughly 10 minutes of robot demonstrations were sufficient to post-train bottle-cap opening with two five-fingered robot hands.

HYBRID COMPUTING FRAMEWORK LOOKS BEYOND PEAK TOPS/W FOR AI EFFICIENCY

Researchers propose a hybrid computing framework that moves beyond traditional peak TOPS/W efficiency metrics. The paper demonstrates that combining digital orchestration with analogue, photonic, and neuromorphic accelerators delivers a scalable path toward energy-efficient AI systems. Rather than forcing all workloads onto pure digital logic, the proposed system dynamically routes tasks so physical substrates earn an expanding role only where they offer a measurable, system-level efficiency advantage while digital software manages noise, integration, and fallbacks.

CHINA'S TOP AI STILL TRAINED ON NVIDIA CHIPS AS SWITCH TO LOCAL TECH LAGS

Leading Chinese artificial intelligence firms continue to rely on Nvidia GPUs for training frontier AI models, delaying a full transition to domestic silicon alternatives. Despite Beijing's aggressive drive for semiconductor self-reliance and heavy investments in local accelerator developers like Huawei, technical hurdles persist. Domestic AI chipmakers face ongoing challenges with software ecosystem maturity, inter-chip interconnect bandwidth, and cluster stability during massive-scale training runs.

TLDR IndexPage 3