Articles
In-depth research, comprehensive guides, and long-form thoughts on modern technology.
Intelligence Is Free, Now What? The Coming Shift in Data Systems
As AI inference costs collapse, researchers at UC Berkeley argue data systems must be rebuilt for swarms of agents that speculate, remember, and even design their own infrastructure.
The State of Simulation for Physical AI in 2026
Robot simulation has evolved from debugging tool to core training infrastructure, with GPU-accelerated engines like Isaac Lab, MuJoCo Warp, and Newton defining the stack.
Microsoft Verifies Rust Cryptography in SymCrypt Using Lean and AI Agents
Microsoft is using Rust, the Lean proof assistant, and AI agents to formally verify production cryptographic code, starting with ML-KEM and SHA3 implementations.
Dell Technologies Capital Explains Why AI Will Not Kill SaaS
Dell Technologies Capital managing director explains why AI will not kill SaaS, how deep tech founders survive, and why distribution decides AI winners.
Black Founders Face Series A Gap as AI Reshapes Startup Economics
AI lowered the cost of building startups, not scaling them. For Black founders, fully funded seed rounds are now the critical barrier to reaching Series A.
Interactive World Simulator for Robot Policy Training
An action-conditioned video prediction model serves as an interactive world simulator for scalable robot policy training and evaluation, running at 15 FPS on a single RTX 4090.
Torque-Driven RL for Quadruped Locomotion
A torque-driven RL framework for the Unitree B1 quadruped achieves 3.5 m/s speeds and stair climbing without exteroceptive sensors, using NVIDIA Isaac Lab.
FARO: Feasibility-Aware Robot Motion Optimization
FARO introduces a nested kino-dynamic framework for rapid feasibility checking and trajectory generation, enabling real-world humanoid loco-manipulation.
The State of Simulation for Physical AI in 2026
Simulation has become foundational for Physical AI, with open-source engines like MuJoCo, Isaac Lab, and Newton enabling scalable robot learning and policy training.
ByteDance Astra: Dual-Model Robot Navigation
ByteDance's Astra uses a dual-model architecture with Astra-Global for localization and Astra-Local for path planning, achieving 99.9% accuracy in unseen environments.
Automated Failure Attribution in LLM Multi-Agent Systems
New research from Penn State and Duke introduces automated failure attribution to pinpoint which agent and step cause task failures in LLM multi-agent systems.
China's Answer to Its AI Talent Gap: Recruiting Teenagers
Facing a projected shortfall of 5 million AI workers by 2030, Chinese tech giants are now scouting and training students as young as 13.