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Insights on AI training data, robotics, evaluation, and building better AI.

Why Egocentric Video Is the Future of Robot Learning
Physical AIJuly 10, 20267 min read

Why Egocentric Video Is the Future of Robot Learning

One hour of first-person human video can be worth ten of teleoperation — and in 2026 a pair of glasses is out-scaling the robot fleets. Here's the evidence, the dataset stack behind it, and what separates data that transfers from data that quietly poisons a policy.

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Physical AIJul 10

The VLA Revolution: One Brain for Every Robot

π0.7, GR00T N1.7, Gemini Robotics — Vision-Language-Action models now train once across many robots and adapt to a new one with a LoRA fine-tune. The bottleneck moved from the model to the data. Here's the architecture shift, the numbers, and why collecting alone is a losing strategy.

6 min read
The VLA Revolution: One Brain for Every Robot
Physical AIJul 10

World Models & Game Data: Teaching Robots to Imagine

Genie 3, Cosmos 3, Dreamer 4 — world models became the defining AI battleground of 2026, with LeCun and Fei-Fei Li both betting their next decade on them. Robots don't play games, but they learn to predict consequences. Here's why labs collect game data, and why real robot video still grounds it all.

6 min read
World Models & Game Data: Teaching Robots to Imagine
Physical AIJul 10

Data Quality Is the Hidden Moat in Physical AI

DROID took 13 institutions and 12 months to collect 76,000 clean episodes. Cheap data farms ship whatever they record. AI-native QC — rejecting the broken 20–30% before a human looks — is what separates lab-grade data from noise, and in 2026 automation is quietly rewriting the economics of the moat.

6 min read
Data Quality Is the Hidden Moat in Physical AI
EngineeringApr 15

RLHF vs SFT: Choosing the Right Post-Training Approach for Your AI Model

A practical guide to understanding when to use Reinforcement Learning from Human Feedback versus Supervised Fine-Tuning, with real-world examples and decision frameworks.

3 min read
RLHF vs SFT: Choosing the Right Post-Training Approach for Your AI Model
RoboticsApr 12

Building Training Data for Physical AI: From Motion Capture to Robot Learning

How to design and capture high-quality motion data for humanoid robots, manipulation tasks, and sim-to-real transfer pipelines.

3 min read
Building Training Data for Physical AI: From Motion Capture to Robot Learning
EngineeringApr 10

How to Evaluate AI Terminal Agents: Beyond Code Generation Benchmarks

Why HumanEval is not enough, and how multi-step reasoning benchmarks like Terminal Bench measure what matters for production AI agents.

3 min read
How to Evaluate AI Terminal Agents: Beyond Code Generation Benchmarks

19 articles