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Insights on AI training data, robotics, evaluation, and building better AI.
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.
Read moreDesigning Coding Benchmarks That Actually Work
Lessons from building 500+ benchmark tasks on what makes an evaluation meaningful versus what makes it look impressive.
The Future of LLM Post-Training: What Changes in 2026
How post-training evolves beyond simple RLHF toward multi-stage pipelines and domain-specific alignment.
Building Annotation Teams That Scale: Lessons from 48K+ Contributors
How to recruit, train, and manage a distributed annotation workforce at scale.
Designing End-to-End Data Pipelines for AI Training
Architecture patterns for production AI data pipelines — from ingestion to model-ready datasets.
AI Safety Starts with Training Data: A Practical Guide
How training data quality impacts AI safety outcomes.
What AI Labs Actually Look For in Data Partners
Inside the evaluation criteria frontier AI teams use when choosing data providers.
19 articles