Blog
Insights on AI training data, robotics, evaluation, and building better AI.
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.
Read moreBuilding 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.
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.
Why Data Quality Beats Data Quantity in AI Training
Lessons from 250+ AI training projects on why fewer, higher-quality examples consistently outperform massive low-quality datasets.
A Practical Guide to Motion Capture for Robot Training
Comparing optical, inertial, and vision-based motion capture systems for producing robot training data at scale.
Scaling Data Annotation from 1K to 100K Without Losing Quality
The operational playbook for scaling AI training data production while maintaining annotation quality and consistency.
Security Considerations for AI Training Data Pipelines
How to protect sensitive training data, maintain data isolation, and meet enterprise security requirements in AI data operations.
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