Blog
Insights on AI training data, robotics, evaluation, and building better AI.
Will One AI Model Rule Robotics? Not Until the Data Catches Up
Our CEO Tam Le joined the robotics panel at SF Tech Week's AI Frontier Summit, where panelists split on one model versus many. Our take, and how Tbrain builds the data robots learn from.
Read moreHow 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.
The Unique Challenges of Medical AI Training Data
Why medical AI requires specialized annotators, stricter quality control, and domain-specific workflows compared to general AI training.
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