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 Agentic AI Workflows Transform Data Operations
Using AI agents to automate quality control, delivery, and monitoring in AI training data pipelines.
Designing 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.
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