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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 moreThe 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.
How 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.
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