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 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.
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.
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.
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