RL Know vs RL Do: Why AI Learned to Act
AI training has three stages: pre-training builds capability, RL Know refines judgment, RL Do trains action. That last one is why AI feels different now.
Coding
Writing on agentic engineering, AI coding workflows, and building software with multi-agent systems.
Why 100 Agents?
SkillsBench tested 7,308 agent runs. The data confirms: good engineering -- focused skills -- consistently improves agent outcomes.
Lessons from Building a 100+ Agent Swarm in Web3
The full synthesis: context rot, DRYP, skills vs. agents, and why good practice -- not grand design -- is how you get to 100+.
DRYP -- Don't Repeat Your Prompt
Applying the DRY principle to AI agent instructions: why duplicated prompts are a refactoring opportunity.
5 Rules for Agentic Engineering
Five non-negotiable rules for moving from vibe coding to real AI agentic software engineering.
Skills vs. Agents
The framework that made 70+ AI agents manageable: the distinction between skills and agents.
More Context Is Not Better
Why stuffing AI with more context makes results worse, and how a skills-specialist agent fixes it.