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Thoughts on tech projects, cybersecurity, infrastructure, and things I'm learning.
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Thoughts on tech projects, cybersecurity, infrastructure, and things I'm learning.
Get a weekly email with what I learned, summaries of new posts, and direct links. No spam, unsubscribe anytime.
12 community skills evaluated, 35 design rules extracted, 4 knowledge base files created, 5 agents deployed. I built a complete UI/UX design and quality system for Claude Code in a single day.
247 game AI parameters, 7 candidate use cases, 5 agents, 1 honest verdict: no. But the research process itself uncovered three real configuration problems in my Vector Memory server that had been silently degrading search quality for weeks.
50 instincts, 13 semantic clusters, 7 accepted candidates, 5 promoted skills. I built the third tier of a continuous learning pipeline that synthesizes behavioral patterns into reusable agents, skills, and commands.
I reviewed an AI-generated recommendation to convert my custom agents into 'captains' that spawn parallel sub-agents. Here's what I learned about factual assessment, corrected parallel structures, sandbox constraints, and when to use this pattern (or keep it simple).
From 70K tokens per session to 7K. A 7-agent audit, 23 evaluation documents, 11 component scorecards, 5 optimization patterns, and an 8-agent implementation team. This is the full story of cutting context consumption by 90%.
A comprehensive configuration overhaul that transformed my Claude Code workflow from serial execution to parallel agent orchestration. 7 custom agents, 9 rules reorganized, file protection hooks, and the philosophy of why every AI-assisted developer should go agentic-first.