After 2 failed attempts in early 2024, Claude 3.5 Sonnet (October 2024) finally had the instruction-following capability needed. Built a production deployment orchestrator in 3 days that saved weeks per use.
I’ve learned through experience that there’s a fundamental truth about AI-assisted development: AI enforcement is not assured. You can write the most detailed skill file. You can craft the perfect system prompt. You can set up MCP servers with every tool imaginable. But here’s the uncomfortable truth: the AI decides whether to follow any of it. That’s not enforcement. That’s hope. TL;DR: LLMs are probabilistic and can’t guarantee workflow compliance. Skills and MCP tools extend capabilities but don’t enforce behavior. Claude Code Hooks solve this by providing deterministic control points—SessionStart, PreToolUse, and PostToolUse—that ensure critical actions always happen. As AI-generated code scales, you need automated validation systems that codify architectural rules, business constraints, and design patterns. Workflow orchestration must live outside the AI. ...
Can machines think like humans? Explore the fascinating comparison between biological brains (20 watts, continuous learning) and artificial neural networks (megawatts to train, frozen after training). Understand thinking, creativity, and consciousness.
Built a fully functional Codecov MCP server in 2 hours using Claude Code to extend Claude Code itself. From zero to working server with authentication, API integration, and real-world lessons learned.
My CICD setup is to leverage GitHub workflows fed by secrets and vars from my springcloudconfig server. ...
After multiple failed attempts, I finally cracked the code for using LLMs to generate complex, production-ready software. Learn how AI went from a frustrating tool to a revolutionary development partner.