There is a gap between experimenting with AI on a side project and systematically delivering AI-assisted code in production. Track 3 closes that gap with a methodology that holds under deadline pressure.
Most developers who experiment with AI get impressive first drafts and then a production problem. Tests pass but the code behaves differently in production. Context truncates silently in the middle of a critical function. Accountability is diffuse when something goes wrong. These are structural problems, not tool problems.
An eight-part series — required pre-reading for Track 3. The agentic loop, VS Code setup, CLAUDE.md configuration, and team scale.
A three-part series on Model Context Protocol. What MCP is, how to connect Claude to your tools, and the enterprise workflow that actually closes the loop.
A four-part series for developers. Composing skills into pipelines, wiring them to tools with MCP and hooks, the context architecture, and what makes a skill production-grade.
Claude Code is not an autocomplete tool with a chat interface. It is an agentic loop that reads files, runs commands, edits code, and calls other agents — until the task is done. Understanding the architecture changes how you use it.
→ Methodology"It works but I don't know why" is a ticking time bomb. Here is why vibe coding destroys codebases — and how you avoid the trap.
→ MethodologyThe practical workflow that separates productive AI-assisted development from wasted tokens. Read the project. Plan on disk. Flush the context. Build from the plan. Review every line. This is not a suggestion — it is the process.
→ ProductionAI makes the first 10% of any initiative effortless. The 90% that delivers real value requires existing expertise to even know what needs building. The demo trap isn't a developer problem — it's an organizational pattern.
→A full-day workshop for the entire development team. You leave with working configuration, a shared methodology and code you actually checked in during the workshop.
Duration: 8 hours