LLM workflows and AI tooling for real software work.

Problem

AI capability is easy to demo and hard to operationalize. Useful systems need context, tool access, memory, guardrails, workflow design, and interfaces that make the model's output actionable.

Architecture And Implementation

• ChatGPT, Claude, Cursor IDE, and AI-assisted development workflows.
• Model Context Protocol integrations and tool-oriented agent surfaces.
• Custom memory framework concepts and reasoning workflow experiments.
• Custom llama.cpp builds and local model workflow exploration.
• Fine-tuning logic exploration and AI image, video, and audio generation platforms.

Tradeoffs

I separate durable engineering from demo magic. Models can generate ideas, but product systems need routing, state, tool boundaries, validation, memory strategy, and human-readable failure modes. That is why my AI work emphasizes MCP-style interfaces, local model experiments, Python support scripts, and TypeScript product surfaces instead of one-off prompt chains.

Testing And Deployment

I validate AI-facing work through conventional software checks first: type checks, build checks, route verification, generated-content parsing, and explicit asset-path scans. For local model and Mythogon-related work, the deployment concern is keeping inference, orchestration, and tooling boundaries debuggable.

Outcome

This shows I can learn fast, evaluate new tools critically, wire AI systems into real product surfaces, and communicate the difference between a prompt trick and a maintainable workflow a team can test, deploy, and improve.