Creative Technologist · Design Systems Architect · Human-Machine Co-Author
I’ve been building the logic of generative systems since before “generative AI” was a category — a rule-based analog computing game at RISD in 2004, a rules-driven interactive map in 2003, an AI tooling layer for my own practice today. The tools change. The question doesn’t: how do you build a system precise enough to be consistent and flexible enough for a person to make it their own.
Currently building:
orchestrator-discipline— multi-agent AI work doesn’t fail loudly, it fails silently: work that should parallelize runs serially, subagent reports get trusted as facts, summaries arrive unreadable. An installable Claude Code skill that writes down the delegation disciplines preventing each — in ~130 tokens of always-on context, because a discipline skill that bloats every session is refuting itself.google-workspace-mcp— a minimal MCP stdio server exposing Google Workspace write operations that another popular CLI's own MCP server intentionally leaves out. Single file, zero dependencies, raw JSON-RPC 2.0.root-to-anywhere— Claude's built-in Drive connector can't write directly into subfolders. Rather than work around that by hand every time, I paired a file-naming convention with a scheduled Apps Script that relocates and cleans up automatically.gemini-vectorize— a zero-dependency CLI that chains three different AI models (Gemini, Recraft) and a custom cleanup pass into one idempotent image-to-vector pipeline.
Also maintain a small suite of prepress/SVG/print-automation tools (process-images, svg-color-rinse, svg-knockout, and others) — production-grade image and print engineering, the same discipline applied to a different medium.
Elsewhere:
🌐 gaidula.com — practice, writing, longer-form work
💼 LinkedIn
✉️ dan@gaidula.com
25 years as a creative technologist. 30 years as a designer. One throughline.