Notes // AI-assisted Systems

AI-assisted systems architecture

17 years of full stack systems architecture by Ryan Fitzpatrick. Enterprise and consumer software. Client/server systems. Ongoing research into where AI-assisted development is genuinely useful in shipping work.

Notes from web, game, and distributed-systems work on where AI helps with the job and where it quietly gets in the way.

About these notes

AI-assisted software, written up after it shipped

Posts, experiments, and the occasional postmortem on how AI tooling changes software work. The throughline: when it earns its place in a real codebase and when it doesn't.

What is this site for?
Tracking what works and what doesn't when AI is part of the software production loop. Architecture calls, tooling experiments, and the awkward bits that don't fit a tidy blog tour.
Who is this site for?
Developers and engineering leads who want to know whether AI tooling holds up under real cost, speed, and reliability pressure, not just demo well.
How do I find something specific?
Topic chips at the top, search box inside the blog page. Both feed the same index.

Blog // Notes and Research

Writing

Posts on coding agents, software architecture, Three.js, and what AI tooling does (and doesn't) change about production systems.

05/28/2026 · AI Research

Squire: Giving AI a Place to Work

A proposal for Squire: a local, context-engineered awareness service that runs inside apps, uses CLI tools as capabilities, and prepares useful work without taking over user judgment.

Chatbots move the user out of the workflow. Squire moves the model into the workflow: a local awareness layer that understands the app, reads its structures, and prepares the next useful thing.

  • Local AI
  • CLI Tools
  • AI-assisted development
  • Context Engineering
  • Agentic Software
  • Developer Tools
  • Workflow Automation
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05/10/2026 · AI Research

LLM Assisted Game Development Workflow

A practical first-person guide to using LLMs in game development, covering code, assets, prototyping, debugging, multiplayer, tools, and workflow management.

AI-assisted game development is no longer just autocomplete. This guide shows what LLMs can realistically add to a game dev workflow today, from design docs and gameplay code to assets, debugging, performance, and multi-agent workflows.

  • AI Game Development
  • LLM Workflows
  • Game Dev
  • Coding Agents
  • Three.js
  • Godot
  • Multiplayer
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