Hello! I'm Timothy Jordan and I believe in improving life through engineering and art.
A controlled benchmark: a clean llms.txt discovery layer cuts an AI agent's tokens, but no in-page link (a head tag or a visible footer link) got the agent to use it on its own. One line in the prompt did. The lever is the instruction, not the markup.
We toggled each of a14y.dev's 11 agent-readiness features on and off and measured what each one is worth to an AI agent. A markdown mirror and a real meta description do most of the work, the features don't simply stack, and one of them actively makes things worse.
We scored the 50,074 most-visited websites for how well an AI agent can discover, parse, and comprehend them. The median scores 52 of 100, not one scored excellent, and roughly three in four haven't shipped the agent-readability layer that halves what an agent spends to use a site.
In the early days of web search, webmasters tried to cheat search algorithms. We're seeing the same thing playing out by those who are trying to game model training with slop content.
Running an a14y audit and shipping the top fixes raised the test site's score from 37 to 89, and roughly halved Claude's token use and tool calls on the same evaluation.
How to structure developer documentation so that AI agents like Claude Code, ChatGPT, Cursor, Copilot can discover, retrieve, and cite it accurately. Three layers: discovery, retrieval, and tools.
Agent readability is a set of best practices that make your website parseable, navigable, and citable by AI agents like ChatGPT, Claude, Cursor, and Copilot. A site-wide spec across discovery, structure, and context.
Walk-through of the Vercel Slack agent skill, which takes a coding agent (Claude Code, OpenCode, Cursor) from idea to a deployed Slack bot in a single session — handling app config, OAuth, webhooks, and infra.
I’ve redeployed timothyjordan.com to Vercel to transform their DX for the agentic era.
My farewell letter to 16 years at Google, some accomplishments, and a lot of gratitude.