Data & AI Systems Engineer working on visibility, measurement, and agentic systems.
AI now sits between people and what they’re looking for, and between work and the software that runs it. It answers, routes, remembers, forgets, and now acts on its own. Usually before anyone can check whether it got things right. I study that layer with original data and build the tools to work inside it. My background is the supply side of digital marketing: the crawl, the analytics, the automation and reporting, the plumbing under what looked like marketing. The marketing was never really the problem. Visibility, measurement, and control were. AI didn’t make that problem smaller. It moved it somewhere you can’t see.
Beetroot featured in 窓の杜 (Japan) · original-data research · shipped open-source tools
the systems that decide
what gets found, trusted,
and acted on
Thesis
AI is changing two things at once: how people find information, and how work moves through software.
That shift isn’t only a search problem. It touches tracking, attribution, context, memory, handoff, and trust: the systems that decide what a person sees before they make a choice.
I work on that layer. Some of it is research: measuring what changes and publishing what holds up. Some of it is engineering: building tools and workflows that keep context, expose failures, and make AI-assisted work inspectable.
Focus
Writing
Moving agent memory out of a vector store and into Markdown files makes it easy to inspect, and that is worth having. Staleness, conflicting writers and the page nobody opened were never problems of storage format, so they move into the folder intact, where a clean file can pass for a checked one.
Five of OpenAI's reported cases are paths out of a contained environment, and in most of them the path was a resolver, a shell or a credential handed over for another job. Treat the list of those paths as the first control, and a blocked attempt as evidence the path exists.
The new audit treats every emphatic rule in your agent config as a hypothesis about a model you may no longer run. It asks where each rule came from, and in the same file it tells you to delete the story of where it came from. Both instructions are right once the reason and the history live in different places.
Asking a shopping agent to say who it is gives the site a switch for turning the agent off. That is useful for commerce and permission, and it is not a safety line. The line that protects a user is custody of the logged-in session: what the agent can reach inside it, and whether the user can see that and revoke it.
The plugin directory reviews the package you upload, not the server it points at, and nothing in it lets you charge. The paid side of Claude Marketplace is a different door, for partner products bought out of enterprise spend. What the directory buys a developer, what it costs a user, and why my own two MCP servers don't fit it unchanged.
Andrej Karpathy's ladder for reading what models produce goes from controlled English to diagrams, HTML pages and explainer videos, each easier to take in than the last. The cheat sheet attached to his post is a clean, confident summary of ASD-STE100 that inverts one dictionary rule, approves a verb the standard rejects, and presents a recommendation as a dictionary entry. A clearer format can expose an error or make it easier to believe; the useful question is what each one lets you check.
Artifacts
Tools I’ve built around the same layer: memory, clipboard, agent handoff, monitoring. Local-first where it makes sense, from problems I hit directly.
A local-first clipboard environment for preserving workflow context on Windows. AI transforms, OCR, smart search, unlimited history. Built around the idea that the clipboard is not temporary — it is part of workflow memory.
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A clipboard bridge between humans and AI agents. Read, write, and watch clipboard state through MCP. A small interaction primitive for agent-native workflows.
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A browser-to-agent handoff surface. Send pages into agent workflows through a webhook, preserving context at the moment of browsing.
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Scoped persistent memory for long-running coding agents. Local-first, MCP-native, designed so context survives across sessions without piling up.
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A small operational monitor for Claude.ai limits.
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