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
You optimize a tool for what it can do. An agent selects a tool for whether it can predict the result and read the answer when it comes back empty. Those are different properties, and the distance between them is why the more capable tool is so often the one that goes untouched.
A monitor that pages on a process's absence is answering the question you can already see the answer to. Whether a job is gone is cheap to detect and almost never the thing that matters. Whether it failed or was deliberately stopped, by a deploy, a reboot, an operator, is what a page turns on, and a bare exit carries only a sliver of that story.
A pinned software library is an artifact you can inspect and rerun: if the output changed and you didn't, something you can read changed, and you can find out what. A model breaks that. It shifts behind an unchanged name, loses capacity without notice, and drifts in disposition, so a regression arrives with no cause you can point to and you waste a week blaming your own prompt. Treat it as a dependency you measure, not a foundation you trust: pin the version so you own the moment it changes, and fix a small panel of cheap metrics in advance so you can tell the vendor's drift from your own.
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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