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
Once an agent will push any number you give it, the model stops being the part worth arguing about. The part you own is the evidence that the number and the goal move together, and that evidence expires as soon as the optimizer leaves the region where you checked it.
Mention, answer inclusion and citation come apart the moment you sample an assistant more than once. One prompt can carry a brand's entire presence, a parser can move its citation rate 2.5x, and a hallucinated license still counts as a mention. A workflow for technical teams, built on 359 answers.
Cache diagnostics names the first place a request stopped matching the one before it. The useful part is reading that verdict next to the cache read count, and covering the parameter changes it cannot see yourself, because those are the misses hardest to find by eye.
Claude Code just stopped repositories from switching on telemetry export. The narrower fix hides a wider fact: a committed settings file is code that runs as you, and in a headless run nothing asks first. Interactively a dialog does ask, and I accept it without reading.
A request that returns 400 has already told you what to fix. The changes that cost you on this upgrade come back as 200: a response that no longer starts with text, an effort level that dropped a notch because you never set it, and an alias that moved you to a new model without a deploy.
The bill went up by $96 a year, which is nothing. What changed underneath it is that the number is no longer derivable: the included allowance has no published size, the free credit allotment has no published size, and the annual credit costs more than the monthly one. A price you cannot compute from published figures is an estimate, and an estimate is a different kind of dependency.
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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