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
An installed tool holds whatever authority you already have, and handing it over is the one security decision that gets no second look: taken once, in the least deliberate act of the whole install, and never revisited. You can contain what it can reach or read what its code actually touches; trusting the name does neither.
Input from outside gets a discipline and the agent's own output does not, so the summary it writes to survive a reset and the memory it recalls come back in as instructions with a standing nothing revoked. The author the harness records for them is the model itself, which is the exact reason the check lets them through.
A rule you write into an agent prompt or a CLAUDE.md is advisory: the model reads it on every path but only weighs it, and weighing is not refusing, so it holds most of the time, and most of the time is not what a load-bearing rule is for. To make one actually hold it has to move to where compliance is not optional, a check the harness runs and enforces on its own, regardless of what the model decided. With a limit: a gate binds only over the paths it covers, and some rules cannot be settled at any gate at all, because their violation shows up in the world and not in the action it would inspect.
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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