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
Re-reading a translated document proves nothing, because judging the output asks for the exact fluency you handed to the machine. So you stop reading harder and move the judgment back into a language you can actually read.
The failures people blame on their agents (many converging on the same wrong answer, a disagreement that hardens into sabotage, a success reported for work that never ran) are not gaps a stronger model closes. They are the oldest failures in distributed computing, and they yield to engineering the layer between the agents rather than to more intelligence inside each one.
Every capability you expose to an agent spends context on its schema up front, so the price of its abilities tracks the size of your catalog and not the size of the job. The fix is not choosing CLIs over servers but treating the whole tool surface as a budget you spend only when the work reaches for a definition.
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.
Learn more →
A clipboard bridge between humans and AI agents. Read, write, and watch clipboard state through MCP. A small interaction primitive for agent-native workflows.
Learn more →
A browser-to-agent handoff surface. Send pages into agent workflows through a webhook, preserving context at the moment of browsing.
Learn more →
Scoped persistent memory for long-running coding agents. Local-first, MCP-native, designed so context survives across sessions without piling up.
Learn more →
A small operational monitor for Claude.ai limits.
Learn more →