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
A hands-on run of Codex CLI 0.162.1 on a throwaway repository: where the checkouts land, which commit each entry point begins at, why your .env and uncommitted edits stay behind, how to merge the work back, and what cleanup leaves on disk.
Reading a skill before you run it is the standard advice, and it has two holes. A copied skill has no update channel, so the copy you read can drift from its source without telling you, and for a skill that ships Python, the file you read is not always the file the interpreter loads.
The handoffs are vendor-documented now: a review command, a session transfer, an importer in each direction. Who should get which job is documented nowhere. Seven splits, each traced to its origin and marked documented or reported, plus the sandbox defaults that differ and the integrations that were removed this year.
Every practice in the guides is a verb of choosing: trim, defer, retrieve, compact. Choosing needs a list. The one Claude Code ships itemizes your configuration by source and size, then reports the whole conversation as a single number, and nowhere says how long anything stays.
The API moved from navigator to document in May, lost three methods before that and turned its registration call into a promise after. A script written to satisfy a detector in April can expose zero tools to a current browser. The working registration, a test that needs no agent, and where a plain MCP endpoint is the better door.
The hardware rules can be checked with a pulled cable. Unauthorized proxying through consumer subscriptions, responsibility for what an agent does with its tools, and a lower enforcement bar cannot be checked from outside, and they came in without comment.
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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