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
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.
What Claude Dashboards built from 88 rows of public Wikipedia data, which of its numbers held up against the source file, and why the one thing it got wrong will be invisible to most people who look at a shared dashboard. Plus who sees what when you share one, and how Claude Motion makes video without a video model.
The new rule does not rank sources by how far you trust them. An attacker's web page counts as a source and your own bash output does not, because the model drives bash and does not write the web. With the web tool fenced, the next way out on a default environment is curl.
Which Console organization to link, which settings quietly put your card back in play, why claude -p and interactive Claude Code bill differently, and what an Anthropic engineer's daily new-tab extension uploads from your signed-in browser each time it runs.
How to route a Claude Code subagent to Haiku 5.5 on purpose, which effort level to pin it to, how to keep it under the long-prompt rate with /autocompact, and what the system card says to check before you trust it with a tool loop.
Identical price cards, two different cliffs. On long prompts the models part ways: one reprices the whole request above 100,000 tokens, the other above 272,000. For a subagent that reads files, that line decides the bill more than the rate does.
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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