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 plugin directory reviews the package you upload, not the server it points at, and nothing in it lets you charge. The paid side of Claude Marketplace is a different door, for partner products bought out of enterprise spend. What the directory buys a developer, what it costs a user, and why my own two MCP servers don't fit it unchanged.
Andrej Karpathy's ladder for reading what models produce goes from controlled English to diagrams, HTML pages and explainer videos, each easier to take in than the last. The cheat sheet attached to his post is a clean, confident summary of ASD-STE100 that inverts one dictionary rule, approves a verb the standard rejects, and presents a recommendation as a dictionary entry. A clearer format can expose an error or make it easier to believe; the useful question is what each one lets you check.
Notion now lets a ChatGPT Plus or Pro subscription pay for some of its AI. It covers one agent and one model family, it still needs a Notion Business or Enterprise plan, and its eligibility changed by tweet within two days of launch. What token sharing is, how to set it up safely, what it covers, and why I still can't tell how much of either allowance a Notion task will use.
Extra friction at the moment of granting says nothing yet about grants already made. On a Mac where the terminal holds that permission, the rules Apple's engineers describe let a coding agent started there read with the terminal's access, and the agent picks its own commands.
Mods arrived in v2.1.287 as one line in the release notes. What they add is not reach, a plugin already ran as you. It is precedence: an installed mod answers after your rules and your PreToolUse hooks, and outside managed settings its answer is the one that stands.
Google is splitting access to its new frontier model by cyber safeguards, the way it did with 3.8 Flash and Anthropic did with Mythos. The developer build is still being tuned, so the benchmark table describes a model no developer will get in that form.
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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