Max Nardit

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.

Selective collaborations →About me →

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

Operating 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

Current areas

Visibility & discovery: How people, businesses, and tools stay findable when AI systems answer, summarize, route, and act.
Measurement & tracking: How to know what’s working when clicks, cookies, referrals, and dashboards stop telling the whole story.
Agentic systems: Memory, handoff, orchestration, tool boundaries, and recovery for agents that touch real workflows.
Operational evidence: Original-data research, field notes, and shipped tools. Findings over forecasts.

Writing

Recent writing

All articles →

September 11, 2026

Your agent config is infrastructure now

Past a couple of agents, their definitions stop being config you edit and become infrastructure, and the trouble with infrastructure is that past the point you can hold it in your head, its declared state and its running state drift apart by default and nothing reports it. The fix is the discipline servers learned: declare the set as files, diff it against reality, pin what you applied, and refuse to apply over a change made behind your back instead of converging past it. With one catch that is easy to miss. A reconciler only ever covers what you remembered to declare.

September 10, 2026

The connector is an untrusted author

Who wrote a span is a fact the harness already holds. Whether that span is safe to obey is not. So the fix is not a cleverer reader: it is to give the tool and the peer the standing of the foreign thing they are, instead of the standing of the plumbing they arrived through.

September 9, 2026

The agent reaches for the tool it can read

You optimize a tool for what it can do. An agent selects a tool for whether it can predict the result and read the answer when it comes back empty. Those are different properties, and the distance between them is why the more capable tool is so often the one that goes untouched.

Artifacts

Selected 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.

Beetroot

Open Source

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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Clipboard MCP

Open Source

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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