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 →

August 24, 2026

Your instruction files are addressing, not fragmentation

Merging every agent instruction file into a shared standard looks like deleting needless repetition, and for project setup it genuinely is. But most of what these documents hold is not a scattered copy of one thing; it is separate addresses, sorted by who a line applies to and who is allowed to write it. One of those boundaries is also a trust boundary, which is why the tidy fix of one universal file quietly hands repository text the authority of your own standing rules.

August 18, 2026

You can't verify a translation by reading it

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.

August 17, 2026

Your multi-agent system is a distributed system

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.

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

Free

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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