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

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August 10, 2026

Wake me for the decision, not the keystroke

Per-command permission prompts train the operator to approve on reflex, and about one in three planted-dangerous requests get waved straight through. The interrupts still worth keeping are the rare, legible ones: an action that cannot be undone, one that reaches outside the sandbox, or an approach that has already failed three times.

August 9, 2026

The injection you can't review your way out of

Code injection gets stopped at the seam where data becomes code, by refusing a whole syntactic class rather than weighing each case. It holds wherever that boundary is decidable, which is the exact property natural-language prompt injection never has.

August 8, 2026

Put the more agentic model in the reviewer's seat

The traits that make a model more agentic (it expands scope, verifies itself unprompted, writes longer) are a liability in a bounded unattended worker and an asset in a reviewer. So the fleet's workers run on Claude Opus 4.8 while Opus 5 does the reviewing. A field note on matching a model's disposition to its role, not its benchmark.

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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