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.
Beetroot featured in 窓の杜 (Japan) · original-data research · shipped open-source tools
the systems that decidewhat gets found, trusted,and acted on
01 / 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.
02 / Focus
Current areas
- 01
Visibility & discovery
How people, businesses, and tools stay findable when AI systems answer, summarize, route, and act.
- 02
Measurement & tracking
How to know what’s working when clicks, cookies, referrals, and dashboards stop telling the whole story.
- 03
Agentic systems
Memory, handoff, orchestration, tool boundaries, and recovery for agents that touch real workflows.
- 04
Operational evidence
Original-data research, field notes, and shipped tools. Findings over forecasts.
03 / 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.
- 01BeetrootA local-first clipboard environment for preserving workflow context on Windows.
- 02Clipboard MCPA clipboard bridge between humans and AI agents.
- 03Share2AgentA browser-to-agent handoff surface.
- 04Agent RecallScoped persistent memory for long-running coding agents.
- 05Claude Usage DeckA small operational monitor for Claude.ai limits.
04 / Writing
Recent writing
All articles →Claude Code fork subagents in claude -p: what 2.1.302 changed for headless runs
Offered is not used: a headless model may never pick a fork, and the official docs still describe the old default. Checking which subagent your pipeline got, why a background fork hands a parser two answers, and where switching it off pays.
Claude Code statusline and ccusage: the native rate limits, and a 5-hour block that ends at a different time
Two tools answer one question with different evidence: the server reports a reset time, a log parser infers one. When they disagree by hours, believe the server and keep the parser for history. The dollar figure, on a subscription, is an exchange rate.
Claude Code vs Cursor: seven ways developers split the work, now that neither tool is stuck on one surface
Each vendor erased the old editor against terminal line from its own end. What still divides them is quieter: a config folder that Cursor reads live while Claude Code imports Cursor rules once, and permission systems that ignore each other's deny rules.
MCP vs API for AI agents: the API catalog is a directory, and the choice is who sits in the middle
An API catalog does not compete with an MCP server: it can list one. With that settled, the choice between MCP and a plain HTTP API described by OpenAPI comes down to whether a host application integrates your service for the model, and whether anyone has already decided to put you in front of it.
Claude Code Review is GA: the review layers around it, and which ones you can copy without a Team plan
Most of what makes the Claude Code team's process work is GitHub settings and written rules, and those run on any plan. What does not copy is where the rules come from. The managed service picks them from a branch the pull request cannot touch; a local review reads whatever is on disk, including a rules file the change under review just edited.
Claude API OpenTelemetry: tracing the SDK and the API without counting tokens twice
A span's input token count already includes cache reads and writes, so the query that feels natural if you know the Messages API counts the cached prefix twice. Setup for the SDK half, tested against a local mock; the Console destination from the docs; and the subtraction that gets you back to uncached input.
05 / Contact
Get in touch
Email or Telegram both reach me. Telegram is faster.