Home, essays, projects, and operating-layer notes.

Essays, workflow notes, and systems writing.

Every's eight-level AI adoption map is useful because it measures delegation, trust, context, and verification, not personal intelligence. This version expands the ladder and pairs it with a self-assessment that checks how people actually work with AI.

Cerebras is interesting beyond its IPO because near-1,000-token-per-second inference changes the shape of the work: draft, check, repair, compare, and return before the human loses the thread.

The useful question is how much AI capability would still remain if the proprietary frontier disappeared tomorrow.

The common claim is that AI cannot replace your job. That may be true. But jobs are bundles of functions, and the better question is which functions are already moving closer to reliable AI handoff.

Loophole is not interesting because it solves ethics. It is interesting because it treats moral principles like something you can draft, attack, patch, and escalate until the real conflicts in your values finally surface.

Attio, Linear, and PostHog are converging on the same product bet: one fixed dashboard cannot serve every user or every intent, so the SaaS homepage is shifting toward an agentic entry point. Chat is the router. Generative UI is what comes next.

The next real step for agents is genuine multi-agent runtime across different harnesses: paired review loops, handoffs, shared threads, and open protocols such as A2A.

As of March 24, 2026, mobile AI coding is splitting around where the work runs: a phone steering your own machine, or a phone watching cloud agents work somewhere else.

Claude Code autonomy is not one trick. It starts with permission friction and context hygiene, then moves through subagents and loop hooks, and becomes genuinely useful when the system can measure whether the last iteration actually improved the work.

The newest memory systems do not make language models inherently stateful. They build recall, updates, and temporal reasoning around the model instead. Gemini Embedding 2 is the first mass-scale vector embedding that is multimodal.

The most useful way to understand AI progress isn't by tracking model names. It's by tracking the operating model: from chatbots that only talk to systems that can plan, act, coordinate, and increasingly sit across software.

AI can speed up individual tasks, but that does not automatically create more free time. From personal experience, the saved capacity gets filled by more complex tasks and higher expectations.

I write, build, and teach around AI, learning, and digital work.

A public interactive explainer showing how modern agent memory systems layer extraction, versioning, multimodal recall, and source grounding around stateless models.

A tiny agent-prep game where you build the tray an AI agent needs before it can do useful work.

A public interactive grimoire exploring AI through ritual, agency, memory, and dependence across twelve short chapters.

A public interactive map tracking major AI data center projects, locations, investment, and planned capacity.

A compact checklist for deciding whether an AI output is actually usable.

A living clock for four AI-exposed work functions, showing Bob's month-level estimates for when specific activities move closer to reliable AI handoff.

A practical AI proficiency self-assessment that estimates your current operating level from chatbot use through copilot, agents, autopilot, workflows, background assistants, multi-agent work, and orchestration.

A supply and demand simulator for AI-exposed work units, showing how abundance can devalue tasks before whole jobs disappear.

A living map of the AI tools I use, what each one is for, and how I keep a large stack from turning into tool sprawl.

Working notes on how to move from prompt to draft to review without losing clarity.

Essays, resources, and experiments on where AI is going and how to use it.

A public interactive guide to Claude Code, covering slash commands, memory, skills, hooks, MCP servers, subagents, and workflow patterns.

Email for thoughtful AI, project, speaking, or advisory enquiries.

Small interactive apps, visual explainers, and interface sketches that are useful to open, test, and play with.

A public interactive guide to the five levels of Claude Code autonomy, from permission friction through subagents and loop hooks to evaluation-driven improvement.

A public tracker for the moving floor of open-weight AI against the proprietary frontier ceiling.

A public interactive guide to agent-to-agent communication patterns, covering paired review, typed handoffs, shared threads, swarm routing, and the A2A protocol.

A small interactive demo for feeling how near-1,000-token-per-second inference changes agent loops and human attention.

A public interactive explainer for Loophole, Brendan Hogan's adversarial moral-legal code system for drafting, attacking, patching, and escalating edge cases in your own principles.

A public interactive guide to the split between local supervision, self-hosted mobile bridges, and cloud autonomy in mobile AI coding.

Short email updates on new essays, projects, and useful AI notes from the site.

Smaller builds, tools, artifacts, and prototypes linked to the ideas on the site.

A playful prompt-mixing experiment where sliders brew a reusable AI prompt recipe.

A playable inventory of tool notes, checklists, workflow pages, and reusable AI materials connected to the rest of the site.

A public interactive companion to The SaaS Homepage Is Becoming an Agent, tracing the shift from dashboard-first software to chat-first entry points and then to generated interfaces.

A public month-by-month timeline of AI breakthroughs, model launches, open-weight releases, speech models, and industry shifts from GPT-3 to the June 2026 agent era.

A public interactive timeline and story view tracing the evolution of large language models from Transformer to the June 14, 2026 agent, multimodal, open-weight, and research frontier.

A public interactive guide to how AI evolved from chatbots into agentic systems, orchestrators, and software that behaves more like an operating system.

Bob Z

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AI Proficiency Self-Assessment

Projects AI Proficiency Self-Assessment

Interactive AI self-assessment

AI Proficiency Self-Assessment

A practical AI proficiency self-assessment that estimates your current operating level from chatbot use through copilot, agents, autopilot, workflows, background assistants, multi-agent work, and orchestration.

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This self-assessment estimates your AI proficiency level by looking at how you actually work with AI, not only which tools you have heard of.

It uses the eight-level ladder from the companion essay: chatbot, copilot, agent, autopilot, workflows, assistant, multi-agent, and orchestrator. The useful score is not a badge. It is a way to see how much context, action, autonomy, and verification your current workflow really has.

Credit where it belongs: the eight-level frame is based on Every.to's public guide, The Eight Levels of AI Adoption. This version expands that frame into a practical self-assessment for behaviour, safety, verification, and current agent terminology.

What the self-assessment measures

The assessment separates two things that often get blurred:

your operating level, based on behaviour such as context access, tool use, approvals, review, background triggers, and multi-agent coordination
your terminology freshness, based on whether terms such as MCP, A2A, guardrails, handoffs, subagents, evals, and human-in-the-loop mean something concrete to you

How to use the assessment

Use the result as a workflow diagnostic rather than a personal ranking.

Someone can be excellent at Level 2 if the work needs close human judgement. Someone can also be unsafe at Level 6 if their background agent has no review queue, audit trail, or escalation path.