Writing¶

Six levels of complexity in a Codex morning brief

This is the easiest way I know to teach someone how to use AI.

Not by starting with models, agents, or some abstract taxonomy of what the technology can do. Start with a job people already understand, then make that job quietly more powerful.

The morning brief works because almost everyone already has one. They just assemble it badly.

I think the morning brief is the first Codex workflow that normal people actually understand.

You wake up, open Slack, check your calendar, click into email, forget why you opened email, go back to Slack, then realize you have a meeting in seven minutes and no idea what happened yesterday.

The appeal is simple: help me remember what is going on.

When we were talking about Codex onboarding, this was the first workflow that felt both boring enough to teach and strong enough to matter. It starts as a dumb little orientation prompt. If you keep pushing it, it turns into a pretty good model for how people actually graduate into using Codex seriously.

Start with the thing a beginner can understand. Then add one real capability at a time until the shape of the whole system becomes obvious.

I think there are six real levels.

Codex-maxxing

I was already using coding agents a lot before Codex. Mostly, though, I used them through interfaces built for coding work: making diffs, changing repos, and shipping code.

Around November, I started pushing them into knowledge work too. I made presentations in Slidev, used agents more like note-takers with voice input, and kept looking for other artifacts a coding agent could help me produce: an index.html, a PDF, a spreadsheet, a slide deck.

The latest Codex app upgrades are the first thing I've used that make that broader mode feel native. Codex is still excellent for coding, but the more interesting shift is that it gives my work somewhere to live.

What changed my behavior was learning to give work an operating loop: a durable thread, shared memory, tools that can act on my computer, ways to steer and resume the task, and a surface where I can review the artifact itself.

What Music Do You Listen To?

People often ask me what kind of music I listen to, and in my mind I almost never remember. Today I wanted to write a little bit more about the music I listen to and share some of my favorite albums with you. A lot of this has been co-written with the Spotify API, so everything is as true as it can be.

Sunsetting 567 Labs and Open Sourcing the Course Content

I am sunsetting 567 Labs. The courses close today, February 2, 2026.

Things

Some links may include affiliate attribution. Recommendations are based on personal use.

In the past 2 decades I went from sharing a bed with my parents renting out the unfinished basement of some Canadian family to doing quite well for myself. This is all the stuff I use, plan to use, and what's on my upgrade roadmap. Each item includes why it works for me.

Do Your Engineers Know How to Leverage AI?

This past spring, two senior engineers at different companies received the same challenge from their CEOs: "We need to move faster. Use AI to get there."

What Is the Coding Agents Speaker Series?

I hosted a series of conversations with the teams behind the most successful coding agents in the industry—Cognition (Devin), Sourcegraph (Amp), Cline, and Augment. Coding agents are the most economically viable agents today—they're generating real revenue, being used daily by professional developers, and solving actual business problems at scale.

This makes them incredibly important to study. While other agent applications remain largely experimental, coding agents have crossed the chasm into production use. The patterns and principles these teams discovered aren't just theoretical—they're battle-tested insights from systems processing millions of real-world tasks.

This series captures those hard-won lessons, revealing what works and what doesn't when building agents that actually deliver economic value.

Context Engineering Series: Technical implementation patterns for agentic RAG systems, including tool response design, context management, and system architecture. This Speaker Series provides strategic insights, while Context Engineering offers implementation details.

**[RAG Master Series](./rag-series-index.md)**: Comprehensive guide to retrieval-augmented generation systems. Many coding agent insights (like why simple approaches beat complex ones) apply directly to RAG system design and optimization.

What Is the RAG Master Series?

Retrieval-Augmented Generation (RAG) has become the foundation of modern AI applications that need to access and reason about external knowledge. This comprehensive series distills years of consulting experience helping companies build, improve, and scale RAG systems in production.

RAG systems are fundamentally different from other AI applications - they combine the complexity of information retrieval with the unpredictability of language generation. This series provides a systematic approach to mastering both aspects, from basic implementations to enterprise-grade systems serving millions of users.

This guide covers everything from fundamental concepts to advanced optimization techniques, anti-patterns to avoid, and real-world case studies from successful deployments across industries.

If you want hands-on help, I recommend reaching out to my friend Nila: nila.is. Please mention you came from me.

Domain Experts: The Lever for Vertical AI — Chris Lovejoy (Anterior)

I hosted a session featuring Chris Lovejoy, Head of Clinical AI at Anterior, who shared valuable insights from his experience building AI agents for specialized industries. Chris brings a unique perspective as a former medical doctor who transitioned to AI, working across healthcare, education, recruiting, and retail sectors.

Text Chunking - Anton (ChromaDB)

I hosted a special session with Anton from ChromaDB to discuss their latest technical research on text chunking for RAG applications. This session covers the fundamentals of chunking strategies, evaluation methods, and practical tips for improving retrieval performance in your AI systems.