Skip to content

Applied AI

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.

How Should We Choose Agent Frameworks and Form Factors?

This is part of the Context Engineering Series. I'm focusing on agent frameworks because understanding form factors and complexity levels is essential before building any agentic system.

What Do We Actually Mean When We Say We Want to Build Agents?

Field note from a conversation with my friend Nila, who helps companies navigate AI implementation decisions: nila.is. Nila focuses on implementations and workflows; I focus on writing down strategy and execution patterns on this blog.

When companies say they want to build agents, I focus on practical outcomes. What specific functionality do you need? What business value are you trying to create?

How Do We Prototype Agents Rapidly?

This is part of the Context Engineering Series. I'm focusing on rapid prototyping because testing agent viability quickly is essential for good context engineering decisions.

If your boss is asking you to "explore agents," start here. This methodology will give you evidence in days, not quarters.

Most teams waste months building agent frameworks before they know if their idea actually works. There's a faster way: use Claude Code as your testing harness to validate agent concepts without writing orchestration code.

Decomposing RAG Systems to Identify Bottlenecks

There's a reason Google has separate interfaces for Maps, Images, News, and Shopping. The same reason explains why many RAG systems today are hitting a performance ceiling. After working with dozens of companies implementing RAG, I've discovered that most teams focus on optimizing embeddings while missing two fundamental dimensions that matter far more: Topics and Capabilities.

How to Lead AI Engineering Teams

Have you ever wondered why some teams seem to effortlessly deliver value while others stay busy but make no real progress?

I recently had a conversation that completely changed how I think about leading teams. While discussing team performance with a VP of Engineering who was frustrated with their team's slow progress, I suggested focusing on better standups and more experiments.

That's when Skylar Payne dropped a truth bomb that made me completely rethink everything:

"Leaders are living and breathing the business strategy through their meetings and context, but the people on the ground don't have any fucking clue what that is. They're kind of trying to read the tea leaves to understand what it is."

That moment was a wake-up call.

I had been so focused on the mechanics of execution that I'd missed something fundamental: The best processes in the world won't help if your team doesn't understand how their work drives real value.

In less than an hour, I learned more about effective leadership than I had in the past year. Let me share what I discovered.

SWE vs AI Engineering Standups

When I talk to engineering leaders struggling with their AI teams, I often hear the same frustration: "Why is everything taking so long? Why can't we just ship features like our other teams?"

This frustration stems from a fundamental misunderstanding: AI development isn't just engineering - it's applied research. And this changes everything about how we need to think about progress, goals, and team management. In a previous article I wrote about communication for AI teams. Today I want to talk about standups specifically.

The ticket is not the feature, the ticket is the experiment, the outcome is learning.

Effective Communication in AI Engineering: Moving Beyond Vague Updates

The right way to do AI engineering updates

Helping software engineers enhance their AI engineering processes through rigorous and insightful updates.

In the dynamic realm of AI engineering, effective communication is crucial for project success. Consider two scenarios:

Scenario A: "We made some improvements to the model. It seems better now."

Scenario B: "Our hypothesis was that fine-tuning on domain-specific data would improve accuracy. We implemented this change and observed a 15% increase in F1 score, from 0.72 to 0.83, on our test set. However, inference time increased by 20ms on average."

Scenario B clearly provides more value and allows for informed decision-making. After collaborating with numerous startups on their AI initiatives, I've witnessed the transformative power of precise, data-driven communication. It's not just about relaying information; it's about enabling action, fostering alignment, and driving progress.

A surprising reason to not list your consulting prices

As I've shared insights on indie consulting, marketing strategies, and referral techniques, a recurring question from my newsletter subscribers is about pricing. Specifically, many ask if they should lower their rates or make them public.

In this article, we'll delve into the counterintuitive reasons why listing your consulting prices might not be the best strategy, regardless of whether you're aiming to appear affordable or exclusive. We'll explore the potential drawbacks of transparent pricing, introduce more effective alternatives like minimum level of engagement pricing, and provide actionable strategies to help you maximize your value and earnings as a consultant.

Building on the foundation laid in my previous posts about building a consulting practice and using the right tools, this piece will add another crucial element to your consulting toolkit: strategic pricing.

Implementing Naturalistic Dialogue in AI Companions

Ever think, "This AI companion sounds odd"? You're onto something. Let's explore naturalistic dialogue and how it could change our digital interactions.

I've been focused on dialogue lately. Not the formal kind, but the type you'd hear between friends at a coffee shop. Conversations that flow, full of inside jokes and half-finished sentences that still make sense. Imagine if your AI companion could chat like that.

This post will define naturalistic dialogue, characterized by:

  1. Contextual efficiency: saying more with less
  2. Implicit references: alluding rather than stating
  3. Fragmentation: incomplete thoughts and imperfections
  4. Organic flow: spontaneity

We'll then examine AI-generated dialogue challenges and propose a solution using chain-of-thought reasoning and planning to craft more natural responses.