2025¶

Context Engineering: 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.

Part I — What I actually talk about when companies say they want to build agents

Field note from a conversation with Vignesh Mohankumar, a successful consultant who helps companies navigate AI implementation decisions. Vignesh and I are both AI consultants helping companies build AI systems—he focuses on implementations and workflows, while I help with overall strategy and execution.

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?

Context Engineering: Rapid Agent Prototyping

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.

Two Experiments We Need to Run on AI Agent Compaction

Two core insights:

1. If in-context learning is gradient descent, then compaction is momentum.
2. We can use compaction as a compression system to understand how agents actually behave at scale.

This is part of the Context Engineering series. I'm focusing on compaction because it's where theory meets practice—and where we desperately need empirical research.

Slash Commands vs Subagents: How to Keep AI Tools Focused

The main idea: When AI tools do messy tasks, they can either stay focused or get confused by too much information.

This is part of the Context Engineering Series that shows how to build better AI tools based on what I've learned from coding assistants and business systems.

Context Engineering Series: Building Better Agentic RAG Systems

I've been helping companies build agentic RAG systems and studying coding agents from Cognition, Claude Code, Cursor, and others. These coding agents are probably unlocking a trillion-dollar industry—making them the most economically viable agents to date.

This series shares what I've learned from these teams and conversations with professional developers using these systems daily, exploring what we can apply to other industries.

Beyond Chunks: Why Context Engineering is the Future of RAG

The core insight: In agentic systems, how we structure tool responses is as important as the information they contain.

This is the first post in a series on context engineering. I'm starting here because it's the lowest hanging fruit—something every company can audit and experiment with immediately.

The $100M Money Models: The Complete Report

This is my study notes from the live event https://youtu.be/6_CCutkM11 given by Alex Hormozi.

The $100M Money Models: The Complete Report

Foreword: The Event That Broke the Internet (and World Records)

On August 20, 2025, Alex Hormozi hosted a live event to launch his third book, $100M Money Models. More than a simple book launch, it was a masterclass in business scaling, monetization, and a live demonstration of the very principles he teaches. With Guinness World Records judges present, the event aimed not just to educate but to make history.

This report is a structured and comprehensive summary of that event. It captures the core teachings, frameworks, case studies, and offers presented, transforming a live stream into a timeless manual for entrepreneurs. It is designed to be a definitive guide to the concepts that have allowed Hormozi to build a portfolio of companies under Acquisition.com that generated over $250 million in revenue in 2024.

RAG for Coding Agents Lightning Series

I find this to be a pretty interesting topic because I personally believe that coding agents are probably executing at the frontier of agentic ray systems.

The world of autonomous coding agents is rapidly evolving, with fundamental disagreements emerging about the best approaches to building reliable, high-performance systems. This Lightning Series brings together the minds behind some of the most successful coding agents—from SWE-Bench champions to billion-dollar products—to debate the core architectural decisions shaping the future of AI-powered development.

Quick Links

If you just want to sign up, you're going to have to visit every single tab, open these links, and sign up to each one.

• RAG in the Age of Agents: SWE-Bench as a Case Study from Colin Flaherty of Augment Code

• Lessons on Retrieval for Autonomous Coding Agents from Nik Pash of Cline

• Why Devin Does Not Use Multi-Agents from Walden Yan of Cognition AI

Lovable, Monetization, and the Vibe Coder Economy

| These are all just notes from a 30-minute conversation I had with somebody. A fun little exercise, as you will see.

When people ask me what a hot take is, here's mine: more agent tools and AI tools should be pricing on outcomes and trying hard to figure out what that means. This aligns with my broader thoughts on pricing AI tools as headcount alternatives.

The question hit me personally as a small investor in Lovable and a consultant focused on value-based pricing: Why am I not building my consulting business, my courses, my job board on Lovable instead of spreading them across Stripe, Maven, Circle, Kit, and Podia, It's because I could only possibly pay $100/month, and for that, they could not possibly offer me the features I need to.

RAG Anti-Patterns with Skylar Payne

I hosted a Lightning Lesson with Skylar Payne, an experienced AI practitioner who's worked at companies like Google and LinkedIn over the past decade. Skylar shared valuable insights on common RAG (Retrieval-Augmented Generation) anti-patterns he's observed across multiple client engagements, providing practical advice for improving AI systems through better data handling, retrieval, and evaluation practices.