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There's a reason Andrej Karpathy doesn't use RAG for his personal knowledge system. He tried it. It's brittle. The retrieval layer fails on nuanced queries. The embedding pipeline is another moving part. The cost compounds at scale. His replacement method has 3 components: 1. Raw sources - untouched markdown. Never let AI modify ground truth. 2. Wiki layer - AI-authored pages that cross-reference the raw sources. Grows every session. 3. Claude.md - a schema file that loads before every LLM call. This is the memory layer. The insight: the LLM doesn't need a vector search to recall things. It needs a well-structured index and the discipline to keep it clean. At human-scale knowledge bases (a few thousand files), full context loading beats retrieval every time. We built this live using Claude Code as the vault operator - autonomous ingest, link resolution, and a self-healing lint loop that keeps the wiki consistent. The interesting engineering problem was the index design. Claude needs to be able to navigate the vault in 2 reads: index first, then the relevant page. If the index is dirty, everything downstream breaks. Anyone else running a similar architecture? Curious how others are handling vault maintenance at scale.

This is my biggest career win to date. 3 weeks waiting. Birthday week. And this landed. My AI Avengers Lab cleared initial review for the Claude Partner Network. Anthropic committed $100M to this ecosystem. We're in it from day one. What does a Claude Partner actually do? Not prompt engineering. Not chatbot demos. We build complete AI practices - the systems, workflows, and infrastructure that replace entire SaaS stacks. Our own operation is the proof: → 3,000+ contact CRM, managed entirely by Claude as Co-founder → Outreach, content, ops - zero manual overhead → Lean by design. → Each of our team member has Claude as a Coworker This turns everything we've built internally into something we can build for you. We're identifying our founding team of ten right now - the people we'll anchor this practice around. Training starts soon. If you've been watching AI Avengers Lab from the outside, this is the moment to get in. Thank you @Nate Herk for educating us .

Not a gotcha - a real question I've been sitting with. The automation agency model works because there's a skill gap. Client doesn't know how to build the workflow, you do, you charge for the bridge. Claude Code is compressing that gap faster than any tool I've seen. I've been running my entire business ops through a terminal for 90 days. No n8n. No Zapier. No make.com. Emails, CRM, content, YouTube - all of it. I'm not a developer. I have a MacBook and a business. The workflows write themselves. I describe what I need, Claude builds the script, runs it, iterates it. After 2 weeks it stops doing heavy lifting because it wrote the scripts once and now just runs them. The builders who figure this out first aren't dead - they're the most valuable people in the room. But the value shifts from "I'll build this for you" to "I'll teach your team to talk to Claude and get this done themselves." Your automation knowledge still matters. Understanding what a workflow actually is still matters. But the interface is changing underneath all of us. What are you doing to stay ahead of that shift?

I built 500 AI workflows, and these are the 5 that actually sell in 2026. In this video, I break down 5 “boring” AI workflows that businesses actually want in 2026, based on building 500 AI workflows myself. If you’re trying to figure out what businesses really want (not just what sounds cool), this is what’s working right now. These are simple, high-demand AI workflows you can build, sell, and scale in 2026. Hope you enjoy!