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Most free AI courses are a teaser. A few videos. Then a paywall. Anthropic’s Academy is not that. Thirteen courses. Listed at $0. Built by the team that ships Claude. No subscription. No upsell after the last lesson. That is worth an evening. Not because a certificate will hire you. Because the work teaches how the model fails, not just how to chat with it. If you just landed in Clief Notes and you still treat Claude like a smarter search box, start here. You do not need to be a developer. You do need one loop you can finish. Three tracks - AI Fluency. How to think with the model. Where it lies. Where it helps. Everyone starts here. - Developer. API, Claude Code, MCP. User to builder. - Specialized. Students, educators, nonprofits, AWS, Google Cloud. Only after 101. The instinct to kill: skipping Fluency and jumping to agents. That instinct is wrong. You will ship slop faster. Where to start tonight New to AI: Claude 101, then AI Fluency: Framework and Foundations. Want to build: Building with the Claude API, then Introduction to MCP. Education or nonprofit: Claude 101, then your specialized course. Most courses are 1 to 4 hours. Two in a weekend is enough. How you get in 1. Open Anthropic Academy on Skilljar. 2. Make a free account. 3. Pick a course marked $0. 4. Finish the lessons. 5. Download the certificate if you want the paper. https://anthropic.skilljar.com/ The certificate is a bonus. The useful part is a working sense of when Claude is wrong. That is the skill that compounds. Do Claude 101 this weekend. Bring back one thing that broke your current prompt habit. Drop it under this post. I want the boring version, not the certificate screenshot.

Ori Eval is live on OpenRouter. If you already route models through OR, this is the systematic version of "should we switch." Public benches and Twitter recs measure a fixed task set. They tell you how a model behaved on someone else's prompts, someone else's tools, someone else's budget. With 500+ models on OpenRouter, "just switch to the new one" is expensive guesswork. Same leak as picking a CMS because a roundup said it was number one. OpenRouter shipped Ori Eval for that. It is not a leaderboard. It is a way to score models on your app. What it actually does: - Scans the repo for every place a model gets called - Asks what you care about: accuracy, latency, cost, tool-call correctness, whatever you name - Writes a *.eval.ts file. You do not have to be an eval person - Runs your agent against candidate models through OpenRouter, so the comparison can cross labs - Pins the harness and the model for the run. If the score moves, it is the model, not the furniture shifting - Checks whether tools were called or not, and grades open-ended answers with an LLM judge - Can sit in CI so a worse model does not ship. Re-run when a new one drops The sample they show is a table: catch rate, p50 latency, dollars per PR, pass/fail against your criteria. Then a recommendation, plus a cheaper "value" pick if volume grows. I have not run this on my agents yet. Treating it as garage until someone in here drops a real table. Cost is real. It calls live models. OpenRouter's own skill says 10 to 30 minutes and it can spend more than the credit on the key. First pass lands in a throwaway workspace. The eval is not yours until you decide to keep the file. Try it Tell your coding agent: run curl -fsSL https://openrouter.ai/skills/spawn-ori-eval and follow the instructions in its output to get started Already on the OpenRouter MCP? /spawn-ori-eval By hand: curl -fsSL https://openrouter.ai/labs/ori/install.sh | bash

As some of you know I have been working on a few modules for the classroom WELCOME TO THE WISE ARCHITECTS CORNER - THE LEGENDS · Clief Notes For a while now the sign on the door read 👷 Coming Soon 🚧 The First Rung Is Now Open Welcome. This is where we will demonstrate how AI works, where we build human-AI systems that last, and where the person at the keyboard stays the author of the whole thing. You do not need a technical background for any of it. You bring the curiosity, I bring the blueprints, and we build the understanding together. 🪜 Here is the climb This whole corner is one ladder, five rungs, and each rung raises what you can do with the machine: (1) Prompt engineering, talking to the model (2) Context engineering, giving it what it needs (3) Memory engineering, making it remember (4) Loop engineering, making it check its own work and then repeat (5) Graph engineering, running a whole system of them, with you in the chair You walk in asking "how do I even talk to this thing?" and you walk out running a system of them. That is the journey, and the top of this ladder is a system of machines, the bottom is one sentence, and the bottom is open today. 📚 What is open today The whole first rung, Prompt Engineering, all six posts: the Series 1 Overview, three classes, the payoff with its quick card, and my perspective on the rung everybody says is dead. 📆 A few things before you start One series a week. Rung two lands next week, rung three the week after, and this corner is going to grow fast, five weeks to the top. Follow these modules in order. You will find that each rung stands on the one below it, the second assumes you took the first, the third assumes you took the second, and the path only works if YOU climb from the bottom. Start at the map. The Start Here post shows you how every series in this corner runs and points you to the first door, the Series 1 Overview. Do not skip it (I know, you want the techniques, they are one door further and they are not going anywhere, the map is a short read, and it saves you a whole series of wondering where you are).

A founder asked me a question this week that I couldn't answer on the spot: do other businesses have a system like this? So I went and looked. Short version: outside this room, I can't find them. Everyone here runs some version of it already, a folder of markdown that Claude reads and maintains, and I'd stopped noticing how unusual that is. I'd like the room to tell me where the others are hiding, or whether we're further ahead than we think. The system in question is the one I've been installing at a three-person shop since August. The part that's now real: a knowledge base in a git repo that their own Claude reads before it does any work, a file register that watches their Drive and logs every arrival and every move, and a pitch engine that runs a brief through four human approval gates with the gaps written on the slide. Every filing carries who approved it or which rule filed it. Every page carries where it came from. When something ends up in the wrong place, the record says whether a person put it there or our rule did. None of the pieces are exotic. Markdown, git, a spreadsheet, an Apps Script, skills. What's different is that the knowledge is compiled and maintained, not retrieved. The agent doesn't search a pile of PDFs every time. It reads pages it helped write, with the provenance on them, and the rules for what it may and may not do live in the same repo. Here's what I found when I went looking for who else does this. The big holding companies in that industry have "operating systems" now. Real ones, sold to clients as the reason to hire the group. That's a data platform with agents on it, top down, owned by the group. Not a thing three people could own. Everyone in the middle has AI on top of files. The numbers from this year's surveys: about two thirds of agencies run brief generation in production, a third draft client reports with it, and the single biggest problem the independents report is getting AI into their actual workflows. Every "AI knowledge base" guide I read describes the same thing. Search over the shared drive. A memory server. Chat with your documents. Useful. Also not a record of anything.