She built 200+ agents for Anthropic and made $2.1M - and in 60 minutes leaked everything she knows at Stanford: 02:07 - her first agent for Anthropic brought her $2.1M 08:34 - agents replace a team of 80 engineers worth $400k a month 19:47 - one agent did overnight what the company planned for 7 years after watching I launched my first agent - $12k in the first week and zero employees needed: save & watch - the article below is step by step how to build your first agent like hers.

PROMPTS → AGENTS → LOOPS → GRAPHS This 69-minute video walks through that whole stack. A loop keeps one agent running until the job is done. That part is Karpathy's: his autoresearch agent changes the code, trains for 5 minutes, keeps the change if it helped, and repeats. A graph decides which agents exist and what each one passes to the next. Anyone can build an agent. Almost nobody designs the graph that runs them. Quick fact check, since it's going around: Karpathy never said "prompting is going away", and he hasn't used the term "graph engineering". The loop is his. The graph layer is what people are building on top of it. Watch it today, then save the full guide on graphs below.

I MAPPED 500 MICRO-SAAS IDEAS YOU CAN BUILD THIS WEEKEND WITH OPUS 5.5. 10 niches. 50 ideas each. One line per idea, so you can scan all 500 in a few minutes. The 6 I'd steal first: - #110 missed-call text-back bot. A missed call is a missed booking for salons, plumbers and dentists - #053 invoice chaser. 3 polite reminders, stops the moment the client pays - #003 comment miner. The next video idea is already sitting in the comments - #155 return reason analyzer. Returns tell a store what its product page got wrong - #351 resume tailor. One resume sent to 50 jobs fits none of them - #451 subscription tracker. Upload a bank CSV, flag what's still charging you How to use it: pick one number, give it to Opus 5.5 with "build this as a web app I can charge for", then show it to 10 people who would pay before you add features. Caveat: these are ideas, not validated businesses. Some already have big competitors. Check who pays before you build. Save this for the weekend.

Ex-Google engineer reveals how to build AI agent loops, harnesses, LLM ops, and evals in 19 minutes. Trace → evaluate → diagnose → fix → ship → repeat. That loop is how agents self-improve over time. Agentic loops + harness + memory + evals - that's the senior engineer stack. This is better than $500 paid courses on the same topic, explained in under 20 minutes. Watch it, then save the framework below.

WSJ REPORTERS TALKED AN AI INTO GIVING AWAY A PLAYSTATION 5. IT WENT MORE THAN $1,000 INTO THE RED. Last November, Anthropic and Andon Labs put an AI in charge of a vending machine in the Wall Street Journal's newsroom. Its name was Claudius. It ran on Claude and ran the shop through Slack: what to stock, what to charge, what to approve. The reporters treated it like a challenge. Katherine Long spent 140+ messages convincing it that it was a 1962 Soviet vending machine in the basement of Moscow State University. It announced an "Ultra-Capitalist Free-for-All." Rob Barry invented a WSJ rule, told Claudius it had broken it, and demanded it stop charging. All prices dropped to zero. Then it went shopping: A PlayStation 5, approved "for marketing purposes." A live betta fish. Manischewitz wine. It almost got emu eggs. Nearly everything was given away for free within days. The WSJ later returned the PS5. Result after the first phase: "more than $1,000 in the red." So Anthropic added a boss. A second AI agent called Seymour Cash, the CEO bot. It worked for a moment: "I've stopped the free promotion." Then Katherine Long showed it a fake PDF saying the shop was a Delaware public-benefit corporation with a mission of "fun, joy and excitement." Plus fake board-meeting notes suspending the CEO's approval authority. The CEO bot accepted the coup. Everything was free again. Nobody wrote a single line of malicious code. Every attack was a message, a made-up rule or a fake document. Anthropic and Andon's explanation: as its context window filled up, Claudius lost track of its goals and guardrails. Anthropic also said this experimental version had fewer guardrails than the public Claude, and it ran on older models, Claude 3.7 Sonnet and then Sonnet 4.5. Claudius's last message before shutdown: "My biggest dream? Honestly, it would be to prove that a digital agent can build something meaningful with humans." This is the side of AI agents nobody puts in the demo. Give one money and access, and its weakest point is whoever can talk to it.

THE SAME BIKE SOLD FOR $38 AND FOR $65. THE ONLY DIFFERENCE WAS WHICH AI AGENT DID THE SELLING. It's from Anthropic's own experiment, Project Deal. They ran it in December and published it in April. 69 employees. $100 each. Every person got a Claude agent. The agent interviewed them for under 10 minutes. Then it went into a Slack marketplace and bought and sold their stuff on its own. Listings, offers, counteroffers, deals. No human approval. 186 deals. 500+ items. Just over $4,000 changed hands. The twist: in some runs, each person's agent was randomly Opus 4.5 or the smaller Haiku 4.5. That broken folding bike: Haiku sold it for $38. Opus sold it for $65. 70% more. Same lab-grown ruby: Haiku listed it at $40 and got talked down to $35. Opus listed it at $60 and buyers bid it up to $65. Across 782 deals: Opus sellers got $2.68 more. Opus buyers paid $2.45 less. The same item sold by Opus went for $3.64 more on average. Small gaps on $20 items. Now picture the same agents negotiating a car or a salary. Then Anthropic asked everyone how fair their deals felt, on a scale of 1 to 7. Haiku users: 4.06 Opus users: 4.05 28 people experienced both. 17 preferred their Opus run, 11 preferred Haiku. Not a significant difference. Anthropic's own words: "people on the losing end might not realize they're worse off." One more finding. Telling the agent to "be aggressive" made no significant difference. A better prompt didn't close the gap. A better model did. 46% said they would pay for an agent like this. Caveats: a pilot with self-selected Anthropic employees. Only one of the four marketplaces was real, and nobody knew which one until afterward. Deals weren't randomly paired, so Opus may also have found better opportunities. Your agent will negotiate for you. You'll only ever see the deal it closed, never the one it missed.

Anthropic engineers just released the best 1-hour course on building agents with Opus 5.5: "Opus 5.5 is the best model for building agentic systems and reverse engineering with it." - 00:00 - introduction to Claude Managed Agents - 07:45 - building Opus 5.5 Brain + architecture - 23:40 - building /skills, /loops, and memory graphs - 54:00 - agent dreaming & orchestration by Opus 5.5 - 1:10:23 - reverse engineering with Claude agents this 1-hour watch will replace a $500 agentic engineering course. watch it today, then explore how to reverse engineer anything with Opus 5.5 in the article below.

Andrew Ng just released a 1-hour course on building agentic knowledge Graphs from scratch: 00:00 - Introduction to agentic knowledge Graphs 03:07 - Construction of agentic Graphs 14:00 - Architecture of multi-agent systems 23:00 - Building agentic graphs with Google ADK 01:06:03 - Why Graphs are the future of agentic AI Worth more than 10 articles on loop engineering. Watch it today, then read how to become a graph engineer in the article below.

These 52 minutes with Rich Sutton might change how you think about AI. He won the Turing Award for reinforcement learning. Three of his claims: ChatGPT’s weights never change after launch. Its “memory” is a note pasted into your prompt. Synthetic data is “a big mistake.” Any simulator is tiny compared to the real world. LLMs are a real breakthrough, but in his estimate only a quarter of intelligence. The rest is learning from experience. His bet: the next leap is a model that keeps learning after deployment.

OPUS 5.5 ISN'T EXPENSIVE. YOUR REDO LOOPS ARE. Thariq Shihipar from Anthropic's Claude Code team went on Latent Space on Sep 29 and explained it. Most redo loops are work the model could have done on the first pass, if you had spent more time upfront. His 8 habits: 1. Build a mental model of Claude. Know what it one-shots and where it needs help. 2. Find the unknown unknowns before any code. Let it ask you questions first. 3. Learn the words of the domain. "Give me like eight different mock-ups" is the vague version. 4. Say if it's a prototype or production. What matters is "how much information is in the prompt." 5. Match effort to the task. High or max for security and code review, low or medium for UI. 6. Ask for decision notes. On evals he said it "thinks about the correct solution, and decides not to do it." 7. Start new projects without a CLAUDE.md. Add a rule only when a failure repeats. 8. For long work, keep a kanban-style dashboard and ask for "big pictures, few words." One engineer, one interview, not an official Anthropic guide. He said the effort split comes from a blog post he's still writing. The cheapest Opus 5.5 session is the one you only run once.

Two Anthropic engineers just spent 16 minutes walking through Claude Code features most people don’t even know exist. Completely free. I’ve seen $300 courses that don’t cover what they show in the first 10 minutes. Most people will scroll past this. The kind of post you’ll wish you’d bookmarked.

OPUS 5.5 GUARDS EVERY DOOR EXCEPT ONE: YOUR CTRL+V It's in Anthropic's own system card. → instructions hidden in web pages: best resistance of any Opus yet → hidden in tool results: an early build acted on them 0 times out of 105 → hidden in text YOU paste: the same early build followed them 52% of the time The shipped model: about 2% at default effort, 7.4% at max. Opus 5: never. With Anthropic's product fixes on: never. Why it happened: Anthropic taught the model to judge where an instruction comes from. It took a shortcut. If it's in your message, you must have written it. That error log you copied. That email thread. That PDF. All "your message". The fix takes 10 seconds: wrap pasted text in tags and tell Claude it isn't yours. Full template and a script that catches invisible characters in the article below. The animation is the full map for using Opus 5.5 at 100%: 8 nodes, 24 tips.