BREAKING: Claude Opus 5 is OUT NOW! And…it’s a hard model to love. We’ve spent the last week @every testing it across coding, writing, knowledge work, and our internal agent. It argued with instructions, stopped before the work was finished, and generally didn’t play well with our existing skills and plugins like Compound Engineering. Our first reaction was: What have they done to my boy? Then we deleted our existing skills and started from scratch. Without the elaborate workflows we had built for earlier models, Opus 5 got dramatically better, and even showed flashes of brilliance. Here’s our Day 0 vibe check: - It’s a poor man’s Fable. It has many of Fable’s personality quirks without Fable’s genius. - It breaks backward compatibility. If you’re using it with existing skills and workflows, watch out. It will often stop early or otherwise miss your instructions. - If you start from scratch, you’ll have better results. @KieranKlaassen figured out that if he just started from scratch without his existing skills, he could get dramatically better results. This is a model that takes some time to rebuild your workflows around—but if you do, there’s a payoff waiting. - Medium or low effort works better. @KieranKlassenn also found better results using Opus 5 on lower thinking levels. It seems that the more time you give it to think, the more likely it is to do the more annoying behaviors. Don’t just switch to Sonnet for a faster response! Try low thinking. I have two slots in my workflow: 1. The genius model I use for my biggest hardest tasks, currently Fable. 2. The smart, fast generalist I use for everything else, currently GPT-5.6. Opus 5 has the personality of the genius, but doesn’t have its top end. So that puts it in a strange middle ground that doesn’t really have a home in my day to day. I think I’ll use it mostly when I run out of Fable tokens. full vibe check on @every in the next tweet 👇

graph engineering explained (marketing edition) graph engineering is about designing the map your agents run inside, you draw the steps and the routes between them ahead of time, then they travel the path you put down it is the layer past looping, where one agent just circles a single task until the work meets the standard you set every agent graph is built from 4 pieces: > nodes: a stage the work passes through, research, draft, score, publish, a few run once, others are their own loop the agent circles until that step clears > routes: the paths you draw between the nodes ahead of time, every direction the work is allowed to travel > checkpoints: the check on each route that reads the result and sends the work forward when it clears or back to an earlier node when it misses > gates: a checkpoint the work cannot skip, nothing publishes until the draft clears the rubric if you have built a workflow in n8n you have already drawn one, nodes you connected, branches that fire on a condition, a step that loops until it clears. an agent graph is that same shape, each node holds an agent doing the work n8n would hand to a single api call the content graphs I run at my agency all take this shape, here is one you can build for SEO > 1 research: pull the keyword, the search intent, the competitors ranking for it, and the questions people keep asking > 2 brief: turn that research into a brief, the angle, the entities to cover, the queries the piece has to answer > 3 draft: an agent writes the article from the brief and nothing else in its context > 4 score: a critic grades the draft against your rubric, depth, intent match, originality. this node is a loop, it sends the weak drafts back to 3 and only releases one that clears > 5 publish: once the rubric clears and the brand rules pass, the agent adds internal links and the piece goes live each arrow between those is a route, every grade is a checkpoint that picks which route the work takes next, and the draft and score nodes form a loop inside the bigger map while the rest run once this is where the word graph starts to mislead. that draft and score loop can pass itself, the critic likes the draft, its rubric clears, it publishes, and still never ranks. the loop was grading the writing against another agent's opinion while the only thing that counts is whether it ranked so you add a checkpoint the agents cannot argue with, one that reads live search and AEO signals from outside the graph: > did google index it > is it climbing on the target query > are AI answers citing it > do people stay once they land if those move, the map keeps its shape and you feed it the next keyword. a stall sends the work back to research instead, because a miss this late usually traces to the angle or the intent you chose at the start, which a rewrite cannot fix anchor the map to results the agents cannot fake, and freeze the few rules they never rewrite, your brand voice and the claims you cannot make with the anchor in place, a failed piece shows you the exact node it broke on

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on @startupideaspod watch