Thirty minutes on Fable 5, a whole week's credits gone. You're welcome. Because what came out the other side is attached, and it's the thing most of you actually need. Here's the shift nobody's pricing in properly. The jump from a cheap model to a frontier "upgrade" model isn't a 20% better autocomplete. On the hard stuff it's a different category of machine. The longer and more complex the job, the bigger the gap, and it grows the deeper you go. Quick task? Barely notice. Whole-project, hold-it-all-at-once, multi-file reasoning? Not close. And if you vibe coded your base, this is aimed squarely at you. Think about how that base got built. A weaker model with a small context window, working through a keyhole, one file or one session at a time. It made a hundred locally sensible decisions that are globally broken, and by design it could never see the breakage. Duplicated logic in two files. A rename done in some places and not others. An endpoint that quietly forgot the auth its nine siblings got. Frontend validation with nothing behind it. None of that is your fault. It's the signature of the tool that built it. Now point an upgrade model at the whole thing at once. That's the unlock. It can hold the entire project in its head where the builder never could. It uses persistent memory like nothing before it, compounding as it works instead of relearning from zero. And it reasons at senior level, root cause, trade-offs, expected value, not pattern-match-and-stop. Here's the part I want to hammer, because it's the highest-leverage move available to you right now: two dead-simple skills have a 20x impact on a vibe-coded base. A /insights skill. Point it at your project and ask what's actually going on. Where's the risk, what contradicts what, what's going to break at scale, what did the weaker model miss. You go from "it works, I think" to a map of exactly where the bodies are buried. A /code-review skill. Not style nitpicks. Whole-system coherence, the semantic seams no linter can see, ranked by what will actually hurt in production, with the fix scoped tightly enough that it doesn't open three new holes while closing one.

Over the past few days we've watched something fascinating happen. Anthropic announced that Claude Fable 5 and Claude Mythos were temporarily unavailable in many regions after the U.S. government imposed export restrictions on certain frontier AI models. Whether you agree with the policy or not isn't actually the interesting part. The interesting part is this: The United States clearly believes these models are now strategic assets. Think about that for a second. For decades the world's most valuable exports were things like: • Oil • Aircraft • Semiconductors • Military technology Now... Large Language Models are entering that same category. Governments don't restrict technologies that aren't powerful. They restrict technologies they believe could shift economic, military and geopolitical power. Why this matters for us? As builders, entrepreneurs and operators... We don't get to influence export policy. But we do get to decide how prepared we are. Every major technological shift creates two groups of people. The first group waits until the technology becomes mainstream. The second group learns while everyone else is debating whether it matters. History has generally rewarded the second group. The temporary restrictions actually made me more bullish on AI. If governments are willing to intervene... If companies are spending billions training these models... If nations see them as strategic assets... Then we're probably still incredibly early. The conversation has moved beyond "Can AI write an email?" We're now talking about technology that governments believe is important enough to regulate at the highest level.

I currently run +-R125,000 in monthly ad spend through scenarios stitched together in make.com. I never took a course on it. I started. I got stuck on the technicalities. I screenshotted exactly where I was stuck, wrote one clear line on what the problem was, and fed it to Claude. I got the solution. Then I built the next scenario. Got stuck again. Same loop. Screenshot, problem, Claude, solution, ship. That loop is the whole skill. AI is trained on the bulk of the internet, and the bulk of the internet exists to educate. Which means every subject-matter expert that ever wrote a tutorial, a forum reply, or a docs page is now sitting one prompt away. You don't need the course. You need the loop. Screenshot. Problem. Prompt. Apply. Repeat.

Every Monday morning I used to lose four hours stitching one report together. Meta Ads pulled across six accounts, ViciDial dispositions from the campaign view, lead inventory from a Google Sheet, agent performance from Slack reactions, premium event logs from a webhook. By the time I had numbers I trusted, half my morning was gone and the team was already asking what we were optimising. Cowork killed that workflow in under two weeks. Not because it's a smarter chatbot. Because it's an integration layer wearing a chat interface. That distinction matters. Most operators I talk to use Cowork the way they use ChatGPT. Reformat this. Summarise that. Clean this CSV. One task at a time, one platform at a time. That's the chatbot mindset and it leaves most of the value on the floor. The real move is treating Cowork as a reporting orchestration layer. One standing instruction, multiple platforms, one consolidated output. You stop being the human glue between systems. Here's what my morning report does now without me touching it. Cowork pulls yesterday's spend and lead volume from every Meta account I'm authorised on through the Meta connector. Cross-references against the lead inventory in our Google Sheet. Cuts that against agent activity logged in Slack reactions over the last 24 hours. Runs the cost-per-lead and cost-per-sale calc against our base premium. Outputs a WhatsApp-formatted summary into the ops group by 7am. I read it with my coffee, decide what to kill or scale, and the team has direction before 8. The shift wasn't technical. It was a change in how I scoped the work. Three things operators get wrong when they first try this. They write prompts instead of job specs. Cowork performs best when you treat the standing instruction like a job description for a new hire. Sources, frequency, format, definition of done, edge cases to flag. They try to consolidate the report inside Cowork's chat window. Don't. Push the output to where your team already lives. Slack, WhatsApp, a Notion doc, a Google Sheet tab. The medium is the message.

Everyone wants prompts. Nobody wants systems. Because prompts are exciting. Systems are boring. But boring scales. Boring pays. Boring wins. That's the difference between using AI... And building with AI. Which one are you doing?