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I watched a video this week from a developer with a big YouTube following. His complaint about building with AI landed close to home. The machine never stops to ask whether the thing you want is a good idea. It builds what you asked for, and somewhere along the way you stop owning what got built.
Mine does stop and ask. I set it up that way on purpose, because I want to hear it when an idea is bad. A week ago, it pushed back on me three times in one evening. It was confident every time, and it was wrong.
I created an active research library on my Mac Mini. I call it ParaCorpus. It goes out and collects articles, papers, and video transcripts on its own, so my AI tools can work from real sources instead of guessing. Some websites don’t hand over the article, though. They hand over a “verify you are not a robot” screen, and my library was saving those screens as if they were the real thing.
I name my AI tools. Holden is my Claude chat, the one I build most of my systems with. He wanted to fix it with rules: watch for the words those screens use, and throw out anything that matches. It sounds sensible. It kept breaking. One version threw out a real article because it quoted the phrase “verify you are not a robot.”
I asked why we weren’t using Jev. Holden pushed back three times that evening, sure of himself every time. It was the night my last story went out. Eventually I typed, “I don’t understand why you’re so resistant.”
Jev is a new kind of AI from a company called TypeSafe. It doesn’t write prose. It judges. You give it a question and something to look at, and it can sort it into categories, give it a score, or answer yes or no with a number between 0 and 1 for how sure it is. This problem only needed the yes or no. Is this page a robot check? 0.98.
That’s the magic of it. With no prose, there’s nothing to interpret. The result can’t wander, hedge, or bury itself in a paragraph. It means one thing. It’s also fast and cheap, about a seventh of a second per answer, which makes it practical to ask about every single page it collects.
I asked Holden to stop arguing and try it. My guess was that a real robot check would score around 0.9. He tested 202 saved pages in under 15 seconds. The three robot checks came back 0.99, 0.99, and 0.98. Nothing else topped 0.68.
Vindicated, a little. Mostly surprised the answer came back that clean.
Holden admitted his resistance had been “rote, not reasoned.” The rules he kept quoting were ours, too. We’d written them a few weeks earlier for a different job. Good rules. Wrong problem.
Holden’s model was trained before Jev existed. My best guess is that when it showed up, he had no instinct for it, so he reached for what he knew, and then for what we’d written down. Even the smartest model doesn’t know what came out after it was built.
Back in Model Context Protocol, I wrote that you can’t blindly trust your gut, because AI is moving faster than your intuition. This was the other half. The model has a gut too, and it was trained on the past, same as mine.
I didn’t out-know Holden. He knows more than I do about almost everything. What I had was a different kind of instinct. I see a process like a circuit board, though I’ve never designed a circuit: a switch here, a gate there, something flowing through. When I first heard about Jev in September, I explored it fast, and then it clicked. I could see exactly where it belonged.
Some decisions are exact. Is this payment over $100? A computer can follow a rule and never miss. A lot of decisions are fuzzy, though, and I run into them constantly. Is this page the real article? Is this video worth my time? Does this new note replace something I saved last month? Which project owns this idea?
You can write rules for fuzzy decisions. I’ve watched teams try for my whole career. Every rule handles one case and uncovers two more. It’s whack-a-mole, and you never run out of moles.
Think about knowing how warm a room is. You could try to track every molecule of air, and you’d never finish. Or you could read the thermometer. Rules chase molecules. A good question reads the thermometer. Our test even caught junk no one had thought to look for, pages that were nothing but a menu.
In my circuit-board picture, Jev is a new primitive, a basic building block for everything else. A fuzzy question goes in, a clear signal comes out, and the rest is plain wiring: keep it or throw it out. Claude can make the same call. It also wants to write you three paragraphs about it, which is exactly the complaint from that video. Jev hands back a number, and the system knows exactly what it means.
Jev isn’t perfect. It still makes mistakes, and it can only judge what it’s shown. Like Holden, it depends on a good question and the right context.
When the machine does the typing, the scarce skill is seeing where the parts go, and noticing when a new primitive shows up that the machine hasn’t met yet.
In my experience, most AI won’t push back much out of the box. Mine does because I asked it to, and a lot of the time it has a point. When it doesn’t, what’s worked for me is staying curious about the new parts, watching for patterns I haven’t seen before, and running the test when my gut keeps saying there’s something there. The model’s instinct was trained on yesterday. Yours can keep learning, as long as you keep asserting your own thinking.
Smarter than me. Not always right.
Uncomplicated systems. Uncommon results.
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