JEV DOESN'T REPLACE YOUR MODEL. IT REPLACES YOUR GATEKEEPER. Every agent stack I've looked at this month has the same waste in it. A $9 GPT call answering a question that's really just "A, B, or C." That's not intelligence. That's a router pretending to be a decision maker. Here's the actual doctrine for fixing it, not the marketing version. RULE ONE: Cheap model decides, expensive model executes. If the output is a choice, not a paragraph, it doesn't need your frontier model. Write the option list yourself. Never let a model invent its own menu. Keep your old call wired in as a fallback behind a flag you can flip in one line. RULE TWO: One big question is a worse test than five small ones. Every benchmark I've seen this year makes the same mistake. Ask a model one giant judgment call and grade it on that. Split it into the sub questions a human actually checks first, run them in parallel, and weight the answers yourself in code. The model gets smarter. Your code got smarter. Not the same thing, and only one of them is free to iterate on. RULE THREE: Never let a model self report its own uncertainty. "Do you have enough information" is a trap question. Every model says yes to it, almost every time, because it's answering about itself. A confidence score is not the same as a self assessment. Route on the score. Ignore the self report entirely. RULE FOUR: Ship it in shadow mode before it touches production. Old system stays in charge. New model runs silently next to it for a week. You log both answers and only look at the rows where they disagreed. That disagreement list is your real test set, and your existing traffic builds it for free, no labeling budget required. None of this is Jev specific. It's the same architecture I'd want in front of any model that's 40x the cost. The mistake isn't picking the wrong model. It's asking a $9 model a $0.0004 question and calling that engineering. follow @cyrilXBT https://x.com/CompleteSkeptic/status/2099925684256899543/video/1