The magic wears off! Why knowing more about AI makes you want it less
A new study finds that the more you understand AI, the less excited you are to use it. The mechanism isn't fear, it's the death of magic.
ReadRandom thoughts and working notes on building AI agents for production. Only this, and nothing more.
A new study finds that the more you understand AI, the less excited you are to use it. The mechanism isn't fear, it's the death of magic.
ReadPrompt injection can't be escaped away like SQL injection. The fix isn't preventing it, it's surviving it: scope permissions in the infrastructure, gate the irreversible, and assume the attack succeeds.
ReadAn agent that works but costs four dollars a session doesn't ship. Three moves that cut agent cost without touching its logic: profile the tokens, cache the context, route the easy steps, and budget the rest.
ReadWhen an agent fails on step six, intelligence won't tell you why. A trace will. What an agent trace must capture, and the cold-reconstruction test for whether yours is real.
ReadAn eval suite that exists but isn't enforced ships regressions anyway. How to wire evals into your deploy pipeline as a gate, with baselines, noise-aware thresholds, and loud overrides.
ReadYour agent's confident diagnosis and its correct one look identical until you hold the ground truth. How to build the eval harness that tells them apart.
ReadThe agent loop everyone writes first dies with its process and double-charges on retry. Two structural moves fix it: split the decider from the doer, and make progress durable.
ReadThe agents that survive production aren't the most capable ones. They're the ones whose builders knew where competence ended and said so in code.
ReadA production AI agent is eight components, not a prompt. Here's the parts list, the failure each part prevents, and the four ways agents fail.
ReadMost AI agent pilots, 88% of them, never reach production. The gap isn't model intelligence. It's five engineering questions your demo can't answer.
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