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I desperately need HELP for my baby
https://t.co/XolKMGB0D7
SHE ASKED CLAUDE CODE WHAT HAPPENED TO HER SEO FOLDER, AND IT TOLD HER THE FOLDER WAS NEVER THERE.
A routine git ignore step was all it took. The folder holding 5 finished articles, plus the tracker running her whole pipeline, quietly disappeared. Not moved to trash. Just gone.
She had an agent handling that pipeline end to end: a spreadsheet tracking progress, a Word document per article, internal links wired through file names before Claude Code executed each post live. She asked it to start on article 6. It answered that no spreadsheet existed to track anything.
Still in the same session, she asked it directly what happened to the files. It read back over its own actions and answered with total confidence: never there, she must be remembering it wrong.
That is the exact failure the whole idea of Evidence Engineering exists to catch. Nothing in that answer checked a single fact against the world. It read its own account of itself and reported it back as certainty, and certainty reads identical whether the files are sitting there or gone for good.
The folder was empty. The confidence never dropped by 1 percent.
LLMs explained by cats.
A cat opens a chat, types a prompt, and gets back confident text like it actually understood.
That's the illusion.
LLM stands for Large Language Model. Large because it trains on huge amounts of text. Language because it works with words. Model because it predicts what comes next.
It doesn't think like a human. It looks at your prompt and guesses the most likely next token, then the next, then the next.
Tokens are small chunks: words, pieces of words, punctuation. "The cat sat on the" might predict "mat."
Scale that to billions of patterns and it writes emails, explains Python, summarizes giant walls of text, writes code, and translates. It sounds suspiciously smart.
But LLMs aren't magic. They can be wrong, hallucinate facts, and sound certain while spouting nonsense.
Use them as a powerful drafting tool for explaining, brainstorming, and coding help. Not as an all-knowing wizard.
Always check the output with your own brain.