Fully converted to the belief that vector embeddings are the answer to nearly every data and search problem — and not shy about evangelizing that view.
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Someone with an almost supernatural command of vector embeddings — the person on the ML team who intuitively understands high-dimensional semantic space and consistently produces embedding pipelines that just work beautifully. An embedding god can look at a similarity matrix and immediately diagnose what the model is misunderstanding. They're the rare engineer who has internalized the math well enough to reason about it like intuition, and the team always tags them when the retrieval system starts acting weird.
Someone who has fully swallowed the AI hype and now sees large language models as the solution to every problem — personal, professional, existential. LLM-pilled people automate their grocery lists, therapy journaling, and first dates. They cite token counts in casual conversation and get mildly offended when you say you still Google things. It's not quite a cult, but they will absolutely send you a custom GPT you didn't ask for.
Manifest pilled describes someone who has been fully convinced by manifestation ideology and now applies law-of-attraction thinking to virtually every area of their life. Once manifest pilled, you don't look for jobs — you script them into existence. You don't date — you align with your future partner's energy. The label can be affectionate among believers or gently mocking from skeptics. Either way, it signals a deep and somewhat irreversible conversion to the manifestation worldview.
Fully converted to the belief that vector embeddings are the answer to nearly every data and search problem — and not shy about evangelizing that view. Someone who is embedding pilled will steer every technical conversation toward semantic search, insist that keyword matching is obsolete, and get visibly excited about cosine similarity in inappropriate social contexts. It's said affectionately within ML circles about that one engineer who won't stop recommending you rewrite your search stack around embeddings.
He's completely embedding pilled — he wants us to vectorize our internal wiki, the customer emails, everything.
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