LLMs are just giant probability machines pretending to think [P]

In three linesEducational post explaining LLMs as probabilistic machines. Breaks down architecture (embeddings, positional encoding, attention, feed-forward, LM Head) using a simple example: predicting « vault » after « The investor walked to the bank ». Emphasizes LM Head as a giant vocabulary of candidate tokens and that intelligence emerges from scaling probability + context + mathematical matching.

Summary generated by Claude — human-verified