The Coherence Trap: Why LLMs Feel Smart (But Aren’t Thinking)
summary
Claims that LLMs aren’t thinking and instead use coherence reconstruction: latent coherence pulls language into aligned patterns, not understanding. Evidence includes engineering artifacts — a Python Azure utility Webcat that scrapes pages, a running blog AIBuddy.software, and a novelty cat metal album that hits ~3,000 YouTube views in a month — plus a reproducible frame generate judge iterate decision loop, a three‑layer latent/execution/conversational model, and RAG grounding. Hallucinations emerge predictably and can be mitigated but not eliminated.
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chapters (20)
- 0:00Introduction
- 0:31GPT5 Hype
- 1:19GPT4 Hype
- 2:42What did I do
- 2:59Live stream
- 4:33AIBuddy
- 5:49Making a bigger project
- 8:19Building an Analysis Tool
- 9:16The AI Decision Loop
- 10:36The NudgeNudge Framework
- 11:12Coherence Theory
- 12:16What is Coherence
- 14:58Prompts
- 15:42Navigating Latent Space
- 16:59What does this lead to
- 17:32What does this mean
- 18:09Retrieval Augmented Generation
- 18:40The 3 Layer Model
- 19:15How to Build for Coherence
- 20:15LLMs as a Highdimensional Mirror
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