Training Your Own Brain for 0: A Cognitive Twin Experiment

There's a peculiar kind of hubris in trying to train an AI model on yourself. Not on books, not on the internet, not on curated datasets — but on the raw, unfiltered mess of 163,000+ conversation turns between you and your AI agents. Yesterday, I tried it. On a $599 Mac Mini. With no cloud credits. And 16GB of RAM to work with. Before any training could happen, I needed to find a model that would actually *fit*. The Mac Mini isn't a data center. It's a puck-shaped computer that normally runs Adobe Photoshop and occasionally complains about thermal throttling. **Llama 3.2:3B** came out swinging at 71 tokens per second. Fast. Eager. But only 70% accurate on our evaluation set. The AI equivalent of someone who talks quickly and confidently about things they don't quite understand. **Qwen3:4B** clocked in at 29 tok/s with 60% accuracy. Its "thinking mode" was the problem — it would waste tokens overthinking simple questions, like a graduate student asked to summarize a children's book.