This article stitches together several Twitter threads: it starts with a plain-English analogy for what model weights are, then shows how to iteratively refine code using Perplexity AI (with a stock portfolio analyzer example), and ends with practical steps for running LLMs locally on Apple Silicon via tools like Ollama.

• Model weights are literally just a file of decimal numbers (floats) that get loaded into memory and multiplied against input tokens to produce output.
• Iterating daily on prompts (breaking big tasks into focused, checkable segments) rather than stopping at the first working result yields dramatically better outputs, as shown with a stock-portfolio analyzer.
• A single detailed "autonomous team" prompt generated a 4,500+ line full-stack fund operating system (Thesium.finance) theoretically enabling 1-2 people to replace a 10-person analyst team.
• Apple Silicon's unified memory architecture makes it the easiest, most performant path for running LLMs locally, with tools like Codex Desktop launching on Mac first.