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.

This unrolled thread covers four topics: a plain-language explanation of model weights, strategies for refining code with Perplexity AI’s Computer, an AI-native fund system built end-to-end, and tips for running LLMs locally on Apple Silicon using Ollama. It walks through each use case with examples and practical advice.

• Perplexity's Computer platform generated 4,500+ lines of full-stack code for Thesium.finance, an AI-native fund aiming to replace a Bloomberg terminal and ten analysts with two humans overseeing automated trading agents.
• Getting usable output from AI coding tools requires iterative refining through multiple prompts, not just a single initial request.
• Apple Silicon's shared CPU/GPU memory makes it well-suited for running LLMs locally, letting users host models and keep data/inference private without needing to understand the underlying math.