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.

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.

This article compares running AI agents locally on a Mac Mini with Ollama and open-source models versus hosting them on a cloud server using Claude or Gemini APIs. It breaks down upfront and monthly costs—about $35/month local amortized versus roughly $73 for Gemini and $123 for Claude—and highlights performance, privacy, and usage trade-offs.

This article walks through why and how to run large language models locally, covering privacy, cost, offline access, and control. It breaks down hardware needs, quantization, PC versus Mac setups, and starter software to get models up and running.