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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.