Imagine you’re gauging whether you’ll make your flight on time: you look at traffic, departure time and distance, then give each factor a “strength” before multiplying them together for a final score. In LLMs, those strength values are the model’s weights. They’re just decimal numbers—floats—saved in a file. Load that file into memory, feed in your text tokens, and the model spits out its output. That file of floats is literally the model you’re running. Since Perplexity.ai launched its Computer product, its author has been building and refining projects every day rather than stopping at the first working prompt. He uses a stock‐portfolio analyzer as a running example, showing how he tweaks prompts and workflows to squeeze more out of the system. His process involves iterating on code, breaking down big prompts into focused segments and checking each piece for reliability before moving on. One standout build is a full-stack, AI-powered fund operating system—over 4,500 lines of code that, in theory, lets 1–2 humans replace a team of ten analysts. He prompted the system to act as an “autonomous engineering, product and research team” to create an MVP called Thesium.finance. That prompt spells out end-to-end goals—research through execution—so the AI agents can maintain live investment theses while humans supervise the interface. On the hardware side, he swears by Apple Silicon for local LLM work. The unified memory architecture boosts GPU performance and simplifies setup. He points to tools like the Codex Desktop app, which arrive first on Mac, and argues that for anyone wanting to run models on their own machine—no deep AI background required—Macs offer the smoothest path.