Laurentiu Raducu’s technique for learning with LLMs, born from frustration with their explanations (“too simplistic and, depending on the number of emojis used, a bit annoying”): instead of asking a model to explain a topic, ask it to build a playable low-poly simulation of it — a RollerCoaster Tycoon-style animated game — then push it to GitHub Pages. The flow: in plan mode (Claude Code or OpenCode), have the model build the foundational knowledge base for the topic; have it review that knowledge base for accuracy; then have it build the simulation with UX requirements (responsive, controls to stop the flow); push to a repo and enable Pages. The result is an animation you can follow step by step — “100% accurate and free of hallucinations” per the author — and learning by watching a process beats reading. His worked example is ChipTycoon: chip manufacturing from sand collection to a finished chip delivered to a data center, motivated by researching AI data-center bottlenecks and realizing he didn’t understand chip production. Improvements: map the low-poly objects to realistic 3D models (his picture-to-3D skill), and add challenges — answering questions about previous steps “retains knowledge tremendously.” Same pattern applied to rocket engines, how LLMs work (Token Town), F1 engines, and EUV lithography. The insight: the simulation is the explanation — every step has to be concrete and sequential, and if the animation doesn’t make sense, that’s a gap in your understanding.
How I Use LLMs to Learn Complex Topics — Laurentiu Raducu
The simulation-is-the-explanation pattern: instead of asking an LLM to explain a topic, have it build a low-poly playable simulation of it — then push it to GitHub Pages.