How I use LLMs to learn complex topics
Building interactive low-poly simulations with LLMs — after verifying a curated knowledge base — creates a more effective, hallucination-free way to master complex technical topics than reading explanations or bulleted lists.
Key Points
- Standard LLM explanations feel too simplistic and emoji-heavy, making them hard to follow for deep technical topics.
- The author uses a three-step flow in plan mode (CC/OpenCode): build foundational knowledge for a topic, verify its accuracy, then generate a low-poly interactive simulation (Rollercoaster Tycoon-style) with responsive controls.
- The resulting simulations — such as ChipTycoon tracing sand-to-chip flow, rocket-engine, token-town for LLM internals, engineworks for F1 engines, and euv-lithography — are hallucination-free and visually map concepts to objects.
- Retention improves further by adding challenges, quizzes on prior steps, and intuitive puzzles; realism can be enhanced by mapping 3D assets from the author’s unreal-game-assets-creation-skill.
- [AI Synthesis] This workflow turns passive reading into active, visual, systems-level learning that scales across semiconductor, aerospace, and AI infrastructure domains.