Many engineers use generative AI to build proofs of concept, internal tools, or dashboards. But learning something new is also among the most popular uses. The problem, according to developer Laurentiu Gabriel, is that the style of LLM explanations is often too simple and, depending on the number of emojis, even annoying.
It was with this in mind that he created a different method. Instead of just asking for an explanation, he uses the model to build an interactive visual simulation of the subject. The result, according to him, is a form of learning that ‘sticks’ much more than reading endless materials or digesting bullet-point lists.
The idea came when he was analyzing AI bottlenecks that can slow down data center construction. He realized he knew little about chip production. So he thought: what if there were a game that showed the chip manufacturing process? Learning by mapping concepts to objects within the game seemed promising. He tested it and it worked.
The flow is simple. First, in planning mode, using tools like CC or OpenCode, he asks the model to build fundamental knowledge about the topic. Then, he asks for a review of the accuracy of that foundation. Next, he requests the creation of a low-poly style simulation, similar to Rollercoaster Tycoon, with UX elements like responsiveness and pause controls. Finally, he publishes it in a repository with GitHub Pages.
The result is a beautiful animation, ‘100% accurate and free of hallucinations’, he guarantees. He applied the method to learn about chip manufacturing and launched the site ChipTycoon. There, you follow a cart from collecting sand to delivering the finished chip to a data center. Visually, you can see the cart change as the product goes through manufacturing stages.
Of course, the low-poly design may require imagination to visualize what happened to the quartz sand after the furnace. To improve, Laurentiu suggests using a skill that transforms photos into 3D objects and mapping those objects into the simulation. Another tip is to add challenges and questions about previous steps of the process, which helps retain knowledge. Intuitive puzzles are also welcome.
The initiative shows a creative use of LLMs: transforming passive learning into an active and visual experience. Instead of relying on generic explanations, the user builds a tool that internalizes knowledge in a playful way. It’s an approach that can be replicated for any complex topic.
According to Laurentiu Gabriel, the method works better than reading endless materials or trying to digest AI-generated lists. The combination of simulation, interaction, and accuracy review seems to be the key. For those who want to learn something new, it’s worth trying.
The ChipTycoon project is available online, and the author encourages exploring other pages he has created. The idea is simple but powerful: using AI not just to answer, but to build experiences that truly teach.