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Testing Claude Visuals against Thinky3D live 3D simulations on 5 identical topics: honest observations on where each approach wins

I've been using Claude Visuals heavily since it dropped and wanted to share some structured observations plus a side-by-side comparison I put together to stress-test where it shines and where alternative approaches add value. Context on why I care about this specifically: a few weeks ago at a hackathon my friend and I built an open source learning tool "Thinky3D" that takes a similar idea to Claude Visuals but goes 3D instead of 2D. Having spent a lot of time in the weeds on "how do you get an LLM to reliably generate runnable interactive visuals" gave me a genuine appreciation for how hard what Anthropic shipped actually is. When Claude Visuals dropped I was naturally curious how the two approaches would compare on identical prompts, so I made a direct side-by-side video on 5 topics: black holes, DNA, Möbius strips, pendulums, and pathfinding algorithms. Video: https://www.youtube.com/watch?v=kOWrQiObnO4 Here is what I actually found, with specific examples: Where Claude Visuals is genuinely strong (and in my testing, wins outright): Speed. Claude Visuals are near-instant. Generating a novel 3D simulation takes noticeably longer because the model has to write a full component. Right-sized for the task. For topics like compound interest, binary tree rebalancing, or flowcharts, a 2D interactive visual is honestly the correct answer. Adding a third dimension is gratuitous. Computer science (pathfinding test). Claude's node graph with visited/queue/path state was actually more legible for understanding the algorithm logic than my 3D maze version. The 2D abstraction is doing real work here. Where 3D simulations added something Claude Visuals does not currently seem to do: Spatial physics. The black hole gravitational lensing case was the clearest gap. Showing a warped spacetime grid with light bending around an event horizon is hard to do in 2D without it becoming a diagram. Depth felt necessary, not decorative. Topology. The Möbius strip twist slider from 0° to 360° with edge tracers gave a very different feel for the single-boundary property than a static mesh. Being able to watch a flat ribbon become a Möbius surface as you drag the twist value was the strongest "aha" moment in my tests. DNA helix structure. A slider that unwinds the helix from ladder to double helix visually demonstrates the structural relationship in a way I have not been able to get out of a 2D explanation. Technical note for this community: Getting an LLM to reliably generate runnable React Three Fiber code in a browser sandbox was genuinely brutal. Hooks declared inside conditionals, THREE.js constructor instances passed as React children, geometry method calls on React elements, missing return statements. Hundreds of failure modes. I ended up building a Babel AST validation pass, a Safe React proxy that auto-fixes misused THREE instances at runtime, and a patch-based correction loop that sends runtime errors back to the model as minimal search-and-replace edits. I suspect Anthropic is solving similar problems under the hood for Claude Visuals and I would genuinely love to know how they handle it, especially the sandboxing layer and how they prevent generated code from crashing the chat UI. If anyone wants to poke at the code, the source is here: https://github.com/Ayushmaniar/Gemini_Hackathon Would genuinely love feedback from this community on where to take it next. Broader take after spending weeks on this: I think we're close to the point where learning physics, chemistry, math, or biology from static textbook diagrams is going to feel as dated as learning to code from a printed manual. Curious if anyone here disagrees, or has a different take on where this is heading. Claude visuals: https://thenewstack.io/anthropics-claude-interactive-visualizations/ submitted by /u/Interesting_Swing857 [link] [comments]