Claude builds 3D Hamiltonian Monte Carlo animation in one shot with anaglyphs

2026-07-07 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

Bob Carpenter demonstrates that Claude Opus 4.8, using its "hard" thinking mode, successfully generated a complex 3D Hamiltonian Monte Carlo (HMC) animation with anaglyph 3D encoding from a single, detailed prompt. The LLM produced a self-contained 627KB HTML file in approximately 10 minutes, featuring interactive controls for adjusting correlation in a 3D normal distribution, step size, number of steps, and animation speed. This one-shot generation, which visualizes HMC sampling from a highly correlated 3D normal target, surprised the author, who had anticipated multiple iterations. The resulting web application allows users to zoom, rotate, and toggle 3D effects, highlighting the advanced capabilities of frontier LLMs in creating sophisticated statistical visualization tools.

Key takeaway

For prompt engineers or data scientists developing interactive visualizations, this demonstration shows you can achieve sophisticated, one-shot web application generation. Frontier LLMs like Claude Opus enable this. Your approach to creating complex statistical tools should now consider direct LLM prompting. This method can significantly reduce development time. Explore detailed, single-shot prompts for generating interactive 3D models or other data exploration interfaces. This capability could reshape how you prototype and deploy visualization assets.

Key insights

Frontier LLMs like Claude Opus 4.8 can generate complex, interactive web applications for statistical visualization from a single prompt.

Principles

• LLMs can generate complex, interactive web apps.
• Detailed, single prompts achieve sophisticated results.
• LLM-powered tools simplify statistical visualization.

Method

Provide a single, detailed prompt to a frontier LLM (e.g., Claude Opus 4.8) specifying the desired 3D animation, target distribution, interactive controls, and output format (HTML).

In practice

• Use Claude Opus for complex visualization tasks.
• Experiment with detailed, single-shot prompts.
• Explore LLMs for generating interactive web tools.

Topics

• Hamiltonian Monte Carlo
• LLM Code Generation
• Claude Opus
• Statistical Visualization
• 3D Anaglyph
• Prompt Engineering

Best for: Research Scientist, AI Scientist, Prompt Engineer, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.