Over the past few weeks, GPT-6 Astra, Claude Code with Opus 5.5, and other recent foundation models have surprised many of us in graphics, CAD, and robotics. They can now work directly with Blender, CAD kernels, game engines, physics simulators, and real robots very well. New examples appear almost daily, faster than the publication cycle can capture. 📝To keep track, we maintain a Living Survey of frontier AI for Design & 3D Modeling & Robotics: 🌐 mit-cdfg.github.io/Survey-AI… Currently, it organizes 243 cases from 345+ public showcases and developer reports across 3D modeling, CAD and industrial design, animation, simulation, and robot control, each linked to its original source. (If you are working on related projects, it's better to check this out: ) Different from a traditional survey, three ideas guide it: 1. Horizon scanning beyond publication lag In an era where frontier foundation model capabilities evolve rapidly, traditional academic publishing cycles could lag behind public community developments and empirical findings. This platform establishes a centralized, high-velocity empirical synthesis repository to provide researchers and engineers with timely visibility into ongoing developments across 3D generation, parametric CAD, and embodied robotics—anchoring these observations in objective evaluations of strategic opportunities and critical safety boundaries. 2. Demonstrations as a distributed record of use We analyze the corpus of over 345 publicly documented showcases and developer reports as an extensive, distributed "crowdsourced user study." This framing captures how models operate when prompted across diverse geometry kernels (CGM, Open CASCADE), DCC software (Blender), physics simulators (Isaac Sim, MuJoCo, Genesis), and physical robot hardware—revealing real-world workflow friction, prompt overhead, and boundary failures that static benchmarks miss. 3. Ranking by open verifiability A central challenge is balancing the timely collection of rapidly emerging results with the need for high-quality, evidence-based evaluation. Our approach is to rank cases by open verifiability—the amount of evidence that others can directly inspect. Equally impressive demos can carry very different evidence, so every case is ranked by what others can inspect: - Rank 1 · Demo + Implementation Code: public code, scripts or CAD/robot harnesses (availability/verifiability) - Rank 2 · Demo + Interactive Web Link: a live web app, 3D viewer or cloud CAD project - Rank 3 · Demonstration Media Only: video or screenshots only Note: Rank 1 means the evidence is inspectable, not necessarily reproducible from a clean setup. Gallery: mit-cdfg.github.io/Survey-AI… Contributions are very welcome via pull requests on GitHub. We especially welcome Rank 1 contributions (demos with code) and Rank 2 contributions (demos with interactive webpages). Many thanks to our collaborators: Jamison Meindl(@meindljamie), Akihisa Watanabe(@Akihisa_Wat ), Anna Deng, Tianyu Huang(@tyanyuy3125), Igor Sadalski(@igorsadalski ), Harrison Liang, Minghao Guo(@GuoMh14 ), Benjamin Tod Jones, Wojciech Matusik(@wojmatusik ).