These scans seem perfect for fabrication experiments.

I’ve been trying a workflow where the mesh is inverted and used to generate a 3D-printed mold, then I gelcast zirconia ceramic into it and sinter it. The result is a dense ceramic version of the sculpture.

If you downscale the models they work well as small desktop statues or relief friezes, and ceramic casting can preserve surprisingly fine detail from the scan.

I rank with those who think human-like intelligence will require embeddings grounded in multiple physical sensory domains (vision, touch, audio, chemical sensing, etc.) fused into a shared world representation. That seems much closer to how biological intelligence works than text-only models. But if this path succeeds and produces systems with something like genuine understanding or sentience, there’s a deeper question: what is the moral status of such systems? If they have experiences or agency, treating them purely as tools could start to look uncomfortably close to slavery.

In the 90s people hoped Unified Modeling Language diagrams would generate software automatically. That mostly didn’t happen. But large language models might actually be the realization of that old dream. Instead of formal diagrams, we describe the system in natural language and the model produces the code. It reminds me of the old debates around visual web tools vs hand-written HTML. There seems to be a recurring pattern: every step up the abstraction ladder creates tension between people who prefer the new layer and those who want to stay closer to the underlying mechanics.

Roughly: machine code --> assembly --> C --> high-level languages --> frameworks --> visual tools --> LLM-assisted coding. Most of those transitions were controversial at the time, but in retrospect they mostly expanded the toolbox rather than replacing the lower layers.

One workflow I’ve found useful with LLMs is to treat them more like a code generator after the design phase. I first define the constraints, objects, actors, and flows of the system, then use structured prompts to generate or refine pieces of the implementation.