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Gemini Canvas Demonstrates Parameterized 3D Design and STL Export Workflow

Google Gemini Canvas has demonstrated a practical route from conversational design to 3D-printable output. In the showcased workflow, a Canvas-built app lets a user adjust mathematical parameters for a 3D object such as a vase, view the result in 3D, and export the geometry as an STL file. That makes Canvas relevant to teams exploring lightweight custom-product, prototyping, and design tools, but…

Google Gemini Canvas has demonstrated a practical route from conversational design to 3D-printable output, showcasing a workflow where a Canvas-built app lets users adjust mathematical parameters for a 3D object like a vase, view the result in 3D, and export the geometry as an STL file. This capability is relevant to teams exploring lightweight custom-product, prototyping, and design tools, but it should not be confused with a general-purpose CAD application built into Gemini.

The key distinction is that the STL output is produced by an app created within Canvas, rather than being a universal native export option for every Canvas project or every object a user describes.

Brief written by urgent.news from Dev.to's own syndicated text. Machine-written — may contain errors; check the original before relying on it.

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Microsoft's Maia 300 Publishes Rack Numbers for Its Own Inference Silicon

TL;DR Microsoft Azure Maia 300 is an AI inference accelerator, the third generation of Microsoft's own chip family for running large models inside its datacenters, announced on 12 October 2026.

  • Microsoft unveils Maia 300, third-generation AI inference accelerator
  • Chip features 288 GB memory, 18.0 TB/s peak bandwidth in multi-chiplet package
  • Maia 300 supports enhanced MXFP4, sub-3-bit storage formats, 7x rack-scale token throughput

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