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don't let them fool ya with video game assets! Working feature tree, working assembly, 100% AI generated! Text-to-CAD is not the hard part and it is not the interesting part!

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Matt Ferraro

2,907 subscribers

17,286 görüntüleme • 14 gün önce •via X (Twitter)

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Text to CAD is here and apparently that's not even the hard part. What comes next?
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Text to CAD is here and apparently that's not even the hard part. What comes next?

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The scary part about AI in CAD is not that GPT can extrude a shape. It is that it can now build a mechanism where multiple parts actually have to agree with each other. Using GPT + Autodesk Fusion MCP, this project turns into a mechanical iris assembly with a base plate, eight overlapping blades, a drive ring, a cover, mounting holes, and relationships between the components. One blade is easy. The annoying part is repeating it around the same center, keeping every pivot aligned, fitting the mechanism between the surrounding plates, and making the whole assembly stay coherent as more geometry gets added. That is where MCP starts getting interesting. GPT is no longer treating Fusion like a place to dump some 3D geometry. It is working inside the actual CAD structure, where components, relationships, repeated parts, and the assembly itself all matter. That is a much bigger deal than text-to-3D. Because once an AI agent can reliably build and modify mechanisms instead of isolated shapes, a surprisingly large chunk of mechanical CAD starts looking like something you describe, inspect, and correct rather than model feature by feature.
0:34

The scary part about AI in CAD is not that GPT can extrude a shape. It is that it can now build a mechanism where multiple parts actually have to agree with each other. Using GPT + Autodesk Fusion MCP, this project turns into a mechanical iris assembly with a base plate, eight overlapping blades, a drive ring, a cover, mounting holes, and relationships between the components. One blade is easy. The annoying part is repeating it around the same center, keeping every pivot aligned, fitting the mechanism between the surrounding plates, and making the whole assembly stay coherent as more geometry gets added. That is where MCP starts getting interesting. GPT is no longer treating Fusion like a place to dump some 3D geometry. It is working inside the actual CAD structure, where components, relationships, repeated parts, and the assembly itself all matter. That is a much bigger deal than text-to-3D. Because once an AI agent can reliably build and modify mechanisms instead of isolated shapes, a surprisingly large chunk of mechanical CAD starts looking like something you describe, inspect, and correct rather than model feature by feature.

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AI in CAD just went from “make me a part” to “build me the damn machine.” Using Autodesk Fusion MCP, the agent is working inside a real mechanical assembly with a chassis, shafts, gears, bearings, brackets, repeated hardware, and multiple components that all have to stay aligned with each other. That is a completely different problem from generating one clean 3D object. You can inspect the assembly from different angles, isolate sections, dig into individual joints, and keep working on the same engineering project instead of starting from another prompt-generated blob. The important part is not that AI can create geometry anymore. It is that it can operate inside the structure of a real CAD project where every component depends on what is around it. Once this gets reliable, a lot of mechanical CAD starts looking suspiciously simple: describe the system, inspect what the agent built, then tell it what to change.
0:29

AI in CAD just went from “make me a part” to “build me the damn machine.” Using Autodesk Fusion MCP, the agent is working inside a real mechanical assembly with a chassis, shafts, gears, bearings, brackets, repeated hardware, and multiple components that all have to stay aligned with each other. That is a completely different problem from generating one clean 3D object. You can inspect the assembly from different angles, isolate sections, dig into individual joints, and keep working on the same engineering project instead of starting from another prompt-generated blob. The important part is not that AI can create geometry anymore. It is that it can operate inside the structure of a real CAD project where every component depends on what is around it. Once this gets reliable, a lot of mechanical CAD starts looking suspiciously simple: describe the system, inspect what the agent built, then tell it what to change.

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📐Early GPU Acceleration Progress - Part 3 AutoDesk Fusion working with OpenGL backend. This is really big for CAD folks. AutoDesk plans to remove OpenGL in new versions of Fusion, but I think you can keep using the current one and have it working. #winboat #linux #opengl #CAD
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📐Early GPU Acceleration Progress - Part 3 AutoDesk Fusion working with OpenGL backend. This is really big for CAD folks. AutoDesk plans to remove OpenGL in new versions of Fusion, but I think you can keep using the current one and have it working. #winboat #linux #opengl #CAD

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The scary part about AI in CAD is not that it can draw a part. It is that it can now understand a messy engineering project well enough to search it, isolate the right components, change their parameters, and reorganize the whole thing from plain English. This guy is working inside Autodesk Fusion and asks the AI to create a complex geometric structure, find specific fasteners inside a large assembly, hide everything else, change dimensions across different screw sizes, and then rearrange the hardware into entirely new layouts. No manually hunting through the browser tree. No opening fifty components one by one. No “which M12 bolt was that again?” The model is treating the CAD file less like a drawing and more like a database it can reason over and modify. That is a much bigger deal than text-to-3D. Because once MCP-style agents can reliably understand what is already inside a real engineering project, the boring half of CAD work starts looking suspiciously automatable.
1:26

The scary part about AI in CAD is not that it can draw a part. It is that it can now understand a messy engineering project well enough to search it, isolate the right components, change their parameters, and reorganize the whole thing from plain English. This guy is working inside Autodesk Fusion and asks the AI to create a complex geometric structure, find specific fasteners inside a large assembly, hide everything else, change dimensions across different screw sizes, and then rearrange the hardware into entirely new layouts. No manually hunting through the browser tree. No opening fifty components one by one. No “which M12 bolt was that again?” The model is treating the CAD file less like a drawing and more like a database it can reason over and modify. That is a much bigger deal than text-to-3D. Because once MCP-style agents can reliably understand what is already inside a real engineering project, the boring half of CAD work starts looking suspiciously automatable.

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AI is not replacing the engineer, the accountant, or the lawyer. It is handling the first 80% so they can focus on the part that actually matters. Aaron Levie on the future of jobs with Casey Newton: "All of the expertise and domain knowledge is in that last 20%, not the text that got generated."

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The interesting part about GPT in CAD is not that it can make a shape. It is that through MCP, GPT can work directly inside Onshape and turn a plain-English description into actual CAD geometry you can keep editing. In this case, the model builds a radial mechanical part with a central hub, repeated ribs, an outer ring, and a full pattern of mounting holes, all inside the real Onshape document. There is no fake “3D-looking” render hiding the hard part. The geometry exists in the CAD workspace, can be rotated, inspected, modified, and used as the starting point for the next iteration. That changes the workflow from manually clicking through every feature to describing the structure, letting GPT build the first pass, then correcting what matters. Once MCP gets reliable enough, the boring part of CAD starts looking less like modeling and more like supervising.
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.House Republicans is working to implement President Trump's FULL agenda, not just part of it. The American people are expecting us to deliver on this, and we will.
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NVIDIA is making it possible to speak with your voice to the AI characters of the game you're playing. It may look like picking from a NPC dialogue tree but the impressive part of this demo is that the generative AI is reacting to natural speech. Watch the full video (with sound) here: Source: The Verge

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