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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.show more

Rina

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16,772 görüntüleme • 10 gün önce •via X (Twitter)

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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.

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.

Rina

32,114 görüntüleme • 8 gün önce

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.

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.

Rina

14,154 görüntüleme • 8 gün önce

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.

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.

Rina

33,039 görüntüleme • 13 gün önce

KYRALL TURNS "I NEED THIS PART" INTO SECONDS, WITHOUT OPENING CAD you describe the part. the AI generates it. no SolidWorks. no Fusion. no hours of setup. why this matters to you: modeling a part in CAD is slow and technical. sketch, constrain, extrude, fillet — for every bracket, housing or fixture. and simple one-off parts eat the same ceremony every time. for an engineer, that bottleneck stalls the whole project. Kyrall skips it: → prompt → 3D part ready → exports to STEP and STL, the formats you already use → for when you need the geometry now, not in three days it doesn't compete with CAD suites on millimeter control. it competes on getting you to a usable part first. idea → part → keep building. link below :)

KYRALL TURNS "I NEED THIS PART" INTO SECONDS, WITHOUT OPENING CAD you describe the part. the AI generates it. no SolidWorks. no Fusion. no hours of setup. why this matters to you: modeling a part in CAD is slow and technical. sketch, constrain, extrude, fillet — for every bracket, housing or fixture. and simple one-off parts eat the same ceremony every time. for an engineer, that bottleneck stalls the whole project. Kyrall skips it: → prompt → 3D part ready → exports to STEP and STL, the formats you already use → for when you need the geometry now, not in three days it doesn't compete with CAD suites on millimeter control. it competes on getting you to a usable part first. idea → part → keep building. link below :)

marcus

31,238 görüntüleme • 1 ay önce

Can frontier language models turn a technical drawing into real CAD? We ran the experiment. Gemini 3.1 Pro, Claude Opus 4.7, and GPT-5.4 got a set of technical drawings and were asked to reproduce a part as a valid 3D CAD model. Still a far way to go, but clearly a sign of life.

Can frontier language models turn a technical drawing into real CAD? We ran the experiment. Gemini 3.1 Pro, Claude Opus 4.7, and GPT-5.4 got a set of technical drawings and were asked to reproduce a part as a valid 3D CAD model. Still a far way to go, but clearly a sign of life.

Michael Rabinovich

172,642 görüntüleme • 4 ay önce

AI designs functional hardware! 🔧 Claude Opus 4.7 is leading CAD benchmarks, and AI is starting to design functional hardware autonomously. The shift happening: CAD as a tool → CAD as an autonomous system. The model didn't just sketch geometry, it generated mechanically valid designs with joints and motion constraints. It optimized for real-world functionality, not just visual correctness like earlier LLMs. This mirrors what happened in coding last year. In software, engineers moved toward requirements, architecture, and orchestration while code became the compiled outcome of AI systems. Mechanical engineering may follow the same path: from designing parts → defining intent. This changes the role of engineering teams. Instead of manually designing every component, engineers will define constraints, validate outputs, and focus on system-level architecture. Engineers spend less time on geometry and more time on optimization and integration. AI handles the mechanical validity checks that traditionally required experienced designers. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

AI designs functional hardware! 🔧 Claude Opus 4.7 is leading CAD benchmarks, and AI is starting to design functional hardware autonomously. The shift happening: CAD as a tool → CAD as an autonomous system. The model didn't just sketch geometry, it generated mechanically valid designs with joints and motion constraints. It optimized for real-world functionality, not just visual correctness like earlier LLMs. This mirrors what happened in coding last year. In software, engineers moved toward requirements, architecture, and orchestration while code became the compiled outcome of AI systems. Mechanical engineering may follow the same path: from designing parts → defining intent. This changes the role of engineering teams. Instead of manually designing every component, engineers will define constraints, validate outputs, and focus on system-level architecture. Engineers spend less time on geometry and more time on optimization and integration. AI handles the mechanical validity checks that traditionally required experienced designers. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

25,458 görüntüleme • 4 ay önce

You can build your 3D models directly from a prompt into Claude code. All thanks to this beautiful python project, build123 a parametric modeling framework for 2D and 3D CAD. It is built on the Open Cascade geometric kernel and it is suitable for 3D printing.

You can build your 3D models directly from a prompt into Claude code. All thanks to this beautiful python project, build123 a parametric modeling framework for 2D and 3D CAD. It is built on the Open Cascade geometric kernel and it is suitable for 3D printing.

Marco Franzon

20,819 görüntüleme • 4 ay önce

For a long time I've been teaching a self-extending design system to generate aerospace hardware from pure physics no CAD, just code and constraints. Tonight I hooked my CEM up to Fusion 360 via MCP after a design and it rebuilt it! Real feature trees a machinist can open, edit, and cut toolpaths from. The system that designs in code can now speak the language of every shop floor on Earth. So to answer if you can transfer your LEAP 71 work to CAD. Yes, you can.

For a long time I've been teaching a self-extending design system to generate aerospace hardware from pure physics no CAD, just code and constraints. Tonight I hooked my CEM up to Fusion 360 via MCP after a design and it rebuilt it! Real feature trees a machinist can open, edit, and cut toolpaths from. The system that designs in code can now speak the language of every shop floor on Earth. So to answer if you can transfer your LEAP 71 work to CAD. Yes, you can.

Dylan Small

15,054 görüntüleme • 1 ay önce

Blender tutorials are about to age very badly. In this demo, Kimi K3 gets two character references and a plain-English request. Minutes later, it builds the first version inside Blender. The user asks for a better horn, Kimi edits the same scene, and the character starts moving. This is not another AI tool generating one lucky image. It is working with an actual editable 3D project, then changing the geometry after feedback instead of restarting from zero. It still needs human direction, because the first result is rarely the good one. But blockouts, repetitive edits, Blender Python, and basic iteration are becoming a conversation rather than hours of clicking through menus. Kimi K3 + Blender MCP is basically turning 3D modeling into: describe it, inspect it, complain about it, and let the AI fix it.

Blender tutorials are about to age very badly. In this demo, Kimi K3 gets two character references and a plain-English request. Minutes later, it builds the first version inside Blender. The user asks for a better horn, Kimi edits the same scene, and the character starts moving. This is not another AI tool generating one lucky image. It is working with an actual editable 3D project, then changing the geometry after feedback instead of restarting from zero. It still needs human direction, because the first result is rarely the good one. But blockouts, repetitive edits, Blender Python, and basic iteration are becoming a conversation rather than hours of clicking through menus. Kimi K3 + Blender MCP is basically turning 3D modeling into: describe it, inspect it, complain about it, and let the AI fix it.

Rina

19,092 görüntüleme • 29 gün önce

Andrew Ng, on the biggest shift in AI right now: “The most important trend is agentic AI workflows.” In one slide, he showed the part most people still miss: GPT-3.5 scored 48% on a coding benchmark, GPT-4 reached 67%, but GPT-3.5 inside an agentic workflow jumped to around 95%. That is not a prompt trick. It is the model planning, using tools, checking its own work, fixing mistakes and running the loop again. Everyone is still arguing about which model is smarter, while the real edge is moving to the system wrapped around the model. Watch the clip, then build the workflow around your own process below.

Andrew Ng, on the biggest shift in AI right now: “The most important trend is agentic AI workflows.” In one slide, he showed the part most people still miss: GPT-3.5 scored 48% on a coding benchmark, GPT-4 reached 67%, but GPT-3.5 inside an agentic workflow jumped to around 95%. That is not a prompt trick. It is the model planning, using tools, checking its own work, fixing mistakes and running the loop again. Everyone is still arguing about which model is smarter, while the real edge is moving to the system wrapped around the model. Watch the clip, then build the workflow around your own process below.

Gipp 🦅

17,267 görüntüleme • 2 ay önce

OpenAI shipped a new speech-to-speech model today: gpt-realtime-2 This is the first speech-to-speech model good enough to use in my voice agents that do "real work." Or real play, for that matter. Here's gpt-realtime-2 as the brain of the ship AI in Gradient Bang. The voice-to-voice response and tool calling times here are unedited, so you can see exactly what the interaction with the model is like in an agent with a very complex system instruction and frequent tool calls. (I did clip out the subagent task execution segments, after gpt-realtime-2 starts a subagent via a tool call. Subagents in this config used gpt-5.2 "medium" effort.)

OpenAI shipped a new speech-to-speech model today: gpt-realtime-2 This is the first speech-to-speech model good enough to use in my voice agents that do "real work." Or real play, for that matter. Here's gpt-realtime-2 as the brain of the ship AI in Gradient Bang. The voice-to-voice response and tool calling times here are unedited, so you can see exactly what the interaction with the model is like in an agent with a very complex system instruction and frequent tool calls. (I did clip out the subagent task execution segments, after gpt-realtime-2 starts a subagent via a tool call. Subagents in this config used gpt-5.2 "medium" effort.)

kwindla

55,222 görüntüleme • 3 ay önce

SplatOverflow: Asynchronous Hardware Troubleshooting TL;DR: That's probably the most creative application of 3DGS I have ever seen :) Abstract (excerpt): SplatOverflow creates a novel boundary object, the SplatOverflow scene, that users reference to communicate about hardware. The scene comprises a 3D Gaussian Splat of the user’s hardware registered onto the hardware’s CAD model. The splat captures the current state of the hardware, and the registered CAD model acts as a referential anchor for troubleshooting instructions. With SplatOverflow, maintainers can directly address issues and author instructions in the user’s workspace.

SplatOverflow: Asynchronous Hardware Troubleshooting TL;DR: That's probably the most creative application of 3DGS I have ever seen :) Abstract (excerpt): SplatOverflow creates a novel boundary object, the SplatOverflow scene, that users reference to communicate about hardware. The scene comprises a 3D Gaussian Splat of the user’s hardware registered onto the hardware’s CAD model. The splat captures the current state of the hardware, and the registered CAD model acts as a referential anchor for troubleshooting instructions. With SplatOverflow, maintainers can directly address issues and author instructions in the user’s workspace.

MrNeRF

20,093 görüntüleme • 1 yıl önce

🚨You can now use the new upcoming OpenAI model GPT 5.2 inside Cursor. Here is the full walkthrough. - Open the editor, go to settings and then the model tab. Add a custom model and enter the text "gpt-5.2-high" and "gpt-5.2". - After that you can select the model and ask questions. To verify, I started my test on the usage page which had zero gpt-5.2-high requests and consumption. After the test I could see the details in usage and the cost incurred while using it. Enjoy

🚨You can now use the new upcoming OpenAI model GPT 5.2 inside Cursor. Here is the full walkthrough. - Open the editor, go to settings and then the model tab. Add a custom model and enter the text "gpt-5.2-high" and "gpt-5.2". - After that you can select the model and ask questions. To verify, I started my test on the usage page which had zero gpt-5.2-high requests and consumption. After the test I could see the details in usage and the cost incurred while using it. Enjoy

AshutoshShrivastava

424,035 görüntüleme • 8 ay önce

Say hello to text-to-cad, an open source tool for generating 3D models in Codex! Prompt and edit complex 3D models. Export STEP, STL, GLB, DXF and URDF files. Built for CAD newbies. Link to github below. CAD is hard. As a software engineer getting back into robotics, I’ve been humbled by new tools like Onshape. Struggling to kick old habits, I started prompting Codex to generate 3D models and had some limited succes. After a few iterations I found a recipe that actually works: 1. Generate a python script for every STEP file. The agent can easily edit each part’s source without touching the raw STEP file. Use build123d > cadquery. 2. Reference specific faces and edges in prompts for precise edits. I built a basic local ui to inspect / cache STEP B-reps to make this easier. 3. Maintain markdown explaining important part features in plain English so the model can index on project context quickly. 4. Verify results with screenshots and geometry. Models don’t have great spatial awareness, but they can interpret images and verify constraints very well. For the best results I’ve been using GPT 5.4 xhigh / Opus 4.6+. Fair warning, this will burn through tokens, I recommend the Pro/Max plans if you’re planning on building anything serious. PRs welcome!

Say hello to text-to-cad, an open source tool for generating 3D models in Codex! Prompt and edit complex 3D models. Export STEP, STL, GLB, DXF and URDF files. Built for CAD newbies. Link to github below. CAD is hard. As a software engineer getting back into robotics, I’ve been humbled by new tools like Onshape. Struggling to kick old habits, I started prompting Codex to generate 3D models and had some limited succes. After a few iterations I found a recipe that actually works: 1. Generate a python script for every STEP file. The agent can easily edit each part’s source without touching the raw STEP file. Use build123d > cadquery. 2. Reference specific faces and edges in prompts for precise edits. I built a basic local ui to inspect / cache STEP B-reps to make this easier. 3. Maintain markdown explaining important part features in plain English so the model can index on project context quickly. 4. Verify results with screenshots and geometry. Models don’t have great spatial awareness, but they can interpret images and verify constraints very well. For the best results I’ve been using GPT 5.4 xhigh / Opus 4.6+. Fair warning, this will burn through tokens, I recommend the Pro/Max plans if you’re planning on building anything serious. PRs welcome!

Jake Fitzgerald

52,883 görüntüleme • 4 ay önce

Assembling is easy but to reach where China is we need control over every element in the chain from steel to the raw materials that go into batteries and wires and most importantly the machine that builds machines. For that, CAD has to become like Insta in the hands of our generation. We're just starting, just a matter of time 🤞

Assembling is easy but to reach where China is we need control over every element in the chain from steel to the raw materials that go into batteries and wires and most importantly the machine that builds machines. For that, CAD has to become like Insta in the hands of our generation. We're just starting, just a matter of time 🤞

Dravidian Insights

20,785 görüntüleme • 9 ay önce

A neural network can begin as a flat sheet and learn the shape of hidden data A self-organizing map turns learning into geometry. Each data point pulls one winning neuron toward it, but nearby neurons move too, and so the whole lattice bends without losing its neighborhood structure. The strange part is that the network is not given the roll shape. It discovers the shape through competition and local cooperation. Paper: Self-Organized Formation of Topologically Correct Feature Maps Authors: Teuvo Kohonen Year: 1982

A neural network can begin as a flat sheet and learn the shape of hidden data A self-organizing map turns learning into geometry. Each data point pulls one winning neuron toward it, but nearby neurons move too, and so the whole lattice bends without losing its neighborhood structure. The strange part is that the network is not given the roll shape. It discovers the shape through competition and local cooperation. Paper: Self-Organized Formation of Topologically Correct Feature Maps Authors: Teuvo Kohonen Year: 1982

Mathelirium

130,081 görüntüleme • 3 ay önce

Adena Friedman, Chief Executive Officer of Nasdaq: "Part of that promise of the markets is to make it so every American can share in that great economy, and today, with the Trump Accounts, the next generation of Americans—every American—will have the opportunity to be a part of and share in this prosperity of this nation."

Adena Friedman, Chief Executive Officer of Nasdaq: "Part of that promise of the markets is to make it so every American can share in that great economy, and today, with the Trump Accounts, the next generation of Americans—every American—will have the opportunity to be a part of and share in this prosperity of this nation."

Rapid Response 47

48,385 görüntüleme • 1 ay önce

Terence Tao, UCLA professor and the most decorated mathematician alive: "Funds pay $750K to combine weak signals into one real edge. I proved the thing that makes it work and makes it dangerous: in any long enough sequence, hidden structure is unavoidable. it always accumulates. the whole job is telling the real structure from the noise that only looks like it." this free lecture is the most decorated mathematician alive on the exact problem sitting underneath every factor model, and it costs nothing. at the board it's simple. Tao's lifelong theme is the line between structure and randomness. The Erdős discrepancy problem asks a deceptively simple thing: can you write an endless string of plus-ones and minus-ones that stays perfectly balanced forever? Tao proved you cannot. No matter how cleverly you try, imbalance, hidden structure, is forced to accumulate as the sequence grows. There is no such thing as a long stream of pure, structureless noise. That's the whole idea, minus the jargon. Which is exactly why a multi-factor model can work, and exactly why it can kill you. Stack enough weak signals and real structure will appear, because at scale structure is unavoidable. But so will fake structure, patterns that exist only because the data is long enough to force them. Same point as the post above: finding structure is guaranteed. Knowing which structure is an edge is the rare and expensive part. the mathematics is free and public. what nobody can sell you is the judgment to tell the structure the market will pay you for from the structure that exists only because you looked hard enough. That judgment is the alpha, and it takes years to build.

Terence Tao, UCLA professor and the most decorated mathematician alive: "Funds pay $750K to combine weak signals into one real edge. I proved the thing that makes it work and makes it dangerous: in any long enough sequence, hidden structure is unavoidable. it always accumulates. the whole job is telling the real structure from the noise that only looks like it." this free lecture is the most decorated mathematician alive on the exact problem sitting underneath every factor model, and it costs nothing. at the board it's simple. Tao's lifelong theme is the line between structure and randomness. The Erdős discrepancy problem asks a deceptively simple thing: can you write an endless string of plus-ones and minus-ones that stays perfectly balanced forever? Tao proved you cannot. No matter how cleverly you try, imbalance, hidden structure, is forced to accumulate as the sequence grows. There is no such thing as a long stream of pure, structureless noise. That's the whole idea, minus the jargon. Which is exactly why a multi-factor model can work, and exactly why it can kill you. Stack enough weak signals and real structure will appear, because at scale structure is unavoidable. But so will fake structure, patterns that exist only because the data is long enough to force them. Same point as the post above: finding structure is guaranteed. Knowing which structure is an edge is the rare and expensive part. the mathematics is free and public. what nobody can sell you is the judgment to tell the structure the market will pay you for from the structure that exists only because you looked hard enough. That judgment is the alpha, and it takes years to build.

Rossst.03

213,599 görüntüleme • 1 ay önce