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Nurb – Agentic CAD for 3D printing

Hacker News

Nurb – Agentic CAD for 3D printing

I've had a love/hate relationship with Fusion over the years. I can model stuff there but it's just always slow and painful and Fusion has a deep desire to crash on me. I started dabbling with the Fusion MCP a couple of months ago and found that to be a much more pleasant way to interact with it. But then I realized I was just using Fusion as a rendering engine, which was terribly overkill. So I built Nurb. You just describe what you want ("Make an adapter that connects my shop vac hose to the dust port on my table saw") and it asks for clarification, does web searches for part measurements/details and then generates an interactive viewer as well as optimized STL files to print. The whole system hangs on the convention that a part is a function, and its keyword defaults are its parameters. That concept drives the CLI, the viewer’s sliders, the tests, and the agent’s interface. There’s no schema, no project file, no init step. A project is any directory with a parts/ folder. Under the hood it’s build123d on the OCCT kernel, so parts are B-rep solids with working chamfers, fillets, and STEP export, not meshes. That also means the model already knows the API, since build123d is in every training set. The agent can’t see, so nurb check is its eyes. Printability rules (overhangs, thin walls, slivers, stability) run against the exact solid and come back as text with coordinates, so the agent can fix what it can’t look at. The rules were calibrated by porting my real parts catalog from Fusion. It’s source-available under FSL-1.1-MIT. Install is a one-liner on https://nurb.dev , which also sets up the necessary SKILL.md file for you. Works great with all major LLMs!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, agentic · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
89%89% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way, para · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training, active · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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