CREOSON AI MCP Interface (tested - what is next?)

Here is a new experiment running on a mid-2017 MacBook Pro with a 100% Local AI (Gemma4) talking to a remote Windows 11 Machine that has a CREOSON MCP Endpoint (in beta).

This was just a simple connection test - asked it if it can see the CREOSON MCP endpoint?  And this was the response.

The cool thing about this…. the Local AI knows “nothing” about Creo Parametric before this interaction.  The MCP provided enough context to give the Local AI a bit of courage. 🤣

NOTE: I do not recommend running a Local AI on a mid-2017 MacBook Pro. 🙄  But it definitely works.

Dave

I have fielded quite a few questions regarding Local AI and how to do that effectively.

My “Go To” Answer on this comes down to three (3) factors:

1. The Model - you need a language model capable of contextualizing and reasoning your content
2. The Harness - the glue that orchestrates your intent, your skills, language model, MCP endpoints 
3. Inference Speed - the hardware required to run your language model (capacity and speed)

Those are (IMHO) the most important - inference runtime/engine for the model is another factor (geeky stuff 🤓).

I wanted to show more than “Claude Code” as an interface to Creo Parametric (everyone does that, figured some would appreciate a radically different approach).   So I picked this stack as a test.

1. Model - Gemma 4 31B (31B indicates the size of the model 31-BILLION Parameters)
2. Harness - OpenCode (an Open Source harness, like Claude Code or others)
3. Inference Speed - Cerebras (running the exact same Gemma 4 31B you can run locally)

Cerebras hosts the Gemma 4 31B Model, the OpenCode Harness knows how to talk to it via an Cerebras API Key, and OpenCode also knows where the CREOSON MCP is located (a Win11 Machine on my local network) - oh.. OpenCode is running on my MacBook Pro - orchestrating ALL this traffic.

The reason I chose Cerebras is simple:

• Cerebras has the fastest commercially available AI Inference on the planet,
• The Local AI Model I wanted to test (Gemma 4 31B) was running on the Cerebras platform, 
• Everything else was local to my computer/network

So this setup enabled me to show how important inference is in a relevant engineering example. Open a model, change all the names to have a prefix, generate drawings for every model, ultimately generate all the PDFs. Here was the result:

So - this is literally one of those pinch my-self days because it did not seem real.  We provided a few things (very quickly).

My instructions to Gemma 4 31B via OpenCode, were properly contextualized with the CREOSON Agent Skills and orchestrated to CREOSON MCP running on another laptop connected to Creo Parametric from my MacBook Pro. (a cigarette worthy experience).

The point of this post is simple.  AI is getting REALLY GOOD, REALLY CHEAP, and REALLY CAPABLE.  Local AI specifically will be one of the most important factors in Engineering - this will require good models, good harnesses, and good hardware. 

The next year will be explosive for AI in engineering - the Hardware is getting bigger, the models are getting smaller, and the orchestration of all this is getting easier. You are going to have many choices.

Hang on.. this is going to be fun!

Dave