generative design and dynamo blog by weeklydynamo Newsletter Source

  Research Log [2025–09–21]: Building and Troubleshooting an Automated Structural Analysis Workflow with Dynamo and Robot 1. Objective To establish and document a seamless, automated workflow connecting the parametric design tool Dynamo with Autodesk Robot Structural Analysis . This initiative covers the entire pipeline from model generation and analysis to result verification, with a systematic documentation of key errors encountered and their resolutions. 2. Summary of Work A comprehensive Dynamo script was developed utilizing the ‘Structural Analysis for Dynamo’ package. This script generates a structural model (members, nodes) and defines its core properties, including materials, sections, support conditions, and loads. The workflow successfully transfers this model to Robot for structural analysis and then brings the calculated member force and stress results back into Dynamo for color-based visualization. Throughout this process, several critical issues were identified...

  [September 13, 2025] Today’s Research Log: Verifying the Integration of Dynamo with the Gemini Vision API and its Limitations 1. Objective 🎯 The goal was to build an automated workflow within the Autodesk Dynamo environment. This workflow would use Python scripts to connect with Google’s multimodal AI model, aiming to receive image files from the AI that were either modified or newly generated. 2. Summary of Work 💡 I wrote a Python script to call the Google Gemini Vision API from Dynamo and attempted to integrate the API while resolving a series of network and model errors. Through systematic debugging and testing, I discovered that the initial goal of generating images with the ‘Nano Banana’ (Gemini 2.5 Flash) model is not currently supported by the API. As a result, while I successfully created a stable workflow that takes an image as input and returns a text analysis, I concluded that the original goal of image output could not be achieved. 3. Detailed Process & ...

  I have always been an active user of cloud-based AI like Google’s Gemini to enhance the efficiency of my Generative Design (GD) workflows and uncover optimal insights from countless design alternatives. Since the release of Gemini 2.5 Pro, its exceptional analytical capabilities have been a massive help in identifying patterns in complex data and suggesting design directions. However, it’s crucial to note that the quality of the AI’s response varies significantly depending on how well you craft your prompt. But as I moved from experimentation to practical application — using the Gemini Pro model via an API key to iteratively analyze tens or even hundreds of design alternatives — I ran into a very real-world problem: the burn rate of API tokens. The more powerful the model, the more tokens were consumed for each analysis. For a GD workflow that requires free-form idea exploration and unlimited testing, this quickly became a significant cost burden. This cost issue, combined with c...

  Image-to-Geometry Workflow example The video below shows the current results of our ongoing project, where we are experimenting with outputting the form of a recognized image directly into Dynamo Geometry. While we first tested automated modeling from images and colors several years ago, this latest experiment is focused on significantly simplifying and advancing that core process. Press enter or click to view image in full size Press enter or click to view image in full size In the video above, it seems that the shape of the image is well read, but it can be seen that the shape is broken as it goes back. Therefore, we have updated how ai understands and defines the pattern of shape. A subsequent video demonstrates our new, more sophisticated approach where the workflow is decoupled into three distinct stages. While these stages are logically separate, they are currently combined into a single, powerful Python node. Press enter or click to view image in full size Here is a breakd...

  Hello! Recently, I experienced a frustrating slowdown on my computer while testing AI models. For anyone facing a similar problem, I’ve put together a detailed guide on the cause and how I fixed it. Press enter or click to view image in full size 1. The Mysterious Process: WorkloadsSessionHost I was in the middle of a project, using AI models like  mstyai  and  Gemini , when my computer started to feel sluggish. When I opened Task Manager, I discovered the root cause: multiple instances of a process called  WorkloadsSessionHost  were consuming a huge amount of my system's memory. This process is typically associated with remote desktop or virtual environments. Since I wasn’t using any of those, I was baffled as to why it kept running automatically. 2. Cause Analysis: The AI Work Was the Culprit To figure out the identity of  WorkloadsSessionHost , I analyzed my system logs and services. I found that this process was deeply connected to a service call...