WeeklyDynamo: Generative Design and Dynamo for AEC

At Autodesk University 2025 I had about three minutes. The segment sits at 27:30 inside Sol Amour's session Exploring the Dynamo Product Road Map and Vision for the Future , which runs 46:30 in total, and the subject was water treatment plant layout generated with Generative Design. Three minutes is a reasonable amount of time to demonstrate that something works. It is nowhere near enough time to show another engineer how to rebuild it. This month the same material had to become a twelve-hour course, delivered over two days to practising engineers at a public-sector training academy. Expanding a three-minute demo by a factor of two hundred and forty sounded absurd when I first wrote the number down, and then I built the curriculum and discovered that the ratio was never the interesting part. A demo and a course are not the same content played at different speeds. They are largely different content, and the portions that survive the translation are not the portions I would have ...

  Part 6 of a series — a Dynamo tunnel geometry that was already finished, a 39-sheet Excel workbook it had never been wired to, the web viewer built to referee the two, and the Revit add-in at the end of it. For six weeks the web viewer said the geometry was fine. Then the same polylines went into a Revit add-in and three of the bands would not build at all. This is the last stretch: an add-in, thirteen versions in one afternoon, and a root cause that only appeared once I stopped looking at screenshots.

A long-form project note about translating a pathfinding concept into a Dynamo and Generative Design workflow, including CAD geometry, Revit Generic Model caching, Data.Remember, scalar GD outputs, and the failures that shaped the final structure. From Concept to Dynamo: The Trial-and-Error Behind a Generative Design Workflow Building the concept is one task. Moving it into a real .dyn workflow is another. A few days ago I published Automation Lab: Runnable Pathfinding Experiment . That post introduced a small browser-based experiment: place start points, end points, and obstacles on a canvas, then generate route options around blocked areas. On the web, the idea is easy to understand. A point is a point. An obstacle is a drawn shape. A route is a line. The coordinate system is controlled, the geometry types are clean, and every object is created by the application itself. That makes the browser experiment useful as a concept layer. It can show the intent quickly: generate a ro...

From Generative Design Optimization to Quantity Takeoff Automation: Building a Scalable AEC Process System In many AEC workflows, Generative Design optimization and Quantity Takeoff automation are treated as separate topics. One belongs to the front end of design exploration.   The other belongs to the back end of documentation, estimation, or reporting. But in real projects, especially repetitive and high-complexity projects, that separation is too artificial. The real opportunity is not to optimize a layout in isolation, and not to extract quantities only after the design is already fixed. The deeper opportunity is to build a connected system where: - spatial logic - room classification - layout rules - object libraries - placement automation - parameter logic - quantity extraction are designed as one continuous workflow. That is the difference between isolated automation and scalable process architecture. The real problem: many AEC automations stop at one layer This is a re...

 AI Can Now Build App Features. Revit Add-in Development Is Changing with It. We have entered a new phase of software development. AI is no longer only suggesting snippets, writing helper functions, or completing boilerplate.   It is increasingly capable of planning, building, testing, and revising actual application features. That shift matters for every software domain.   But in AEC, it matters in a very specific way: **it changes who can build internal tools, and how fast they can do it.** And if that is true for web apps, it is increasingly true for Revit add-ins as well. For many years, custom add-in development in Revit followed a familiar pattern.   A problem emerged inside a design firm, construction company, or engineering organization.   The team documented the request, aligned requirements, secured budget, and then asked an external software company or specialized vendor to build the tool. That model still exists.   But t...

Why AI in AEC Stalls: The Real Problem Is Not the Model, but the Workflow Data Structure Everyone says AI is coming to AEC. And yet, in real projects, fully working AI workflows are still rare. Why? Most people first suspect the model. They say the AI is not accurate enough. The LLM is unstable. The vision model is weak. The output hallucinates. The prediction is unreliable. Those concerns are not meaningless. But in practice, the failure point often appears much earlier. AI in AEC usually does not stall first at the model layer. It stalls at the workflow layer. More specifically, it stalls where data, rules, geometry logic, naming systems, and execution structure have not been organized into a form that AI can actually read, trust, and use. That is the real bottleneck. The wrong diagnosis: blaming the model too early AEC teams often assume that poor AI results come from weak AI. But in many cases, the model is not the first bottleneck. The workflow is. The project may already contain...

AI is now being inserted into almost every conversation about AEC automation. But in many cases, the question is still too vague. The industry keeps asking: “How can we use AI?” A better question would be: **Where exactly should AI sit inside the automation process?** That difference matters because automation is not a single action. It is a chain of decisions, transformations, validations, and outputs. Some parts of that chain benefit greatly from probabilistic inference. Other parts require deterministic control. If AI is placed in the wrong layer, the workflow becomes unstable. If it is placed in the right layer, it can create real leverage. This is the core issue. The value of AI in AEC does not come from making everything intelligent.   It comes from assigning intelligence to the right layer of the system. In my view, that is where the conversation needs to mature. AI should not be treated as a blanket replacement for automation.   It should be treated as one co...