generative design and dynamo blog by weeklydynamo Newsletter Source

 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. 1. A viewer pass is not a Revit pass The viewer's verdict on R1/R2/R3 closure: the lining band reads as clean and shut. Everything in Part 6 starts from the fact that this view is not evidence of a buildable loop. The shared boundary between R3, the side detail and the slab, reviewed in the viewer. Contact looks exact here — Revit's kernel is the only thing that can actually adjudicate it. Everything up to 07-23 was about fixing the section schema in the viewer. The ...

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...