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From Concept to Dynamo: The Trial-and-Error Behind a Generative Design Workflow

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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...
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Automation Lab: Runnable Pathfinding Experiment

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Automation Lab Runnable Pathfinding Experiment A focused browser prototype for testing route generation around obstacles. Place start points, end points, and obstacles on the canvas, then compare the generated route with alternative path patterns. Run Pathfinding Experiment
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Building a Local AI Organization: From One Assistant to a Governed Knowledge System

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Why useful AI work needs roles, result gates, and governed memory - especially for AEC/BIM automation. 0. Opening - Why One Assistant Was Not Enough Most AI workflows still begin with a single assistant. Ask a question. Get an answer. Copy the result somewhere else. Start again next time. For simple tasks, this is enough. If I need a short summary, a draft email, a quick explanation, or a small code snippet, one assistant can be extremely useful. But the moment the work becomes continuous, the single-assistant model starts to break. In my case, the work does not live inside one prompt. It crosses multiple layers: AEC automation projects Revit and Dynamo workflows Python scripts Generative Design experiments AI model training notes research papers blog articles YouTube and LinkedIn content presentation decks project-specific standards client-facing deliverables long-term knowledge management A single assistant can answer well in the moment. But it usually does not...
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A Practical Comparison of Generative Design Tools: Dynamo, Grasshopper, and AI Design Platforms

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Generative design is often described as a way for software or AI to create design options automatically. In practice, the more important point is not automation itself. The real value comes from defining design variables, evaluation criteria, and a search strategy so that many alternatives can be generated, compared, and improved. For AEC workflows, this matters because design decisions are rarely based on a single criterion. A building option may need to balance area efficiency, daylight, cost, constructability, code constraints, structural logic, and documentation requirements. Generative design tools help explore that decision space, but each tool is built around a different workflow and level of control. The Basic Structure of Generative Design Most generative design workflows can be understood through three components: a generator, an evaluator, and a solver. Component Role Generator Creates design alternatives from input variables and constraints. Evaluator Measur...
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From Generative Design Optimization to Quantity Takeoff Automation: Building a Scalable AEC Process System

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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...
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