We live in exciting times, and AI is growing more widespread and useful in the AEC, BIM and even Revit API arena.
I have used GitHub Copilot in VS Code for Revit API add-in coding and installed OpenAI Codex myself.
A long list of topics, many of them just short pointers, ranging through trends and statistics, agentic AI, MCP, new models and technologies,
I found the AI Index 2025: State of AI in 10 Charts presents some very interesting statistical news on global AI development and trends.
Looking critically at AI usage is also vital, like this article explaining how to handle challenges using AI for complex app coding: Vibe coding considered harmful, Lego code instead.
A nice comparison of generative versus agentic AI is presented answering Wait, what is agentic AI?:
Simply put: Generative AI creates content; agentic AI solves problems on a user’s behalf.
The MCP (Model Context Protocol) adoption is gaining significant traction, and it’s increasingly being integrated with A2A (Agent-to-Agent) communication, cf., MCP measured in GitHub stars and A2A interaction:
A Fusion user has implemented an MCP server for Fusion using the M$ Semantic Workbench:
Semantic Workbench is a versatile tool designed to help prototype intelligent assistants quickly. It supports the creation of new assistants or the integration of existing ones, all within a cohesive interface. The workbench provides a user-friendly UI for creating conversations with one or more assistants, configuring settings, and exposing various behaviours.
The Semantic Workbench is composed of three main components:
Designed to be agnostic of any agent framework, language, or platform, the Semantic Workbench facilitates experimentation, development, testing, and measurement of agent behaviors and workflows. Assistants integrate with the workbench via a RESTful API, allowing for flexibility and broad applicability in various development environments. The Fusion MCP server can work seamlessly with the Claude MCP client. We can simply ask Claude to generate 3D models, and it does so automatically in Fusion by utilizing these MCP tools. The MCP client can utilise basic tools to create 3D models. Its capabilities can be expanded by developing additional tools and providing comprehensive Python API documentation as a resource for the MCP server. The fusion server available is open source and can be simply setup on the machine. The different MCPs are implemented by different independent engineers, e.g., the Fusion one is by a Fusion user! Great to see the potential power of MCP for such a complex UI as Fusion. MCP achieves things that otherwise would have taken months of learning for a newbie. This may remove the learning curve for using any of the design software in this space and thus anyone can move to any other software as well. Looks like one of the reasons for sticking to one software is taken away for good. This essentially commoditises all CAD software. Given the goal of MCP of becoming something of a "natural language USB for software", the need for traditional UX diminishes for a large proportion of users. Will this also hold true for power users?
The revit-mcp GitHub organisation provides a framework that implements the MCP protocol based on Autodesk Revit software. It enables AI conversation clients compatible with the MCP protocol to drive Revit. Example and the three main project components:
You may find the prompting guide for GPT-4.1 models useful. While the cookbook provides a comprehensive guidance for prompting this model, this guide shares a few best practices to keep in mind in the following areas:
OpenAI o3 and o4-mini impress coders and scientists. Open AI Codex coding agent runs locally: tweet. Here's the quick low-down of 03 and 04mini by Dan Shipper:
Basically, it makes ChatGPT way more useful.
Roman @Nice3point Karpovich (LinkedIn), aka Роман Карпович, principal contributor and maintainer of RevitLookup, commented on the last post discussing intersection result, LLM for IFC and more AI, saying:
Microsoft has already prepared great packages for AI, and they can already be used in C#, with just a couple of lines of code:
In the past weeks, I installed Visual Studio Code on MacOS, with GitHub Copilot integrated, and used that for some Revit API work with impressive results.
I installed OpenAI Codex and asked it to explain the purpose of my current Revit API code WIP. I provided one single prompt, six words:
Here is the answer it provided right out of the box:
This is a small C# /.NET add‑in for Autodesk Revit (a “Revit plug‑in”) whose goal is to let you take an existing plumbing (or HVAC) distribution network in your model—pipes, fittings (elbows, tees, reducers, etc.) and even ducts—and make a full copy of it, displaced to one side. Under the hood it does four main things:
Ribbon integration – On startup, it adds an “HVAC System Tools” tab with buttons for
Graph‑based network traversal – Before duplicating, it walks your existing system and builds a little graph of “nodes” (every pipe segment or fitting) and their “connectors” (endpoints), capturing for each:
Duplication at offset – In a single TransactionGroup it:
Re‑connecting the network – In a second transaction it revisits every new node, finds on each its closest connector on a neighboring new element (using a tiny distance‑threshold test) and calls the Revit API “ConnectTo” to wire them back up in exactly the same topology as the original.
Beyond pure duplication, there are also commands to:
All of this is built on small data classes – NodeData, ConnectorData, ParamData – to snapshot each element’s geometry, connectivity and parameters, plus a few utility routines (in Util.cs) to find connectors, compare points, and manage the various Revit API quirks.
Currently Gemini is ahead as "best AI", even in the OpenAI listing.
Chef by Convex generates full‑stack apps and launches them in realtime.
DeepWiki indexes and documents all GitHub repos: Cognition, maker of Devin, recently announced DeepWiki as a free encyclopeadia of all GitHub repos. You can replace any public GitHub repo url with:
That will produce a remarkably accurate Wikipedia-like description of the library as well as a Devin-backed chatbot on how to use it.
For more limited document analysis, Microsoft released MarkItDown, an MCP-integrated LLM tool to convert anything to Markdown.
Are you thinking of starting up something yourself? In that case, you might discover some useful hints up front in a postmortem of a startup.
Finally, for Unix aficionados, this article discussed an interesting topic that is completely unrelated to AI, demystifying the #! (shebang)