Thunderhead Explorer

When DuckDB published Jev and DuckDB: Plain-English Conditions in SQL , I wanted to try it on my North Sea wells and pipelines right away. The idea is simple and powerful. You write a plain-English condition inside a SQL query, and a decision model answers it for each row. But those Jev functions send the row data to the hosted TypeSafe API, and that needs an API key. I already had Laya, an open-weight decision model, running on my Mac from Lunar Laya . So I asked a direct question: can the same SQL run on a local model, inside DuckDB, with no network at all? The result is duckdb-jev-spatial . It is a native DuckDB extension, written in Rust, that registers seven Jev functions. Laya Rust and ONNX Runtime run the model inside the DuckDB process. The DuckDB spatial extension does the geometry. In this post, I show how the pieces fit together, what the NorthSea data shows, and where the model is wrong. Two kinds of conditions A model that reads one well row cannot find the pipelin...

One photo, one sentence. “Locate the Sydney Opera House”, and a few seconds later the scene is in 3D, as Gaussian splats you can tilt around. Recently I came across a small project called Odyseus Spatial VLM by Mercurius Technologies. Two things grabbed me right away. The first was the name. I grew up on Greek mythology, and seeing Odysseus on a GitHub page brought back a lot of childhood memories. The second was the idea: take a vision language model (VLM), the kind of model that can look at a picture and answer questions about it, and ask it not just what is in the photo, but where it is, in 3D, in meters. I liked it so much that I forked it, played with it for a while, and kept adding things. It grew into its own project, which I named Apollo Spatial VLM . (Why Apollo? Mercury and Apollo have a lovely story together. I tell it at the end of the README.) How it works You upload a photo and type what you are looking for, someth...

When Unsloth published its guide on how to train your own decision model , I immediately thought of the North Sea. In Lunar Laya , I argued that a model should take a state and some text, and return a structured, measurable answer. Here was a way to turn any small LLM into exactly that kind of model, with a probability attached to every answer. So I had to try it on FELN. A quick recap for new readers. FELN (Find Existing Location with N layers) is the little query language behind my RAG , LoRA , Liquid and WebGPU experiments. A question such as “Find gas wells within 5 km of oil pipelines” becomes a plan with three parallel lists: layers , where and relations . The first layer is the one returned; any other layer only filters it through a spatial relation. All those projects generate the whole plan in one go. This time, I wanted to pull out the very first decision and give it to a model that does nothing else: which layers does this question need, and which one is primary? One...

Every time I open my feed these days, somebody has wrapped an API in an MCP server. Weather? MCP. Calendar? MCP. I fully expect my toaster to ship one next week. Don't get me wrong, I like MCP and I use it. But I kept staring at the ArcGIS REST API, with years of well-documented endpoints for search, geocoding, routing, demographics and maps, and asking myself a simple question: does an agent really need another server in front of all of that to be agentic? So I built arcpi to find out. The old way and the new way, in one script arcpi runs on pi , a minimalist agent harness that lives in the terminal. I gave it four small Markdown skills, written from the Esri REST documentation, that tell the model which endpoint to call and with which parameters. And one TypeScript extension that makes the HTTP call as the signed-in user. That's it. No ArcGIS MCP server, no build step, no dependencies. The magic is code mode. Instead of calling one tool, waiting, thinking, and call...

This past July, I had the pleasure of presenting ProCowork at the Esri User Conference in San Diego. For those who could not make it (or were busy chasing the next session across the convention center), here is the short version of what I showed, and why I built it. I have been playing with GenAI and GIS for a while now. Asking a language model to write ArcPy is old news. What I really wanted was a model that could do the work, on my open project, inside my running ArcGIS Pro session, while I watch. ProCowork is an experimental ArcGIS Pro add-in that puts the Claude Code engine in a dockable chat panel. You type a request, Claude writes the Python, the add-in runs it against the live map, and the code and the results come back into the same panel. So I can type things like: “List the layers in the current map.” “Add a DOUBLE field POP_DEN to Parcels and set it to POP / AREASQMI.” “Select parcels where POP_DEN > 5000 and zoom to...

In my last lander post I spent a few paragraphs on Jev , the System One decision model TypeSafe AI opened to early access this month. You hand it a block of state and typed questions, and it returns a probability for every choice. No text to parse. I also made a claim I had not tested: my lander talks to its model through one call, agent.predict(prompt, QUESTIONS) , so swapping in a different decision model should be an adapter, not a redesign. Then I read about Contrastive Language Models (CLM). It takes the same contract and builds it in the open: a block of state in, typed questions, a probability per choice out, with source code and released 8B heads you can run yourself. Its server even answers on an endpoint called /v1/systemone . Jev I can only read about. CLM I could actually test. I had to find out. The adapter part turned out to be true. It was everything after that which kept me busy. The small CLM I trained on my Mac, with no overrides. The lander starts upside do...