Thunderhead Explorer

In my last post, Where Is Jev? , I found storage tanks in an overhead image. I asked a vision-language model one fixed question about each tile, and SAM 3 found the tanks in the tiles that passed. This time, I want to try the opposite approach. I do not ask a question, and I do not write a prompt. I point at one tank, and the app finds the chips that look like it. The tool is Clay Embedding Explorer , a small web app that I built around the Clay v1.5 foundation model. It shows chip embeddings on a map, over the source image. In this post, I explain what an embedding is here, how the pipeline works, and what the tank numbers say. One of the numbers looked bad at first, and the reason was more interesting than the number. The image and the labels I use the same image as last time: one RGB GeoTIFF, 8,200 × 6,087 pixels at 0.5 m per pixel, in EPSG:3857. Web Mercator stretches distances, so the true ground size of one pixel at this latitude is 0.457 m. A FileGDB has 97 hand-drawn tan...

Remember Where's Waldo? You do not look at the whole page at once. You scan it left to right, top to bottom, one cell at a time. When you find the cell with Waldo in it, you look closely and spot him. I wanted to try that idea on an overhead image of an industrial site with a lot of storage tanks. In my last post , a Jev-style decision model answered a plain-English condition for each row of a table. Here, the rows are image tiles. I cut the image into tiles and ask a vision-language model one fixed question about each tile: does it contain a tank, part of a tank, or no tank? If the answer is no tank, I skip the tile. If not, the tile goes to SAM 3 , and SAM 3 finds the tanks. No model is trained for this task. The project is where-is-jev . In this post, I show how the pieces fit together, what the numbers say, and where the method is wrong. The data The test image is one RGB GeoTIFF: 8,200 × 6,087 pixels at 0.5 m per pixel, in EPSG:3857. A FileGDB has 97 tank polygons (th...

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...