Thunderhead Explorer

Two of my recent lander experiments kept nagging at me. In One Lander, Three Controllers , an adaptive MPC learned its engine during the flight and landed through a power loss with no training data at all. In Lunar Laya , a small decision model on Apple MLX turned a text description of the flight into structured choices, but the text carried hints from a fixed feedback law that assumed a healthy engine. Cut the engine power and that model crashed every time, faithfully following bad advice. So I asked the obvious question: what happens if the engineer writes the hints for the trainee? The result is Lunar MPC + Laya . An actual recorded flight. Laya proposes every command from telemetry alone; adaptive MPC vetoes about one in three. The engine loses 60% of its thrust at ten seconds, the estimate drops to 40%, and the lander touches down at seventy. The engineer and the trainee The game is the same arcade-style lander from Lunar Laya: three pads on bumpy terrain, a tilt control, a...

I keep coming back to Lunar Lander. It gives me a very visible way to ask a simple question: what does a model actually decide? Turn too much, burn too late, or miss the pad, and the result is right there on the screen. After exploring learning and control approaches to this problem, I wanted to try another angle: give a small model the current state and a text instruction, then ask it for a decision that the application can execute and measure. That experiment is now public as Lunar Laya , an Atari-inspired lander using Laya with local inference on Apple MLX. I believe the input should be a state and a text, even a structured text. The output should always be structured and very deterministic, so it can be controlled and measured. This implementation is a representation of that idea. The fact that the model can be small enough to run locally in an MLX environment is proof that it can be done for this kind of bounded decision task. An actual trained-model flight using FP16 MLX ...

After LoRA on a 4B model and a 1.2B model on the Mac , I kept asking myself a simpler question. How small can the model be, and how plain can the training loop be, before this North Sea task stops working? So I went back to the basics: GPT-2 medium, 355 million parameters, fully fine-tuned with Andrej Karpathy's nanoGPT . No adapters, no chat template, no grammar, no catalog in the prompt. Just a question in, a JSON query out. That is nanofel . The task is the same FELN translation: a question about wells, pipelines, and discoveries becomes a plan with layers , where , and relations . The twist here is the data format. nanoGPT trains on one flat stream of tokens and samples random windows from it, so each training pair is simply: Q: Which gas wells in Norway are within 2 kilometers of an oil pipeline? A: {"layers":["Wells","Pipelines"], "where":["content_type = cast(2 as SMALLINT) and (country = 'NO')","Pipelin...

After teaching a small model to query the North Sea , I wanted to take the experiment onto a Jetson. Could I train on a powerful workstation, then move the model, application, and spatial database onto an NVIDIA Jetson Orin? And could it produce the expected FELN JSON accurately enough to be useful? That is the experiment behind feln-qwen . The job is specific: translate a question about wells, pipelines, and discoveries into FELN. The JSON identifies the layers, their attribute filters, and the spatial relationships between them. Layer order matters. Finding pipelines near discoveries is a different request from finding discoveries near pipelines! A huge thank you to Geodata for the beautiful NorthSea data . It gave this experiment a rich geospatial vocabulary and meaningful relationships to work with. I deeply appreciate the work and care behind this dataset. I chose Qwen3.5-9B and first verified stock-model inference on the Orin. The target machine had to support the model b...

I have been experimenting with small language models that translate everyday questions into structured GIS queries. With FELN Liquid , I wanted to explore the training side on the Mac itself. Could I take a compact model, teach it the conventions of a North Sea catalog, and complete the experiment locally? The first complete run used LiquidAI's LFM2.5-1.2B-Instruct with LoRA through MLX on an Apple M4 Max. Training took 48 minutes and 37 seconds. Including checkpoint evaluation and the final test, the workflow finished in just under an hour. That is a practical turnaround for trying an idea and inspecting what it learned. The task is familiar from the other FELN projects: turn a request into a JSON plan containing layers , where , and relations . A compact Layers.json catalog accompanies each request, supplying the names, types, aliases, codes, and hints that make the data meaningful. For example, “Show all wells with water depth > 350 meters” should produce: { ...

I have been exploring how small language models can turn a plain-English question into a useful GIS query. With FELN LoRA, that meant training a model on a GPU and running it locally on my Mac. For feln-webgpu , I wanted to bring the language model, the database, and the map into the web application itself. Type “Give me all oil discoveries,” and the browser generates a query plan, compiles it to SQL, executes it, and draws the results. The example in the repository finds 562 discoveries, with 200 displayed. We can inspect the plan and SQL beside the map. I like being able to see what the model actually asked the database! The question, query plan, SQL, and resulting features in one browser tab. The pieces are a fine-tuned LiquidAI LFM2-350M model, Transformers.js running on WebGPU, DuckDB-Wasm with its spatial extension, and the ArcGIS Maps SDK for JavaScript. Once the assets have loaded, query generation and database execution happen on the user's machine. There is ...