Blog de Joe Freeman

In freemansoft/Flutter-AdaptiveCards a demonstration Dart chat server hands a question to a local Ollama model. It asks for the answer as Adaptive Card JSON, a strict, closed-vocabulary schema that a Flutter client renders as interactive UI rather than as text. A set of probes in that repository puts identical questions to different local models. Probe context_fill_probe.dart  produced every figure in this article. It runs 25 test cases, one question each. A test case passes only if the reply used an element type that would answer the question. Every other probe in that set had been asking into a nearly empty window. That window holds a system prompt, one question, and at most a short canned exchange showing the model the shape of a card. The literature documents that a long context degrades model behavior. What it does to a model's adherence to an output format is measured less often. The test probe simulates a long conversation by filling three q...

In freemansoft/Flutter-AdaptiveCards a demonstration Dart chat server hands a question to a local Ollama model. It asks for the answer as Adaptive Card JSON, a strict, closed-vocabulary schema that a Flutter client renders as interactive UI rather than as text. A directory of probes measures which local models manage that and how well. Every one of those probes had been asking its question into a nearly empty context window. Each call carried a system prompt, one question, and at most a short seed exchange. A real conversation fills the window. What changes when it is full? Two findings came out of filling it, measured on an Apple M1 Max with 64 GB and an Apple M5 with 16 GB. Both hosts ran Ollama 0.33.3, and the M1 Max results reproduced under Ollama 0.34.0. What the runner allocates follows one rule , with no counterexample in fifty-one runs across the two Ollama versions. It is often not what the request asked for. A history m...

In freemansoft/Flutter-AdaptiveCards  a demonstration Flutter client sends questions to a Dart chat server. The server passes each one to a local Ollama model and asks for the answer as Adaptive Card JSON. The client renders the card that comes back. To measure which models manage that, a directory of probes sends a fixed set of questions straight to Ollama, one model at a time. Each probe builds its requests the way the chat server does and judges every reply with the server's own card detector. Several of those results looked like something a model did when the cause was the test setup: the machine, the Ollama runtime, the harness or the probe. Each rule below checks for one of those mistakes before it reaches a published number. Five of the eight rules come from a measurement that went wrong. The other three guard against a known weakness in the setup: the per-call time limit, the point in a long run where a model is measured, and what the card detector cann...