If you want AI agents running on your own machine, with your own models, and no data leaving your computer, this is the article :). This is part three of the series. In part one we set up PostgreSQL, and in part two we covered the LLM concepts (models, parameter, quantization, context, capabili...
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This is Part 2 of a 5-part series. Part 1 built a rule-based agent for a single ride-share zone — no LLM, just structured numbers and deterministic rules. It worked, but with one obvious hole.
The obvious way to understand a neuron is to find the inputs that make it fire hardest and read them. It is also the way that produces confident, wrong explanations, and the fixes — automated explanation with automated scoring — are interesting precisely because of how they are scored. Where th...
There are two ceilings on schema depth and they behave completely differently. One is published, enforced at request time and easy to discover. The other is not a limit at all — it is a gradual change in what a failure looks like, and it has no error message.
A neural radiance field is a small neural network whose weights encode one specific scene — not a model of scenes in general, a model of your kitchen. Gaussian splatting keeps the idea of fitting a scene to photographs and throws away the network, which is why it renders in milliseconds instead ...