From Prompt to Token: How an LLM Generates a Response
How a causal language model turns a prompt into the next token: tokenization, layers, logits, sampling, and the cache that makes the loop possible.
Ideas
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How a causal language model turns a prompt into the next token: tokenization, layers, logits, sampling, and the cache that makes the loop possible.
Grain, time, inclusions, an owner, and a known way to be wrong. A teaching note on KPI design with a labeled example and no client metrics.
A venture inside CincyLogic, founded August 2026. Assessment-led practice intelligence for independent specialty and community practices. The public entry is the Independent Practice Resilience Assessment.
An experimental data and analytics systems project. A public description of the mechanism is not ready.
Public experiments in understanding and explaining language-model systems.
A held place for scheduling systems, process tools, and diagrams that are not products yet.
Nothing in this type yet.