One Model, Many Conversations: LLM Serving, Memory, and Reasoning
How serving engines share one model across users, isolate KV caches, batch token generation, preserve context, and spend more inference compute on reasoning.
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Practical Architecture Writing
Practical architecture decisions, tradeoffs, and operating lessons from software that has to survive business change.
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From GGUF to GPU: How llama.cpp Actually Runs a Model
Trace llama_decode through GGML graphs, CUDA kernels, GPU memory, and next-token generation—and see why expensive LLM inference can still be fast.
An LLM Is Not the Program: What Is Actually Inside the Model File
Follow an LLM request past the API and into GGUF tensors, learned parameters, attention, and the transformer layer that turns model data into logits.
Membership Reconciliation: When an Audit Stops at 10,000 Records
How a silent 10,000-record cap, ambiguous identities, and competing sources of truth changed a production membership reconciliation.
Build Around Business Capabilities, Not Vendor Features
Why durable business capabilities should define platform boundaries, while commerce, payment, and ERP vendors remain replaceable implementation details.