How I Use AI to Make Software Easier to Own
How I use AI to investigate, implement, challenge assumptions, and remove unnecessary complexity while retaining responsibility for engineering decisions.
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Practical architecture decisions, tradeoffs, and operating lessons from software that has to survive business change.
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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.
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.
The Complete Guide to AI Workflow Automation for SMBs
A practical guide to AI workflow automation for small and mid-sized businesses: use cases, ROI, implementation steps, risks, tools, and when custom software makes sense.