How to Estimate the Cost of an AI API Workflow
Estimating an AI API workflow means pricing every billable step, not just the final model call. Map the pipeline into steps, count input and output tokens including…
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John C. Mullen
Explore the published guides and contributions from John C. Mullen.
87 published articlesHow to Remove Sensitive Information Before Sharing AI Logs
Sanitising a log before you paste it into an AI chat, support ticket, or issue tracker has two stages: catalogue what is actually sensitive, then either redact…
Building Reliable AI Content Workflows with Human Review
A reliable AI content workflow gives each task to the party that can do it safely, keeps every claim linked to a source, and names a human…
AI Model vs AI App: What Is the Difference?
An AI model is the engine; an AI app is the car you drive. The distinction decides who bills you, whose privacy policy applies, who handles support,…
How to Test an AI API Connection from WordPress
Testing an AI connection in WordPress has two distinct layers: whether the site can talk to an AI provider, and whether the plugin's REST endpoints answer correctly…
How to Choose an AI Tool for Your Actual Workflow: A Practical Selection Guide
The right AI tool is the one that fits a specific task you already do, survives a fair test on your own examples, and earns a place…
RAM vs VRAM for Local AI: What Each One Does
When you run a local LLM, VRAM is where inference happens on the GPU, while system RAM provides capacity for loading models, CPU inference, and offloading. Both…
Connecting to Ollama from Another Device on Your Network
Ollama listens on 127.0.0.1:11434 by default, so nothing outside the host machine can reach it. Making it available to a phone, laptop, or second PC means changing…
How to Compare Local Language Models with Reproducible Tests
A reproducible local model comparison pins the model, runtime, hardware, prompt template, context settings, and sampling parameters before measuring quality, latency, memory, and failure rates. This guide…
AI Hardware Explained: RAM, VRAM, Quantization, and Model Size
Running a local language model is mostly a memory-planning problem. This guide explains how parameter count, quantization, VRAM, RAM, context length, and runtime overhead determine whether a…
How Large Language Models Work: Tokens, Context, Training, and Inference
Large language models are next-token predictors, and almost every practical constraint they have follows from four ideas: how text becomes tokens, how the context window limits what…
How to Record a Reproducible Local Model Benchmark
A reproducible local model benchmark is a record, not a number. It needs the exact model bytes and hashes, the runtime version, the hardware and power state,…