Applied artificial intelligence, local inference workflows, model runtimes, and practical developer techniques for working with modern AI systems.
Criticism of generative AI often becomes contempt for people who use it. The fair standard is human agency, not tool purity—what you contributed, corrected, and learned.
Context limits aren’t vibes—they’re math. Learn truncation behavior, token budgets, and RAG chunking so your model stops forgetting the important part.
Tokens are the meter your model charges in: context, latency, and cost. We'll make tokenization visible using your existing local Ollama service—first via a CLI token inspector + heatmap, then with a live-updating browser playground.
Run a local LLM on macOS, Linux, or Windows, call it over HTTP like a real service, stream output in Node/Python/Go/C++, and measure TTFT + throughput to understand what's actually happening.
How to wire AI into your CI pipeline, release process, and observability stack without handing the keys to an unreliable robot.
Transform AI code assistants from unreliable oracles into trusted teammates. Learn the Spec → Tests → AI → Review workflow, three failure modes to avoid, and concrete patterns for leading AI agents effectively in your daily coding sessions.
