AI Models.

AI Models Tool Use and Agents: A Model That Can Act Needs Boundaries Once a model can call tools, its mistakes have consequences. Design the permissions and checks before the clever part. Nov 5, 2026 · 4 min read AI Models Reading Benchmarks Without Being Misled by Them A benchmark score summarizes a narrow test. The number is useful only when you know what the test measures and whether its questions leaked into training. Nov 5, 2026 · 4 min read AI Models What a Model Learns From Its Data, and What It Cannot Learn Training data shapes what a model knows, how it writes and what it overlooks. The gaps are as informative as the strengths. Nov 5, 2026 · 5 min read AI Models Alignment in Plain Language: Helpfulness, Honesty and Caution Teaching a model to be helpful, honest and careful involves trade-offs. Understanding them explains refusals, hedges and occasional over-caution. Nov 5, 2026 · 4 min read AI Models Quantization: Smaller Models and What Is Lost Storing a model's numbers with fewer bits shrinks it and speeds it up. The loss is real, and it depends on the task. Nov 5, 2026 · 4 min read AI Models Latency and Streaming: Why the First Token Matters People judge an AI feature by how soon something appears, not by when the answer is finished. Streaming changes that experience. Nov 5, 2026 · 4 min read AI Models Getting Structured Output From a Model Reliably A program needs a predictable shape. Constraining the format, validating it and planning for failure turns a chatty model into a dependable component. Nov 5, 2026 · 4 min read AI Models Reading a Model Card Like an Engineer A model card lists what its makers chose to say. The useful part is often what it leaves out and how the tests were run. Nov 5, 2026 · 4 min read AI Models Routing Each Request to the Right Model Sending every request to the largest model is expensive and often unnecessary. A simple routing layer can save money without hurting quality. Nov 5, 2026 · 4 min read AI Models What 'Memory' in an AI Assistant Really Means Remembering a preference across sessions usually means a stored note that gets added to the context. It is useful, and it has rules worth understanding. Nov 5, 2026 · 4 min read AI Models Why the Same Prompt Can Return Different Answers Sampling, hardware differences and model updates all change output. Knowing the sources of variation helps you test and explain results. Nov 5, 2026 · 4 min read AI Models Models Get Retired: Planning for the Next Version The model you build on will be replaced. Designing for that change, with tests and swappable components, is part of the work. Nov 5, 2026 · 4 min read