Coverage of major AI model families and how they're evolving.
14 articles
Vision-language models let AI read a chart, a screenshot, or a scanned document the way it reads text, but the underlying mechanism still trips on details a human eye catches instantly.
Chain-of-thought prompting used to be the trick that unlocked better answers. With reasoning models doing that step internally now, here's whether asking one to 'think step by step' still does anything.
What AI model cards actually disclose about a model, and the gaps evaluators need to check for themselves.
Latency, privacy, and unit cost are real wins. Memory bandwidth and thermals decide whether any of it ships. A look at what runs locally in 2026.
The capability gap is six to twelve months. The real decision is data control, traffic shape, and who carries the operations burden.
A mixture of experts activates a slice of a huge model on each token. Here is how routing works, what it costs in memory, and which parameter count matters.
Model names and version numbers have become genuinely confusing, and understanding how versioning actually works helps explain why the same product name can behave differently over time.
A context window is what a model can see in one conversation; memory is something else entirely, and confusing the two explains most AI "forgetting" complaints.
Synthetic data — text, images, or examples generated by an AI model rather than collected from the real world — now fills a growing share of what trains the next generation of models.
Reasoning models spend extra computing time working through a problem step by step before answering, which changes both what they get right and how much they cost.
Fine-tuning retrains a model on your own examples, while prompting just gives it instructions at request time — and picking the wrong one wastes real money.
Model distillation trains a small, fast AI model to mimic a larger one, so products can run cheaper and faster without starting from scratch.
