Explainers
Evergreen explainers and reference guides, continuously maintained.
-
How AI Agents Work in Practice: Tools, Loops and Where They Go Wrong
Explains how AI agents turn a chatbot into something that takes actions, how the plan, act and check loop works, and why…
-
How Are AI Models Tested? Benchmarks, Evals and Red Teams Explained
Explains how AI models are tested, from exam-style benchmarks to red teams and outside evaluators, and why a single score rarely tells…
-
Probes explained in 2 minutes
A probe is a small classifier that reads an AI model’s internal signals, rather than its words, to detect things like deception.
-
World-action models explained in 2 minutes
A world-action model predicts both what a robot should do next and what the world will look like after it does it.
-
Total vs active parameters explained in 2 minutes
In a mixture-of-experts model, only a small share of the parameters does the work on each word, which is why two numbers…
-
NVFP4 explained in 2 minutes
NVFP4 is a way of storing an AI model’s numbers in just 4 bits each, so big models need far less memory…
-
Open Weights vs Closed Models: What “Open” Actually Means in AI
An open-weights model hands you the trained file to run yourself; a closed model only lets you use it through someone else’s…
-
How Do Robots Learn? Teleoperation, Simulation and Vision-Language-Action Models
Robots learn skills from three sources, people steering them, practice in simulation and models that turn words and images into movement, and…
-
How Self-Driving Actually Works: SAE Levels 0–5 and the State of Play
The SAE levels are the only precise vocabulary for autonomous driving, and almost every consumer claim abuses them. Here is the framework…
-
The AI Compute Stack: Why Chips Decide the Race
AI capability is downstream of hardware. Understanding the compute stack (accelerators, memory, packaging, power) explains most of the industry’s strategic behaviour.