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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 and what…
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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.
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AI Benchmarks Explained: What the Scores Actually Measure
Benchmark numbers drive coverage, procurement and valuations. Most of the widely-quoted ones are saturated, contaminated, or not comparable between models.
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Why AI Models Hallucinate
Hallucination is not a bug in language models. It is a predictable consequence of how they are trained and, crucially, of how they are…
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Tool Use and MCP: How AI Connects to the Real World
Tool use is how a model stops merely talking and starts doing. MCP is the standard that stopped every integration from being bespoke.
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Fine-Tuning vs RAG vs Prompting: Choosing the Right Approach
Three ways to make a model do what you want, routinely confused with each other. The deciding question is whether your problem is knowledge,…
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Reasoning Models Explained: Chain-of-Thought and Test-Time Compute
Reasoning models spend extra computation thinking before answering. The gains on hard problems are real, the costs are substantial, and the tradeoffs are not…
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RAG Explained: Giving AI Access to Knowledge It Was Never Trained On
Retrieval-augmented generation looks up relevant information and hands it to the model at question time. Large context windows were supposed to kill it. They…
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AI Agents Explained: What They Are and What They Can Actually Do
An agent is a model given tools and a goal, running in a loop until the job is done. The honest picture of where…
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Tokens and Embeddings: How Machines Represent Meaning
Models do not read words or understand meaning directly. They read tokens and operate on vectors. This distinction explains a whole class of otherwise…
