Learn AI
Evergreen explainers on fundamental AI concepts — no account, no tracking, just reading.
Beginner
What is AI?
A plain-language introduction to artificial intelligence and what it can (and can't) do.
What is an LLM?
Large language models explained: how they predict text and why scale matters.
What is a Transformer?
The neural network architecture behind nearly every modern AI model.
What is a Token?
The basic unit of text that language models actually read and write.
What is Context Length?
How much a model can 'remember' at once — and why longer isn't always better.
What is Multimodal AI?
Models that see, hear, and read — not just text.
Open vs Closed Models
The most important strategic choice in AI: open weights vs proprietary APIs.
Intermediate
What is RAG?
Retrieval-Augmented Generation: giving models access to your documents.
What is Fine-Tuning?
Teaching a pre-trained model new skills, styles, or domain knowledge.
What is Inference?
Running a trained model: the step that costs money at scale.
What is Quantization?
Shrinking models by using fewer bits per number — with minimal quality loss.
What is GGUF?
The file format that made running LLMs locally practical.
What are AI Agents?
AI systems that don't just answer — they act.
What is MCP?
Model Context Protocol: a standard way to connect AI to tools and data.
What are Embeddings?
Turning text into numbers so machines can measure meaning.
What are AI Benchmarks?
How the industry measures model capability — and why scores need context.
Advanced
What is Mixture of Experts?
How models like Mixtral get big-model quality at small-model cost.
What is a Vector Database?
Storage engineered for similarity search over embeddings.
Running AI Locally
A practical guide to running models on your own hardware.
What is Attention?
The mechanism that lets models focus on what matters.