01
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
Free AI library
Technical guidesFree forever.
177 plain-English guides, structured learning paths, and an open library — built by an independent 501(c)(3) nonprofit so anyone can understand modern AI.
177Free guides
1Topic tracks
~2 minPer guide
~6hReading time
Start here
Five outcome-based courses
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
02
Responsible AI User
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
03
AI at Work
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
04
AI Policy & Society
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
05
Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
Topic tracks
Browse by track
Jump into the area you care about. Every track has multiple plain-English guides.
FundamentalsCore concepts: how AI works at a high level.84 guides Language AILLMs, NLP, ChatGPT, prompting, and text understanding.155 guides Visual AIImage generation, computer vision, video, and design.133 guides Audio AIVoice, music, podcasting, and audio-first AI.117 guides CompaniesProfiles of OpenAI, Anthropic, Google, Meta, and more.121 guides ApplicationsHow AI shows up in coding, search, agents, and daily life.111 guides IndustriesAI in healthcare, finance, education, energy, and more.91 guides TechnicalChips, fine-tuning, RAG, vectors, and AI infrastructure.177 guides SocietyBias, privacy, copyright, safety, and the AI future.30 guides
Full library
All guides
177 of 1019 guides shown. Filter by track or search above.
Technical
Speculative Streaming and Multi-Token Prediction
Speculative streaming and multi-token prediction speed up language model generation by guessing several future tokens at once and verifying them in a single…
TechnicalBERTScore and Semantic Evaluation
BERTScore measures how well machine-generated text matches a reference by comparing meaning, not exact words.
TechnicalLinear Attention and Performer Kernels
Linear attention replaces the quadratic softmax attention in Transformers with a math trick that scales linearly with sequence length.
TechnicalYaRN and Context Length Extension
YaRN (Yet another RoPE extensioN) is an efficient technique for stretching a model's usable context window far beyond what it was trained on.
TechnicalPositional Interpolation for Long Context
Positional Interpolation (PI) is a simple, influential technique that extends a Transformer's context window by squeezing new position indices into the range…
TechnicalMixture of LoRA Experts
Mixture of LoRA Experts (MoLE) combines many small, cheaply-trained adapters with a learned router so a single base model can flexibly specialize across…
TechnicalSpeculative Edits for Code Models
Speculative edits make AI code editing feel instant by predicting that most of a file will stay unchanged and only verifying the small parts that differ.
TechnicalActivation Steering and Representation Engineering
Activation steering nudges a model's behavior by directly adding or subtracting vectors inside its hidden activations at runtime, no retraining required.
TechnicalSparse Autoencoders for Interpretability
Sparse autoencoders (SAEs) are a tool that pulls apart the tangled internal activations of a neural network into a much larger set of cleaner…
TechnicalSuperposition and Polysemanticity in AI Interpretability
Learn superposition and polysemanticity in AI interpretability: why models pack features into shared directions, why neurons become polysemantic…
TechnicalLogit Lens and Tuned Lens
The logit lens and tuned lens are interpretability techniques that peek at a transformer's hidden states layer by layer to see what the model is 'thinking'…
TechnicalDeepSpeed and Megatron Training Stacks
DeepSpeed (Microsoft) and Megatron-LM (NVIDIA) are the software stacks that make training models with billions of parameters across thousands of GPUs…
Finished reading? Prove it.
Check what you learned with topic quizzes, then explore our structured courses and current certification requirements. Core guides remain free to read.