AI & Agent Systems
How language models actually work — from tokens and attention to inference, RAG, and the agent runtimes that wrap them. Start at intuition, end at production behavior.
- 01How LLMs Work — Complete Guide
- 02Neural Networks
- 03Tokenization
- 04Embeddings
- 05Attention
- 06Transformers
- 07Loss & Gradient Descent
- 08Probability & Sampling
- 09Training Pipeline
- 10Data & Annotation
- 11Inference Pipeline
- 12Context Engineering
- 13Building Enterprise AI Agents
- 14Trip Planner Agent
- 15Retrieval-Augmented Generation (RAG)
- 16Agents & Orchestration
- 17Agent Runtimes & State
- 18Memory & State
- 19Tools & MCP
- 20AI Platforms & Infrastructure
Other domains
Identity & Security
Principals, credentials, sessions, tokens, and the protocols that carry them — JWT, OAuth, OIDC, SAML, SCIM — through to workload identity, delegated authority, and agent identity.
Distributed systems that stay upEnterprise Systems
Distributed systems, caching, messaging, consistency, APIs, data architecture, and the observability that tells you whether any of it is working.
Named solutions to recurring problemsArchitecture & Design Patterns
Circuit breakers, sagas, the outbox, bulkheads, idempotency, CQRS, and the other patterns you reach for when the diagram meets production.
Now break itFailure Modes
The ways systems actually fail: cascading failure, retry storms, cache stampedes, split brain, token replay, stale authorization. What triggers them, how they present, and how to contain the blast radius.