FastAPI and Starlette Security for AI Agents: Production Hardening
Vulnerabilities, patches, and a hardening checklist for Python teams deploying AI agent backends.
AI Explainers & Guides
Cornerstone guides on the topics that matter for production AI. We publish a new explainer every week.
How Large Language Models Work: A Technical Explainer
Understand tokens, attention, and next-token prediction without the PhD-level math.
GPT-5 vs Claude 4.5 vs Gemini Ultra: Choose the Right LLM
A technical comparison for production teams selecting a frontier model in 2026
What Are AI Agents? A Practical Guide for Builders in 2026
How autonomous agents differ from chatbots, what frameworks actually work, and where they reliably fail.
Fine-tuning vs RAG vs Prompt Engineering: A Decision Framework
When to customize an LLM: cost, latency, and accuracy tradeoffs explained for engineering leaders.
What is Retrieval-Augmented Generation (RAG)? A 2026 Guide
How RAG fixes LLM hallucinations by letting models fetch real data before generating answers.
Coming soon
- What is the Model Context Protocol (MCP)? A practical guide for AI builders
- Vector databases for RAG: Pinecone, pgvector, Chroma, and how to choose
- Prompt engineering in 2026: patterns that actually work in production
- How to reduce LLM hallucinations: RAG, grounding, and evaluation tactics
- Embedding models compared: OpenAI, Cohere, open source, and retrieval quality
- AI coding agents in 2026: Copilot, Codex, Cursor, and what works in real repos