The case for recursive self-improvement has a hidden premise: that a machine can tell which version of itself is actually better. Sixty years of research keeps running into that one step. Where self-improving systems reached superhuman results, where they collapse instead, and what decides which, with a complete runnable Python experiment.
A new free, browser-based lab for the math that powers machine learning. Drag a gradient descent path, transform space with a matrix, feel a derivative, and shape a probability distribution, each with a step-by-step lesson on the same page.
Loops are the future of AI agents, and the popular story about why is wrong. A deep, research-grounded look at how agent loops really work, why longer loops fail, what actually decides whether one is reliable, where that breaks down in practice, and how to build loops that hold, with a complete, runnable Python implementation.
A deeply researched, production-grade guide to prompt injection in 2026. Why detection cannot be trusted, the lethal trifecta, and the by-design defenses that hold even when the model is fooled, with complete Python implementations of capabilities, taint tracking, plan-then-execute, the dual-LLM quarantine, egress control, MCP hardening, and a red-team harness.
A complete, plain-language guide to context engineering for AI agents. Learn what the context window is, why long context degrades, the Write, Select, Compress, and Isolate strategies, and how to build a production-minded, security-conscious, and scalable context pipeline in Python.
A research-backed guide to retrieval-augmented generation, explaining RAG from first principles through retrieval, chunking, embeddings, reranking, GraphRAG, agentic RAG, evaluation, security, and production architecture.
Build a complete Claude-powered knowledge base agent in Python, with API key setup, safe tools, an agent loop, summaries, concept pages, and rebuilds.
A short, practical guide to how AI coding agents are changing software engineering, senior engineering work, code review, and beginner learning.
How to use AI agents without flooding the internet with low-value content. A practical guide to quality guardrails, human review, and workflows worth publishing.
How AI agents can turn e-invoicing and fiscalization into explainable tax compliance workflows while preserving cryptographic trust and human oversight.
A researched guide to why HTML artifacts, visual interfaces, and interactive simulations are becoming a better output layer for AI agents.
A practical 2026 guide to using SKILL.md, AGENTS.md, and CLAUDE.md so coding agents reuse your standards instead of replanning every small task.
A clear 2026 guide to how AI is reshaping science, medicine, energy, work, and governance, with updated evidence and practical arguments.
A beginner-friendly Python guide to build a reliable AI agent from scratch, understand every step, and harden it for production.
A clear number-theory explainer for when 5n ends in 0, why parity matters, and how to check the pattern with divisibility and modular arithmetic.