AI is starting to improve its own tools, training and research workflows. Explore the science behind recursive self-learning, what works, and where it fails.
Explore our work in AI, systems engineering and applied science, including research projects and published technical explanations.
Engineering articles from our blog, listed separately from research projects.
AI is starting to improve its own tools, training and research workflows. Explore the science behind recursive self-learning, what works, and where it fails.
MCP connects AI agents to real tools, data and business systems—but that creates new security boundaries. This guide explains the most important MCP risks, OAuth controls, prompt injection defenses, tool security and production best practices for 2026.
AI agents often work beautifully in demos and fail under real production conditions. Learn 10 practical failure modes across tools, memory, evals, security, observability and recovery—and the engineering patterns that make agents more reliable.
Retries, tools, approvals, memory, tracing, replay, fallbacks and parallel agents look like separate problems. A 20-year-old idea from programming-language research suggests they may be different versions of the same deeper problem.
Most AI-tool lists repeat ChatGPT, Claude and Gemini. We researched 15 lesser-known free tools that can actually save time across meetings, research, automation, notes, visuals and deep work.
Learn how ChatGPT MCP apps work and how to build one using the Apps SDK, a remote MCP server, tools, interactive UI, authentication, and production-ready patterns.
Model Context Protocol is becoming a core integration layer for AI agents. This guide explains MCP architecture, servers, tools, security, 2026 changes, business use cases, and when to use it.