This page covers six small open infrastructure projects that each do one job: an embedded database for connected devices, a reverse proxy, browser tools for key-value stores, a policy language that keeps answers ready as data arrives, a setup compiler for AI coding agents’ machines, and a network sandbox for agents. They’re separate projects, each with its own site and repository. What they share is an old habit. SQLite showed that a database can be one file linked into a program; nginx showed that a web server can be small and fast.

For about fifteen years the promise of on-demand manufacturing was the same: upload a file, get a price, get a part. That promise was kept. What nobody built was the layer above it, the part where something other than a human does the uploading. That layer arrived in 2026, quietly, in the form of protocol announcements rather than product launches. The trend worth naming is the collapse of three previously separate stacks into one addressable pipeline.

Agentic AI refers to a class of artificial intelligence systems designed to act autonomously toward defined goals, making decisions, initiating actions, and adapting behavior based on changing conditions and feedback. Unlike traditional AI models that primarily respond to direct inputs with outputs, agentic AI systems operate with a degree of independence, often orchestrating multiple steps, tools, or processes to achieve an objective over time. The term has gained prominence alongside advances in large language models, automation frameworks, and multi-agent systems, where software entities increasingly resemble goal-driven actors rather than passive tools.

A working reference for the vocabulary of modern AI — organized by conceptual layer, not alphabetically. Each definition is written for practitioners and informed generalists: precise enough to cite, plain enough to share. Foundation Layer Large Language Model (LLM) A neural network trained on vast quantities of text to predict and generate language. LLMs learn statistical patterns across billions of documents, enabling them to answer questions, write code, summarize text, and engage in dialogue.

Autonomous systems sit in that slightly uneasy space between tools and actors. They are built by humans, constrained by code and hardware, yet increasingly capable of making choices that feel less like execution and more like judgment. At a basic level, they are machines or software that perform tasks without continuous human guidance—self-driving cars navigating city streets, industrial robots adjusting production flows in real time, or software agents managing logistics, trading, or customer interactions.