A practical knowledge base for AI protocols, agents, core concepts, frameworks, and tools.
Learn what Quantization means and how it is used. Quantization compresses model weights from 16-bit floats down to 8-bit, 4-bit, or lower integer representations, trading a controlled amount of precision for smaller memory footprint and faster inference. It is the standard way to run large models locally and to cut inference cost
Learn what Scaling Law means and how it is used. Scaling laws are empirical regularities: a model's prediction error falls as a predictable power law in parameters, data, and compute. They turned "how good a model does this budget buy" into something you can estimate in advance, and they are the direct justification for the compute build-out of recent years
Learn what Shadow AI means and how it is used. Shadow AI is employees using AI tools on company data without IT or security approval. What separates it from shadow IT is that the data itself leaves, not just a system entry point — and bans rarely work, so the viable path is providing a compliant alternative
Learn what SLM (Small Language Model) means and how it is used. A small language model is one small enough to run on ordinary consumer hardware and fast enough to serve a single user. In agent systems, most model calls are repetitive, narrow, fixed-format subtasks — and handling those with a small model is often cheaper and more stable
Learn what Spec-Driven Development means and how it is used. Spec-driven development treats versioned specifications as the single source of truth, with code generated and maintained against them by humans and AI agents. It targets three characteristic failures of coding agents: intent drift, context decay, and outputs no one can judge for lack of acceptance criteria
Learn what Synthetic Data means and how it is used. Synthetic data is training data generated by a model or a program rather than collected from the real world. It has become a necessity as high-quality real data runs short and in domains like robotics where collection is prohibitively expensive — but used carelessly it amplifies bias and degrades models
Learn what Transformer (Architecture) means and how it is used. The Transformer is a neural network architecture introduced in 2017. It dropped the word-by-word recurrent structure of its predecessors and used attention to read an entire input at once. Today it is the skeleton of nearly every large language model, multimodal model, and mainstream image or video generator
Learn what Voice Agent means and how it is used. A voice agent is an AI that listens, speaks, and actually gets things done, chaining speech recognition, reasoning, tool calls, and speech synthesis into a real-time loop. What separates it from an old voice assistant is that it has no scripted decision tree; what separates it from a text agent is latency and interruption handling
Learn what Agent Plugins means and how it is used. Agent Plugins is an open packaging format released in August 2026. A plugin uses plugin.json to bundle Agent Skills and MCP server configuration into one distributable directory that compatible agent clients can discover and load.
Learn what Agent Swarm means and how it is used. An Agent Swarm is a system in which relatively independent AI agents explore in parallel, share discoveries, and coordinate results. It can expand search coverage and total work, but agents based on similar models may repeat errors, duplicate effort, or deadlock on complex dependencies.
Learn what Agentic Payments means and how it is used. Agentic Payments lets an AI agent initiate and complete a payment within user-defined boundaries for identity, amount, counterparty, purpose, and time. The point is not an unlimited AI credit card, but machine-enforceable authorization, risk controls, execution, and auditable receipts.
Learn what ARD (Agentic Resource Discovery) means and how it is used. Agentic Resource Discovery (ARD) is a federated open specification that lets AI agents find, describe, and verify MCP servers, A2A agents, Skills, APIs, and other capabilities across organizations without hard-coding the entire tool catalog into context.
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