8 topics · newest 8 Nov 2026
Learn what an agent harness is, how its loop runs a task and what its parts are: context, tools and permissions, what is kept between sessions, and hooks. Tests by OpenAI and Anthropic show how much a harness changed a model's results, and both companies describe harnesses for particular kinds of work.
· 8 min
Keyword search, semantic search and knowledge graph search are three ways to retrieve data for an LLM or an AI agent. This guide covers how each one works, how they differ, how hybrid search combines them with rank fusion and a reranker, and the best use cases for each.
· 9 min
Sigmoid and softmax turn the scores of a classifier into probabilities. The sigmoid turns one score into a probability between 0 and 1. The softmax turns several scores, one per class, into probabilities that sum to 1. This article explains both with formulas and examples, and shows when to use each.
· 8 min
Hindsight is a memory system for AI agents. This guide explains why agents need memory, why entities and a knowledge graph help, how the operations retain, recall and reflect work, how Hindsight differs from RAG, and its use cases.
· 8 min
MCP, the Model Context Protocol, connects AI applications such as Claude, ChatGPT and AI agents to tools and data. This guide covers how MCP works, its architecture, how it differs from an API, its use cases and its limits.
· 8 min
This guide explains what AI workflows and agent skills are, how they differ, how to choose, and how real systems use the two together.
· 6 min
Hermes Agent is an open-source AI agent from Nous Research that runs on your own computer or server, uses tools to carry out tasks and remembers what it learned. This guide explains why to use it, its folder structure, how it works and learns, cron jobs, limits and use cases.
· 14 min
Jev is TypeSafe's AI model for decisions, not for writing. You give it a text and questions with fixed answer options, and it tells you how likely each option is.
· 5 min