The OpenClaw agent framework is an open‑source platform that lets developers build autonomous agents for edge AI and security. It runs as a single binary, supports multi‑profile setups, and includes self‑healing and token‑lock isolation.
Self‑Adapting LLMs let a model learn from its own conversations. The SEAL framework from MIT lets the model write study sheets and self‑edit, making it useful for education, support, and autonomous agents.
OpenCrabs 0.2.2 brings precise token counting and smarter memory management to AI agents. The update fixes inflated token totals, aligns billing with provider counts, and improves compaction and search.
OpenCrabs 0.2.2 brings accurate token counting and a three‑tier memory system to help developers build smarter AI agents. This article explains the new features and how to use them.
YOLO26 Edge AI Vision is a lightweight, high‑accuracy object‑detection model designed for edge devices. It’s 30 % smaller than YOLOv8, runs 25 fps on a Raspberry Pi 4, and achieves 58 % mAP on COCO.
A clear, practical guide to the Microsoft Agent Framework RC. Learn A2A messaging, MCP, design patterns, and steps to build safe multi-agent systems.
YOLO26 Edge AI Vision is a lightweight, high‑speed object‑detection model that runs on edge devices like Raspberry Pi and Jetson Nano. It offers sub‑millisecond inference and high accuracy, making it ideal for real‑time applications.
OpenCrabs 0.2.2 brings precise token counting and a new memory architecture, helping agents stay within context limits while delivering better performance.
The OpenCrabs 0.2.2 update refines token counting with the official `tiktoken` library and introduces a three‑tier memory architecture, making AI more reliable and easier to manage.
Persona Looting is a new malware that steals the cryptographic skeleton key of OpenClaw agents. This article explains how it works, why it matters, and how to protect your AI workflows.