Adafruit highlights the development of “pycoClaw,” a fully-featured AI agent implemented in MicroPython and running on a $5 ESP32-S3. This agent boasts capabilities like recursive tool calling, persistent memory using SD card storage, and a touchscreen UI, all built with an async architecture and optimized for performance through C user modules. The project is open-source and supports various hardware platforms, with ongoing development for RP2350, and is showcased alongside other Adafruit news including new product releases, community events, and resources for makers.
This article explains the differences between Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and AI Agents, highlighting that they solve different problems at different layers of the AI stack. It also covers how ChatGPT routes prompts and handles modes, agent skills, architectural concepts for developers, and service deployment strategies.
OpenViking is an open-source context database designed specifically for AI Agents. It unifies the management of context (memory, resources, and skills) using a file system paradigm, enabling hierarchical context delivery and self-iteration.
The article discusses the evolution from RAG (Retrieval-Augmented Generation) to 'context engineering' in the field of AI, particularly with the rise of agents. It explores how companies like Contextual AI are building platforms to manage context for AI agents and highlights the shift from prompt engineering to managing the entire context state.
Vercel's research shows that embedding a compressed 8KB docs index in AGENTS.md achieves a 100% pass rate for Next.js 16 API evaluations, while skills maxed out at 79%, even with explicit instructions. This suggests that passive context provision via AGENTS.md is more effective than active retrieval with skills for framework-specific knowledge in AI coding agents.
FailSafe is an open-source, modular framework designed to automate the verification of textual claims. It employs a multi-stage pipeline that integrates Large Language Models (LLMs) with retrieval-augmented generation (RAG) techniques.
A tutorial showing how to use the MCP framework with EyelevelAI's GroundX to build a Retrieval-Augmented Generation (RAG) system for complex documents, including setup of a local MCP server, creation of ingestion and search tools, and integration with the Cursor IDE.
A curated repository of AI-powered applications and agentic systems showcasing practical use cases of Large Language Models (LLMs) from providers like Google, Anthropic, OpenAI, and self-hosted open-source models.
Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
A course teaching everything you need to know to start building AI Agents. Includes 12 lessons, code samples, and multi-language support.
