Exploring secure environments for testing and running AI agent code, including options like Docker, online IDEs, and dedicated platforms.
Vercel has open-sourced bash-tool, a Bash execution engine for AI agents, enabling them to run filesystem-based commands to retrieve context for model prompts. It allows agents to handle large local contexts without embedding entire files, by running shell-style operations like find, grep, and jq.
mcp-cli is a lightweight CLI that enables dynamic discovery of MCP servers, reducing token consumption and making tool interactions more efficient for 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.
Simon Willison’s annual review of the major trends, breakthroughs, and cultural moments in the large language model ecosystem in 2025, covering reasoning models, coding agents, CLI tools, Chinese open‑weight models, image editing, academic competition wins, and the rise of AI‑enabled browsers.
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.
This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.
This article is a year-end recap from Towards Data Science (TDS) highlighting the most popular articles published in 2025. The year was heavily focused on AI Agents and their development, with significant interest in related frameworks like MCP and contextual engineering. Beyond agents, Python remained a crucial skill for data professionals, and there was a strong emphasis on career development within the field. The recap also touches on the evolution of RAG (Retrieval-Augmented Generation) into more sophisticated context-aware systems and the importance of optimizing LLM (Large Language Model) costs. TDS also celebrated its growth as an independent publication and its Author Payment
A simple, open format for guiding coding agents, used by over 60k open-source projects. It's a dedicated, predictable place to provide the context and instructions to help AI coding agents work on your project.
