The Universal Tool Calling Protocol (UTCP) is an open standard that describes how to call existing tools directly, eliminating the need for wrappers. It focuses on direct communication with tool endpoints (HTTP, gRPC, WebSocket, CLI, etc.) to reduce latency and maintain existing security and billing systems.
An overview of the top AI-powered coding tools available in 2025, including Cursor, Claude Code, Windsurf, VSCode, and others, highlighting their features and popularity among developers.
| **Tool** | **Developer** | **Key Features** | **Notes** |
|-------------------|----------------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------|
| **Cursor** | Anysphere | AI-powered autocomplete, code rewriting, agent mode, integrates with Web resources | Most popular AI-focused IDE, fork of VSCode |
| **Claude Code** | Anthropic | Works in MacOS/Linux terminal, command-line interface, high control over workflow | Preferred by developers who like command-line tools |
| **Windsurf** | Codeium (acquired by OpenAI)| AI autocorrect, agentic AI, Cascade interface, works in other IDEs (e.g., Jetbrains) | Rebranded from Codeium, acquired by OpenAI for $3 billion |
| **VSCode** | Microsoft | Extensions support, GitHub Copilot (agent mode in preview), slow AI integration | Popular IDE, but slower to adopt AI features compared to startups |
| **Vibe Coding Tools** | Various (e.g., Lovable, Replit, Bolt, Firebase) | Chat interface, no software installation, browser-based, minimal coding required | Designed for "vibe coding," where users describe what they want to build |
| **Other Tools** | OpenAI, Mistral, Apple, etc. | Codex (OpenAI), Mistral Code, Apple’s AI tools, Zed, Devin (AI software engineer) | Rapidly evolving, many new tools emerging frequently |
| **Tool** | **Developer** | **Key Features** | **Notes** |
|-------------------|----------------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------|
| **Cursor** | Anysphere | AI-powered autocomplete, code rewriting, agent mode, integrates with Web resources | Most popular AI-focused IDE, fork of VSCode |
| **Claude Code** | Anthropic | Works in MacOS/Linux terminal, command-line interface, high control over workflow | Preferred by developers who like command-line tools |
| **Windsurf** | Codeium (acquired by OpenAI)| AI autocorrect, agentic AI, Cascade interface, works in other IDEs (e.g., Jetbrains) | Rebranded from Codeium, acquired by OpenAI for $3 billion |
| **VSCode** | Microsoft | Extensions support, GitHub Copilot (agent mode in preview), slow AI integration | Popular IDE, but slower to adopt AI features compared to startups |
| **Vibe Coding Tools** | Various (e.g., Lovable, Replit, Bolt, Firebase) | Chat interface, no software installation, browser-based, minimal coding required | Designed for "vibe coding," where users describe what they want to build |
| **Other Tools** | OpenAI, Mistral, Apple, etc. | Codex (OpenAI), Mistral Code, Apple’s AI tools, Zed, Devin (AI software engineer) | Rapidly evolving, many new tools emerging frequently |
Mistral Small 3.2 is a minor update to the Mistral Small 3.1 model, offering improvements in instruction following, repetition errors, and function calling. The article details the author's experience running the model locally using Ollama and GGUF quantizations, including generating an SVG image and describing it with the model itself.
LLM 0.26 introduces tool support, allowing LLMs to access and utilize Python functions as tools. The article details how to install, configure, and use these tools with various LLMs like OpenAI, Anthropic, Gemini, and Ollama models, including examples with plugins and ad-hoc functions. It also discusses the implications for building 'agents' and future development plans.
A summary of a workshop presented at PyCon US on building software with LLMs, covering setup, prompting, building tools (text-to-SQL, structured data extraction, semantic search/RAG), tool usage, and security considerations like prompt injection. It also discusses the current LLM landscape, including models from OpenAI, Gemini, Anthropic, and open-weight alternatives.
Guidance on choosing the best AI model for GitHub Copilot projects, considering speed, depth, cost, and task complexity. Models discussed include GPT-4.1, GPT-4o, Claude 3.5 Sonnet, o4-mini, o3, Gemini 2.0 Flash, and GPT-4.5.
This article details the Model Context Protocol (MCP), a new approach to integrating Large Language Models (LLMs) like Azure OpenAI with tools. MCP focuses on structured data exchange to improve reliability, observability, and functionality, moving beyond simple text-in, text-out interactions. It aims to standardize how LLMs interact with tools, enhancing their ability to utilize those tools effectively.
This document details how to use function calling with Mistral AI models to connect to external tools and build more complex applications, outlining a four-step process: User query & tool specification, Model argument generation, User function execution, and Model final answer generation.
Charm makes the command line glamorous with tools like Huh for interactive forms, Lip Gloss for styling, and Glow for markdown reading. They offer various libraries and applications for building terminal user interfaces.
Explains how LLMs can use tools through 'execution rounds,' providing examples and guidance on model selection.
