China's own Open Source AI development Community (dare l say HugginFace 😆) featuring all the latest Chinese AI (perhaps more) with a Comprehensive open-sourced framework to assist model development and building of AI applications https://modelscope.cn/home #modelscope #hugginface #LLMs #Qwen #Deepseek #Wan

Developer favorite platform for building AI agents and LLM apps. OpenAI Agents SDK, CrewAI, Autogen, and 400+ LLMs and agent frameworks. https://www.agentops.ai/ https://github.com/AgentOps-AI/agentops #aiagents #agents_ops #dev_ai_agents #nocode

https://www.fine.dev/ Fine | AI Coding Tool for Startups | AI Developer Agents Fine is an AI Coding agent for software developers and programmers at startups to use LLMs to write code and complete dev tasks. Sign up to Fine, the AI coding tool for startups.

https://openrouter.ai/ OpenRouter provides a unified API that gives you access to hundreds of AI models through a single endpoint, while automatically handling fallbacks and selecting the most cost-effective options. Get started with just a few lines of code using your preferred SDK or framework. #openrouter #unifiedllms #llmsaggregator #huggingface

https://www.promptingguide.ai/agents/introduction In this guide, we refer to an agent as an LLM-powered system designed to take actions and solve complex tasks autonomously. Unlike traditional LLMs, AI agents go beyond simple text generation. They are equipped with additional capabilities, including: Planning and reflection: - AI agents can analyze a problem, break it down into steps, and adjust their approach based on new information. - Tool access: They can interact with external tools and resources, such as databases, APIs, and software applications, to gather information and execute actions. - Memory: AI agents can store and retrieve information, allowing them to learn from past experiences and make more informed decisions. This lecture discusses the concept of AI agents and their significance in the realm of artificial intelligence.

https://youtu.be/Iabue7wtE4g?si=NlvvXGn80ZN80MxE In this video, I go through hands-on how to use the Anthropic computer use models and tools. Explain how they work and also show how you can get it started with Docker on your own computer. For more tutorials on using LLMs and building agents, check out my Patreon Patreon: / samwitteveen Twitter: / sam_witteveen Computer Use: https://www.anthropic.com/news/develo... Computer Use Docs: https://docs.anthropic.com/en/docs/bu... 👨‍💻Github: https://github.com/anthropics/anthrop...

https://lmstudio.ai/ LM Studio is a desktop app for developing and experimenting with LLMs locally on your computer. Key functionality A desktop application for running local LLMs A familiar chat interface Search & download functionality (via Hugging Face 🤗) A local server that can listen on OpenAI-like endpoints Systems for managing local models and configurations

https://x.com/AndrewYNg/status/1882125891821822398 Our first short course with @AnthropicAI! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, @Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here: https://deeplearning.ai/short-courses/building-towards-computer-use-with-anthropic

https://modelcontextprotocol.io/introduction MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools. Useful links *https://github.com/modelcontextprotocol/servers *https://github.com/modelcontextprotocol

Smithery [https://smithery.ai/ ] is a registry of Model Context Protocols, designed to help developers find the right tools to build their AI agentic applications. Smithery addresses this challenge by providing: A centralized hub for discovering model context protocols Standardized interfaces for tool integration and configs Easy-to-use resources for agent development Community-driven protocol sharing and collaboration Model Context Protocol The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLMs and external data sources and tools. It is a universal standard for connecting AI systems with the context they need, eliminating information silos and fragmented integrations. By providing a standard way to connect AI systems with data sources, MCP simplifies the development and maintenance of agentic applications. This makes it easier to build agents like intelligent IDEs, chat interfaces and custom AI workflows. Instead of writing custom implementations for each new data source, developers can use MCP as a single, standardized protocol. This approach not only makes systems more maintainable but also ensures better scalability as your AI applications grow and evolve.