A deep dive into Kimi K2.6, Moonshot AI latest open-source foundation model that beats GPT-5.4 and Claude Opus 4.6 on several key benchmarks, with a live demo of redesigning a Hugo blog.
How to use OpenAI’s Life Science Research plugin with Claude Code, Codex, and open source models for bioinformatics research.
Learn how to create, install, and share agent skills for Claude Code and other AI assistants. Skills are markdown files that teach agents specialized workflows and best practices.
Learn how to use Google’s MedGemma 1.5, an open-source 4B parameter model for medical image classification, 3D CT/MRI interpretation, and clinical Q&A
Build an open-source alternative to Claude Cowork using OpenAI Agents SDK. Grant folder access, run shell commands, and let an AI agent handle tasks autonomously.
Create a multi-agent customer service system with handoffs, shared context, and specialized tools using the OpenAI Agents SDK.
Learn how to build AI agents with OpenAI’s Agents SDK. Create multi-agent systems, use built-in tools, and run agents with any LLM provider.
Learn how to build AI agents with smolagents, Hugging Face’s minimalist framework. Create custom tools, share them on the Hub, and build a chatbot UI in minutes.
Explore HuggingChat’s Omni model router and how to connect MCP servers for tools
Learn how agents actually work by building one from scratch using only LLM API calls. No LangChain, no LlamaIndex, just pure Python.
Learn how to create a multi-agent deep research pipeline that uses smolagents, Firecrawl, and open models to scrape and research the web.
Learn how to implement authentication for your remote Model Context Protocol (MCP) server using OAuth 2.1.
Learn how to create and deploy a remote Model Context Protocol (MCP) server. This guide will show you how to expose your tools and resources to any AI assistant, whether it’s a local model or a hosted service like OpenAI.
In this lesson, we will go through an introduction of LlamaIndex. We will see what you can do with it, how it deals with RAG and its main components. We will then implement a RAG pipeline with their famous 5-liner, which allows you to chat with your data in 5 lines of code. This tutorial is based on the original LlamaIndex documentation.
In this tutorial, we use Exa and CrewAI to build a team of AI research agents who, given any topic, can perform the following tasks for us: research and summarize the latest news on the given topic, verify that the sources are correct and that the articles are relevant to the selected topic, compile the top stories into a newsletter using an HTML template.
In this tutorial, you will learn how to create a crew of AI agents to automate your Instagram content strategy. In it, these agents are able to do the following things for you: perform research, find SEO keywords, create a calendar of posts, write the copy of each post, and generate the AI images for each post.
In this crash course, we will explore the basics of CrewAI. We will start by installing the necessary libraries and setting up our development environment. Then, we will create a simple crew using a sequential process. Finally, we will run the crew and see how it works.
In this tutorial, we will learn how to chat with a MySQL (or SQLite) database using Python and LangChain. We will use the LangChain wrapper of sqlalchemy to interact with the database. We will also use the langchain package to create a custom chain that will allow us to chat with the database using natural language.
In this article, we will show you how to setup a Python development environment for AI. We will go through installing the python version that you need, a version manager, a package manager, a code editor, and a notebook environment. By the end of this article, you will have a fully functional Python development environment.
In this article, we will create a web application that predicts whether a tumor is malignant or benign. To do that, we will first train a model using the Logistic Regression algorithm. Then we will use the model to predict the diagnosis of a tumor. And finally, we will use Streamlit to create the web application.
In this article, we will see how to create a Chrome extension. The extension we will build uses the latest version of the Chrome Manifest (manifest.json), which is V3. If you don’t know what is the Manifest, don’t worry. We will see that in a second.
In this tutorial, we will discuss what linear regression is and how to use it. We will also use R to implement a linear regression model to a test dataset.