Courses

Master OpenCode from scratch and learn how to build real-world AI applications using multiple models—without vendor lock-in or expensive subscriptions. This complete OpenCode course covers everything you need to go from beginner to advanced, including setup, model integration, agent workflows, and real project building. You’ll learn how to use OpenCode with tools like Ollama for running models locally, and OpenRouter for accessing multiple cloud models—all in a single workflow. We’ll also explore how to use powerful open models like Gemma 4 and optimize cost using model switching strategies. 🔥 What you’ll learn: OpenCode setup (Windows, Mac, Linux) Local AI with Ollama Cloud models via OpenRouter Multi-agent workflows (Plan vs Build mode) Cost optimization techniques Building real apps (REST APIs, UI apps, automation tools) Sessions, debugging, and workflow management 🎯 Who this is for: Developers & engineers AI enthusiasts Startup founders Anyone building AI-powered applications If you want full control over your AI stack—this is the course to start with.

Visit sintra.ai/leonvanzyl or use my promo code leonvanzyl at checkout to receive limited time offer of 72% off on all plans 🚀 Free resources: source code, prompts, workflows + peer discussion https://skool.com/leonvanzyl 🧪 Agentic Labs: AI coding courses, live Q&A, weekly builder challenges, direct access to me + a serious builder community https://skool.com/agentic-labs OpenCode is the open source coding agent that is quickly becoming a serious alternative to Claude Code. In this opencode tutorial, you will learn how to install OpenCode, connect free AI models through OpenCode Zen or premium providers like OpenAI, and master advanced features including agent skills, sub agents, MCP servers, and background agents. ⏱️ CHAPTERS: 00:00 - OpenCode intro and overview 00:39 - Installing OpenCode in seconds 01:25 - Connecting providers and OpenCode Zen 02:20 - Free AI coding models walkthrough 03:46 - Scaffolding a Next js project 04:55 - Subscriptions vs API keys 06:00 - Connecting OpenAI and GPT 5 07:30 - Installing agent skills from skills sh 10:30 - AGENTS md memory file setup 13:00 - Sub agents and context window protection 15:30 - Design system for consistent UI 15:50 - Sponsor message 18:00 - Building the AI Project Planner app 22:30 - Saving plans for future sessions 24:30 - Creating custom sub agents 28:30 - Playwright MCP server setup 30:30 - Auto testing the app with browser MCP Business & sponsorship enquiries: leon@cognaitiv.ai #opencode #agenticcoding

Claude Code Tutorial: Beginner to Advanced in 20 Minutes. Join our FREE community with free prompts & guides: https://www.skool.com/ai-launchpad/about Chapters: 0:00 - Intro 1:10 - What is Claude & Setup 3:54 - Plan mode & prompting 6:18 - First results & Iteration 8:18 - Claude.md & MCP 12:12 - Context management & extended thinking 16:10 - Deploy your builds 17:07 - Skills & Hooks & Routines 18:39 - Outro For business inquiries including sponsorships and collaborations, please email: business@zinhomedia.com

Get started with Codex, OpenAI’s coding agent, in this step-by-step onboarding walkthrough. You’ll learn how to install Codex, set up the CLI and VS Code extension, configure your workflow, and use Agents.md and prompting patterns to write, review, and reason across a real codebase. This video covers: 00:00 | Installing Codex (CLI + IDE) 04:00 | Setting up a repo and getting your first runs working 08:12 | Writing a great Agents.md (patterns + best practices) 14:14 | Configuring Codex for your environment 16:26 | Prompting patterns for more consistent results 27:02 | Tips for using Codex in the CLI and IDE 34:59 | MCP Configuration 46:50 | Advanced workflows: headless mode + SDK 52:02 | Codex resources to get you started Sign up: https://openai.com/codex/ Codex overview + docs: https://developers.openai.com/codex Codex Cookbook: https://cookbook.openai.com/topic/codex Install + setup VS Code extension: https://marketplace.visualstudio.com/items?itemName=openai.chatgpt Agents.md standard: https://agents.md Agents.md repo: https://github.com/agentsmd/agents.md Prompting + workflows Prompting guide: https://developers.openai.com/codex/prompting/ Exec plans (Agents.md patterns): https://cookbook.openai.com/articles/codex_exec_plans Config reference: https://github.com/openai/codex/blob/main/docs/config.md#config-reference Updates Changelog: https://developers.openai.com/codex/changelog Releases: https://github.com/openai/codex/releases

Watch us build a complete real-time polling app using ONLY AI! Andrew Stewart (@srcrip) & Daniel Bergholz (@DanielBergholz) join me as we let Claude Code + Tidewave do ALL the coding. No manual code writing allowed! We built authentication, real-time voting, dynamic forms, live updates, and a polished UI - all through AI prompts. The result? A fully functional Phoenix LiveView app with 13 commits in just 2 hours! 00:00 - Introduction and sponsor message 04:00 - Setting up the live coding challenge and polling the audience 09:00 - Introducing Claude Code and the wild card project selection 15:25 - Installing and configuring Claude Code for the first time 23:30 - Setting up Tidewave MCP for Elixir integration 32:00 - Creating the Phoenix project and initial setup 40:00 - Building polls schema and embedded options 48:00 - Implementing Phoenix authentication with gen.auth 1:05:00 - Creating live views and dynamic forms 1:20:00 - Adding real-time voting functionality with PubSub 1:27:00 - Debugging with Tidewave's powerful runtime integration 1:35:00 - Building authorization and user permissions 1:45:00 - Adding vote changing functionality and UI polish 1:55:00 - Creating a stunning homepage with Claude's design skills 2:05:00 - Setting up GitHub workflow and creating pull request 2:15:00 - Wrap-up and reflections on AI-assisted development Tools Used: Claude Code (agentic terminal AI) Tidewave MCP (Elixir runtime integration) Phoenix LiveView + Tailwind CSS Judge Our Vibe Code: Open PR: https://github.com/ElixirMentor/pulse_vote/pull/1 Connect: Andrew: @srcrip Daniel: @DanielBergholz Support Elixir Mentor: https://elixirmentor.com/?utm_source=elixir-mentor #TideWave #ClaudeCode #ElixirLang #PhoenixFramework #AIcoding #VibeCoding #LiveView #ElixirMentor

Learn how to use the OpenAI's Assistants API to build powerful AI assistants. In this course, we'll explore how to leverage the Assistants API by OpenAI to create dynamic, intelligent web apps using Streamlit. What we'll cover: 1️⃣ Function Calling with the API: Learn to seamlessly integrate the Assistants API into your applications, enabling advanced AI functionalities right at your fingertips. 2️⃣ Knowledge Retrieval: Discover how to use the API to extract information, answer questions, and make your applications smarter and more responsive. 3️⃣ Code Interpreter Capabilities: Dive into the API's ability to interpret and generate code, a game-changer for automating tasks and enhancing your app's capabilities. 4️⃣ LLM Fundamentals: Gain a solid understanding of Large Language Models (LLMs) and how they form the backbone of OpenAI's Assistants API. This module demystifies the technology and provides a foundation for advanced application development. The course features a series of hands-on projects and real-world examples to apply what you've learned. By the end of our session, you'll have the skills and confidence to build your own intelligent web apps using Streamlit and OpenAI's Assistants API. ⭐️ Code ⭐️ Personal trainer: https://github.com/pdichone/vincibits-personal-trainer-assistant Personal trainer: https://github.com/pdichone/vincibits-personal-trainer-assistant Study-Buddy: https://github.com/pdichone/vincibits-study-buddy-knwoledge-retrieval ✏️ Created by @vincibits Twitter (X): @buildappswithme ❤️ Try interactive AI courses we love, right in your browser: https://scrimba.com/freeCodeCamp-AI (Made possible by a grant from our friends at Scrimba) ⭐️ Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:01:02) What’s This Course About - What Will You Learn? ⌨️ (0:01:33) Pre-requisites ⌨️ (0:02:44) Python and Dev tools Set up ⌨️ (0:04:22) VS Code Installation ⌨️ (0:05:31) OpenAI Account - Generate an API Key ⌨️ (0:07:53) What is the Assistants API & Benefits - Comparison Between the Chat Completion API and the Assistants API ⌨️ (0:18:16) Assistants API Building Blocks ⌨️ (0:24:04) Creating an Assistants API - Manually (Personal Trainer) ⌨️ (0:38:20) Creating an Assistants API - In Code (Personal Trainer) ⌨️ (1:04:15) Build a News Summarizer Introduction: Function Calling - A Streamlit Application ⌨️ (1:25:39) Create an AssistantsManager Class For our News Summarizer ⌨️ (2:10:46) Using the AssistantManager Class to Create an Assistant and run it as a Streamlit App ⌨️ (2:28:23) Knowledge Bases Retrieval Tools - How it Works & Introduction to Embeddings ⌨️ (2:35:25) Build a Study Buddy Streamlit Application ⌨️ (3:22:24) Run the Study Buddy Streamlit Application and Test ⌨️ (3:29:27) Wrap up and Final Considerations. 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

Code with Claude—our first developer conference—took place on May 22, 2025 in San Francisco. Code with Claude was a hands-on, one-day event to announce Claude 4 models as well focusing on exploring real-world implementations and best practices using the Anthropic API, CLI tools, and Model Context Protocol (MCP).

AI Fluency: Framework & Foundations is a course developed by Anthropic, Prof. Rick Dakan (Ringling College of Art and Design) and Prof. Joseph Feller (University College Cork). View the full free course, including all videos, exercises, and resources, at https://www.anthropic.com/ai-fluency

Potencialize seu Chatbot com a Técnica RAG: Busca Vetorial e Conteúdo Personalizado Neste vídeo, Fábio Vedovelli explora a técnica de *Retrieval Augmented Generation* (RAG), integrando conteúdos específicos em chatbots com GPT-4. Ideal para otimizar respostas de IA com informações exclusivas. Código-fonte: https://github.com/vedovelli/ag-ui-screencast Destaques do Vídeo - **Integração de Dados:** Aprenda a transformar artigos do TabNews em dados estruturados usando FireCrawl e armazená-los em bancos de dados vetoriais como Postgres com pgVector. - **Busca Semântica:** Configure buscas vetoriais para que o chatbot identifique semelhanças de significado, oferecendo respostas precisas e contextualizadas. - **Implementação Técnica:** Utilize tecnologias como Mastra para backend e CopilotKit para interface de usuário, criando uma experiência de chat intuitiva. - **Desenvolvimento Moderno:** Descubra como React Router e CopilotKit podem construir interfaces front-end eficientes para comunicação direta com a AI. Palavras-chave RAG, GPT-4, Busca Vetorial, Postgres, TabNews, React, Mastra, CopilotKit, AI, FireCrawl Este vídeo é perfeito para desenvolvedores e entusiastas de tecnologia que querem aprofundar seus conhecimentos em inteligência artificial e integrar funcionalidades avançadas em suas aplicações. Assista agora e transforme seu entendimento sobre AI e buscas semânticas! Sobre o Criador Fábio Vedovelli é especialista em desenvolvimento de software, apaixonado por compartilhar conhecimento através de screencasts detalhados. Confira outros vídeos no canal para mais conteúdos sobre tecnologia!