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Learn the basics of artificial intelligence. This course is for absolute beginners. This course was created by learnaianywhere.org and released to support the goals of National AI Literacy Day. You can learn more at https://learnaianywhere.org and download the resources used. For more on AI Literacy Day, check out ailiteracyday.org or their YouTube playlist: https://www.youtube.com/watch?v=vihMGH6aqTY&list=PLGhqQNp1xV3McvqOTfJmX2mAuuZqOKMEs ❤️ 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 ⭐️ - 00:00 Welcome To The Course - 01:46 Prerequisite - 02:51 Symbol Key - 03:19 Lesson 1: What is AI - Objectives - 03:35 Lesson 1: What is AI - What Is AI AI or Not: https://learnaianywhere.org/courses/ai-literacy-for-everyone/ai-or-not - 05:39 Lesson 1: What is AI - How AI Can Help Us Recap Quiz: https://learnaianywhere.org/courses/ai-literacy-for-everyone/recap-quiz-1 - 07:14 Lesson 1: What is AI - Project Time - 09:06 Lesson 2: The Key Parts of Machine Learning - Objectives - 09:27 Lesson 2: The Key Parts of Machine Learning - What is Machine Learning - 12:01 Lesson 2: The Key Parts of Machine Learning - Neuropocket Tutorial - 14:39 Lesson 2: The Key Parts of Machine Learning - AI Tools Can Make Mistake Recap Quiz: https://learnaianywhere.org/courses/ai-literacy-for-everyone/recap-quiz-2 - 16:27 Lesson 2: The Key Parts of Machine Learning - Project Time - 17:20 Lesson 3: How Do Machines Train - Objectives - 18:06 Lesson 3: How Do Machines Train - The Describer Drawer Game - 19:51 Lesson 3: How Do Machines Train - The Describer Drawer Game Demonstration - 23:07 Lesson 3: How Do Machines Train - What Is An Algorithm - 25:37 Lesson 3: How Do Machines Train - The Human Learning Algorithm - 30:25 Lesson 3: How Do Machines Train - The Machine Learning Algorithm Recap Quiz: https://learnaianywhere.org/courses/ai-literacy-for-everyone/recap-quiz-3 - 38:05 Lesson 3: How Do Machines Train - Project Time - 39:07 Lesson 4: Can Machines Be Responsible - Objectives - 39:34 Lesson 4: Can Machines Be Responsible - Bearly A Dog Challenge - 40:14 Lesson 4: Can Machines Be Responsible - What is Bias - 44:06 Lesson 4: Can Machines Be Responsible - Who is Responsible - 46:50 Lesson 4: Can Machines Be Responsible - Data Privacy - 50:46 Lesson 4: Can Machines Be Responsible - Responsible AI Recap Quiz: https://learnaianywhere.org/courses/ai-literacy-for-everyone/recap-quiz-4 - 51:20 Lesson 4: Can Machines Be Responsible - Project Time

Try https://waku.one, Me https://seanchen.io, Open Source: https://github.com/ShenSeanChen/waku-agent I An LLM is an incredibly powerful brain that knows everything about humanity and nothing about you or the software you run. This video walks through the four buzzwords that turn that raw brain into a system you can trust: agent harness, loop engineering, LLM Ops, and eval. No jargon, step by step, technical or not. You can build anything. You can learn anything. 💪 Timeline 0:00 Intro: the 4 AI agent buzzwords 0:42 What an AI agent run actually is 1:52 The memory system: procedural, semantic, episodic 3:37 What "harness" really means (the horse) 5:07 Storing and updating memory (databases, skills, summarizer agent) 8:28 Retrieval: RAG, SQL vs semantic search 10:00 Tool calling and why agents loop 10:52 Loop engineering and end-loop guardrails 13:21 A Claude Code hooks example 14:18 Eval and LLM Ops: why you need them 15:55 Tracing every run (Langfuse, LangSmith) 16:43 Evaluation: LLM as a judge 17:20 Diagnosing what broke 18:16 The gate: ship the fix or fix the bug 18:57 Zoom out: the full system 19:53 Wrap up Launch Full-Stack App Template: https://github.com/ShenSeanChen/launch-mvp-stripe-nextjs-supabase My AI Agent Startup AutoManus.io is an AI sales lead manager for made-to-order product sales. We embed directly where conversations happen (WhatsApp, email, web chat) to capture inbound, automate follow‑ups, and kill CRM busywork; live with 4 pilots and 2 contracts in motion, pre‑seed backed by Character VC. Focused on US/UK/Germany, raising to expand engineering and scale deployments. ☕️ Buy me a coffee: https://buy.stripe.com/5kA176bA895ggog4gh - X: https://x.com/ShenSeanChen - Sean's Discord: https://discord.gg/tvECErKcFr - AutoManus Discord: https://discord.gg/5HhcNjCR - LinkedIn: https://linkedin.com/in/shen-sean-chen - Github Link: https://github.com/ShenSeanChen - Instagram: https://www.instagram.com/sean_ai_stories Sub Count when launched: 13,050 Top Github Repos - LaunchMVP NextJS,Supabase,Stripe (1015 stars): https://github.com/ShenSeanChen/launch-mvp-stripe-nextjs-supabase - ReactNextJSTips (114 stars): https://github.com/ShenSeanChen/yt-react-nextjs-tips - DeepResearchAgentBackend (70 stars): https://github.com/ShenSeanChen/yt-DeepResearch-Backend - SupabaseTipsKanban (64 stars): https://github.com/ShenSeanChen/yt-supabase-tips-kanban - RAGVectorDB (56 stars): https://github.com/ShenSeanChen/yt-rag - KimiK2Claude (37 stars): https://github.com/ShenSeanChen/yt-kimi-k2-claude - DeepResearchAgentFrontend (33 stars): https://github.com/ShenSeanChen/yt-DeepResearch-Frontend - AgenticRAG (13 stars): https://github.com/ShenSeanChen/yt-agentic-rag You Can Build Anything Playlist (Full-Stack Dev Without CS Degrees) - Core7: 95% React/NextJS Use Cases https://www.youtube.com/watch?v=vZzFlAjz4rA&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=16 - Core6: 95% Supabase Use Cases https://www.youtube.com/watch?v=Pnoq2tIif3s&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=15 - Core5: StripeNextJSSupabase https://www.youtube.com/watch?v=ad1BxZufer8&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=8 - Core4: AuthSupabaseGoogle https://www.youtube.com/watch?v=AZAU3ClAWWE&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=6 - Core3: FastapiGCPPro https://www.youtube.com/watch?v=u3sfqpzzo-U&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=5 - Core2: SupabaseHero https://www.youtube.com/watch?v=V0kmgkyPsVQ&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=4 - Core1: LaunchAnApp https://www.youtube.com/watch?v=iQ2izvGG4tQ&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=3 - Launch WhereToMeet w/ GoogleMaps, Calendar, Supabase, Cursor: https://www.youtube.com/watch?v=5xMjX1QxGUY&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=2 - Launch EncourageMe w/ Cursor, Supabase, Vercel https://www.youtube.com/watch?v=hnvL4xa2DHA&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR

An overview and starting point for Claude Code. Want to learn more about coding with AI? Check out my course: https://www.traversymedia.com/coding-with-ai Get Cluade Code To Follow Along: https://clau.de/TraversyMedia Get the Neon free plan: https://get.neon.com/6uuwMG3 Follow Neon to not miss course giveaways: - X/Twitter: https://x.com/neondatabase - Linkedin: https://www.linkedin.com/company/neondatabase Github Gist With Prompts: https://gist.github.com/bradtraversy/9937e0ecfc8a5b269ce6a885636670dc Crypto Dash Repo: https://github.com/bradtraversy/crypto-dash Timestamps: 0:00 - intro 2:23 - What Is Claude Code 3:56 - Installing Claude 6:43 - First Launch 7:26 - Models & Usage Limits 10:44 - Effort 11:14 - Using Claude In VS Code 12:16 - The First Prompt 16:10 - Shell Mode 17:30 - Modes 19:15 - Settings & Permissions 22:55 - VS Code Extension 24:05 - Refactoring Code 27:03 - Context, Tokens & Clearing 29:20 - Global Sessions 33:14 - Working on an Existing Project 35:48 - Claude.md File & /init 39:28 - Plan Mode 44:58 - Adding Favorites Feature 48:05 - Skills 53:27 - MCPs 57:51 - Sub-Agents

🔥 Entre para a minha comunidade de automação - https://link.devaprender.com/entrar Curso Claude Code - https://youtu.be/KoFOPpUWi98 Curso Github Copilot - https://youtu.be/uW5OzVgtofU ✅Quer pedir um orçamento de um BOT(robô) ou sistema? - Solicite aqui: https://devaprender.com/orcamentos/ ✅Curso lógica de Programação: https://youtu.be/epf-WQdVis0 Links mencionados no video ------------------------------------------------------------------------------------------ nodejs download - https://nodejs.org/en/download opencode download - https://opencode.ai/download opencode mcp - https://opencode.ai/docs/mcp-servers/#context7 Skills - https://skillsmp.com/ ------------------------------------------------------------------------------------------ Resumo⤵️ Nesse curso completo de OpenCode, eu vou te mostrar como usar essa ferramenta de IA, para se conectar a qualquer provedor de IA, como o OpenAI, Claude Code, DeepSeek, OpenRouter. Ou seja, tanto opções pagas, como opções gratúitas! Aqui você irá aprender a usar o OpenCode do zero, desde de como instalar, configurar um provedor e personalizar e usar as principais funcionalidades como escolher modelos, nível de raciocínio, usar skills, usar servidores mcp e até mesmo como monetizar esse conhecimento. CONTATO COMERCIAL(não respondo dúvidas de videos) ------------------------ jhonatan@devaprender.com 00:00 - O que iremos aprender 00:50 - Como instalar OpenCode 03:18 - Por que usar OpenCode vs Claude Code ou outros? 04:39 - Como instalar qualquer provedor PAGOS 08:43 - Como instalar qualquer provedor GRATUITO 11:33 - Como usar Servidores MCP 14:49 - Como personalizar o OpenCode 16:25 - Como usar SKILLS 19:09 - Quando usar OpenCode no meu fluxo de trabalho? 20:10 - Como monetizar o conhecimento de criação de apps/automações?(parte 1) 20:51 - Como usar o modo Plan/Edit 21:05 - Como monetizar o conhecimento(parte 2) 21:48 - O que estudar a partir de agora?

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.

⚡ 72% OFF Sintra AI employees with code LEONVANZYL: https://sintra.ai/leonvanzyl 🚀 Agentic Labs — exclusive AI coding courses, live Q&As, workshops & deep dives: https://skool.com/agentic-labs 🎁 Free RAMP course + all the resources from my videos: https://skool.com/leonvanzyl Claude Code is powerful, but it locks you into one provider and one bill. OpenCode is the open source alternative that gives you the same agentic workflow with any model you want — including completely free ones. In this walkthrough you'll go from install to advanced workflows: agent skills for better output, subagents that protect your context window, memory files that keep the agent on your rules, and automated browser testing. Together we build a full AI project planner and let the agent test it in a real browser — without me touching a thing. You'll also see why I never pay per token, and the one file that instantly fixes an agent that won't listen. ▶️ Watch next: I Locked Claude Code in a Docker Sandbox and Let It Go Full YOLO — https://youtu.be/iUDuN9dyuTY ⏱️ Chapters 00:00 - OpenCode vs Claude Code: What You Get Free 00:39 - OpenCode Setup & Install 01:25 - OpenCode Zen: Free AI Models 04:55 - Subscriptions vs API Keys (Stop Overpaying) 07:30 - OpenCode Skills from skills.sh 10:30 - AGENTS.md Memory Files 13:00 - Subagents & Context Window Protection 15:50 - Sponsor: Sintra AI 28:30 - OpenCode MCP: Playwright Auto Testing Connect X: https://x.com/leonvz TikTok: https://www.tiktok.com/@leonvanzyl Business & sponsorships: leon@cognaitiv.ai

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!