This is a complete course on learning Generative ai and agentic with Langchain and Langgraph. We have included all the topics from RAG, vectorless rag, Deep agents, Guardrails, LLM Evaluation and LLM Gateways Techniques Github Links Langchain : https://github.com/krishnaik06/Langchain-V1-Crash-Course Langgraph: https://github.com/krishnaik06/Agentic-LanggraphCrash-course RAG: https://github.com/krishnaik06/RAG-Tutorials Vectorless RAG: https://github.com/krishnaik06/RAG-Tutorials/blob/main/PageIndex_Vectorless_RAG_CrashCourse%20(1).ipynb Deep Agents: https://drive.google.com/file/d/1SVjvgqvKfF-FPAIqpEZdKMQLhe13NLFD/view Guardrails : https://github.com/krishnaik06/Langchain-V1-Crash-Course/blob/main/updatedlangchain/langchain_guardrails_crash_course.ipynb LLM Evals : https://github.com/krishnaik06/RAG-Tutorials/blob/main/1-rag_evaluation.ipynb LLM Gateways: https://github.com/krishnaik06/Langchain-V1-Crash-Course/blob/main/llm_gateway_tutorial.ipynb Timestamp 00:00:00 Introduction 00:02:31 Langchain Course 02:35:12 Langraph Course 05:02:29 RAG Course 07:10:43 Vectorless RAG 08:02:11 Deep Agents 08:45:43 Guardrails 09:22:55 LLM Evaluation 10:30:25 LLM Gateways ------------------------------------------------------------------- Learn from us visit https://krishnaik.in/liveclasses

Matt will be back on stage at the World's Fair next week! see https://ai.engineer/wf and use YOUTUBEPROMO for new tickets only. Join 6000 AI engineers at the "Superbowl of AI"! --- A hands-on workshop covering the full lifecycle of AI-assisted development, from turning ambiguous requirements into agent-ready plans to running autonomous coding agents that ship production features. You'll learn to stress-test vague briefs into structured PRDs, slice work into thin "tracer bullet" vertical slices, and run an AI agent with TDD. You'll watch it select tasks, write tests, implement code, and commit. You'll then refine your prompts based on where it struggles, graduate to fully autonomous (AFK) runs, and learn to design codebases that maximize agent effectiveness. You'll walk away knowing how to: - Turn ambiguous requirements into agent-ready issues - Slice work into vertical tracer bullets an agent can grab independently - Run AI agents human-in-the-loop and autonomously with TDD - Design codebase architectures that AI agents love to work in For: Engineers ready to move beyond chat-based AI assistance and build a real workflow for shipping features with autonomous coding agents. Speaker info: - https://x.com/mattpocockuk - https://www.linkedin.com/in/mapocock/ - https://youtube.com/@mattpocockuk Timestamps: 00:00:00 - Introduction 00:00:14 - The Thesis of AI Engineering 00:04:20 - Phase 1: Research & Prototyping 00:12:45 - Phase 2: The Grill Session 00:22:10 - Phase 3: Writing the PRD 00:35:50 - Phase 4: Slicing Work into Issues 00:48:15 - Phase 5: Implementation with AI Agents 01:05:30 - Phase 6: Human-in-the-Loop Review 01:18:45 - Phase 7: Deployment & Monitoring 01:28:10 - Designing Codebases for AI Effectiveness 01:34:06 - Final Takeaways & Summary

Learn all about Claude Code. This is the ultimate guide to turning Anthropic’s Claude into a seamless part of your development workflow. Whether you're scaffolding a brand-new project or navigating a complex, multi-file codebase, this is how you code faster without sacrificing quality. 🔗 IMPORTANT LINKS 🔬 Practice with Hands-On Labs: https://kode.wiki/claude-code 🎓 Get the Full Course here: https://kode.wiki/claude-code-course ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp ⭐️ Contents ⭐️ 0:00:00 Introduction 0:00:30 Prerequisites and Initial Setup 0:09:25 Start your first session 0:23:58 Understanding Codebases with Claude 0:32:21 Installing Claude on Ubuntu Virtual Machines 0:49:34 Building a project from scratch 0:49:34 Getting project ready for public release 0:56:49 Creating Unit Tests for our Project 1:03:20 Autonomous Task Completion 1:17:11 Environment and Configuration Management 1:30:17 Managing Long Sessions Effectively 2:07:52 Writing Effective Development Prompts 2:23:39 Multi-file Project Navigation 2:32:10 Working with APIs and External Services 2:46:16 Initial Project Analysis 2:56:24 Design Pattern Implementation Review 3:05:50 SOLID Principles Evaluation 3:24:04 Error Handling & Resilience Audit 3:36:04 Exception Flow Analysis 3:52:12 Resilience & Fault Tolerance 3:53:09 Code Quality Metrics & Standards 4:02:17 Code Duplication Detection 4:12:14 Naming Conventions & Readability 4:19:23 Test Coverage & Quality Analysis 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

Hermes Agent is the best AI tool out there. Here is your COMPLETE tutorial. FULL Hermes Agent bootcamp in the Vibe Coding Academy coming up: https://www.skool.com/vibe-coding-academy 2nd Youtube Channel: https://youtube.com/@AlexFinnLabsOfficial Sign up for my free newsletter: https://www.alexfinn.ai/subscribe Follow my X: https://x.com/AlexFinn Henry Intelligent Machines (my new startup): https://meethenry.ai My $300k/yr AI app: https://www.creatorbuddy.io/ Prompts: I want you to schedule a task for yourself to do every single night at 2am. It should be a micro app, UI, or system that helps us get closer to my personal goals and ambitions. It should save us time, make us more productive, or just be generally useful. Make sure this is scheduled for every single night at 2am. hermes-agent.nousresearch.com Timestamps: 0:00 Intro 1:50 What Hermes Agent is 3:13 Hermes vs OpenClaw 5:17 Hermes vs Claude Code 7:57 Hermes installation 9:28 Choosing a model 13:44 First things to do 18:02 Hermes dashboard 21:00 Hermes Kanban board 23:15 Use case 1 25:40 Use case 2 27:47 Use case 3 29:40 Memory and skills 34:30 Mission control 37:38 Troubleshooting 38:42 Security

🚀 Work with us - https://aifoundations.io/apply ✨ Join Ai Foundations - https://aifoundations.io This is the only Claude tutorial you’ll ever need. With this guide you can go from beginner to pro using Claude. I cover the basics like signing up, settings, memory, prompting and we work our way up to the intermediate features like Claude projects, artifacts, inline visualizations, data analytics and more. Then we finally dive into Claude desktop, where I teach you how to use Claude Cowork with computer use, scheduled tasks, and connectors for Notion, Gmail and Google Calendar. I even show you how to build Claude skills from scratch. After you have a beginner's understanding of Claude we dive into Claude Code. I show you how to deploy a real website in Netlify and even control Claude through a remote environment on Anthropics servers. We cover github, environment variables, pretty much everything you need, explained in beginner terms. I even cover exporting your ChatGPT memories and bringing them over to Claude. If you've been hearing about Claude AI, Claude Cowork, or Claude Code and wondering how it all fits together: this is the video you’ve been searching for. I walk through everything step-by-step leaving no stone unturned. Utilize the chapters and resources below to get the most out of this video. If it’s your first time watching, I recommend watching all the way through at least once, then coming back to spend extra time on specific chapters! Resources Mentioned: Join Ai Foundations - https://aifoundations.io DBS Framework Skill - https://aifoundations.io/resources/the-dbs-framework-skill Claude Code Mastery - https://aifoundations.io/products/claude-code-mastery Sign up for Claude - https://claude.ai Chapters: 0:00 - Claude Tutorial Intro 0:25 - How to Use This Guide 0:40 - Claude Pricing Comparison 1:22 - Sign Up for Claude 1:49 - Claude Onboarding 3:10 - Claude Chat Layout 3:50 - General Settings 4:53 - Notifications 6:00 - Claude Appearance 6:35 - Claude Voices 6:51 - Privacy and Billing 8:02 - Claude Memory 8:26 - Exporting Memories from ChatGPT 9:21 - Tool Settings 10:14 - Claude Artifact Settings 11:23 - Code Permissions 12:10 - Connector Settings 13:00 - Language Settings 13:15 - Claude Status and Support 13:50 - Gifting Claude 14:23 - Keyboard Shortcuts 14:40 - Claude Models 16:15 - Attachments and Connections 17:10 - Writing Styles in Claude 17:53 - Talking to Claude Voice 19:00 - Organizing Chats 19:40 - Incognito Chats 20:20 - GCAO Prompt Template 21:50 - Prompting Claude 24:50 - Ai Foundations Offers 25:38 - Claude Chat Search 26:34 - Claude Projects Guide 27:18 - Project Instructions 29:40 - Projects Guide Continued 30:56 - Claude Artifacts Library 32:37 - Creating Claude Artifacts 36:40 - Sharing Artifacts 37:15 - Mini App Artifacts 38:35 - Inline Visualizations 39:00 - Visualizing Data Analytics 41:15 - Claude Desktop 42:45 - Claude Cowork Setup 44:00 - Claude Quick Chat 45:00 - Cowork Settings Continued 47:00 - Organizing Files in Cowork 48:20 - Claude Computer Use 49:30 - Cowork Scheduled Tasks 51:22 - Cowork Projects 52:40 - Cowork Prospecting Project 55:00 - Claude in Chrome Extension 56:10 - Cowork Project Results 57:05 - Claude Dispatch Setup 57:50 - Cowork Connectors 58:20 - Claude Notion MCP Connector 59:30 - Using Connectors 1:00:18 - Claude Gmail Connector 1:02:40 - Claude Skills 1:04:18 - DBS Framework Skill 1:05:30 - Uploading Skills 1:06:00 - Creating a Basic Skill 1:07:36 - Testing the Recipe Skill 1:09:25 - Creating an Advanced Skill 1:10:20 - Google Calendar Connector 1:12:24 - Testing the Calendar Skill 1:13:10 - Claude Code Prerequisite 1:14:20 - Claude Code Mastery 1:15:23 - Two Ways to Use Claude Code 1:15:48 - Claude Code Environments 1:17:45 - Claude Code Models 1:18:10 - Claude Code Effort 1:18:34 - Claude Code Permissions 1:18:50 - Claude Code Enhancements 1:20:20 - Folder Setup 1:21:30 - Creating Claude MD 1:22:12 - Opening Files Quickly 1:22:31 - Building a Personal Website 1:25:15 - Planning with Claude Code 1:28:05 - Plan Mode in Claude Code 1:28:45 - Editing My Site 1:29:19 - The Complete Website 1:30:00 - Deploying with Netlify Connector 1:31:12 - Setting a Domain Name 1:31:45 - Adding Environment Variables 1:34:00 - Testing Email Connection 1:34:30 - Creating Remote Skills 1:36:18 - Github Repo Setup 1:37:00 - Initial Commit in Github 1:38:00 - Claude Code Cloud Environments 1:39:00 - Claude Code on my Phone 1:39:55 - Netlify + Github Deployment Setup 1:41:00 - Updating My Website 1:42:00 - Website Complete 1:42:20 - Claude Code Scheduled Tasks 1:44:24 - Scheduled Tasks with Skills 1:47:30 - Updating My Website from Anywhere 1:49:50 - Claude Code Terminal 1:50:30 - Claude Code Commands 1:51:10 - Claude Code Terminal Test 1:51:58 - Final Thoughts #claude #claudecowork #claudecode

This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are developed, along with mental models of how to think about their "psychology", and how to get the best use them in practical applications. I have one "Intro to LLMs" video already from ~year ago, but that is just a re-recording of a random talk, so I wanted to loop around and do a lot more comprehensive version. Instructor Andrej was a founding member at OpenAI (2015) and then Sr. Director of AI at Tesla (2017-2022), and is now a founder at Eureka Labs, which is building an AI-native school. His goal in this video is to raise knowledge and understanding of the state of the art in AI, and empower people to effectively use the latest and greatest in their work. Find more at https://karpathy.ai/ and https://x.com/karpathy Chapters 00:00:00 introduction 00:01:00 pretraining data (internet) 00:07:47 tokenization 00:14:27 neural network I/O 00:20:11 neural network internals 00:26:01 inference 00:31:09 GPT-2: training and inference 00:42:52 Llama 3.1 base model inference 00:59:23 pretraining to post-training 01:01:06 post-training data (conversations) 01:20:32 hallucinations, tool use, knowledge/working memory 01:41:46 knowledge of self 01:46:56 models need tokens to think 02:01:11 tokenization revisited: models struggle with spelling 02:04:53 jagged intelligence 02:07:28 supervised finetuning to reinforcement learning 02:14:42 reinforcement learning 02:27:47 DeepSeek-R1 02:42:07 AlphaGo 02:48:26 reinforcement learning from human feedback (RLHF) 03:09:39 preview of things to come 03:15:15 keeping track of LLMs 03:18:34 where to find LLMs 03:21:46 grand summary Links - ChatGPT https://chatgpt.com/ - FineWeb (pretraining dataset): https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1 - Tiktokenizer: https://tiktokenizer.vercel.app/ - Transformer Neural Net 3D visualizer: https://bbycroft.net/llm - llm.c Let's Reproduce GPT-2 https://github.com/karpathy/llm.c/discussions/677 - Llama 3 paper from Meta: https://arxiv.org/abs/2407.21783 - Hyperbolic, for inference of base model: https://app.hyperbolic.xyz/ - InstructGPT paper on SFT: https://arxiv.org/abs/2203.02155 - HuggingFace inference playground: https://huggingface.co/spaces/huggingface/inference-playground - DeepSeek-R1 paper: https://arxiv.org/abs/2501.12948 - TogetherAI Playground for open model inference: https://api.together.xyz/playground - AlphaGo paper (PDF): https://discovery.ucl.ac.uk/id/eprint/10045895/1/agz_unformatted_nature.pdf - AlphaGo Move 37 video: https://www.youtube.com/watch?v=HT-UZkiOLv8 - LM Arena for model rankings: https://lmarena.ai/ - AI News Newsletter: https://buttondown.com/ainews - LMStudio for local inference https://lmstudio.ai/ - The visualization UI I was using in the video: https://excalidraw.com/ - The specific file of Excalidraw we built up: https://drive.google.com/file/d/1EZh5hNDzxMMy05uLhVryk061QYQGTxiN/view?usp=sharing - Discord channel for Eureka Labs and this video: https://discord.gg/3zy8kqD9Cp Educational Use Licensing This video is freely available for educational and internal training purposes. Educators, students, schools, universities, nonprofit institutions, businesses, and individual learners may use this content freely for lessons, courses, internal training, and learning activities, provided they do not engage in commercial resale, redistribution, external commercial use, or modify content to misrepresent its intent.

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

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. Check out my coding with AI course: https://www.codingwithaicourse.com/ 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.

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.vanzyl@gmail.com #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