🔗 Official links from the video: ➡️ Claude Fable 5 & Mythos 5 announcement: https://www.anthropic.com/news/claude-fable-5-mythos-5 ➡️ Claude Mythos: https://www.anthropic.com/claude/mythos In this video, I break down everything Anthropic just announced about Claude Fable 5, the first model in the new Claude 5 family and part of the Mythos-class tier that sits ABOVE Claude Opus. Fable 5 is beating Opus 4.8 almost everywhere, and it's the first model ever to break 90% on complex agentic tasks. I'll walk you through the benchmarks, the even more powerful Claude Mythos 5 model, how to switch to Fable 5 inside Claude Code, and the pricing and token costs you need to know before you start using it. 💡What you'll learn ✅ Why Claude Fable 5 beats Opus 4.8 on agentic coding, tool use, and long-running tasks. ✅ How Fable 5 became the first model ever to break 90% on complex agentic tasks. ✅ What Claude Mythos 5 is and how Project Glasswing fits into the rollout. ✅ How Anthropic made Fable 5 safe for public use by limiting its cybersecurity capabilities. ✅ The exact benchmark numbers for Fable 5, Mythos 5, and Opus 4.8 side by side. ✅ How to update Claude Code and switch to the Fable 5 model with a single slash command. ✅ The full Fable 5 pricing breakdown and why it burns your tokens 2x faster than Opus 4.8. This breakdown gives you the full picture of Claude Fable 5 and the Mythos-class models in 2026, the benchmarks, the safety story behind the cybersecurity limits, the real pricing per million tokens, and exactly how to start using Fable 5 today. Whether you're building agentic workflows in Claude Code or calling the Claude API directly, you'll know precisely what's changed and how to make the most of it. Book a free Consultation: 🔗 https://cal.com/genovaflow-ai/discovery-call Let us build your project: 🔗 https://genovaflow.com/#book

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Grab the free template from the attached file down below 👇 In this video, I will show you the easiest way to make a RAG AI agent using n8n for free. This automation allows you to chat with an AI agent that generates responses based on files you feed into a knowledge base integrated with MongoDB. We will build a system that can process PDFs and CSVs from Google Drive, embed the data using Gemini, and store it for retrieval. 💡What you'll learn ✅ How to build a custom AI agent in n8n that answers questions using your own knowledge base. ✅ Learn how to set up MongoDB Atlas as a free vector database to store chat memory and document embeddings. ✅ Discover how to configure Vector Search in MongoDB to perform semantic searches on your data. ✅ How to build an automated pipeline that downloads files from Google Drive and inserts them into your database automatically. ✅ How to verify and test your RAG agent with real-world files like inventory spreadsheets and financial PDFs. ✅ How to get MongoDB Credentials for n8n ✅ How to set up vector search in MongoDB This tutorial guides you through the entire process of creating a Retrieval-Augmented Generation (RAG) system without writing code. You will learn how to use the "Vector Store" tool to let the AI retrieve information and how to handle different file formats by parsing text and turning it into numbers (embeddings). By the end, you'll be able to ask your AI complex questions about your specific business data and get accurate answers. Book a free Consultation: 🔗 https://cal.com/genovaflow-ai/discovery-call Let us build your project: 🔗 https://genovaflow.com/home/contact 14‑day FREE Trial on n8n: 🔗 https://n8n.partnerlinks.io/ox7johicleqv Got questions about the video? Drop them in the comments below ⬇️ Sponsorship: 📧 [email protected]