I just built an open NotebookLM clone!
Here's what it can do for you:
- Process multi-modal data
- Scrape websites and YouTube videos
- Create a unified knowledge base
- Lets you do RAG over it
- Remember every conversation
- Generate a podcast 🎙️
The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step!
So by the end of this video, you'll learn how to:
↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs
↳ Store everything in a vector database for fast retrieval
↳ Add a memory layer that remembers conversations and preferences for a personalized experience
↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model
The podcast generation feature is my favorite part!
There's something powerful about turning written content into conversational audio that you can listen to while doing something else.
The entire code is 100% open-source. I've shared a link in the replies!
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Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this.
Cheers! :)
💡Lo que TODO analista de datos debería saber: conceptos, errores y modelos explicados.🤯
No basta con software o IA: comprender la estadística marca la diferencia entre resultados fiables y engañosos.
Recopilatorio de post importantes👇🧵
#DataScience#Stats#RStats#analytics
The Channel: Alex The Analyst
❯ Data Analyst Bootcamp (Playlist):
https://t.co/qmzbHyby5X
❯ Data Analyst Bootcamp (Single Video):
https://t.co/rx9X2Uftls
Ever wondered how to actually build AI agents from scratch?
Saurav Prateek’s “AI Engineering 101” playlist is probably the best crash course out there right now — 19 videos walking through real agentic workflows using LangChain, Python, and more.
> Build Agentic Workflows (no-code + code)
> Design routing logic & prompt chaining
> Add memory + human-in-the-loop
> Integrate databases, APIs, and search
> Even create Self-RAG and Text-to-SQL Agents
No fluff. Just clean, practical engineering for real-world AI systems.
Do not sleep on this if you’re serious about agents.
Link in the comment.
👀 ¿Buscas conjuntos de datos gratuitos para aprender/practicar/crear tu portfolio de #stats#datascience#MachineLearning?
Comparto algunos interesantes:👇🧵
(Comparte y completa la lista)
🔥FREE Official Machine Learning Course from Microsoft
- Comprehensive curriculum that will teach you Machine Learning from scratch with 12 weeks of hands-on projects, covering everything from regression to reinforcement learning.
- 26 lessons, 52 quizzes, and real-world applications using Python and R
🔄💬 Text-to-SQL Tutorial
Build a powerful natural language to SQL converter using LangChain, Ollama's DeepSeek model, and Streamlit. This tutorial shows you how to create an intuitive interface that automatically converts spoken queries into database-ready SQL.
🎥 Watch now: https://t.co/YZDrjPEcrL