MCP is on fire.

AI agents can now talk to real world tools, apps and actually get stuff done.

This changes everything.

Here are 10 amazing examples: 1️⃣ WhatsApp MCP

Exchange images, videos, and voice notes on WhatsApp!

Pair it with the ElevenLabs MCP server for AI-powered transcription & audio messages with 3,000+ voices.

Check this out👇

Let's build a multi-agent brand monitoring system using DeepSeek-R1 (100% local): Today, we're building a brand monitoring app that scraps web mentions and produces insights about a company.

Tech stack:

- Bright Data to scrape data at scale
- @crewAIInc for orchestration
- @ollama to serve DeepSeek locally

Let's go! 🚀

Let's compare Llama 4 and DeepSeek-R1 using RAG: Today, we're building a Streamlit app to compare MetaAI's Llama 4 against DeepSeek-R1 using RAG.

Tech stack:

- @Llama_Index workflows for orchestration
- @Cometml Opik for evaluation
- @GroqInc for blazing-fast inference (FREE)

Let's go! 🚀

Let's build our own reasoning LLM using Reinforcement fine-tuning: RFT allows you to transform any open-source LLM into a reasoning powerhouse.

No labeled data needed.

Today we'll use:

- @predibase for reinforcement fine-tuning (RFT)
- @Alibaba_Qwen-2.5:7b as the base model

Let's go! 🚀

Eigenvalues and eigenvectors, clearly explained: The concept of eigenvalues & eigenvectors is widely known yet poorly understood!

Today, I'll clearly explain their meaning & significance.

Let's go! 🚀

Effortlessly trace and monitor multi-agent LLM applications!

With just two lines of code, Opik tracks everything happening inside your AI application, including costs. See the CrewAI example below.

100% open-source, self-hosted. Opik offers integrations for nearly all popular frameworks.

But if you don't find what you need, don't worry!

A single decorator will handle it for you.

Check this out👇

Multithreading in Python clearly explained: Ever felt like your Python code could run faster❓

Multithreading might be the solution you're looking for!

Today, I'll simplify it for you in this step-by-step guide.

Let's go! 🚀

Model Context Protocol (MCP), clearly explained: MCP is like a USB-C port for your AI applications.

Just as USB-C offers a standardized way to connect devices to various accessories, MCP standardizes how your AI apps connect to different data sources and tools.

Let's dive in! 🚀

Let's build a RAG app using Google DeepMind's Gemma 3 (100% local): Google just dropped a multilingual and multimodal open-source LLM.

Today, we're building a RAG app powered by @GoogleDeepMind's Gemma3.

Tech stack:

- @Llama_Index for orchestration
- @Ollama to locally serve Gemma 3
- @Streamlit for the UI

Let's go! 🚀

Let's build a Multimodal RAG app over complex webpages using DeepSeek's Janus-Pro (running locally): The video depicts a multimodal RAG built with a SOTA tech stack.

We'll use:
- ColiVara's SOTA document understanding and retrieval to index webpages.
- @firecrawl_dev for reliable scrapping.
- @huggingface transformers to locally run DeepSeek Janus

Let's build it!

Object oriented programming in Python, clearly explained: We break it down to 6 important concepts:

- Object 🚘
- Class 🏗️
- Inheritance 🧬
- Encapsulation 🔐
- Abstraction 🎭
- Polymorphism 🌀

Let's take them one-by-one... 🚀

Bayes' Theorem clearly explained: Bayes' Theorem is a cornerstone of probability theory!

It calculates the probability of an event, given that another event has occurred.

It's like updating your guess with fresh information!

Before we delve into the details, let's take a quick look at its formula:

Let's build a corrective RAG (CRAG) agentic workflow, step-by-step (100% local): Before we dive in, here's a quick demo of our agentic workflow!

Tech stack:

- @Llama_Index workflows for orchestration
- @Linkup_platform for deep web search
- @Cometml's Opik to trace and monitor
- @Qdrant_engine to self-host vectorDB

Let's go! 🚀

AI Engineering Hub just crossed 3k stars on GitHub!

It’s 100% open-source, packed with 30+ hands-on tutorials that many would charge $1,000+ for...

Here's a small sample of what you get for free: 1️⃣ Multi-agent YouTube Trend Analysis App

Learn to gather trending topics using agents & transform data into actionable insights.

Check this out👇
github.com/patchy631/ai-e…

Let's build a multi-agent best flight finder app, step-by-step (100% local): Before we begin, here's a quick demo of what we're building!

You will learn:

- @CrewAIInc for multi-agent orchestration
- @BrowserbaseHQ's headless browser tool
- @Ollama to locally server DeepSeek-R1

Let's go! 🚀

KV caching in LLMs, clearly explained (with visuals): KV caching is a technique used to speed up LLM inference.

Before diving into the internal details, look at the inference speed difference demonstrated in the video:

- with KV caching → 9 seconds
- without KV caching → 42 seconds (~5x slower)

Let's dive in! 🚀

Let's build a multimodal RAG app using Qwen2.5-VL Max (100% local): Before we begin, here a quick demo of what we're building

We'll use:

- ColPali to process complex docs with text, images and more
- @Alibaba_Qwen 2.5 VL as multimodal LLM
- @qdrant_engine as the vector database

Let's go! 🚀

Let's fine-tune DeepSeek-R1 (distilled Llama) 100% locally: Before we begin, here’s what we’ll be doing:

We’ll fine-tune our private and locally running DeepSeek-R1 (a distilled Llama variant).

Tech stack:

- @UnslothAI for efficient fine-tuning.
- @Ollama to run it locally.

Let’s go! 🚀

10 great Python packages for Data Science not known to many: 1️⃣ CleanLab

You're missing out on a lot if you haven't started using Cleanlab yet!

Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.

It's like a magic wand! 🪄✨

Check this out👇
github.com/cleanlab/clean…

Let's build an enterprise-grade, agentic RAG over complex real-world docs, step-by-step: We gonna do RAG over MIG 29 (a fighter aircraft) flight manual, which includes complex figures, diagrams, and more.

(Watch the video below)

Tech stack:

- @CrewAIInc for agent orchestration
- @EyelevelAI's GroundX for SOTA document parsing

Let's go! 🚀

Let's build a trustworthy RAG app that provides a confidence score for each response: Before we dive in, here's a quick demo of what we're building!

Tech stack:

- @Llama_Index for orchestration
- @CleanlabAI's trustworthy LLM
- @Qdrant_engine to self-host a vectorDB
- LlamaParse to make complex docs LLM ready.

You get both score and reasoning! ✨

Let's go! 🚀