A Crash Course on Building AI Agents!

Here's what it covers:

- What is an AI agent
- Connecting agents to tools
- Overview of MCP
- Replacing tools with MCP servers
- Setting up observability and tracing

All with 100% open-source tools! This course builds agents based on the following definition:

An AI agent uses an LLM as its brain, has memory to retain context, and can take real-world actions through tools, like browsing web, running code, etc.

In short, it thinks, remembers, and acts.

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👇

90% of Python programmers don't know these 11 ways to declare type hints: Type hints are incredibly valuable for improving code quality and maintainability.

Today, I'll walk you through 11 must-know principles to declare type hints in just two minutes.

Let's begin! 🚀

Temperature in LLMs, clearly explained (with code): Let's prompt OpenAI GPT-3.5 with a low temperature value twice.

It produces identical responses from the LLM.

Check the response below👇

7 MCP projects for AI Engineers (with video tutorials): 1️⃣ MCP meets Ollama

An MCP client is a component in an AI app (like Cursor) that establishes connections to external tools.

Learn how to build it 100% locally.

Full walkthrough:

I have tested 100+ MCP servers in the last 3 months!

Here are 6 must-use MCP servers for all developers (open-source): 1️⃣ Graphiti MCP server

Agents forget everything after each task.

Graphiti MCP server lets Agents build and query temporally-aware knowledge graphs, which act as an Agent's memory!

Check this👇

Let's generate our own LLM fine-tuning dataset (100% local): Before we begin, here's what we're doing today!

We'll cover:
- What is instruction fine-tuning?
- Why is it important for LLMs?

Finally, we'll create our own instruction fine-tuning dataset.

Let's dive in!

Let's build a real-time Voice RAG Agent, step-by-step: Before we begin, here's a quick demo of what we're building

Tech stack:

- @Cartesia_AI for SOTA text-to-speech
- @AssemblyAI for speech-to-text
- @LlamaIndex to power RAG
- @livekit for orchestration

Let's go! 🚀

Let's build an MCP-powered audio analysis toolkit: Before we dive in, here's a demo of what we're building!

Tech stack:
- @AssemblyAI for transcription and audio analysis.
- Claude Desktop as the MCP host.
- @streamlit for the UI

Let's build it!

AI agents can finally talk to your frontend!

The AG-UI Protocol bridges the critical gap between AI agents and frontend apps, making human-agent collaboration seamless.

MCP: Agents to tools
A2A: Agents to agents
AG-UI: Agents to users

100% open-source. Here's the official GitHub repo for @CopilotKit's AG-UI:

(don't forget to star 🌟)github.com/ag-ui-protocol…

Top 4 open-source LLM finetuning libraries!

From single-GPU “click-to-tune” notebooks to trillion-param clusters, these four libraries cover every LLM finetuning scenario.

Understand which one to use, & when...👇 1️⃣ Unsloth

Unsloth makes fine-tuning easy and fast, turning a mid-range GPU into a powerhouse with a simple Colab or Kaggle notebook.

Perfect for hackers and small teams using 12–24 GB GPUs needing quick LoRA experiments without DeepSpeed configs or clusters

Check this out👇
github.com/unslothai/unsl…

12 powerful tools for your AI Agents!

Here's a breakdown of what each does...👇 1️⃣ FileReadTool

This tool instantly pulls data from the local file system.

Read more👇
docs.crewai.com/tools/file-doc…

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! 🚀

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... 🚀

Self-attention in LLMs, clearly explained: Before we start a quick primer on tokenization!

Raw text → Tokenization → Embedding → Model

Embedding is a meaningful representation of each token (roughly a word) using a bunch of numbers.

This embedding is what we provide as an input to our language models.

Check this👇

Let's build an MCP-powered Agentic RAG (100% local): Below, we have an MCP-powered Agentic RAG that searches a vector database and falls back to web search if needed.

To build this, we'll use:
- @firecrawl_dev search endpoint for web search.
- @qdrant_engine as the vector DB.
- @cursor_ai as the MCP client.

Let's build it!

Function calling & MCP for LLMs, clearly explained (with visuals): Before MCPs became popular, AI workflows relied on traditional Function Calling for tool access. Now, MCP is standardizing it for Agents/LLMs.

The visual below explains how Function Calling and MCP work under the hood.

Let's learn more!

Let's build an MCP server that connects to 200+ data sources (100% local): Before we dive in, here's a quick demo of what we're building!

Tech stack:

- @MindsDB to power our unified MCP server
- @cursor_ai as the MCP host
- @Docker to self-host the server

Let's go! 🚀

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

Before understanding the internal details, look at the inference speed difference in the video:

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

Let's dive in!

Let's build an MCP-powered financial analyst (100% local): Before we dive in, here's a quick demo of what we're building!

Tech stack:

- @crewAIInc for multi-agent orchestration
- @Ollama to locally serve DeepSeek-R1 LLM
- @cursor_ai as the MCP host

Let's go! 🚀

5 levels of Agentic AI systems, clearly explained (with visuals): Agentic AI systems don't just generate text; they can make decisions, call functions, and even run autonomous workflows.

The visual explains 5 levels of AI agency—from simple responders to fully autonomous agents.

Let's dive to learn more about them.