Let's build a pipeline to evaluate and monitor a RAG application, using a 100% open-source tool: Before we start here's a quick demo what we're building:

Tech Stack:

- @Cometml's Opik for eval and observability
- @Llama_Index to build a RAG app

Track everything from, LLM calls to chunking, embedding, generation and evaluation!

Let's build world's fastest RAG stack, step-by-step: We'll search 36M+ vectors in <15ms 🔥

Tech stack:

- @llama_index for orchestration.
- @qdrant_engine as VectorDB (with Binary Quantization).
- @SambaNovaAI for blazing fast LLM inference

This video shows what we are building.

Let's go! 🚀

Let's build an Agentic RAG, step-by-step with code (100% local): Before we begin, take a look at what we're about to create!

Here's what you'll learn:

- @CrewAIInc for multi-agent orchestration
- @qdrant_engine to self-host a vector DB
- @firecrawl_dev for web search

Let's go! 🚀

Let's build a Chat with docs RAG app, featuring ModernBERT (100% local): Before we begin, take a look at what we're about to create!

Here's what you'll learn:

- @Llama_Index for orchestration
- @ollama for locally serving Llama 3.2
- @qdrant_engine to self-host a vector DB
- @nomic_ai for ModernBERT embeddings

Let's go! 🚀

I've been coding in Python for 9 years now. ⏳

If I were to start over today, here's a roadmap: 1️⃣ freeCodeCamp

4 hours Python bootcamp!!

What you'll learn:
- Installing Python
- Setting up an IDE
- Basics Syntax
- Variables & Datatypes
- Looping in Python
- Exception handling
- Modules & pip
- Mini hands-on projects 🔥

Check this out 👇

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! 🚀

f-strings in Python clearly explained: f-strings were introduced in Python 3.6 and have since become a favorite among developers for their simplicity and readability.

Today, we'll start with the basics and dive into all the ninja tricks of using f-strings.

Let's go! 🚀

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! 🚀

Let's build a multi-agent AI news generator using Cohere's new ⌘R 7B: Before we begin, take a look at what we're building!

The app takes a user query, searches the web for it, and turns it into a well-crafted news article, with citations!

Stack:

- @Cohere ultra-fast ⌘R 7B as LLM
- @CrewAIInc for multi-agent orchestration

Let's go! 🚀

Let's build a RAG app using MetaAI's Llama-3.3 (100% local): Before we begin, take a look at what we're about to create!

Here's what you'll learn:

- @Llama_Index for orchestration
- @qdrant_engine to self-host a vector DB
- @Ollama for locally serving Llama-3.3
- @LightningAI for development & hosting

Let's go! 🚀

Decorators in Python, clearly explained: Decorators are one of the most powerful feature of Python! 🔥

However, understanding them can be a bit overwhelming!

Today, I'll clearly explain how decorators work!

Let's go! 🚀

15 cheat sheets you shouldn't miss as a data scientist: 1. Pandas ↔ Polars ↔ SQL ↔ PySpark Translations.

Master data wrangling by understanding how these frameworks relate to one another. Here's your go-to translation guide for smooth transitions!

Let's build a real-time AI voice assistant, step-by-step: Before we start, here's a quick demo of what we're building!

Tech stack:

- @AssemblyAI to convert speech to text in real-time.
- @OpenAI's GPT-4o to generate intelligent responses.
- @elevenlabsio to convert text responses back to speech.

Let's go! 🚀

Let's build a multi-agent financial analyst using Microsoft's Autogen and Llama3-70B: Before we start, here's a demo and walkthrough of what we're building today.

Tech stack:

- Microsoft's Autogen for multi-agent collaboration
- @Qualcomm's Cloud AI 100 ultra for serving Llama 3:70B

Let's go! 🚀

5 open-source agentic frameworks that will give you superpowers as an AI engineer: 1️⃣ Dymamiq

@DynamiqAGI is like a Swiss army knife for AI Engineers, it supports:

- RAG applications.
- Multi agent orchestration
- And managing complex LLM workflows

Everything in one place!✨

GitHub repo: github.com/dynamiq-ai/dyn…

Let's build a PerplexityAI-like personal research assistant using multi-agent orchestration: Before we dive in, let's see what we're about to create!

We'll use:

- @SwarmZeroAI for multi-agent orchestration
- @Serp_api for real-time search
- @FireCrawl_dev for LLM-ready web scraping

Let's go! 🚀

How LLMs understand relative positions of input words, clearly explained: RoPE (Rotary Positional Embeddings) revolutionised the way positional information is encoded in LLMs and it's widely used by models like Llama-3.

Today, I'll clearly explain what they are & how positional embeddings evolved over time.

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…

How LLMs work, clearly explained: Before diving into LLMs, we must understand conditional probability.

Let's consider a population of 14 individuals:

- Some of them like Tennis 🎾
- Some like Football ⚽️
- A few like both 🎾 ⚽️
- And few like none

Here's how it looks 👇

Python *args & **kwargs clearly explained: *args allows you to pass a variable number of non-keyword arguments to a function.

It collects all non-keyword arguments passed to the function and stores them as a tuple.

Consider the following example:

Self-attention in transformers, explained as a directed graph: Self-attention is at the heart of transformers, the architecture that led to the LLM revolution that we see today.

In this post, I'll clearly explain self-attention & how it can be thought of as a directed graph.

Read more...👇