Microsoft released an AI powered data analysis tool!

Data Formulator is an AI-powered tool for analysts to iteratively create rich visualizations.

It's no-code & 100% open-source Github Repo: github.com/microsoft/data…

Let's build a PDF RAG Agent, step-by-step: 1. Here’s what we’re building:

The agent pulls context from the indexed PDF files stored in vector DB. If needed, it can also fall back to web search to provide more relevant answers.

Tech stack:

@milvusio for vector DB as knowledge base
@AgnoAgi for agent orchestration

5 MCP servers that will give superpowers to your AI Agents.

100% open source. 1. Fast API

Turn any FastAPI app into an MCP server!

FastAPI-MCP is a zero-configuration tool for automatically exposing FastAPI endpoints as Model Context Protocol (MCP) tools.

github.com/tadata-org/fas…

I've been coding in Python for 5 years now.

If I were to start over today, here's a roadmap: 1. CS50’s Introduction to Programming with Python:

The course is designed for students with or without prior programming experience who’d like to learn Python specifically.

Check this out: cs50.harvard.edu/python/2022/

5 MCP servers that will give superpowers to your AI Agents.

100% open source. 1. @firecrawl_dev MCP server

Firecrawl MCP lets you scrape, crawl, search, extract, and run deep research at scale.

It supports JS rendering, batch scraping, URL discovery, and full content extraction from web search.

Let's build a RAG app over Excel sheets using Docling and DeepSeek-R1 (100% local): 1. Check out the RAG app we're building!

This app allows you to upload Excel files and chat with them using the DeepSeek-R1 model running locally.

Built entirely in Python using @getreflex

Train and Evaluate all your Machine Learning Models at once!

Introducing Lazy Predict a Python Library that allows you to train, test and evaluate all your ML models at once with just a few lines of code. Model selection is key for any ML Project which includes identify the model that generalizes well and provides the best performance.

This library can save time and effort by training and evaluating all your ML models at once

Here is using LazyRegressor for Regression Problems:

Let's build a RAG app using DeepSeek-R1 (100% Local): 1. Check out the RAG app we're building!

This app allows you to upload PDF files and chat with them using the DeepSeek-R1 model running locally.

Built entirely in Python using @getreflex

Build a LLM app with RAG to chat with GitHub using Llama 3.2 running locally on your computer (100% free and without internet): 1. Install and Import necessary libraries

@getreflex for building the web app in Python
Embedchain for the RAG functionality

Let's build a RAG app using Llama 3.2 running locally on your computer (100% open source): 1. Install and Import necessary libraries

@getreflex for building the web app in Python
Embedchain for the RAG functionality

Build a Customer Data Management Dashboard App in Python (step-by-step explanation with code): 1. Before we begin, take a look at the dashboard we're building!

This interactive dashboard displays customer data and allows you to add, edit, and delete entries in a table.

It also visualizes changes in the data over time. Built entirely in Python using Reflex:

Build a multi-agent AI news assistant using Open AI swarm and Llama 3.2 running locally on your computer (100% free and open source): 1. Install and import necessary Libraries

@getreflex for building the web app in Python
@OpenAI Swarm for multi-agent orchestration
@ollama for running llama 3.2 locally

Also:

• Load environment variables
• Initialize Swarm client
• Install and run Llama 3.2 using Ollama

Generative Pretrained Transformer (GPT) implemented from scratch using Numpy in just 60 lines of code: Although the above code is super slow and doesn't include any training code, backpropagation, GPU/TPU support, batching, etc

This is a good way to get an idea of the implementation of GPT.

Checkout the complete code here:

github.com/jaymody/picoGPT

5 Open Source frameworks to build LLM apps with RAG (100% free): 1. AutoRAG

Opensource framework tool for finding the optimal RAG pipeline for “your data.”

You can evaluate various RAG modules automatically with your own evaluation data and find the best RAG pipeline for your own use-case.

github.com/Marker-Inc-Kor…

Build a Dashboard App in Python (step-by-step explanation with code): 1. Before we begin, take a look at the dashboard we're building.

This is an interactive NBA dashboard featuring player stats built using @getreflex

The Table tab allows you to display the player data, while the Stats tab visualizes relationships between player stats.

Build a Multimodal AI Agent using Gemini 2.0 that can understand videos and search the internet at the same time (100% open source): 1. Install and import necessary Libraries

@getreflex for building the web app in Python
Phidata for building the Agent
Google AI Python SDK & Gemini 2.0 Flash model

Build a multi-agent AI news assistant using Open AI swarm and Llama 3.2 running locally on your computer (100% free and open source): 1. Install and import necessary Libraries

@getreflex for building the web app in Python
@OpenAI Swarm for multi-agent orchestration
@ollama for running llama 3.2 locally

Also:

• Load environment variables
• Initialize Swarm client
• Install and run Llama 3.2 using Ollama

If you are looking for public datasets to work on LLMs, AI and Machine Learning.

Here are 5 Free data sources where you can find one for your next project: 1. Awesome Data Github Repository

You can find direct links to all the Public datasets, and you can find all sorts of datasets like Images, Text, Audio, and Tabular.

github.com/awesomedata/aw…

5 GitHub repositories that will give you superpowers as an AI/ML Engineer: 1. Awesome Artificial Intelligence

A curated list of Artificial Intelligence:

- courses
- books
- video lectures
- and papers with code

github.com/owainlewis/awe…

Neural Network implemented from scratch in Python (step-by-step explanation with code): Below is the simple Neural Network consists of 2 layers:

- Hidden Layer
- Output Layer

First Initialize the size of layers along with the weights & biases.

And also define the sigmoid activation function & it's derivative which is really key to introduce non-linearity.

Build a LLM app with RAG to chat with PDF files using Llama 3.2 running locally on your computer (100% free and without internet): 1. Install and Import necessary libraries

@getreflex for building the web app in Python
Embedchain for the RAG functionality
tempfile for creating temporary files and directories