🚨NEW Whitepaper on AI Agents by OpenAI

The maker of ChatGPT shares how it builds AI Agents.

Get the 34-page white paper here: This Whitepaper covers:

1. Building, evaluating, and deploying AI agents
2. Architectures, tool integration, and scaling
3. Agent ops and evaluation frameworks

Get it here:

I have one more thing before you go.

If you want to become a generative AI data scientist in 2025 ($200,000 career), then I'd like to help:cdn.openai.com/business-guide…

This is wild.

A new paper shows how you can predict real purchase intent without asking people.

~90% of human test–retest reliability.

Here's what's inside the 28 page paper: 1. Problem with direct Likert from LLMs:

When you ask LLMs to output 1–5 ratings directly, the distributions are too narrow/skewed and don’t look like human survey data, limiting usefulness for concept testing.

How to build AI agents:

A great cheat sheet (bookmark for later).

Here's how to use it: 1️⃣ System Prompt: Define your agent’s role, capabilities, and boundaries. This gives your agent the necessary context.

2️⃣ LLM (Large Language Model): Choose the engine. GPT-5, Claude, Mistral, or an open-source model — pick based on reasoning needs, latency, and cost.

Understanding P-Values is essential for improving regression models.

In 2 minutes, I'll crush your confusion. 1. The p-value:

A p-value in statistics is a measure used to assess the strength of the evidence against a null hypothesis.

Understanding probability is essential in data science.

In 4 minutes, I'll demolish your confusion.

Let's go! 1. Statistical Distributions:

There are 100s of distributions to choose from when modeling data. Choices seem endless. Use this as a guide to simplify the choice.

Top 10 Python Libraries for Generative AI You Need to Master in 2025

(The tools behind document agents, intelligent assistants, and next-gen interfaces.)

Everything you need to know: 🧵 1. LangChain

The backbone of intelligent LLM apps.

Build agents that:
✅ Reason
✅ Use tools
✅ Remember conversations
✅ Access APIs

If you're building anything with GPTs, LangChain is your starting point.

langchain.com

AI Engineering Toolkit

A curated list of 100+ LLM libraries and frameworks for training, fine-tuning, building, evaluating, deploying, RAG, and AI Agents.

100% Open Source Get it here:

I have one more thing before you go.

If you want to become a generative AI data scientist in 2025 ($200,000 career), then I'd like to help:github.com/Sumanth077/ai-…

🚨BREAKING: New Python library for agentic data processing and ETL with AI

Introducing DocETL.

Here's what you need to know: 1. What is DocETL?

It's a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks.

It offers:

- An interactive UI playground
- A Python package for running production pipelines

Stop doing Customer Segmentation with plain vanilla Scikit Learn.

Add these 7 Python libraries to your RFM, clustering, and
customer segmentation projects: 1. Data preparation

- load data with pandas
- impute/mask with Feature-engine

Website: feature-engine.trainindata.com/en/latest/inde…

🚨NEW: Python library for LLM Prompt Management

This is what it does: The Python library is called Promptify.

It combines a prompter, LLMs, and pipeline to Solve NLP Problems with LLM's.

You can easily generate different NLP Task prompts for popular generative models like GPT, PaLM, and more with Promptify.

🚨 BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. 🧵 1. Document Parsing Pipelines

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines.

These 7 statistical analysis concepts have helped me as an AI Data Scientist.

Let's go: 🧵 Step 1: Learn These Descriptive Statistics

Mean, median, mode, variance, standard deviation. Used to summarize data and spot variability. These are key for any data scientist to understand what’s in front of them in their data sets.

🚨Introducing Agent Development Kit (ADK) by Google

A simple framework from Google for building, evaluating, and deploying AI agents.

Here's what you need to know (a thread): 🧵 1. What is ADK?

Agent Development Kit (ADK) is a framework from Google for building, evaluating, and deploying AI agents.

It is “model-agnostic” and “deployment-agnostic”: although it’s optimized to work well with Google’s models and infrastructure (like Gemini, Vertex AI), you can use it with other models and deploy it in different environments.

🚨Introducing Nanobot

Build MCP AI Agents with reasoning, system prompts, and tool orchestration.

This is what it does (and how to get started): (thread) 1. Nanobot enables building agents with MCP and MCP-UI by providing a flexible MCP host.

Nanobot is designed to be a standalone, open-source MCP host that can be easily deployed or integrated into your applications. You can use Nanobot to create your own dedicated MCP and MCP-UI powered chatbot.

🚨 Introducing Microsoft Agent Framework.

A new open-source Python library for making agents.

This is what you need to know: 1. The Microsoft Agent Framework is an open-source development kit for building AI agents and multi-agent workflows for Python.

It brings together and extends ideas from the Semantic Kernel and AutoGen projects, combining their strengths while adding new capabilities.

Google just dropped a new Generative AI Python library for SQL Databases.

Introducing Google GenAI Toolbox.

This is what you need to know: 1. Meet the Google GenAI Toolbox

An open-source server designed to simplify building Gen AI tools for your databases. It streamlines development, letting you integrate powerful data tools with just a few lines of code.

Stop doing Customer Segmentation with plain vanilla Scikit Learn.

Add these 7 Python libraries to your RFM, clustering, and
customer segmentation projects: 1. Data preparation

- load data with pandas
- impute/mask with Feature-engine

Website: feature-engine.trainindata.com/en/latest/inde…

🚨 BREAKING: Microsoft launches a free Python library that converts ANY document to Markdown

Introducing Markitdown. Let me explain. 🧵 1. Document Parsing Pipelines

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines.

Tableau is about to die.

Introducing PandasAI, a free alternative for fast Business Intelligence.

Let's dive in: 1. PandasAI

PandaAI transforms your natural language questions into actionable insights — fast, smartly, and effortlessly.

🚨McKinsey just dropped how to build agentic AI (that works)

Here's everything you need to know in 2 minutes: 1. Stop building agents; Start fixing workflows

The mistake every organization makes: falling in love with your new AI agent.

The solution: Identify the pain points in your process. Then use agents to connect analytics and gen AI into 1 seamless process.

🚨The AI agent handbook

Google just dropped a 46-page playbook on how to build and use agents.

This is what you need to know (and how to get it 100% free): The AI agent handbook contains:

- 10 ways to use AI agents
- How to set them up with Google Agentspace
- 100+ prompts for Agentspace