🚨 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.

🚨 BREAKING: IBM launches a free Python library that converts ANY document to data

Introducing Docling. Here's what you need to know: 🧵 1. What is Docling?

Docling is a Python library that simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.

Google just dropped a masterclass on Agents.

Here's what's covered in the 54 page PDF: Here's what they cover:

1. From models to agents

2. What an AI Agent is

3. Agentic problem-solving loop (5 steps)

4. Taxonomy of agentic systems (levels 0–4)

5. Core architecture decisions

6. Multi-agent patterns (design patterns)

This 277-page PDF unlocks the secrets of Large Language Models.

Here's what's inside: 🧵 Chapter 1 introduces the basics of pre-training.

This is the foundation of large language models, and common pre-training methods and model architectures will be discussed here.

Stanford just made fine-tuning irrelevant with a single paper.

It’s called Agentic Context Engineering (ACE) and it proves you can make models smarter without touching a single weight.

Key takeaways (and get the 23 page PDF): Stanford just released a 23 page paper on Agentic Context Enginnering to improve Agents. Key ideas:

1. ACE = Agentic Context Engineering: treat system prompts + agent memory as a living playbook.

🚨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

🚨 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.

Agentic AI: A comprehensive survey of architectures, appications, and future directions (A 37 page PDF)

Here are the best parts: 1. The Dual-Paradigm Framework

The survey argues that Agentic AI research must be categorized to avoid conceptual retrofitting (applying old symbolic models to new systems).

RIP BI Dashboards.

Tools like Tableau and PowerBI are about to become extinct.

This is what's coming (and how to prepare): I've never been a fan of Tableau and PowerBI.

Static dashboards don't answer dynamic business questions.

That's why a new breed of analytics is coming: AI Analytics.

Stop Prompting LLMs.
Start Programming LLMs.

Introducing DSPy by Stanford NLP.

This is why you need to learn it: 1. Why DSPy?

DSPy is the open-source framework for programming—rather than prompting—language models.

It allows you to iterate fast on building modular AI systems.

This 277-page PDF unlocks the secrets of Large Language Models.

Here's what's inside: 🧵 Chapter 1 introduces the basics of pre-training.

This is the foundation of large language models, and common pre-training methods and model architectures will be discussed here.

🚨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

🚨 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.

The AI Agent Development Process.

How to go from idea to production.

(A thread) 🧵 1. What is the AI Agent Development Process?

A repeatable path to ship an agent from idea to production: define → design → build → train → validate → deploy → monitor → improve.

K-means is one of the most powerful algorithms for data scientists.

But it's confusing for beginners. Let's fix that: 1. What is K-means?

Is a popular unsupervised machine learning algorithm used for clustering. It's a core algorithm used for customer segmentation, inventory categorization, market segmentation, and even anomaly detection.

🚨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