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

Introducing Docling. Here's what you need to know: 🧵 Image
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. Image
2. Document Conversion Architecture

For each document format, the document converter knows which format-specific backend to employ for parsing the document and which pipeline to use for orchestrating the execution, along with any relevant options. Image
3. PDF Conversion to Markdown

Here is an example of the DocLayNet paper from arXiv, converted into Markdown format by Docling. Image
4. Core Technology:

Docling includes:

- PDF Backends for parsing
- Layout Analysis Model
- Vision-Based Table Formatter
- OCR for Text Image
5. Every data analyst, data scientist, and data engineer needs to learn Generative AI

99% of them are overlooking AI. This is a massive opportunity for you.

I'd like to help.
On Wednesday, August 20th, I'm sharing one of my best AI/ML Projects for FREE:

How I built an AI Customer Segmentation Agent with Python:

Register here (1,821 registered): learn.business-science.io/ai-registerImage
That's a wrap! Over the next 24 days, I'm sharing the 24 concepts that helped me become an AI data scientist.

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More from @mdancho84

Feb 5
OpenAI, Google, and Anthropic just published guides on:

• Prompt engineering
• Building agents
• AI in business
• 601 AI use cases

9 of the best guides you can't miss: Image
1. AI in the Enterprise by OpenAI

Grab the PDF: cdn.openai.com/business-guide…Image
2. A practical guide to building agents by OpenAI

Download here: cdn.openai.com/business-guide…
Read 14 tweets
Feb 2
🚨McKinsey just dropped how to build agentic AI (that works)

Here's everything you need to know in 2 minutes: Image
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.
2. Not everything needs an Agent

Stop agent-ifying everything.

Ask: "Is this a problem that actually needs solving with agents?"

Alternatives to Agents:

- Automation
- NLP
- Basic Gen AI
- Predictive Analytics
Read 10 tweets
Jan 22
This 277-page PDF unlocks the secrets of Large Language Models.

Here's what's inside: 🧵 Image
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. Image
Chapter 2 introduces generative models, which are the large language models we commonly refer to today.

After presenting the basic process of building these models, you explore how to scale up model training and handle long texts. Image
Read 10 tweets
Jan 21
RIP BI Dashboards.

Tools like Tableau and PowerBI are about to become extinct.

This is what's coming (and how to prepare): Image
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. Image
AI + Data Science is the future:

AI tools like:

- LangChain
- LangGraph
- OpenAI API

Are being combined with:

- SQL Databases
- Machine Learning
- Prediction

And the results are exactly what businesses need: real-time predictive insights. Image
Read 7 tweets
Jan 18
A Research Scientist at Google DeepMind just dropped a 58 page paper on building agents that specialize in game theory.

Here are the most important parts: Image
The problem with existing agents - You need to prompt the LLM to generate actions.

But this doesn't work in games that have perfect or imperfect information.

Instead, they implement a trick:
To describe a complex "world model", researchers simplify as a Partially Observed Stochastic Game.

It's represented below as a causal graph. Image
Read 9 tweets
Jan 18
Stanford just dropped a 457 page report on AI.

It's packed with data on: cost drops, efficiency, benchmarks, adoption.

This report is a cheat code for your career in 2026.

I pulled the most important charts + what they mean for your career: 🧵 Image
First: this isn’t “AI hype.”

It’s measured trends on what’s getting cheaper, what’s getting better, and what’s spreading across the economy and regulation.

(Bookmark this. You’ll reuse it.)
1. Cost + efficiency

The quiet story of 2025: AI is getting dramatically cheaper + more efficient.

The report estimates price-performance improved ~30% per year and energy efficiency improved ~40% annually.

That’s why AI is moving from “demo” to “default.”
Read 16 tweets

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