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.

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: 🧵 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.)

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: 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.

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.

🚨 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.

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: 1. AI in the Enterprise by OpenAI

Grab the PDF: cdn.openai.com/business-guide…

🚨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.

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.

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.

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: 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:

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: 🧵 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.)

This is huge.

A group of 50 AI researchers (ByteDance, Alibaba, Tencent + universities) just dropped a 303 page field guide on code models + coding agents.

And the takeaways are not what most people assume.

Here are the highlights I’m thinking about (as someone who lives in Python + agents): 1) Small models can punch way above their weight

If you do RL the right way (RLVR / verifiable rewards), a smaller open model can close the gap with the giants on reasoning-style coding tasks.

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: 1. AI in the Enterprise by OpenAI

Grab the PDF: cdn.openai.com/business-guide…

🚨 Google published a 69-page prompt engineering masterclass.

This is what's inside: Table of Contents:

- Prompt Engineering
- LLM Output Configuration
- Prompting Techniques
- Best Practices

🚨 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