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.

A senior Google engineer dropped a 482 page PDF on agentic design patterns.

482 pages.

Most AI engineers bookmarked it and never opened it again.

I read the whole thing.

Here are the top 5 patterns (explained in plain English): PATTERN 1 — Single Agent

The simplest and most common starting point.

One model. One system prompt. A bounded set of tools.

The model decides which tool to call, observes the result, and keeps going until it has enough to answer.

🚨BREAKING: Google just DROPPED a masterclass on GPUs

Get it here 100% free: FULL GUIDE: HOW TO SCALE YOUR MODEL: jax-ml.github.io/scaling-book/

PART 12: HOW TO THINK ABOUT GPUS: jax-ml.github.io/scaling-book/g…

I have one more thing before you go.

If you want to become a generative AI data scientist in 2026 ($200,000 career), then I'd like to help:

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 document extractors.

Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools.

Here’s what it does: 🧵 What it does:

→ Extracts structured data from messy text
→ Grounds every field to the exact source location
→ Handles 100+ page docs
→ Generates interactive HTML for verification
→ Works with Gemini + local models

Understanding regression models is essential in data science.

In 4 minutes, I'll demolish your confusion. Let's go: 1. The 6 Diagnostic Checks Every Data Scientist Should Run

Once you've built a regression model, your job isn't done. These 6 checks will tell you whether your model can actually be trusted.

🚨 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.

Understanding regression models is essential in data science.

In 4 minutes, I'll demolish your confusion. Let's go: 1. The 6 Diagnostic Checks Every Data Scientist Should Run

Once you've built a regression model, your job isn't done. These 6 checks will tell you whether your model can actually be trusted.

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.

RIP manual research workflows.

Someone just open-sourced what comes after Karpathy’s AutoResearch.

It’s called AutoResearchClaw.

And this thing is insane. A few weeks ago, Karpathy showed where research was heading:

AI agents running the experiment loop.

That was already a big signal.

AutoResearchClaw takes it even further.

It doesn’t just help with research.

It tries to automate the entire scientific method end-to-end.

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.

K-means is an essential algorithm for Data Science.

But it's confusing for beginners.

Let me demolish your confusion: 1. K-Means

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.

🚨 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.

🚨 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 document extractors.

Google just released LangExtract: Open-source. Free. Better than $100K enterprise tools.

Here’s what it does: 🧵 What it does:

→ Extracts structured data from messy text
→ Grounds every field to the exact source location
→ Handles 100+ page docs
→ Generates interactive HTML for verification
→ Works with Gemini + local models

Data science killed itself.

Not because AI showed up. Because too much of the field confused running a model with understanding one. For years, data science rewarded people for producing outputs:

A model score
A dashboard
A notebook
A prediction
A nice chart

And a lot of that work looked impressive.

Someone built a free 7-week RAG curriculum on GitHub.

And they're right — it's good.

But, you'll need 1 more thing to get an AI/DS job in 2026: Docker. FastAPI. PostgreSQL. OpenSearch. Airflow. Hybrid search. LangGraph. Production monitoring.

That's a serious architecture. Bookmark it. github.com/jamwithai/prod…

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…

80% of data scientists say they want to build AI agents.

Almost none of them can answer this question:

Which agentic pattern should you actually use? There are 7. And picking the wrong one breaks your entire workflow.

Here's the quick breakdown:

Harvard just open-sourced its entire ML Systems curriculum.
Free. Public. 6 pillars. Hundreds of pages.

And it won't get most data scientists any closer to a $150K AI role.

Here's why. The book covers:

1. System Design
2. Data Engineering
3. Model Deployment
4. MLOps and Monitoring
5. Edge AI
6. Responsible AI

🚨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.