How my life is changing as a direct result of attending the #RStudioConf 🧵

#rstats
Just 3 days ago, I had the pleasure of watching the #rstudioconf2022 kick off.

I've been attending since 2018 and watching even longer than that.

And, I was just a normal spectator in the audience until this happened.
@topepos and @juliasilge's keynote showed all of the open source work their team has been working on to build the best machine learning ecosystem in R called #tidymodels.

And then they brought this slide up.
Max and Julia then proceeded to talk about how the community members have been working on expanding the ecosystem.

- Text Recipes for Text
- Censored for Survival Modeling
- Stacks for Ensembles

And then they announced me and my work on Modeltime for Time Series!!!
I had no clue this was going to happen.

Just a spectator in the back.

My friends to both sides went nuts. Hugs, high-fives, and all.

My students in my slack channel went even more nuts.
Throughout the rest of the week, I was on cloud-9.

My students that were at the conf introduced themselves.

Much of our discussions centered around Max & Julia's keynote and the exposure that modeltime got.
And all of this wouldn't be possible without the support of this company. Rstudio / posit.

So, I'm honored to be part of something bigger than just a programming language.

And if you'd like to learn more about what I do, I'll share a few links.
The first is my modeltime package for #timeseries.

This has been a 2-year+ passion project for building the premier time series forecasting system.

It now has multiple extensions including ensembles, resampling, deep learning, and more.

business-science.github.io/modeltime/
The second is my company @bizScienc.

For the past 4-years I've dedicated myself to teaching students how to apply data science to business.

I have 3000+ students worldwide.

Here are some of my tribe that I met at #rstudioconf2022.
The third is my 40-minute webinar.

I put a free presentation together to help you on your journey to become a data scientist.

A few things I talk about:

Modeltime for Time Series.
Tidymodels & H2O for Machine Learning
Shiny for Web Apps
and 7 more!

learn.business-science.io/free-rtrack-ma…

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More from @mdancho84

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
Jan 17
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):Image
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.
2) Python is weirdly hard for models

Mixing languages in pretraining helps… until it doesn’t. Python’s dynamic typing can create negative transfer vs. statically typed languages. Meanwhile pairs like Java↔C# or JS↔TS have strong “synergy.”
Read 15 tweets
Jan 17
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 13 tweets

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