Nick

Persistent learner, data scientist, blogger, #rstats fan. #machinelearning #dataviz

[Nick @zenggyu]

Hey, #rstats community, I just figured out how to host my #blogdown blog using services provided by @gitlab & @cloudera. It works like a charm and has saved me a lot of trouble and money!😊

Here's the post to share how I did it, check it out:

https://t.co/IvOGvpeoLn

[Lunar @LunarResearcher]

Don't spend 2 years learning AI agents the slow way.

Andrew Ng just shared the complete 2-hour roadmap for becoming an agentic AI engineer in 2026.

00:00 - Learn the foundations of AI agents
12:12 - Design agentic workflows
53:27 - Build agents that work in practice
1:20:30 - Create self-improving loops
1:30:19 - Orchestrate multi-agent systems

Most people are still learning how to prompt one model.

Andrew Ng is teaching the full stack:

Agents → Workflows → Loops → Multi-Agent Systems

Prompting is the old workflow.

Building autonomous systems is the new one.

Anthropic pays top engineers up to $750K/year to understand this stack.

Bookmark this and give it two hours today.

Then read the full agent engineering guide below.

[Roan @RohOnChain]

Don't waste 2 years learning to use LLMs like Claude & ChatGPT.

Andrej Karpathy, the godfather of AI, dropped a 2 hour course on how he personally uses LLMs daily.

• 00:00 - LLMs simplified
• 22:49 - which LLM to actually use
• 42:00 - one prompt for full research
• 1:13:57 - how to code with LLMs
• 1:37:04 - NotebookLM podcast generation

This course will teach you more about using LLMs than most AI engineers learn in their entire career.

Bookmark this & give 2 hours today, no matter what. Then read the article below.

[Nick @zenggyu]

@trq212 I think a better approach might be to ask agents to write quarto documents (https://t.co/OJpOe1UWHR), which are mainly markdown, and then render them to html. Agents can still write markdown while we humans get html. The best of both worlds.

[Nick @zenggyu]

@python_spaces AI agents

[James Balamuta, Ph.D. @axiomsofxyz]

Congratulations to Posit on #PositronIDE exiting beta! 🎉

This is huge for #python and #rstats data scientists - having a unified, modern IDE that speaks both languages fluently. Can't wait to see how this accelerates data science workflows!

Try it out: https://t.co/1xt1rKFWip

[Wes McKinney @wesmckinn]

Positron officially out of public beta and has shiny new AI assistant capabilities, check it out! (I've done a lot of work on the Data Explorer table viewer feature)

https://t.co/TRioXCDpUb

[John Coene @jdatap]

I've made my #rstats obfuscator open-source!

It's an R lexer, parser, and transpiler written in #go (std library only) and, though uglified in some places to cater to R quirks and {box}, the foundations are solid.

https://t.co/WmRTfk961K

[John Coene @jdatap]

Go and upvote/contribute to this issue by @JosiahParry on r-universe.

And thanks @opencpu for the amazing work on r-universe!

https://t.co/2lOek0VUCc

[Simon Couch @simonpcouch]

A new release of the bonsai #rstats package just hit CRAN! This release is a quick patch following up on the 0.3.0 release of bonsai, which introduced support for oblique random forests to the tidymodels framework.

Read more on the tidyverse blog: https://t.co/xkqpn3pHEB

[Simon Couch @simonpcouch]

A new release of #rstats package stacks just hit CRAN! Just two small bug fixes.

https://t.co/3j9TLBsQZD

[Daniel Falbel @dfalbel]

tok v0.1.2 is now on CRAN! tok allows using @huggingface tokenizers natively from #rstats.
See the release notes in https://t.co/AaY4kSNqGg

[Simon Couch @simonpcouch]

bonsai 0.3.0 is now on CRAN!🌲 This release introduces support for oblique random forests to #rstats tidymodels. If you're modeling data with many highly correlated predictors and/or few observations, you'll want to check this out.

https://t.co/xkqpn3pHEB

[Jeroen Ooms @opencpu]

Big rewrite of R-universe WebUI! Pages are cleaner, load much faster, better SEO, improved rendering of articles, citation, charts, etc. Some example pages:

https://t.co/0nuSTF7Z7P
https://t.co/tG68kCJUHZ
https://t.co/DHkjCGCj1d
https://t.co/d1p7WrMPL7

[Dirk Eddelbuettel @eddelbuettel]

#r2u update: we now shipped 20 million .deb packages containing CRAN packages making #Rstats deployment and testing on Ubuntu faster, easier and more reliable.

#r2u: Easy. Fast. Reliable. Pick all three.

More details at https://t.co/miK5BmjXu9

[Simon Couch @simonpcouch]

ICYMI, a new release of #rstats broom went out last week with changes to some of the package's more well-used tidiers! Read more: https://t.co/NXqHdPuLGD

[Emily Riederer @EmilyRiederer]

Not sure who needs to hear this but excelling python logging tutorial

Great explanation of underlying mental model, modern best practices, and customization; always wrankles me that intros mostly teach antipatterns (e.g. using primarily root logger)

https://t.co/928biqGtrn

[Simon Couch @simonpcouch]

A new release of #rstats broom is on CRAN! v1.0.6 includes several changes to well-used tidiers from the package, e.g. for lm(), gam(), and survfit() output.

https://t.co/NXqHdPudR5

[Simon Couch @simonpcouch]

tidymodels has long supported parallelizing model fits across CPU cores. The XGBoost and LightGBM engines have their own tools to parallelize model fits. Should tidymodels users should use tidymodels' implementation, the engines', or both?

https://t.co/4MBwNHzzET

[Dirk Eddelbuettel @eddelbuettel]

Happy to share that #r2u now provides #Ubuntu 24.04, #BioConductor 3.19, and of course #Rstats 4.4.0.

20.7k CRAN packages, and 400+ BioConductor packages as `apt` binaries.

#r2u: Fast. Easy. Reliable. Pick All Three.

https://t.co/miK5BmjXu9

[Dewey Dunnington @paleolimbot]

In case you missed it, @krlmlr wrote up an excellent piece on @ApacheArrow Database Connectivity (ADBC), #rstats, DBI, and the future of database connectivity in R! https://t.co/mjNhsgmisb