JARED KNOWLES.

I think the folks at Lander Analytics do really interesting cutting edge work and share a generous amount of what they learn. My own experience using a MacStudio for multiple side by side models is similar, but I need to look into these time series models more!

They raised taxes to pay for a temporary jail for the World Cup and it won’t be ready to open until the tournament is over. There’s always a jail angle.

My former colleague at the Wisconsin Department of Public Instruction, Dr. Carl Bryan, has launched a newsletter and the list of topics sound like everything I love:

• Democracy and democratic participation • Education policy and public schools • School boards and local governance • Critical theory and public life • Queer liberation and belonging • Humanization, resistance, and public institutions • Current events viewed through questions of power and participation

Can’t wait to follow along

This interests me for three reasons: 1) it is the kind of ambitious policy vision we need to see; 2) it models its assumptions and clearly explains them; 3) there are no notable US politicians speaking at the World Inequality Conference. We need leaders who are relevant voices in exactly these conversations.

An AI harness for research. I have not tried it out because it seems far too optimized on writing papers which is not my primary output anymore.

I’m not surprised by the finding that even among AI users, code harnesses are not used. But if you are not finding value from AI for writing quantitative analysis code and research communication outputs its probably because you aren’t using a coding harness like OpenCode or Claude Code.

It’s important to remember that the most popular and accessed article about ChatGPT and student learning was retracted. The thing is, retraction is just a label, and people can choose to honor or not honor that label. So keep your eyes out for people citing the study.

When I was in grad-school it was trendy to call these FUQs - fundamentally unidentifiable (or unanswerable) questions. This is a much more nuanced, engaging, and useful take on how to approach a problem where your variables of interest can’t be varied.

Anil Dash is one of my favorite writers about technology. And this is why you will no longer see any Civilytics or Jared Knowles content on Twitter/X - I deleted all our posts after reading this article.

Simon Wilison has lots of great insights, and this one mirrors what I am finding. I am picking up a lot of “engineering” skills on the fly, while I am working on “vibe coding” things that I couldn’t deploy on my own in the past, but that I feel like I know very well what they should look and feel like.

Per Engzell and Nathan Wilmers provided some real insight on experiments with how well LLMs can produce quantitative social science paper production, even held back by using Stata of all things.

This is an interesting project I am tracking. There are so many AI agent harnesses, but harnesses make such a difference in the capabilities and usefulness of the tools. Particularly smaller LLMs really benefit from strong harnesses. Could be super useful!

Fascinating to see people using AI to replicate the work of academia. I saw some interesting critiques asking if this means papers never were and should not be moving forward the primary unit of output. I think that’s an interesting question.

A client-server protocol for DuckDB could really unlock a lot of agentic data science workflows for me - collaborating on the same database without running into conflicts or waiting in line to push updates.