How to Know What to Learn in AI

There's a disconnect in AI as far back as knowing what to learn—this is how to overcome it

Jun 18 • Logan Thorneloe

2

Can We Run Data Centers in Space?

An overview of the engineering reality of the moonshot and what's going on with energy consumption on Earth

Jun 10 • Logan Thorneloe

12

3

May 2026

Don't Tokenmax—Do This Instead

Dispelling the myth that more tokens = better productivity

May 7 • Logan Thorneloe

13

4

3

April 2026

Decoupled DiLoCo: How Google Is Enabling Multi-Region, Distributed LLM Pretraining

An overview of Google’s new multi-region, distributed AI training methodology and its practical impact

Apr 30 • Logan Thorneloe

13

3

Devin Has Exposed a Major Issue with Software Engineering

And isn't that we're all going to lose our jobs

Published on AI for Software Engineers • Apr 17

Understanding Open Model Licenses

And what to look for in custom licenses

Apr 15 • Logan Thorneloe

15

1

1

March 2026

The Anatomy of an LLM Benchmark

Common patterns used to create the most effective LLM evaluation datasets...

Published on Deep (Learning) Focus • Mar 31

The Difficulties of Scaling Autoresearch | AI for Software Engineers 83

And agentic engineering's scaling impact on the software development and the internet

Mar 28 • Logan Thorneloe

10

3

20 Years of Code Optimized in Two Days | Weekend Reads 4

Your AI reading list 03-15-2026

Mar 15 • Logan Thorneloe

11

1

ICE Has an AI Problem

And a note on surveillance states

Mar 11 • Logan Thorneloe

24

3

3

Better Agents Mean Better Surveillance | Weekend Reads 3

Your reading list to keep up with AI 03-01-2026

Mar 1 • Logan Thorneloe

11

1

February 2026

What you should know about AI speculation

Thoughts on software engineering careers looking forward

Feb 24 • Logan Thorneloe

20

3

5