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October '25 Stars 📚

In October I read ~301 articles from the 44 blogs that I follow on my feed reader. These are the ones that really caught my interested and which I starred:

1. ChatGPT's Atlas: The Browser That's Anti-Web by Anil Dash
2. Sabbaticals keep our attrition at bay by DHH
3. Quoting Geoffrey Litt by Simon Willison
4. Agentic AI and Security
5. Could China devastate the US without firing a shot? by Gary Marcus
6. Wax On, Wax Off by Jason Gorman
7. Avoid the nightmare bicycle by Geoffrey Litt
8. Dane Stuckey (OpenAI CISO) on prompt injection risks for ChatGPT Atlas by Simon Willison
9. Is vibe coding dying? by Gary Marcus
10. AI Will Destroy Everything by Will Locket
11. Game over. AGI is not imminent, and LLMs are not the royal road to getting there. by Gary Marcus
12. Andrej Karpathy — AGI is still a decade away by Simon Willison
13. Is AGI the right goal for AI? by Gary Marcus
14. Is Your Development Team “AI-Ready”? by Jason Gorman
15. Coding without typing the code by Simon Willison
16. Just Talk To It - the no-bs Way of Agentic Engineering by Simon Willison
17. Why You (Probably) Won’t Get Rich Building With LLMs by Jason Gorman
18. The Passionate Programmer: Too Passionate, Perhaps? by Jason Gorman
19. Quoting Gergely Orosz by Simon Willison
20. “You have 18 months” by Derek Thompson
21. Vibe engineering by Simon Willison
22. The developer role is evolving. Here’s how to stay ahead. by Gwen Davis
23. Quoting Nadia Eghbal by Simon Willison
24. Comprehension Debt: The Ticking Time Bomb of LLM-Generated Code by Jason Gorman

Hurdles of building with AI agents

Over the past few weeks I have been testing a few AI code assistants like Claude Code, OpenAI Codex and Github Copilot. I'm using to vibe engineer some personal projects but also to try out new ways of building software at Mindera. Using AI to write most of my code makes me think that the role of a developer might evolve to more of an architect. Someone who thinks about what they need/want to build and then orchestrates AI to actually get it built. But as I do this, I am seeing two types of situations that will make a full evolution like this a bit slower than some predict, or  even challenge it. Namely: 1. When I'm building something that is not as standard as most of the things I build (ie: something that isn't just CRUD pages and typical interfaces), AI tends to struggle more to deliver working solutions. Or at least takes longer to get it right than more conventional examples. I've seen this described as building things that fall off the distribution curve. The tech stack also has an influence here. AI might push for more standardisation as many of these models are much better trained on stacks like ReactJS. Tech choices will need to consider this.  2. When I'm building something that I've never built before versus something that I have built before. For example, the other day I was building user invitation system for a project, similar to the one I built for Kronflow earlier this year. So I was able to see that Claude Code wasn't building the feature in the right way, and I could intervene and ask it to adjust its approach. This means that when I'm building something that I haven't built before, there is a higher chance that it will be written poorly without me realising. Point number one might be a bigger issue than point number two, because "poor" might still be acceptable, as long as it's not insecure and bug-ridden. But if AI doesn't build the thing properly, a human will still need to step in. What this tells me is that to guide AI adoption we...

Enumerable#partition

Today as I was working on a simple feature on a project I asked Github Copilot to review a piece of code and got back the following feedback:

The collection is iterated twice unnecessarily. Consider using `partition` to split it in a single pass, e.g.:
(1..6).partition { |v| v.even? }
=> [[2, 4, 6], [1, 3, 5]]

I gotta thank Copilot. This is a very nice method to know about.

Hello World

This is the initial post of this blog.

The question is: will there be more?