There is much debate about whether the use em-dash is a reliable signal for AI generated content.

It would be more effective to compare this post with the author's posts before gen AI, and see if there has been a change in writing style.

This quote on the abstraction tower really stood out for me:

I saw someone on LinkedIn recently — early twenties, a few years into their career — lamenting that with AI they “didn’t really know what was going on anymore.” And I thought: mate, you were already so far up the abstraction chain you didn’t even realise you were teetering on top of a wobbly Jenga tower.

They’re writing TypeScript that compiles to JavaScript that runs in a V8 engine written in C++ that’s making system calls to an OS kernel that’s scheduling threads across cores they’ve never thought about, hitting RAM through a memory controller with caching layers they couldn’t diagram, all while npm pulls in 400 packages they’ve never read a line of.

But sure. AI is the moment they lost track of what’s happening.

The abstraction ship sailed decades ago. We just didn’t notice because each layer arrived gradually enough that we could pretend we still understood the whole stack. AI is just the layer that made the pretence impossible to maintain.

Even if the bubble pops, the existing large language models will remain, as will AI assisted coding.

Instead, most organisations don’t tackle technical debt until it causes an operational meltdown. At that point, they end up allocating 30–40% of their budget to massive emergency transformation programmes—double the recommended preventive investment.

I can very much relate to this statement. Many contracts I've worked on in the last few years, have been transformation programmes, where an existing product is rewritten and replatformed, often because of the level of tech debt in the legacy system.

I originally shared this after stumbling upon it in one of Martin Fowler's posts.

The article reminds me of how my mother used to buy dress patterns, blueprints if you will, for making her own clothes. This no code library is much the same, because it offers blueprints if you wanted to build your own implementation.

So the thing that interests me is what has more value - the code or the specifications? You could argue in this age of AI assisted coding that code is cheap but business requirements still involve a lot of effort and research.

To give a non-coding example, I've been wanting to get some cupboards built, and every time I contact a carpenter about this, it's quite expensive to get something bespoke made. However, if I could buy blueprints that I could tweak, then in theory, I could get a handyman to build it for a lower cost.

This is a very roundabout way of saying I do think there are some scenarios where the specifications would be more beneficial than the implementation.

Thank you everyone for your input. I have created a separate community, !aicoding@programming.dev, for AI coding related discussions.

I agree with you on that point, and the same could be said about the meat and dairy industry. However I don't think the answer is censoring discussions about cooking beef or chicken.

You can't compare racist posts, which are a form of hate speech and a breach of this instance's code of conduct, with discussions about topics that you don't agree with.

Good question. I have asked this very question https://programming.dev/post/45013854

Expensive as hell! 🤑

Yegge describes Gas Town as “expensive as hell… you won’t like Gas Town if you ever have to think, even for a moment, about where money comes from.” He’s on his second Claude account to get around Anthropic’s spending limits.

I can’t find any mention online of the per-account limits, but let’s conservatively assume he’s spending at least $2,000 USD per month, and liberally $5,000.

I am not surprised that there are parallels between vibe coding and gambling:

With vibe coding, people often report not realizing until hours, weeks, or even months later whether the code produced is any good. They find new bugs or they can’t make simple modifications; the program crashes in unexpected ways. Moreover, the signs of how hard the AI coding agent is working and the quantities of code produced often seem like short-term indicators of productivity. These can trigger the same feelings as the celebratory noises from the multiline slot machine.

Where did you get the impression that the author is an inexperienced developer and finance bro? The introduces himself as someone who started programming from the age of eleven.

I’m Michael Arnaldi, Founder and CEO of Effectful Technologies — the company behind Effect, the TypeScript library for building production-grade systems. I’ve been programming most of my life. I started at 11 with the goal of cracking video games. Since then, I’ve written code at every level: from kernel development to the highest abstractions in TypeScript.