This speech by Oliver Anthony is exactly my social media experience as well. A few people are famous and get all the attention while the rest are "nobodies". I think it's a bit inherent to the massive scale of social media. In a small town, it's hard to be a complete nobody. At least some people know your parents or have seen you in the supermarket, for example.
I think I’m going to skip the comment feature for fx (https://fx.huijzer.xyz). Niall Ferguson recently argued that people in technology often forget about the past and say that now everything is different even though usually it isn’t (https://youtu.be/giZC4pCqB4o). Most of the time, new things are just old things with slight variations.
So what are old things that are great? Publishing text from one paragraph length to multiple paragraphs. This is what newspapers did for more than a century. Then you still have book authors and authors like Dijkstra (EWD notes) that also published. To write a comment, you have to write your own publication. Not some effortless comment a la modern social media.
"Why are big tech companies slow? Because they've packed in as many features as possible in order to make more money, and the interaction of existing features adds an unimaginable amount of cognitive load" (https://www.seangoedecke.com/difficulty-in-big-tech/).
I wish Dijkstra was alive and on Twitter. Quotes like this are perfect for the place: "The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offense."
Hello world
I recently read the article Every programming language needs its killer app to succeed and think the article makes a great point. It made a lot of sense to me. However, the term "killer app" seems to not really work out with the examples. Only Ruby lists Ruby on Rails as its killer app, but that's basically it.
Instead, I think it's about having a killer domain. So, going through the examples from the original article, here is my take:
I was just now listening to an argument that got me more and more interested as the argument went on. The author did a great job of first taking the opposite position, then building a case for their side, and then making a strong counterargument.
Essentially, the argument went something like this:
The Wi-Fi connection is unreliable.
Some people in the household say that the problem is that the router is too old.
However, after testing and reading reviews, it seems that the router is not the problem.
Therefore, the problem must be that the government is corrupt since they send dangerous radio waves to our house.
I have spent quite some hours building open source software. With that, a lot of things have gotten easier over time. For example, setting up tests, CI, documentation, and websites has gotten much easier. However, interface design somehow not. I already asked about it 3 years ago. If anything, it has gotten worse since I nowadays realize more how important it is to get the design right. Especially once libraries become more and more used, there is a real cost to introduce breaking changes.
Recently, I was thinking again about where AI is going. I was wondering what I as a developer should, or should not, be building. I wrote a post about my thoughts and concluded two things:
Firstly, cloud-based AI gets much cheaper every year, namely about 90% cheaper. If your AI application costs $1 per day to run now, next year it'll cost 10 cents, and just a penny the year after that. And while the price drops, the models keep getting better.
Secondly, the best AI tools don't necessarily come from the big technology companies. Take Cursor AI for example. Microsoft had everything needed to make the best AI code editor - the most GPUs, the most popular code editor (Visual Studio), and years of AI experience. But a small startup built Cursor, which many developers now prefer. The same happened with DeepSeek. Google, Meta, Microsoft, and Amazon are all spending billions on developing the best models. But DeepSeek came out of nowhere and delivered great results. This isn't new. The same thing happened with Google in the early 2000s. Altavista was the biggest search engine until Google, a small newcomer, made something better.
Now that DeepSeek released their new model, I'm thinking again about where AI is heading. For some technologies such as electric cars or batteries, I think I have a reasonable idea of where they are heading thanks to learning curves. Due to price decreases, we will probably see more electric cars, drones, ships, and trucks. Oh, and of course stoves that can boil water in 40 seconds. For AI, I'm not sure. In this blog post, I'll try to estimate the learning curve for AI and see if I can use that to make predictions.