This is one of those things where I don’t agree with the argument, but know the person making it knows way more than I do on the subject and has given it way more thought. In these cases it’s usually best to sit back and listen a bit...
The part of the Polgár story that amazes me the most is that all three daughters showed enough interest and engagement in chess for the experiment to work so successfully. Because with my own children, I’ve seen again and again that you can encourage and expose them to certain interests, but they’re their own people - many of the things I exposed them to and tried to get them excited about just weren’t interesting to them.
And that’s completely fine. I was never forceful about it and they have their own deep interests in things that I just never got into or understood. I just find it surprising that in some families, these exceptional skills and interests are so readily passed from one generation to the next.
A bullet-point list of ways you screwed up communicates something entirely different than a long form email filled with flowery "fluff" (as the author puts it).
In fact, if the author feels confident in this theory, I suggest they replace the blog post with this AI-generated bullet-point summary I just made...
I fully agree with the sentiment, and yet marvel at the apparent lack of alternatives for so many business models. I'm mildly surprised, for example, that micro-payments for web content are still not a (widespread) thing.
Consumers hate ads, but they hate paying for things even more apparently.
I think this argument only makes sense if you believe that AGI and/or unbounded AI agents are "right around the corner". For sure, we will progress in that direction, but when and if we truly get there–who knows?
If you believe, as I do, that these things are a lot further off than some people assume, I think there's plenty of time to build a successful business solving domain-specific workflows in the meantime, and eventually adapting the product as more general technology becomes available.
Let's say 25 years ago you had the idea to build a product that can now be solved more generally with LLMs–let's say a really effective spam filter. Even knowing what you know now, would it have been right at the time to say, "Nah, don't build that business, it will eventually be solved with some new technology?"
I see this post getting trashed in the comments for its overly literal interpretation of personality as a reinforcement learning process, but I think there's some value to it as a mental model of how we operate (which is how the opening sentence describes it).
If you can see past some of the more dubious, overly technical-sounding details and treat it as a metaphor, there is for sure a "behavioral landscape" that we all find ourselves in, filled with local minimal, attractors/basins and steep hills to climb to change our own behaviors.
Thinking about where you are and where you want to be in the behavior landscape can be a useful mental model. Habit changes like exercise and healthy eating, for example, can be really steep hills to climb (and easy to fall back down), but once you get over the hump, you may find yourself in a much better behavioral valley and wonder how you were stuck in the other place for so long.
Of course, other kinds of life are possible, which would require other "just right" conditions - but in that case, we'd all be living in that universe, marveling at those coincidences!
I'm not sure... I'm a huge Elixir fan and I trust José to build a great solution, but I've found the rollout to be a bit confusing. There was the announcement that "Elixir is now a gradually typed language" prior to 1.17 - but it seems that most of the changes were behind the scenes, and 1.17 largely didn't expose user-facing type errors or warnings.
Again, I definitely trust them to get it right in the long term, but in the meantime, the progress has been a bit confusing to me.
One interesting note on this approach is that the author suggests using a "fast" sorting algorithm like mergesort/quicksort as the sorting algorithm for best performance. But in practice, you may get better performance from a "worse" sorting algorithm: insertion sort.
The reason is that objects in collision detection systems tend to move relatively small steps between frames, meaning you can keep lists from the previous frame that are already mostly sorted. For sorting these mostly sorted lists, insertion sort tends towards O(n) while Quicksort tends towards O(n^2)!
Although that's really selling it short - it's so much more than that! But in the context of this conversation, it's a good place to look.