Posted by __rito__ 12/10/2025
It would be very interesting to see this applied year after year to see if people get better or worse over time in the accuracy of their judgments.
It would also be interesting to correlate accuracy to scores, but I kind of doubt that can be done. Between just expressing popular sentiment and the first to the post people getting more votes for the same comment than people who come later it probably wouldn’t be very useful data.
Seriously, while I find this cool and interesting, I also fear how these sorts of things will work out for us all.
It's a shame that maintaining the web is so hard that only a few websites are "good citizens". I wish the web was a -bit- way more like git. It should be easier to crawl the web and serve it.
Say, you browse and get things cached and shared, but only your "local bookmarks" persist. I guess it's like pinning in IPFS.
It is not possible right now to make hosting democratized/distributed/robust because there's no way for people to donate their own resources in a seamless way to keeping things published. In an ideal world, the internet archive seamlessly drops in to serve any content that goes down in a fashion transparent to the user.
If you make it possible for people to donate bandwidth you might just discover no one wants to.
The wanting to is in my mind harder. How do you convince people that having the network is valuable enough? It's easy to compare it with the web backed by few feuds that offer for the most part really good performance, availability and somewhat good discovery.
It's not hard actually. There is a lack of will and forethought on the part of most maintainers. I suspect that monetization also plays a role.
Keeps the spotlight on carefully protected communities like this one.
This only manipulates the children references though, never the item ID itself. So if you have the item ID of an item (submission, comment, poll, pollItem), it'll be available there as long as moderators don't remove it, which happens very seldom.
What do you mean?
I suppose they want to make the comments seem "fresh" but it's a deliberate misrepresentation. You could probably even contrive a situation where it could be damaging, e.g. somebody says something before some relevant incident, but the website claims they said it afterwards.
But, I'm just guessing here based on my own refactoring experience through the years, may be a completely different reason, or even by mistake? Who knows? :)
> I don't think there will be any more AI winters.
This isn't enough to qualify as a testable prediction, in the eyes of people that care about such things, because there is no good way to formulate a resolution criteria for a claim that extends indefinitely into the future. See [1] for a great introduction.
Besides, in my experience, only a tiny fraction of HN comments can be interpreted as falsifiable predictions.
Instead I would recommend learning about calibration [2] and ways to improve one's calibration, which will likely lead you into literature reviews of cognitive biases and what we can do about them. Also, jumping into some prediction markets (as long as they don't become too much of a distraction) is good practice.
If you dig in, there are substantial flaws in the project's analysis and framing, such as the definition of a prediction, assessing comments, data quality overall, and more. Go spelunking through the comments here and notice people asking about methodology and checking the results.
Social science research isn't easy; it requires training, effort, and patience. I would be very happy if Karpathy added a Big Flashing Red Sign to this effect. It would raise awareness and focus community attention on what I think are the hardest and most important aspects of this kind of project: methodology, rigor, criticism, feedback, and correction.
And scroll down to the bottom.
According to the ratings for example, one person both had extremely racist ideas but also made a couple of accurate points about how some tech concepts would evolve.
I try to temper my tendency to believe the Halo effect with Warren Buffett's notion of the Circle of Competence; there is often a very narrow domain where any person can be significantly knowledgeable.
> I try to temper my tendency to believe the Halo effect with Warren Buffett's notion of the Circle of Competence; there is often a very narrow domain where any person can be significantly knowledgeable. (commenter above)
Putting aside Buffett in particular, I'm wary of claims like "there is often a very narrow domain where any person can be significantly knowledgeable". How often? How narrow of a domain? Doesn't it depend on arbitrary definitions of what qualifies as a category? Is this a testable theory? Is it a predictive theory? What does empirical research and careful analysis show?
Putting that aside, there are useful mathematical ways to get an idea of some of the backing concepts without making assumptions about people, culture, education, etc. I'll cook one up now...
Start with 70K balls split evenly across seven colors: red, orange, yellow, green, blue, indigo, and violet. 1,000 show up demanding balls. So we mix them up and randomly distribute 10 balls to every person. What does the distribution tend to look like? What particulars would you tune and/or definitions would you choose to make this problem "sort of" map to something sort of like assessing the diversity of human competence across different areas?
Note the colored balls example assumes independence between colors (subjects or skills or something). But in real life, there are often causally significant links between skills. For example, general reasoning ability improves performance in a lot of other subjects.
Then a goat exploded, because I don't know how to end this comment gracefully.
The EU may give LLM surveillance an F at some point.