I do however hate to admit that it might be possible that the world simply wouldn't require as many humans to function (birth rates are already declining in developed countries), however, I am too sober to have that discussion right now :D.

1. Can AI models self improve? 2. Will it replace humans?

IMO, answer to both is "yes", with a big nuance. For 1, imagine millions of AI agents coming up with different ways to improve the model architecture. It will end up happening just because of sheer quantity/statistics at that point, let alone quality. Furthermore, even if it's just a 1% improvement, that's just one iteration. You can basically do the same thing again with the improved model, and get an even better one.

For 2, its also a yes unfortunately. It has been ages since I have had to write tests for my code. I read it, understand it, see if it does what it is supposed to do, and rarely add things that are missing. The rarity of me doing the said intervention has increased significantly over the course of time.

The two big nuances here are a.) Economy b.) Human element [The author touches upon this in the article in a nice way I think, so only talking about economy]. Global cognitive workforce is a $50T market (mostly middle/upper middle class, 55% to 75% of all global labor income). If the said workforce is made to quit their job, then it's just corporations paying each other. Especially given the fact that the same workforce also happens to be their biggest customer. Taking away your customer's purchasing power does not sound beneficial. Thus capitalistic forces will somehow balance it out and hopefully it will settle down. Also, for 1, running millions of AI agents is an expensive task, you have to collect funds from the same said customer base to be able to create a self improving all knowing god of a model, and I would like to believe that we are quite a bit away from that. Although, I could be wrong here and that would just suck ass.

That being said, AI companies will try to get as much as they can from that share and that will cause a serious shift in labor market. The ones to stay will be those who not just use AI, but also understand the work. At the end of the day, if your AI agent decides to run a "rm -rf /" on the production server, it is still you that would have enabled it.

https://news.ycombinator.com/item?id=48808002

https://news.ycombinator.com/item?id=48810092

I tried using this for LPE on a Rocky9 for a couple of hours and thankfully couldn't get it to work. So that means unless you have quite some free time on your hand, or are extremely good at doing what you do, you can't actually use this to get LPE on enterprise distros.

I also fear that the big corporations might use the same to run targeted ads, capitalistic shenanigans. Which they might already be doing through system prompts.

> We would like to thank:

>

> Karen Oyelaran, who found the issue on Day 1 and is currently appealing her GitHub rate limit via a web form that is also AI-triaged

Also similar: Graduate student descent. https://sciencedryad.wordpress.com/2014/01/25/grad-student-d...

Start of AMS like symptoms can easily be mistaken for walking fatigue and dehydration. It is easier to identify if you are at rest, but during the trek that is seldom the case. So when you actually start realizing something is wrong, you already are at an elevated risk. The only thing that works in these cases is to descend and as fast as possible at that.

Considering the fact that AMS will absolutely and a 100% kill you if you play around with it, guides presenting trekkers with an option of helicopter rescue is not that bad, at least if you look at the worst that can happen.

Today, I know very well how to multiply 98123948 and 109823593 by hand. That doesn't mean I will do it by hand if I have a calculator handy.

Also, ancient scholars, most notably Socrates via Plato, opposed writing because they believed it would weaken human memory, create false wisdom, and stifle interactive dialogue. But hey, turns out you learn better if you write and practice.

For example, consider this game: The game creates a target that's randomly generated on the screen and have a player at the middle of the screen that needs to hit the target. When a key is pressed, the player swings a rope attached to a metal ball in circles above it's head, at a certain rotational velocity. Upon key release, the player has to let go of the rope and the ball travels tangentially from the point of release. Each time you hit the target you score.

Now, I’m trying to calculate the tangential velocity of a projectile from a circular path, I could find the trig formulas on Stack Overflow. But with an LLM, I can describe the 'vibe' of the game mechanic and get the math scaffolded in seconds.

It's that shift from searching for syntax to architecting the logic that feels like the real win.

You are correct that these models primarily address problems that have already been solved. However, that has always been the case for the majority of technical challenges. Before LLMs, we would often spend days searching Stack Overflow to find and adapt the right solution.

Another way to look at this is through the lens of problem decomposition as well. If a complex problem is a collection of sub-problems, receiving immediate solutions for those components accelerates the path to the final result.

For example, I was recently struggling with a UI feature where I wanted cards to follow a fan-like arc. I couldn't quite get the implementation right until I gave it to Gemini. It didn't solve the entire problem for me, but it suggested an approach involving polar coordinates and sine/cosine values. I was able to take that foundational logic turn it into a feature I wanted.

Was it a 100x productivity gain? No. But it was easily a 2x gain, because it replaced hours of searching and waiting for a mental breakthrough with immediate direction.

There was also a relevant thread on Hacker News recently regarding "vibe coding":

https://news.ycombinator.com/item?id=45205232

The developer created a unique game using scroll behavior as the primary input. While the technical aspects of scroll events are certainly "solved" problems, the creative application was novel.

He also mentioned that the minio-to-versity migration is a straight forward process. Apparently, you just read the data from mino's shadow filesystem and set it as an extended attribute in your file.