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.

I personally think that Apple and other smartphone companies need to do a minor and major version release like you do with software. Every 3-5 year, do a major release. This way you create significant hardware/software features every major version, a hype that is well backed up, and at the same time keeps you working and improving and still making money out of it through minor versions. Plus, you also don't have to rely on planned obsolescence as people are gravitated towards the major version release naturally.

Normal (voting) person is oblivious to both what the fed does and what will happen if the fed doesn't do it. All that a normal (voting) people care about, are numbers on price tags, and the fact that those numbers aren't as low as they used to be.

This particular comment feels more like an over-concentration on trivialities rather than refutation or critique of opinion.

However watching others and just collecting more datapoints help in the process of learning. You are learning to read and be more observant regardless of judgements.

I found the article really good.