If someone's businesses is already working, then why would they go and get investment money?

It seems like the investors don't realize that if someone does have that much net profit, they actually don't have that much leverage over them to get good equity deals. So they are essentially just paying high and selling low later.

But I still think it's surprising how much legs Python model of wrapping around C/C++/Rust libraries has. I would assume that if you have Python calling the libraries, you can't do lazy evaluation and thus you hit a wall such as Pandas.

But we seen with compiling Pytorch and Polars that you can have your cake and eat it too. Still have the ease of use of Python while having performance with enough engineering.

VR seem like it was dead with Google Glasses and Meta. But recent releases show that the [vision of VR is more real than I thought](https://www.youtube.com/watch?v=MVYrJJNdrEg). Apple releasing their VR definitely provides more legitimacy to the idea.

Technology is always hard to actually implement in the real world. I think have expectations that the world would flip over with new innovations in a matter of months. But it actually takes years of hard work to get people to adopt it. The internet and mobile phones are good examples where the promise was clear but the journey being universally adopted was not easy. *

* For the internet, you have the massive struggle of web standards, getting implementations web browser to align, and achieving acceptable performance. Internet Explorer still haunts the dreams of many web devs. It's remarkable how many fully featured web apps there are like Onshape and Google Maps.

* For mobile phones, they require a massive amount of technology investment and unit economics to get the price cheap enough for normal people. It's amazing how wireless technology gotten to the point where we can stream videos on phones.

Honestly, it seems like only malware authors benefit from the complexity of pdfs.

Multiprocessing std uses fork in linux distros already. I once ran a multiprocess code on Linux and Windows and there was a significant improvement in performance when running Linux.

The confusion here is parallelism vs concurrency. Parallelism is executing multiple tasks at once and concurrency is the composition of multiple tasks.

For example, imagine there is a woodshop with multiple people and there is only one hammer. The people would be working on their projects such as a chair, a table, etc. Everyone needs to use the hammer to continue their project.

If someone needed a hammer, they would take the single hammer and use it. There are still other projects going on but everyone else would have to wait until the hammer is free. This is concurrency but not parallelism.

If there are multiple hammers, then multiple people could use the hammer at the same time and their project continues. This is parallelism and concurrency.

The hammer here is the CPU and the multiple projects are threads. When you have Python concurrency, you are sharing the hammer across different projects, but it's still one hammer. This is useful for dealing with blocking I/O but not computing bottlenecks.

Let's say that one of the projects needs wood from another place. There is no point in this project to hold on to the hammer when waiting for wood. This is what those Python concurrency libraries are solving for. In real life, you have tasks waiting on other services such as getting customer info from a database. You don't want the task to be wasting the CPU cycles doing nothing, so we can pass the CPU to another task.

But this doesn't mean that we are using more of the CPU. We are still stuck with a single core. If we have a compute bottleneck such as calculating a lot of numbers, then the concurrency libraries don't help.

You might be wondering why Python only allows for a single hammer/CPU core. It's because it's very hard to get parallelism properly working, you can end up with your program stalling easily if you don't do it correctly. The underlying data structures of Python were never designed with that in mind because it was meant to be a scripting language where performance wasn't key. Python grew massive and people started to apply Python to areas where performance was key. It's amazing that Python got so far even with GIL IMO.

As an aside, you might read about "multiprocessing" Python where you can use multiple CPU cores. This is true but there are heavy overhead costs to this. This is like building brand-new workshops with single hammers to handle more projects. This post would get even longer if I explained what is a "process" but to put it shortly, it is how the OS, such as Windows or Linux, manages tasks. There is a lot of overhead with it because it is meant to work with all sorts of different programs written in different languages.

Personally, I haven't ran into a problem with Python packaging recently. I was running https://github.com/zyddnys/manga-image-translator (very cool project btw) and I didn't ran into any issues getting it to work locally on a Windows machine with Nvidia GPU.

https://www.youtube.com/@JamesKerLindsay For good deep dives into international politics.

https://www.youtube.com/@PracticalEngineeringChannel For good videos on civil engineering.

I would recommend these channel for older children who can digest the information. They are all understandable for to the layman.

https://jalammar.github.io/illustrated-transformer/ This is a good illustration of the transformer and how the math works.

https://karpathy.ai/zero-to-hero.html If you want a deeper understanding of transform and how they fit in the whole picture of deep learning, this series is far and away the best resource I found. Karpathy goes into transformers by the sixth lecture, the previous lectures give a lot more context how deep learning works.

I googled "wavelength of images" it doesn't seem like I am going in the right direction because it's about finding the wavelength of light from images rather than "wavelength of features" that this blog is talking about.

That's not to say that happen to you didn't happen. Information on the internet evolves rapidly and far from static. One problem is that people game Google with SEO. We end up with an arms race of people gaming signaling for information and Google search having to find different signals for information. The high noise to signal ratio is a very hard problem to solve.

There probably isn't a grand strategy when it comes to awful processes. The process was probably setup by some woefully overstretch person. No one bothered to update the process because there are too many fires from the other awful processes that was also setup by another woefully overstretch person.

I fear that the world doesn't even have the imagination for the destruction that is going to happen.

To be fair, I don't pretend to know how CDProject developed their games. They might already have testing and the timetable was the problem.