aatarax

I suspect front end will be similar to how AI impacts other domains: experts who can leverage the tools will be able to be more productive at the cost of junior engineers. Even before AI, low or no-code frontend solutions already impacted the frontend market.

I suspect that’s true of most commoditized domains where AI has enough training data. Niche domains will be less impacted.

The world sucks for most people. The world is better than its ever been for most people. The world can be improved a lot for most people. Those three things can and are all simultaneously true.

Unfortunately, improving the world requires engaging deeply with issues and many people now prefer to speak in terms of grand historical narratives and emotional arguments that stitch sparse data points into a large story far vaster than the data can support.

My guess: the COVID related inflation was visible, made for a relatable talking point, and our modern day communication technologies made it easy to repeat that talking point over and over.

There are some similarities. A key difference is buddhism’s emphasis on meditation and practice as a key part of learning to act skillfully which I don’t think stoicism emphasizes as much.

Stoicism has always felt more “human scale” to me. It talks in terms of human virtues, and how a good life is only found in virtue. Buddhism always felt more focused on the mechanics of the mind and how understanding can be converted into attaining happiness/enlightment.

That being said, I’m not nearly as knowledgeable about stoicism.

There are many flavors of buddhism. I am partial towards the Thai Forest tradition because it emphasizes practice and experience over dogma and so avoids metaphysical discussions.

> But what troubles me most about Buddhism is its implication that detachment from ordinary life is the surest route to salvation.

The response from the Thai Forest would be to engage in your life skillfully while paying attention to what causes you happiness or suffering and adjusting what you’re doing based on that. Maybe you discover a certain level of detachment is optimal, maybe you find you need to be more involved in your life. You go where the practice takes you.

Dicts in python are for when you have a thing and you aren't sure what the keys are. Dataclasses are for when you have a thing and you're sure what the keys (attributes are). The trouble is when you have a thing and you're sort of sure, but not entirely sure, and some things are definitely there but not everything you might be thinking of.

0:39 in the video is where it says United States

That being said, I agree its criticism of the US is oblique at best and it is more focused on China.

This section sums it up and I agree with the author here

> LLMs are useful if you already have a good mental model and understanding of a subject. However, I believe that they are destructive when learning something from 0 to 1.

Super useful if you have code in mind and you can get an LLM to generate that code (eg, turning a 10 minute task into a 1 minute task).

Somewhat useful if you have a rough idea in mind, but need help with certain syntax and/or APIs (eg, you are an experienced python dev but are writing some ruby code).

Useful for researching a topic.

Useless for generating code where you have no idea if the generated code is good or correct.

It does. It specifically says the throughput of the port in the video is more than the combined throughput of all ports in the United States.

Paper is great for transient stuff that is relevant for at most a week or two, but it really goes downhill when you try to capture information you'll want to revisit in the future. Paper is great for todo lists and in the moment notes, but not so great for knowledge base building.

I used to struggle with a lot of tools before too and now use org-roam for longer term notes. The linking model helps a lot with just _writing_ the stuff down and not worrying about how to organize it too much in the moment.

Don't know about using __all__ for introspection, but I have found it immensely useful for organizing, reading, and communicating code. When a package has a bunch of files inside of it, but only a handful of names exposed in __all__ it helps a lot with orienting yourself around the package.