It's hard to write code for computers, but it's harder to write code for humans

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A nice read, but I think there is a contradiction here that needs to be cleared up:

1) On one hand, the author says that humans learn from examples, not core concepts.

2) On the other hand, the author emphasises the importance of reducing "conceptual overload", by reducing the number of concepts while maintaining their expressiveness.

So it is not that core concepts are not important for learning. Rather, it is essential to have a set of well-defined and well-documented core concepts which cover what the system can do. But of course, you also need plenty of insightful examples, and of course a "Getting Started" guide should start with examples, not core concepts. But if the core concepts are simple and few enough to fit into a "Getting Started", that's a win.

> psychopathic scammers like Robert Martin

That is a pretty wild take. Would you like to elaborate on that?

Bit of a side issue for me: I was working on my Unity game the other day and thought to myself, have IDE's really not progressed all that much in the last 10-20 years?

Default intellisense has definitely gotten a lot better, but apart from that and a few other minor things the whole concept of coding feels pretty much the same today as back then.

The biggest positive change for me is outside of the editor, it has become easier thanks to much more access to libraries, documentation and just the sheer volume of user questions and answer sets we now have access to (and finally some new tools like ChatGPT that can aggregate those answers to on occasion deliver a reasonable answer).

But overall the act of writing code seems to be stuck. As a result I'm currently taking some time out from my game to run some experiments. I don't want to create a new language, but instead I want to try and offload everything I can to the computer, let it do the drudge work while allowing me to create.

Just 3 of the initial things I want to test: - Why do I need to worry about small language specifics like brackets, terminators and so on when tools should be able to auto-complete them for me? What about the private-public access chain (as well as other modifiers such as unsafe) when tools can auto-determine the most efficient set? - You're editing a file (or parts of different files) and are focusing on say 5 methods that are interacting. I want to see all of them on the screen at the same time, without having to struggle to open and manage many windows with for example VS horizontal/vertical sliders. - Data conversion. So I created a HashSet for something but realize I need to change it to a Dictionary or a Tuple, just make it happen. If it requires brainwork then show me all the places that requires supervision where I have to say ok or make an edit myself. In the case of Unity I also want to be able to click on a method and/or data set and tell it to convert it to a Burst Job with its accompanying NativeData sets.

Easier to use libraries over highly complicated (supposedly performant) have a significant advantage in driving more adoption.

Recently I was trying to generate text embeddings from a huggingface model. Nvidia triton and text-embedding-inference (built by huggingface) were my two options.

> why large companies are generally incapable of delivering great developer experience. I wanted to curl up and cry while trying to make nvidia-triton spit out embeddings . The error messages are cryptic and you need to have jedi like intuition to get it to work. I finally managed to get it work after like 2 days of wrangling with the extremely verbose and long-winded documentation (thanks in part to claude, helped me understand with better examples)

Triton's documentation starts off with core-principles and throughout the entire documentation, they have hyper links to other badly written documentation to ensure you know the core concepts. The only reason I had endured this was because of the supposed performance gains triton promised but underdelivered (this highly likely being I had missed some config/core-concept and did get all the juice)

On the other hand, text-embedding-inference has a two line front and centre command to pull the docker image and get running. The only delay was due to my internet speed before it started serving the embeddings. Then deploying this on our k8s infra was a breeze, minor modifications to the dockerfile and we are running. And on top, it's more performant than triton!