8000 hours of Dota 2 had many hours of flow state as the player however..

Comments URL: https://news.ycombinator.com/item?id=48318199

Points: 1

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[1]": https://www.nytimes.com/2026/04/30/opinion/ai-labor-work-for...

(Gift article)

Correct in that Ruby never had a schism and is still massively productive and wideley deployed (e.g. Shopify + Stripe alone represent billions/trillions of dollars through Ruby hotpaths).

Python's general lack of success in this domain is telling and embodies whats I was trying to communicate in the article -- languages with low entropy in syntax, features, ecosystem, and toolchain compound slowly.

The specifics of Python were chosen only due to the language ecosystem being fragmented and inconsistent while Python remains an essential learning, research, and now ML programming language (it was my first language and I still love it).

My thoughts on LLM generated code have changed immensely in the last 9 months as I've taken on teams and projects through my consulting work [1] as a fractional CTO. Python remains a difficult, flakey, and inconsistent programming language for complex production systems. Most other programming languages suffer from fragmented toolchains and ecosystems: JavaScript (famously), PHP, and even C/C++ to a degree.

Languages with a single way to do things benefit the most: Ruby, Rust, Swift (even). Low entropy is the way to go and convention > configuration seems to pay off with LLMs.

Mean cost of management is more important than specific edge examples "X company run on Y language". I think that 'boring' languages with rock-solid compilers, toolchains, testing frameworks, and package managers make for high return on engineering time and production maintenance.

[1]: sancho.studio

https://www.goodreads.com/book/show/35297608-the-second-kind...

It's a riveting account of years of research to discover Quasicrytals from theory, to experiments, to literally hunting in a meteor field in eastern Russia!

However that's not what brain drain means. You would say "Iran had a brain drain in the 70s" not "America was brain draining Iran" makes no sense.

The narrative and data do not support Americans going abroad.

I think you're referring to a lack of competitive education for those coming outside of America and choosing Europe / China to study.

https://en.wikipedia.org/wiki/Human_capital_flight

I can tell you the drag is between your own tools and the real world (which is very messy and inconsistent): taxes, compliance, payroll, amendments, share structures, etc.

Within my island, my books are in order, invoices and time keeping is fully automated, calendars and sales pipelines are connected.

I'm sure there are many businesses whose inner islands are not as orderly. The zillion tools out there all try to bring equanimity to the chaos and yet here we still are with fresh books, quickbooks, and xero...

I'm not certain that racing China in AI is the right reason but it might get us... somewhere.

Dimensionality gets bizarre in 1000-D space. Similarity and orthogonality express themselves in strange ways and each dimension codes different semantic meaning.

Therefore, if the training data is highly consistent you are by definition reducing some complexity and/or encoding better similarity.

In Go the statement

    result, err := Storage.write(...)

    if err != nil { ... }

   try { Storage.write() } catch (error) { ... }

I hated it. I was dreaming of Rust the entire time to release me from the hell of if err != nil dozens of time per day.

After hours with LLMs I've changed my tune. There have been 5 clients of mine (who have excellent engineering teams) but cannot get coherent results out of LLMs using python or Typescript.

I arrived back at Golang being a frustratingly simple, consistent, and low-thrash programming language which inadvertently made itself well represented in the training corpus [1].

My concession is that if you are going to write a median program (reading/writing files, network, db, etc.)...

Pick Golang especially if you've never used it. LLMs are extremely good at it, frustratingly so.

[1] https://jry.io/writing/use-boring-languages-with-llms/

https://jry.io/writing/use-boring-languages-with-llms/

Comments URL: https://news.ycombinator.com/item?id=48084152

Points: 4

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The big idea with LLMs is consistent references in the training corpus produced cheddar output by the language model during inference.

Go is an amazing language for language models because it's actually quite boring predictable while packing a lot of powerful distractions with a world class tool chain supported by Google and strong std library as well.

As a programmer I actually hated writing Go... and wanted to write Rust; but using coding agents makes me appreciate writing Go more.

I can get consistent results out while having concurrency cross compilation and predictability.

https://jry.io/writing/ai-makes-golang-one-of-the-best-langu...