"We are now, like, in the singularity,"

:eye-roll:

Oh wow, I loved AMOS - it is what got me seriously into programming in the first place.

Very cool. By the way, you can render many more datapoints on mobile if you use WebGL. Here’s a similar example - embeddings rendered using a T-SNE layout

https://blog.scottlogic.com/2021/10/15/efficiently-loading-m...

Will check it, it will be really useful. Thanks

I created my own static site generator about 10 years ago. It was only around 100 lines of code:

https://github.com/ColinEberhardt/tiny-ssg

I used it for a few websites, viable replacement for Assemble and other SSGs of that time.

I presume it only recognised the BBC journalists efforts as satire due to the article in which he clearly states that this was his intention? Without that, I’m am confident it would have fallen for it.

Agents Skills are built upon “Five design decisions [that] are the load-bearing ones”

And Open Design (HN front page yesterday) is supported by “Six load-bearing ideas”

The similarities in the way these prompt libraries are documented doesn’t feel coincidental.

Funny coincidence - I visited that valley for the first time just last week, on my way to the Isle of Skye.

And as a further coincidence, I met Jimmy Saville about 25 years ago. I was in Leeds hospital after a heart operation, and this old and somewhat scruffy track suited guy just walks in to the ward and starts talking to me. I had no idea who he was. After he left, a nurse asked “did you speak to Jimmy?”. It was creepy and unnerving seeing first hand how he just got to roam around.

I can confirm, the graffiti-covered Saville residence has almost completely been demolished.

Agreed, I could just about bear it until I hit the “ Six load-bearing ideas” section. Very off-putting.

Can someone please use AI to explain this code smell?

I am not sure it is inherent to LLM code generation as much as the training data and the tuning of the model. Emphasizing verbose code with lots of explicit explanation. Possibly the stuff you see in CS textbooks. And probably lots of vibe code style edits where the LLM fixes a bug, always adding further complexity to the code.

Funny thing is you could create measurable criterias explaining what is wrong with the code. Ie. function line count or cyclomatic complexity and then letting those guide the code generation.

Very true, with the right feedback loop AI would do a wonderful job of refactoring.

But if AI is the primary author and consumer of this code, that would be an unnecessary step. No need to clean it up for our feeble little human minds.

I was just interested in what this file actually does - and am finding it hard to grok, scrolling through on a mobile device!

I think it does all sorts of random things. And I doubt it is particular easy for a LLM to work with compared to a more sanely structured piece.

I used to find Gary Marcus a good antidote to the AI hype, and followed his critique. But honestly, his more recent writings are clutching at straws. This article feels like desperation.

It’s a bit like saying that driving cars still requires human muscles to operate the controls, so human strength has ‘won’, when it is clearly the internal combustion engine that has created the speed advantage of the car.