piinbinary

It can create API tokens that you can put in various harnesses. It's basically just an easy way to buy any model you want with one payment.

I wonder if either (a) high frequency trading firms are going to start running their algorithms on iPhones, or (b) Geekbench doesn't accurately reflect the real world performance in some way.

Assuming Geekbench is a good reflection of real world performance (in e.g. games, video encoding, encryption, decompression, compilation, databases, etc), I'm curious to know what AMD and Intel are doing so wrong that a cellphone chip could outpace their desktop chips. I don't think the A20's lithography is vastly better, nor does the chip have more area. Where's the performance difference coming from?

It's more productive to explain why the idea doesn't work, since people can learn from that.

A bit off topic, but I think I'm starting to get model fatigue. These come out 10x faster than new Javascript frameworks were coming out 10 years ago (at least new models are far easier to adopt).

This makes me want to find a side project for an excuse to give Go another try (I last used it professionally pre-generics).

I do still wish it had discriminated unions (algebraic data types) and some better error handling ergonomics.

Definitely.

But I might not read it super carefully when recipe planning at the start of the week, which can result in scheduling mistakes.

Another pet peeve (which this doesn't solve): Recipes that include "marinate overnight" or some such in the middle (which can be easy to miss), instead of telling you up front that you have to start the day before.

Some of my pet peeves about regular cookbooks are:

- Steps that actually include many different steps.

- Steps that don't repeat the quantities, making me scroll back and forth between the step and the quantity list.

The diagrams in this post very cleanly solve both.

This has some lessons that apply much more broadly than to just airports (e.g. apps that make you jump through hoops and expect you to know what 2fa or SSO mean)

> Most generative AI tools, however, are trained on copyrighted and licensed content, and their output can include content that infringes those copyrights and licenses

To some extent, it feels like the genie is out of the bottle on this. There's so much LLM-generated code out there, and I'm sure plenty of it could be argued to infringe a copyright or license (though I think the legal bar for counting as infringement is set too low), that there's no way to go back and undo it.

That said, OpenJDK might be afraid that someone will decide to make an example of them because they are a high-profile target.

The part of this that's the most interesting to me is the fact that it is worthwhile for the compiler to expend the effort looking for this optimization opportunity. I would expect (x << 2) & -4 to be a fairly rare pattern, and even then removing the & -4 only saves one or two assembly instructions.

ChatGPT tells me that it would take a very large array to detect distance with any accuracy