Unlikely but quite interesting.

My original point is that by changing what metric and how much of the combo you look at the calculated loss can vary significantly: over an order of magnitude difference between the dog-only volume loss and the refilled drink calorie loss.

lies, damned lies, and statistics.

That's why I have percentages for both the dog alone and the combo.

The costco hotdog contains 580 calories, approximately 110 of which is the bun. If the bun shrinks by 30%, the calories of the entire dog shrinks by 5.7%%. But also the $1.50 is for a combo that includes a drink of up to 270 calories, so the caloric content of the combo shrinks by 3.9% -- substantially less than the 37% claimed volume loss (which does not account for the drink).

EDIT - The drink also allows for a refill which would add another 270 calories bringing the total to 1120, and the caloric shrinkage down to 2.9%

When looking at protein the meal loses 1 gram out of 23, or 4% loss, which is less than inflation. Good for all the gym rats.

Edit 2 - fixed percentage calculations

Gotta love single points of failure...

For most problems solvable in code, there are literally infinite valid code representations of possible solutions (at least in higher-abstraction languages, the space is much more bounded at the assembly level). If you consider the subset of those representations that result in sufficiently performant execution, does it really matter which one is running? This is where most programmers will start mentioning clean abstractions and readability and maintainability, but those are concerns of making the code understandable for people reading and modifying that code. The people are still the driving force.

I'm not arguing for slop here - I definitely care about code quality - but it's important to stay grounded in the reason that it matters: people.

Anthropic trains models on AWS's (and GCPs, and Microslop's) infrastructure, then skims margin off of selling inference also on the infrastructure owned by the other companies.

These are extremely different businesses.

If the AI is communicating to me and can't select the appropriate jargon level, it's failing at communicating effectively.

In general I'd recommend starting with the most forgiving meat available to you (IMO pork butt or shoulder are great options) as even when you don't hit the ideal cook you often still get enjoyable results. Repeating the process enough times to learn how to dial in a cook is much easier when the misses don't suck.

...and the build doesn't require any tools that aren't already on your machine, and there are no dependencies to download

Many of the best tools I use are written in languages I don't program in and thus I don't want the build tools and package caches for those languages taking up space on my drive. I'll take the config file any day.

Cool to see this running but if it's at 10%+ loss vs 16bit that's less cool.

Hard to understand that story from what's published here.

So I'm assuming at least a subset of employees could continue using the models during that time.

Doesn't bode well for the valuations of these labs.

https://www.anthropocenemagazine.org/2025/04/new-study-compa...