News for hackers. Stuff that matters.

ubitaco

Re:Em dashes are fucking amazing on Friday July 24, 2026 @09:30AM (#49035269)
I didn't know that AP style recommended spaces either side of the em dash. Here in the UK we generally use en dashes instead, and I thought em dashes always had no spaces. Apparently the wire machines that AP sent their news out by in olden times couldn't always do en dashes.
Re:UTF-8 is a brilliant design on Friday September 12, 2025 @05:56PM (#45227240)
It's slightly buried in the readme on Github:

> how can we store a 24 byte long string, inline? Don't we also need to store the length somewhere?

> To do this, we utilize the fact that the last byte of our string could only ever have a value in the range [0, 192). We know this because all strings in Rust are valid UTF-8, and the only valid byte pattern for the last byte of a UTF-8 character (and thus the possible last byte of a string) is 0b0XXXXXXX aka [0, 128) or 0b10XXXXXX aka [128, 192)

Whereas GP is making reference to a case in which Italian seismologists were literally convicted after their predictions did not come true.
Re:The UX of UUIDs on Thursday April 11, 2024 @06:14PM (#40007368)
I would back "blank" as the most likely to be understood by the other person.
Re:Kalman Filter Tutorial: Kalman Filter from the Ground Up on Saturday October 14, 2023 @12:11PM (#37881649)
If you can make certain assumptions about the system (mainly that sources of noise follow gaussian distributions and are independent), then the Kalman filter gives the best possible estimate of the system state. And it can be computed cheaply, like on the Apollo guidance computer.

You basically need to know some kind of a model for the system to run KF. Whereas ML is all about working out the model automatically.

As for similarities, KF is a really efficient implementation of Bayesian inference. I think that any ML model that isn't fundamentally using Bayesian inference, is fundamentally flawed.

Re:Good data scientist, bad data scientist on Tuesday May 11, 2021 @03:23PM (#27122338)
> Good DS understands the basics of web technology

I'm not a data scientist but a portion of my job is creating pipelines, data analytics and such. I also only have a bare minimum knowledge of web technology. Why is knowledge of web technology part of being a good Data Scientist? Or is this point oriented specifically for data scientists working in web based companies?

Genuinely curious. I could imagine myself working as a DS in the future and that's why I found this article interesting.

I'm also not a physicist, but for the fun of the discussion...

Radiative heat loss scales with the fourth power of temperature. I don't know what temperature the ISS radiators are but suppose they are around 300K. Then I think the radiative surface to keep something cool at 10K would need to be 30^4, or 810000 times larger per unit heat loss. So realistically I think you would need some kind of wacky very low temperature refrigeration to raise the temperature at the radiator, and then maybe radiate the heat into the lunar surface.

This happened to me on an emirates A380, but on landing. The timing was comical because immediately after touching down (and being rained on) the entertainment screen prompts you to rate your flight.
>In fact there is a 25% chance that the person involved in working that out has been asked to commit fraud at some point.

But there's only a 10% chance of that.

The market has spoken