did you mean Pentiment, led by Josh Sawyer at Obsidian?
137 karma · joined August 17, 2015
Fun fact: the `bell` control character is part of the ascii standard (and before that the baudot telegraph encoding!) and was originally there to ring a literal bell on a recipient's telegraph or teletype machine, presumably to get their attention that they had an incoming message.
To keep backwards compatibility today's terminal emulators trigger the system alert sound instead.
For me personally I tried svelte in the past and bounced off because there was too much implicitly happening that I needed to have a deep understanding of to model correctly. This solves basically all those problems for me.
I thought your video[1] especially did a great job walking through the pros this change brings. Thanks for all your great work on this!
1. Link for the curious: https://www.youtube.com/watch?v=RVnxF3j3N8U&t=6s
Let's compare a few situations. In the baseline you're tailing the car in front of you with a focus on not letting anyone cheat and get in front of you, let's say 50 feet away. Your commute is 30 miles, and in this frictionless sphere of traffic you're going 60mph the whole time. You get to work in 30 minutes flat.
In the second scenario you're following the 3-second rule[0]. This would put you ~285 feet behind the car in front of you. Let's say over the course of your commute 20 cars move in front of you. If the average car length is 15 feet, and they all are 50 feet away from each other, when all 20 cars are in place you're a net -(20 * 65) feet away from the original car, or 1300 feet total. At 60 mph that adds ~15 seconds to your total commute time.
Well worth having an easier time avoiding a potential crash IMO! Also has the benefit of helping prevent traffic to begin with[1]
0: https://driversed.com/trending/what-safe-following-distance.
[0] https://platform.openai.com/docs/model-index-for-researchers
> We develop a large multimodal model (LMM), by connecting the open-set visual encoder of CLIP [36] with the language decoder LLaMA, and fine-tuning them end-to-end on our generated instructional vision-language data
Part of its contents come from the "USPTO Backgrounds" dataset. From The Pile's paper:
> USPTO Backgrounds is a dataset of background sections from patents granted by the United States Patent and Trademark Office, derived from its published bulk archives. A typical patent background lays out the general context of the invention, gives an overview of the technical field, and sets up the framing of the problem space. We included USPTO Backgrounds because it contains a large volume of technical writing on applied subjects, aimed at a non-technical audience.
More details in the paper: https://arxiv.org/pdf/2101.00027.pdf
The Pile: https://pile.eleuther.ai/
784 / 815,201 = 0.00096 = 0.1%
* SF: 784
* San Jose: 106
Still high numbers IMO but SF for example has ~800k residents, so this only represents ~0.1% of the total population. Not super convinced yet that it's going to have any immediate impact on housing in the city unless this triggers a much larger avalanche of firings that don't have an associated swarm of smaller companies waiting in the wings to pick good employees up at a discount.
[0] https://www.sfchronicle.com/bayarea/article/Here-s-how-many-...
See also: the pressure to be glue [1]