Ooooh, you don't happen to have the code for the New Yorker decryption in a form you could send, do you? Or put up on github or even just give me the starting prompt…
Okay, a couple of hours later…thanks for the hint as that's fucking dark magic ;) and I now have access to the entire New Yorker again after around 15 years :)
Since I think you'll find the one for Rolling Stone and Playboy, but not The New Yorker (I might be one of the few that has this or something like it)...
I'll have a more proper github repo at some point, but there are bugs I was working through. Some issues are bloated up ridiculously... a 9mb djvu file shouldn't become a 110mb pdf. Most issues will work well though. Hope it helps.
Try Hey Duggee - it's not as explicitly British-coded, but there's a ton of stuff in there if you were watching Spaced in your late teens and now find yourself a parent…
Seconded, Hey Duggee is a fantastic show. In a way it's the anti-Bluey - same delightful vibes, just as playfully animated, but intentionally ridiculous (and, to me, hilarious) stories.
Ha, same here! It really helped my imposter syndrome, as I overheard a couple of guys talking about the ARM assembly they were doing on their Archimedes on the first day…and I hadn't written anything fancier than QuickBASIC at the time…
I no longer work there, but Lucidworks has had embedding training as a first-class feature in Fusion since January 2020 (I know because I wrapped up adding it just as COVID became a thing). We definitely saw that even with just slightly out-of-band use of language - e.g. in e-commerce, things like "RD TSHRT XS", embedding search with open (and closed) models would fall below bog-standard* BM25 lexical search. Once you trained a model, performance would kick up above lexical search…and if you combined lexical _and_ vector search, things were great.
Also, a member on our team developed an amazing RNN-based model that still today beats the pants off most embedding models when it comes to speed, and is no slouch on CPU either…
(* I'm being harsh on BM25 - it is a baseline that people often forget in vector search, but it can be a tough one to beat at times)
Totally. And this has even happened in search. Open source search engines like Elasticsearch, etc did this... Google etc did this in the early Web days, and so on :)
Well, why wouldn't they sell (license) the rights to make Transformers films (which as far as I know is just extending their existing contract with Paramount)?
They still own the underlying IP[^1], so as long as the contract is a decent one, Paramount has to deal with the actual making/distributing the film, and Hasbro just gets the money, and a toy line off the back of the film. Feels like an easier set up than taking the risk on movie-making yourself (which they did attempt with eOne for other properties, but seemingly have decided that it's probably not a good deal with them)
[1] yes, yes, it's a bit more complicated with Takara in the mix too, but you can essentially view it as a Hasbro-owned property
That paper does a terrible job of making Lucene look useful, though. 10qps from a server with 1TB of RAM is not great (and I know Lucene HNSW can perform better than that in the real world, so I am somewhat mystified that this paper is being pushed by the community).