47 karma · joined December 5, 2020
It wouldn't pay off.
Starting a futures exchange on RAM chips, on the other hand...
IMO it might be just a product problem. I opened nebula and:
* The same video had a better title on YT that was actually less clickbaity and more informative - assumedly because of YT algorithm for optimization
* Nebula auto set quality to 480p compared to 1080p in YT - if I wasn't tech-savvy I'd assume it's just worse quality.
* The loading times when you seek to part that's not loaded yet are 10x longer
* I missed comments
The recommendation algorithm is weaker too, I can't tell to what extent this is due to YouTube having simply more data and to what extent it's weaker engineering.
IF N is vocab size and L is sequence length, you'd need to create a NxL matrix, and multiply it with the embedding matrix. But since your NxL matrix will be sparse with only a single 1 per column, it'd make sense to represent it internally as just one number per column, representing the index at which 1 is. At which point if you defined new multiplication by this matrix, it would basically just index with this number.
And just like you write a special forward pass, you can write a special backward pass so that backpropagation would reach it.
I was a bit surprised by the change in tone in a way I probably wouldn't've been if I'd read chronologically.
Translated into Polish is: liczby ofiar państwowych zbrodni nienawisci
Which translates back into: numbers of victims of governmental crimes of hate
So except for the state turning into governmental makes everything a genitive case. I didn't notice the relationship until translating
In my typescript codebase case, it solves a lot of problems, it probably helps that I use tRPC type rather aggressively (i.e. using UUID types, separate dates and datetimes etc).
A few weeks ago it wasn't working nearly as well in my experience.
Like I once wanted to have my own syntax for querying a SQLite, so built my own ORM. The query syntax is defined in like 50 lines of code. Doc for it here [1]
1. https://asjir.github.io/FunnyORM.jl/dev/#FunnyORM.TableQuery