176 karma · joined January 15, 2015
Been running it almost barebones vanilla for a couple of months. Just a bunch of basic extensions and some skills.
Now, if only they could fix the very annoying bug of the history jumping back at the beginning if I am not a the end while the model is reasoning that would great.
But isn’t Zed written in Rust?
x = mapreduce(f, r, arr, init)
equivalent to
x=init
for e in arr
r(x, f(e))
Of course, if r is ever something different than a simple operator, slap yourself. But otherwise it’s an absurdly powerful construct.
His dismissal of the argument Knuth makes regarding the hot loops could have been explored a bit better. I found it weird he didn’t mention the difference of types of programs of then vs now. Even today, in scientific code it is still absolutely the case a lot of the time that a huge chunk of the runtime comes from a single very very hot loop. It might be hidden in a library, but it’s there. Instead he focuses only on “program size”. Knuth samples where very small FORTRAN programs (compared to today’s standards). Today’s program are bigger but the fundamental number crunching primitive of “let’s compute stuff in a loop” remains. It’s just buried under a pile of extra cruft (data loading, parallelism, dispatching etc).
Now we just deal with a lot more programs that are of a whole different class compared to what they where doing with computers in the 70s. We have much more I/O involved. And hot loops don’t like being I/O bound.
I am getting 50t/s of prefill and 5-6t/s of inference.
You can’t get crazy with it, but it’s pretty nice to just let it work away at simpler prompts overnight or during lunch break or while I am working on other things.
Being able to literally a 30gb blob and then being able to talk with it while running on your machine it’s a different kind of magic compared to a far away magic in the cloud.
> Maybe I should document what I did!
Please do!
Me and a bunch of friends worked on a project that used diamond-types as the backing CRDT engine. It does indeed go brrrr. But damn, it took way too long to reconstruct what it was doing (we needed some more fine grained knobs, so we were playing with the frontier directly). We eventually moved out to something a bit better documented, which was a real pity. I really liked the general architecture and simplicity (of the text-only based version at least).
Practically speaking the standard library is just meant as “what’s included in the default sysimage”.
This allows to have different standard libs for different use cases. Think of Linux distributions in a sense. There are already a couple of alternative “stdlibs” out there for different focuses.
It autodetects when you delete an app from the Applications folder runs a quick scan and suggests which other files you might want to delete (So you might save savedata or settings if needed)
Not saying the sole cause, but the population (delayed) shock[1] alone could be a huge factor.
[1] https://en.wikipedia.org/wiki/File:1910-_Fertility_rate_-_Un...
Sometimes I am baffled that sentences like this are used as headlines. I know I’ll sound dismissive… but: duh?!
It’s more silent than membrane keyboards, completely customizable and super lightweight. (Got a carry box that is the size of a small paperback)
You can download all the schematics (also the board) and case files to print them. The assembly requires a bit of soldering. All in all it costed me ~150€ but I printed some extra stuff to try out.
There are also places that sell kits directly so you don’t have to source all bits and pieces yourself
It’s still dynamic in nature. But you can tune how much staticity you want. The spectrum goes from Python to C in terms of staticity. And with tools like JETLS.jl maturing you get a lot of the benefits static analysis.
The data pipeline ecosystem is starting to rival that of R and Python. The fact that you can just use Julia functions while keeping the performance allows you to avoid those weird vectorization gymnastics. The ML ecosystem is also in a great state. JUMP.jl, Touring.jl, the whole SciML ecosystem, autodiffing and gpu computing are all close to best in class in terms of quality. The NN side of ML is a a bit weaker, but just for lack of developer time investment into that side of the ecosystem.