From Newsgroup: sci.physics

Hi,

Why I forget something, that was never
on my mind. This here is hardly
about rockets:

11.4 Giga Lips with a Budget Laptop https://github.com/Jean-Luc-Picard-2021/gigabudget

So stop glue sniffing. Got it? Your are
just confused. I don't care about SpaceX,
that Composer was acquired by

SpaceX was just a factual thing.

Bye

Disclaimer: Who knows, maybe somebody picks
up pi-calculus and/or WAM for rocket engineering,
I do not primarily exclude it.

But its not on my mind, so I cannot forget something
which I don't care about.

Ross Finlayson schrieb:

Forget Space-X and forget that heil-throwing fat-ass, too,
the rockets they bought are wearing out and the ones they
built are blowing up, moving fast and breaking things.

Mild Shock schrieb:

Hi,

The primary motivation behind SpaceX's acquisition
of Cursor (and its underlying Composer model tech stack)
comes down to vertical integration of the entire AI
stackrCocompute, models, and application surface.

Bridging the Coding & Reasoning Gap: While xAIrCOs Grok
had massive raw compute backing it (via massive clusters
like Colossus), its training data heavily leaned on
social media feeds from X.

That made it great for conversational chat, but left
it lagging behind competitors (like OpenAI and Anthropic)
in pure coding, reasoning efficiency, and long-horizon
agentic execution.

Owning the Developer Workflow: Instead of just building
a model and hoping people use it, acquiring Cursor
gives SpaceX the premier developer-facing application
layer that millions of developers already use
for daily coding.

Coupling Cursor's model architecture with SpaceX/xAI's
massive compute infrastructure allows them to aggressively
scale up training for next-gen models.

So its a Win-Win. Got it?

Bye

Mild Shock schrieb:

Hi,

Because I use WebGPU and not WebGL. And
because WebGPU can adresss modern GPU
developed with the NVIDIA Volta evolution,

which happened in 2017. Namley that compute
shaders are not any more subject to the
realization restriction of lock step

execution, but have independent thread state.
And because there is independent thread state
there is also independent time spent for a

a work item by each logical thread, if the
submitted logical thread uses a lot of branching
logic or even loops. But the use of branching

and loops is encouraged in independent thread
state programming of compute shaders. The variables
that can drive such logic are the scalar variables:

Tour of WGSL - Control Flow
https://google.github.io/tour-of-wgsl/control-flow/

Then not to waste GPU compute time, by logical
threads doing nothing. You will need to
introduce some load balancing among multiple

logical threads. And MPMC queues are one way to
readize load balancing. Compute shaders with
producer and consumer entry points are proposed

as fundamental architecture by Thunder Kittens:

ThunderKittens: Simple, Fast, and Adorable AI Kernels
https://arxiv.org/abs/2410.20399

They are used by this SpaceX acquisition:

Composer 2 Technical Report
https://arxiv.org/abs/2603.24477

Thunder Kittens uses Hardware support, i.e. tma_expect().

Bye

Chris M. Thomasson schrieb:

never meant to be used in a GPU.
Dmitry CAS version can be used, but

Why do you even need a mpmc queue
in your compute shader anyway?

Mild Shock schrieb:

Hi,

This is quite fun, how some TLA+ guy fears
the full state of queue like the devil in
itself. But I guess if a service rate is

low and the producer has not much to do to
produce its work items, the arrival rate
has nevertheless to adapt, and dealing

with "full states", which are wrongly
called deadlock here, is the normal:

Tutorial-style talk - BlockingQueue
https://github.com/lemmy/BlockingQueue/tree/main

Prolog is in good position. The bird box
model has a redo port. So sometimes switching
from push to pull, can help without doing

Deadlock Exorcism. You can also translate
the bird box ports into pi-calculus:

A pi-calculus Specification of Prolog
Benjamin Z. Li - University of Pennsylvania
11 Apr 1994, European Symposium on Programming,
Prolog, Unification, Backtracking
https://scispace.com/pdf/a-pi-calculus-specification-of-prolog-3qf2pf04ud.pdf


Have Fun!

Bye

Mild Shock schrieb:

Hi,

Remember when first all local AI was Python
and PyTorch APIs. And then suddently people started
using bare metal C/C++ Code. Here is the story:

How it started:

GPT-J or GPT-J-6B is an open-source large
language model (LLM) developed by EleutherAI
in 2021. As the name suggests, it is a
generative pre-trained transformer model
designed to produce human-like text that
continues from a prompt.
https://www.eleuther.ai/

How it was going [Georgi Gerganov]:

So a few days later comes out the LLaMA, I do
some calculations and I figure out rCLOkay, 65
billion parameters. You probably need about
40 gigs of RAM, with 4-bit quantization. So
this can run on a MacBook. Why not do it?rCY

Why I was able to do it so quickly - basically,
for all that I saw itrCOs pretty much GPT-J architecture
with some modifications, like some extra memorization
layers. ItrCOs minor changes. Basically, again, the
existing code for the GPT-J, I just simply
modified it there, it happened pretty quickly.
https://changelog.com/podcast/532

Georgi Gerganov, Bulgarian, now with Hugging
Face, ggml-cann also running on Chinese AI chips.
ggml Manifesto https://github.com/ggml-org/ggml

Bye

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