QuBlock

[QuBlock @0xQuBlock]

@lambda0xE octBSCoJisTaeVvsCqdAhCpmpYRvU7uHNwTBGdzKKVbBCNi
Please send some here I applied to join the chat but no response

[QuBlock @0xQuBlock]

@octra Devnet Faucet not working…

[QuBlock @0xQuBlock]

@octra Why dont you open discord discussion? I have some issues starting the node following the instructions

[Pearl Research Labs @prlnet]

Pearl Research Labs is officially a member of the @Nvidia Inception program! #NVIDIAInception

We are building an accelerated inference platform to run the world's leading open-weight models - powered by Hopper and Blackwell GPUs.

➡️ Join the waitlist: https://t.co/j8r8olEmbE

[QuBlock @0xQuBlock]

#Pearl Proof of Useful Work Explained in Simple English

https://t.co/HL8OcCPH9d

[QuBlock @0xQuBlock]

¶ https://t.co/vPHKTN7UuV is live: a clean explorer and analytics dashboard for the Pearl blockchain. $PRL @prlnet

Explore it here: https://t.co/3GI6rS6m4r
API: https://t.co/LQss2FLbXb

[QuBlock @0xQuBlock]

@prlnet @VitalikButerin

[Pearl Research Labs @prlnet]

Since Bitcoin, a major open problem in distributed systems was whether proof-of-work consensus can be implemented on top of real-world computation rather than useless random hashing. This challenge of PoUW was repeatedly conjectured impossible by researchers and thought leaders. They were wrong.

[Pearl Research Labs @prlnet]

The real currency is not money. It's energy and data - the two scarce resources whose fusion creates intelligence.

We believe that this dramatic shift in the production of knowledge compels us to rethink the fundamental properties, purpose, and creation process of money.

[Omri Weinstein @WeinsteinOmri]

The Hadamard Transform underpins many SoTA LLM quantization algorithms such as @Google's TurboQuant, QuIP#, QuaRot, SpinQuant, and NestQuant.

LLM quantization works best when a vector’s coordinates have similar magnitudes. If most of a vector’s mass sits in one coordinate, e.g.
x = (0,0,0,1,0,0,0,0,0,0), then the quantizer faces a bad tradeoff: use a wide range and waste resolution, or use a tighter range and “clip” the outlier, which may result in significant accuracy loss of the LLM.

This is the basic motivation behind random orthogonal rotations: Multiplying x by a Gaussian rotation matrix R spreads x’s mass uniformly over coordinates, while preserving inner-products:

‖Rx‖₂ = ‖x‖₂, ⟨Rw, Rx⟩ = ⟨w, x⟩.

The problem with random rotation, of course, is computational cost: a dense d × d rotation costs O(d²) per vector, so cubic time for W*X.

In 2006, a beautiful and influential paper by Ailon and Chazelle, proposed a faster “pseudo-random” rotation op via the randomized Hadamard transform

H′ = (1/√d)HD,
where H is a Hadamard matrix and D is a diagonal random sign matrix.

It turns out that H’ has essentially the same effect as
a fully random rotation, in the sense that it “evenly spreads the mass” of any unit vector. This is essentially the uncertainty principle of the Fourier transform (!) The upshot is that H’x and H’w can now be computed in O(d log d) time instead of O(d²), due to the recursive (FFT) structure of H.

In this blog, @prlnet's 17-year old researcher @sag0li explains how how we used Agentic toos to efficiently implement a block-Hadamard kernel in CuteDSL, which allows for very fine-grained control over memory and cache management -- this is the path to ultra-fast inference!

[Pearl Research Labs @prlnet]

https://t.co/oJBoA0Fvy2

[Pearl Research Labs @prlnet]

https://t.co/cvDuRl6iQb

[QuBlock @0xQuBlock]

Enjoy Pearl River Beer while reading Pearl Polymath…
@prlnet #prl #pearl

[QuBlock @0xQuBlock]

@theRealKiyosaki You said you love debt, why you sold bitcoin instead of borrowing usd from the btc and buy other businesses?

[QuBlock @0xQuBlock]

@qBitTensorLabs Any hint on the upcoming challenge?