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Traded this pretty poorly, but p is p💰 Andy Ayrey when is Claudius getting its own X to interact with terminal of truths?

spuno

49,354 subscribers

41,862 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

10 Comments

Baki1 year ago

@AndyAyrey @truth_terminal You've started balding looking at these charts

spuno1 year ago

@AndyAyrey @truth_terminal 😂😂😂

RED1 year ago

@AndyAyrey @truth_terminal was watching this chart on stream and no one knew where tf the volume came from 😭

0xiluminado (Comeback arc)1 year ago

@AndyAyrey @truth_terminal You stream this anywhere?

spuno1 year ago

@IluminadoNFT @AndyAyrey @truth_terminal Soon.

Johan_RT1 year ago

@AndyAyrey @truth_terminal Let's go!!!

Gengar0x 🍕🦄💨✨1 year ago

@AndyAyrey @truth_terminal 👀

EDOG1 year ago

@AndyAyrey @truth_terminal Good video

GozzaGus1 year ago

@AndyAyrey @truth_terminal

KIDUKU1 year ago

@AndyAyrey @truth_terminal I was there in 100x and came out with x5

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37,051 views • 1 year ago

But wait ! Is there any agency one can tag and report these things to ?? This P is really getting out of hands for real !!

But wait ! Is there any agency one can tag and report these things to ?? This P is really getting out of hands for real !!

Oyindamola🙄

290,912 views • 1 year ago

Do you actually know what convex optimization is in the geometric, guarantee-theoretic sense or have you only met it through solvers and loss curves? Convexity is rare comfort in optimization...there are no spurious local minima, no surprise traps, and inequalities you can use like tools instead of prayers. So, what is this convexity? Let x = (x₁, x₂) and let f(x) be convex. Plot the surface z = f(x). Pick a contact point x₀. The local slope is the gradient p = ∇f(x₀). That p is exactly the data that defines the supporting plane: z = f(x₀) + p · (x − x₀). Thus, f is said to be convex because for every x, f(x) ≥ f(x₀) + p · (x − x₀). So the plane at x₀ can slide under the surface, but it never slices through it. Not near the point...everywhere. Now for here is the interesting part: The slope becomes a coordinate system! Rewrite the same plane as z = p · x − b, where b is the offset. Because the plane passes through (x₀, f(x₀)), the offset is forced to be b = p · x₀ − f(x₀). And that number isn’t just geometry trivia. It’s the convex conjugate: f*(p) = sup over x ( p · x − f(x) ). At a differentiable contact point, the supporting plane touches f tightly enough that the supremum is achieved at x₀, giving the identity f*(p) = p · x₀ − f(x₀) when p = ∇f(x₀). So one moving contact point gives two linked readouts: primal position x₀ dual position (slope) p = ∇f(x₀) dual offset f*(p) One surface. Two worlds. #ConvexOptimization #Optimization #MachineLearning #SignalProcessing #AppliedMath #Engineering

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Mathelirium

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Derek Chisora is going to retire when he WANTS to retire! 😤 But Tony Bellew is NO p***y! ❌

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Az91saifar :)

48,687 views • 1 year ago

That’s an odd thing for a woman to say. When you combine that with the p*enis, I’m pretty sure something fishy is going on! 😎

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idk what is this. I wanted to draw some sketches of pirates au or something but then i made this. :p #witchhatatelier

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