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A Simian Symphony (1/2): #Neural probe recording from CA1 #hippocampus. #Sonification and #visualization driven by Local Field Potential (LFP) and Current Source Density (CSD). Single-unit spikes each associated different piano keys. #b3d #neuroscience #dataviz #electrophysiology

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Tyler Sloan

1,747 subscribers

102,101 Aufrufe • vor 3 Jahren •via X (Twitter)

#Neural#hippocampus#Sonification#visualization#b3d#neuroscience#dataviz#electrophysiology
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Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

I’m excited to share our latest #neuro #visualization. This project spanned many months, and grew in scope to beyond data #visualization to include #sonification. I hope you’ll sit back, put on your headphones, and enjoy a multi-sensory #neuroscience experience.

Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

We explore a compressed pattern of brain activity seen in all mammals, that is thought to underlie the creation and consolidation of memories. The Sharp Wave Ripples (#SWRs) in the #hippocampus are the largest oscillation in the #brain, involving 10’s of thousands of #neurons.

Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

A Simian Symphony (2/2): CA1 unit ensembles during Sharp Wave Ripples (#SWRs). Spike-sorted units are visualized / sonified across 20 ripples, represented by 3D cell morphology of the #hippocampal #neuron type. Morph between anatomical layer to UMAP space. #neurosience #b3d

Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

This video is the result of a fascinating collaboration with @perpl_lab @SAbbaspoor @DiagBiochips @neuroscott. Also grateful to @NeuroMorphoOrg for the neuron models. What follows is a detailed description of what you're seeing in each scene:

Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

We start by zooming into the #brain, where we see a @DiagBiochips DeepArray probe recording in the #hippocampus. We zoom in further to see the probe sites flickering with voltage changes around the neurons, the Local Field Potential (LFP).

Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

Original data is sampled at 30kHz, so we slow down. The audio #sonification is being driven by the LFP, and the background Current Source Density shows the flow of electrical charges between the cell layers. A multisensory experience of 'ripples' at 1/200th of their real speed.

Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

From the #LFP data, a statistical technique ‘spike sorting’ identified individual spiking neurons (‘units’). Groups of neurons fire together as a #cellassembly , a unit of #neural computation. We introduce the spike #sonification, where each unit is given a unique piano key.

Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

We then look at a sequence of individual Sharp Wave Ripples and their neuronal ensembles. The cell types of each unit are inferred from their depth and their spiking statistics, and are represented by their 3D neuron morphology.

Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

We’ve selected 20 ripples and measured by how many times each unit spikes during the ripple. Assemblies in the first 10 ripples are similar to each other, and those in the last 10 are similar to each other.

Profilbild von Tyler Sloan
Tyler Sloanvor 3 Jahren

For this sequence, the piano notes are assigned to units that had spikes in both sets of ripples. Units who spiked only in the first group were assigned the timbre of an acoustic bass, while the units who spiked only in the second group were assigned that of a marimba.

Profilbild von Earl K. Miller
Earl K. Millervor 3 Jahren

It just needs Tony Levin and Bill Bruford.

Profilbild von Eri
Erivor 3 Jahren

@EdwinRobertoRa4 chécate esta sinfonía, doc!

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Spike 1%

45,609 Aufrufe • vor 2 Monaten

Lecture 2 on our Quantum Mechanics Series Schrödinger’s equation doesn’t start from mystery. It starts from a very specific bet…the state of a particle is a complex field ψ(x,t), and whatever dynamics we write down must move ψ forward in time in a way that preserves total probability. We ask a basic question…what equation should ψ satisfy so that |ψ|² behaves like a conserved density, the way mass density does in fluid flow? What is ψ? Think of ψ(x,t) as the amplitude assigned to “the particle is at position x at time t”. It’s not a probability. It’s the object you add first, and only at the end do you square p(x,t) = |ψ(x,t)|² Because ψ is complex, it has magnitude and phase. Write it in polar form ψ(x,t) = r(x,t) exp(i θ(x,t)) Then r² = |ψ|² is the density, and θ will end up controlling flow (the probability current). Where does Schrödinger’s equation come from? Start with two empirical inputs about waves and particles: E = ħ ω p = ħ k Here ħ (“h-bar”) is Planck’s constant divided by 2π. It’s the unit conversion factor between the wave description (frequency ω, wavevector k) and the particle description (energy E, momentum p). In units, ħ has units of joule-seconds, so multiplying ω (1/seconds) gives energy (joules), and multiplying k (1/meters) gives momentum (kg·m/s). It’s the number that tells you how much energy or momentum you get per unit frequency or wavenumber. A plane wave with angular frequency ω and wavevector k is ψ(x,t) = A exp(i(k·x − ω t)) Now notice what derivatives do to this wave: ∂ψ/∂t = −i ω ψ ∇ψ = i k ψ ∇²ψ = −|k|² ψ Multiply those identities by ħ: i ħ ∂ψ/∂t = ħ ω ψ = E ψ −i ħ ∇ψ = ħ k ψ = p ψ −ħ² ∇²ψ = ħ² |k|² ψ = p² ψ So for plane waves, the operators Ê = i ħ ∂/∂t p̂ = −i ħ ∇ act like energy and momentum! Now use the classical nonrelativistic energy relation: E = p²/(2m) + V(x) This is bookkeeping for a particle moving slow enough that relativity can be ignored. The term p²/(2m) is kinetic energy. If p = mv, then p²/(2m) = (m²v²)/(2m) = (1/2)mv². The term V(x) is potential energy. It depends on position because forces come from spatially varying energy. A slope in V pushes the particle. Examples: for a charged particle in an electric potential φ(x), V(x) = q φ(x). Near Earth, V(z) = mgz. The point is total energy equals kinetic plus potential. Turn that into an equation for ψ by replacing E and p with the operators above: Ê ψ = (p̂²/(2m) + V) ψ Compute p̂² = (−i ħ ∇)·(−i ħ ∇) = −ħ² ∇², so we get i ħ ∂ψ/∂t = ( −ħ²/(2m) ∇² + V(x) ) ψ That is the time-dependent Schrödinger equation. The derivation here is a controlled heuristic: we matched the plane-wave identities to the measured relations E = ħω and p = ħk, then imposed the same energy bookkeeping as classical mechanics. Why this equation is the right kind of rule If ψ is the state, we need a rule that preserves total probability: ∫ |ψ(x,t)|² dx = 1 Schrödinger evolution does. You can see it by deriving a continuity equation. Let ρ(x,t) = |ψ|² = ψ* ψ. Take a time derivative: ∂ρ/∂t = ψ* ∂ψ/∂t + ψ ∂ψ*/∂t Use Schrödinger and its complex conjugate: ∂ψ/∂t = (1/(i ħ)) ( −ħ²/(2m) ∇²ψ + Vψ ) ∂ψ*/∂t = (−1/(i ħ)) ( −ħ²/(2m) ∇²ψ* + Vψ* ) Plug in. The V terms cancel exactly, and what remains can be rearranged into a divergence: ∂ρ/∂t + ∇·j = 0 where the probability current is j = (ħ/(2mi)) ( ψ* ∇ψ − ψ ∇ψ* ) This is the best way to explain ehat ψ is: |ψ|² behaves like a conserved density, and the phase of ψ is what drives the current j. So in this series, ψ isn’t a slogan. It’s the object whose modulus squared is the density, whose phase generates flow, and whose time evolution is fixed (up to V) by matching wave relations to energy bookkeeping: i ħ ∂ψ/∂t = ( -ħ²/(2m) ∇² + V ) ψ #QuantumMechanics #SchrodingerEquation #WaveFunction #BornRule #Physics #MathematicalPhysics
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Mathelirium

40,835 Aufrufe • vor 8 Monaten

Generative Adversarial Network (GAN) by hand ✍️ ~ 9 steps walkthrough below The Gen in GenAI came from this landmark paper by Ian Goodfellow et al., 12 years ago. The paper showed that a neural network can not only classify but also turn upside down to generate realistic looking images. The secret? We pit two of them against each other: a Generator turns noise into fake data, and a Discriminator learns to tell fake from real, pushing the Generator to keep doing better. One runs upside down, the other right way up. I drew and calculated one entirely by hand. Goal: generate realistic 4D data out of 2D noise, filling in every cell yourself. = 1. Given = Four noise vectors in 2D, and four real data vectors in 4D. = 2. Generator, first layer = Let us multiply the noise by weights and biases to get new features. = 3. ReLU = We apply the activation, and -1 and -2 are crossed out and set to 0. = 4. Generator, second layer = Let us multiply again. ReLU applies here too, but every value is already positive, so nothing changes. What comes out is the fake data F, made by a two-layer generator out of nothing but noise. = 5. Discriminator, first layer = We feed it both, the four fakes and the four real vectors, through the same weights. It never learns which is which from the layout, only from the numbers. = 6. Discriminator, second layer = Let us reduce each data vector to a single feature Z. Eight vectors in, eight numbers out. = 7. Sigmoid = We turn each Z into a probability Y. A 1 means the discriminator is certain the data is real, a 0 means certain it is fake. = 8. Training the Discriminator = Let us take the gradients as Y minus YD, where YD is what the discriminator should have said: 0 for the four fakes, 1 for the four real. Why so simple? Because pairing sigmoid with binary cross entropy loss makes the math collapse to exactly this subtraction. Its loss uses both halves of the page. = 9. Training the Generator = We do it again, as Y minus YG, and YG is [1, 1, 1, 1]: the generator wants the discriminator to call every fake real. Same predictions, different target, opposite goal. Its loss uses only the fakes. The outputs: Fake data F = [1, 2, 3, 1], [1, 1, 2, 1], [2, 2, 4, 2], [1, 0, 1, 1] Predictions on fakes = [.7, .5, .9, .3] Predictions on real = [.7, .9, .9, 1] Discriminator gradients = [.7, .5, .9, .3] and [-.3, -.1, -.1, 0] Generator gradients = [-.3, -.5, -.1, -.7] The takeaway: the adversarial part is one subtraction done twice. The same eight predictions, scored against two opposite targets, send one set of gradients back through the blue weights and another back through the green ones. 💾 Save this post!
0:23

Generative Adversarial Network (GAN) by hand ✍️ ~ 9 steps walkthrough below The Gen in GenAI came from this landmark paper by Ian Goodfellow et al., 12 years ago. The paper showed that a neural network can not only classify but also turn upside down to generate realistic looking images. The secret? We pit two of them against each other: a Generator turns noise into fake data, and a Discriminator learns to tell fake from real, pushing the Generator to keep doing better. One runs upside down, the other right way up. I drew and calculated one entirely by hand. Goal: generate realistic 4D data out of 2D noise, filling in every cell yourself. = 1. Given = Four noise vectors in 2D, and four real data vectors in 4D. = 2. Generator, first layer = Let us multiply the noise by weights and biases to get new features. = 3. ReLU = We apply the activation, and -1 and -2 are crossed out and set to 0. = 4. Generator, second layer = Let us multiply again. ReLU applies here too, but every value is already positive, so nothing changes. What comes out is the fake data F, made by a two-layer generator out of nothing but noise. = 5. Discriminator, first layer = We feed it both, the four fakes and the four real vectors, through the same weights. It never learns which is which from the layout, only from the numbers. = 6. Discriminator, second layer = Let us reduce each data vector to a single feature Z. Eight vectors in, eight numbers out. = 7. Sigmoid = We turn each Z into a probability Y. A 1 means the discriminator is certain the data is real, a 0 means certain it is fake. = 8. Training the Discriminator = Let us take the gradients as Y minus YD, where YD is what the discriminator should have said: 0 for the four fakes, 1 for the four real. Why so simple? Because pairing sigmoid with binary cross entropy loss makes the math collapse to exactly this subtraction. Its loss uses both halves of the page. = 9. Training the Generator = We do it again, as Y minus YG, and YG is [1, 1, 1, 1]: the generator wants the discriminator to call every fake real. Same predictions, different target, opposite goal. Its loss uses only the fakes. The outputs: Fake data F = [1, 2, 3, 1], [1, 1, 2, 1], [2, 2, 4, 2], [1, 0, 1, 1] Predictions on fakes = [.7, .5, .9, .3] Predictions on real = [.7, .9, .9, 1] Discriminator gradients = [.7, .5, .9, .3] and [-.3, -.1, -.1, 0] Generator gradients = [-.3, -.5, -.1, -.7] The takeaway: the adversarial part is one subtraction done twice. The same eight predictions, scored against two opposite targets, send one set of gradients back through the blue weights and another back through the green ones. 💾 Save this post!

Tom Yeh

16,752 Aufrufe • vor 2 Monaten

Quantum Mechanics Series Lecture 4 Lecture 1 established that ρ(x,t) = |ψ(x,t)|² behaves like a conserved probability density. Lecture 2 showed what drives that flow. We also saw that writing ψ = r exp(iθ) makes the probability current proportional to the phase gradient, making it clear that phase geometry literally steers the motion. Lecture 3 then showed that the centroid of that flow can move almost classically when the packet is tight and the external potential is smooth. However, that raises yet another question. If the centroid can look classical, why does the full wave still spread, bend, split, and interfere in ways no classical particle cloud would? This is because the wave is not driven only by the external potential. It is also driven by its own curvature. Write ψ(x,t) = r(x,t) exp(iθ(x,t)) with ρ = r². Then Schrödinger’s equation gives two coupled real equations. One is the continuity equation you already know. The other looks like a Hamilton-Jacobi equation, but with one extra term: Q = −(1/2m) ∇²r / r This is the so-called Quantum Potential. It depends entirely on how the amplitude bends across space. So, the wave is being shaped not only by V(x,t), but also by the geometry of its own envelope. In the animation, the upper surface is still |ψ| and its skin is still colored by arg(ψ). The glowing threads still trace the probability current. But now a second membrane hangs underneath. That lower membrane encodes the quantum potential Q itself. The porcelain bead marks the quantum centroid. The amber bead follows a classical centroid under the same external V. When those paths separate, the lower membrane tells you why. The difference is not magic but the extra term classical mechanics does not have. The math breakdown: Start from Schrödinger evolution in units with ħ = 1: i ∂ψ/∂t = [ −(1/2m) ∇² + V(x,t) ] ψ Write the state in polar form: ψ = r exp(iθ) Then ρ = |ψ|² = r² From the imaginary part, you recover probability conservation: ∂ρ/∂t + ∇·j = 0 with j = (1/m) Im(ψ* ∇ψ) = (ρ/m) ∇θ So the local velocity field is v = j / ρ = ∇θ / m Now take the real part of Schrödinger’s equation. That gives ∂θ/∂t + |∇θ|² / (2m) + V + Q = 0 where Q = −(1/2m) ∇²r / r This is the classical Hamilton-Jacobi equation with one extra term. That extra term is what makes quantum motion locally different from classical motion. Take a gradient of that phase equation and use v = ∇θ / m. Then the flow obeys an Euler-like equation: ∂v/∂t + (v·∇)v = −(1/m) ∇(V + Q) In other words, there are really two forces in the problem. One comes from the external potential V. The other comes from the wave’s own curvature through Q. That is why Ehrenfest is only approximate. The centroid can still satisfy d⟨x⟩/dt = ⟨p⟩/m d⟨p⟩/dt = −⟨∇V⟩ but the internal shape of the packet evolves under the combined influence of V and Q. When the packet stays broad and smooth, Q is gentle and the motion looks more classical. When the packet develops sharp curvature or interference structure, Q becomes strong and the classical picture breaks down. That is what this scene is designed to show live. #QuantumMechanics #Wavefunction #SchrodingerEquation #BornRule #ProbabilityCurrent #ContinuityEquation #Phase #EhrenfestTheorem #QuantumPotential #Madelung #HamiltonJacobi #MathematicalPhysics #Mathematics #Physics
1:00

Quantum Mechanics Series Lecture 4 Lecture 1 established that ρ(x,t) = |ψ(x,t)|² behaves like a conserved probability density. Lecture 2 showed what drives that flow. We also saw that writing ψ = r exp(iθ) makes the probability current proportional to the phase gradient, making it clear that phase geometry literally steers the motion. Lecture 3 then showed that the centroid of that flow can move almost classically when the packet is tight and the external potential is smooth. However, that raises yet another question. If the centroid can look classical, why does the full wave still spread, bend, split, and interfere in ways no classical particle cloud would? This is because the wave is not driven only by the external potential. It is also driven by its own curvature. Write ψ(x,t) = r(x,t) exp(iθ(x,t)) with ρ = r². Then Schrödinger’s equation gives two coupled real equations. One is the continuity equation you already know. The other looks like a Hamilton-Jacobi equation, but with one extra term: Q = −(1/2m) ∇²r / r This is the so-called Quantum Potential. It depends entirely on how the amplitude bends across space. So, the wave is being shaped not only by V(x,t), but also by the geometry of its own envelope. In the animation, the upper surface is still |ψ| and its skin is still colored by arg(ψ). The glowing threads still trace the probability current. But now a second membrane hangs underneath. That lower membrane encodes the quantum potential Q itself. The porcelain bead marks the quantum centroid. The amber bead follows a classical centroid under the same external V. When those paths separate, the lower membrane tells you why. The difference is not magic but the extra term classical mechanics does not have. The math breakdown: Start from Schrödinger evolution in units with ħ = 1: i ∂ψ/∂t = [ −(1/2m) ∇² + V(x,t) ] ψ Write the state in polar form: ψ = r exp(iθ) Then ρ = |ψ|² = r² From the imaginary part, you recover probability conservation: ∂ρ/∂t + ∇·j = 0 with j = (1/m) Im(ψ* ∇ψ) = (ρ/m) ∇θ So the local velocity field is v = j / ρ = ∇θ / m Now take the real part of Schrödinger’s equation. That gives ∂θ/∂t + |∇θ|² / (2m) + V + Q = 0 where Q = −(1/2m) ∇²r / r This is the classical Hamilton-Jacobi equation with one extra term. That extra term is what makes quantum motion locally different from classical motion. Take a gradient of that phase equation and use v = ∇θ / m. Then the flow obeys an Euler-like equation: ∂v/∂t + (v·∇)v = −(1/m) ∇(V + Q) In other words, there are really two forces in the problem. One comes from the external potential V. The other comes from the wave’s own curvature through Q. That is why Ehrenfest is only approximate. The centroid can still satisfy d⟨x⟩/dt = ⟨p⟩/m d⟨p⟩/dt = −⟨∇V⟩ but the internal shape of the packet evolves under the combined influence of V and Q. When the packet stays broad and smooth, Q is gentle and the motion looks more classical. When the packet develops sharp curvature or interference structure, Q becomes strong and the classical picture breaks down. That is what this scene is designed to show live. #QuantumMechanics #Wavefunction #SchrodingerEquation #BornRule #ProbabilityCurrent #ContinuityEquation #Phase #EhrenfestTheorem #QuantumPotential #Madelung #HamiltonJacobi #MathematicalPhysics #Mathematics #Physics

Mathelirium

20,456 Aufrufe • vor 5 Monaten

🚨 nasa you have to see this 50 Grok Bot agents rebuilt one of your own planets overnight for $200, and it looks better than anything in the archive TRAPPIST-1 e, catalogued in 2017, never once photographed: 0.92 Earth radii, 0.692 Earth masses, a year that lasts 6.101 days, 249 kelvin, orbiting 0.02925 AU from a 2566 kelvin red dwarf, 40 light years out, 7 worlds in the system. every figure burned into that panel came out of NASA's exoplanet archive over one public url and carries its own citation. the cities are mine and the frame says so turns out the build is five moves, and none of them are the ones people demo: 1. pull the data before a single frame exists. the archive answers a plain GET at takes sql, returns csv across all 6,000 confirmed planets. hand one bot that url and it stops inventing numbers forever 2. shard by shot, not by task. a bot drives one screen at a time, so speed comes from more bots and never from a smarter one. one bot per shot, all of them on their own cloud machines at once 3. keep every recorded skill under 10 minutes, because that is the hard cap. split it: "log in and start the render" is one skill, "check it and download it" is a second on a 5 minute schedule. a long job is a chain of short skills, not one long recording 4. do not let bots message each other to coordinate. they loop, and the loop quietly eats your quota. give them one shared folder and have each drop a file when its shot lands 5. run the last bot in a clean context holding the source csv and nothing else, and make it check every number on the panel against the row it came from honest ceiling: 10 minutes per recording and one screen per bot, so 50 of them buys width and not speed bookmark this. all 50 prompts go up in 24 hours ↓
0:29

🚨 nasa you have to see this 50 Grok Bot agents rebuilt one of your own planets overnight for $200, and it looks better than anything in the archive TRAPPIST-1 e, catalogued in 2017, never once photographed: 0.92 Earth radii, 0.692 Earth masses, a year that lasts 6.101 days, 249 kelvin, orbiting 0.02925 AU from a 2566 kelvin red dwarf, 40 light years out, 7 worlds in the system. every figure burned into that panel came out of NASA's exoplanet archive over one public url and carries its own citation. the cities are mine and the frame says so turns out the build is five moves, and none of them are the ones people demo: 1. pull the data before a single frame exists. the archive answers a plain GET at takes sql, returns csv across all 6,000 confirmed planets. hand one bot that url and it stops inventing numbers forever 2. shard by shot, not by task. a bot drives one screen at a time, so speed comes from more bots and never from a smarter one. one bot per shot, all of them on their own cloud machines at once 3. keep every recorded skill under 10 minutes, because that is the hard cap. split it: "log in and start the render" is one skill, "check it and download it" is a second on a 5 minute schedule. a long job is a chain of short skills, not one long recording 4. do not let bots message each other to coordinate. they loop, and the loop quietly eats your quota. give them one shared folder and have each drop a file when its shot lands 5. run the last bot in a clean context holding the source csv and nothing else, and make it check every number on the panel against the row it came from honest ceiling: 10 minutes per recording and one screen per bot, so 50 of them buys width and not speed bookmark this. all 50 prompts go up in 24 hours ↓

Argona

37,258 Aufrufe • vor 1 Monat

Google Ironwood TPU Memory Hierarchy in 9 levels by hand ✍️ 1. Bit – The most basic unit of information, the on–off decision from which every number, tensor, and model state is ultimately constructed. 2. FP8 (1×8 → 8 bits) – Eight bits are grouped to form a floating-point value, typically used for inference, where reduced precision is a deliberate trade-off to maximize throughput and efficiency. 3. BF16 (×2 → 16 bits) – Two FP8-scale chunks are combined to gain more dynamic range and stability, while still staying friendly to high-throughput hardware. 4. Tensor tile (×1024 → 1K) – Data moves through the chip in blocks of 1024 values at a time, defining the granularity at which tensors are fetched and manipulated. 5. Matrix Multiplication Unit (MXU) (×64 → 64K) – A systolic array where matrix multiplication is not abstract but physical, with tensor tiles flowing through fixed hardware to achieve the highest possible throughput. 6. Vector Memory (VMEM) (×2048 → 128M) – On-chip working memory that holds activations, partial results, and intermediates, sized specifically to keep the systolic array busy without stalling. 7. Common Memory (CMEM) (×8 → 1 GB) – A small but critical shared memory sitting between VMEM and HBM, used for staging, accumulation, synchronization, and cross-lane coordination. 8. HBM (×96 → 96 GB) – Off-chip high-bandwidth memory where model weights and large states live, implemented as HBM3e with 16 stacks at 6 GB each, for a total of 96 GB. 9. Dual-Die (x2 → 192GB) – Two tightly coupled compute dies operate as a single logical accelerator, each with its own local HBM, effectively doubling memory capacity and bandwidth while allowing tensors and activations to stream seamlessly across dies as if they lived on one chip. I created this drawing for this week's seminar. I’ll take you through these 9 levels in a beginner-friendly way by hand ✍️. RSVP 👉
0:27

Google Ironwood TPU Memory Hierarchy in 9 levels by hand ✍️ 1. Bit – The most basic unit of information, the on–off decision from which every number, tensor, and model state is ultimately constructed. 2. FP8 (1×8 → 8 bits) – Eight bits are grouped to form a floating-point value, typically used for inference, where reduced precision is a deliberate trade-off to maximize throughput and efficiency. 3. BF16 (×2 → 16 bits) – Two FP8-scale chunks are combined to gain more dynamic range and stability, while still staying friendly to high-throughput hardware. 4. Tensor tile (×1024 → 1K) – Data moves through the chip in blocks of 1024 values at a time, defining the granularity at which tensors are fetched and manipulated. 5. Matrix Multiplication Unit (MXU) (×64 → 64K) – A systolic array where matrix multiplication is not abstract but physical, with tensor tiles flowing through fixed hardware to achieve the highest possible throughput. 6. Vector Memory (VMEM) (×2048 → 128M) – On-chip working memory that holds activations, partial results, and intermediates, sized specifically to keep the systolic array busy without stalling. 7. Common Memory (CMEM) (×8 → 1 GB) – A small but critical shared memory sitting between VMEM and HBM, used for staging, accumulation, synchronization, and cross-lane coordination. 8. HBM (×96 → 96 GB) – Off-chip high-bandwidth memory where model weights and large states live, implemented as HBM3e with 16 stacks at 6 GB each, for a total of 96 GB. 9. Dual-Die (x2 → 192GB) – Two tightly coupled compute dies operate as a single logical accelerator, each with its own local HBM, effectively doubling memory capacity and bandwidth while allowing tensors and activations to stream seamlessly across dies as if they lived on one chip. I created this drawing for this week's seminar. I’ll take you through these 9 levels in a beginner-friendly way by hand ✍️. RSVP 👉

Tom Yeh

30,489 Aufrufe • vor 8 Monaten

🚨PHYSICS NEWS🚨: Physicists Tried to Chop a Photon in Half — and Got an Infinite Swarm Instead 🧨 According to a theoretical study published in *Physical Review Letters* on July 15, 2026 by researchers at the University of Oslo, if you try to “cut” a photon by removing a mirror at the exact moment it is reflecting, the result is not two smaller photons. Instead, you create a complex quantum state that is a superposition containing any number of photons from zero all the way to infinity. The state looks ordinary (single photon on one side, vacuum on the other) when measured locally, but globally it is a bizarre mixture that defies classical intuition. This development offers the broader scientific community a striking demonstration of how quantum fields behave when boundaries change suddenly. It raises deep questions about what “a photon” really is and how quantum states respond to abrupt modifications in their environment. **Uniphics provides a clear, first-principles explanation without invoking mysterious quantum weirdness.** In Uniphics, what we call a photon is simply a propagating spin wave in the ξM-field. These waves are patterns of organized spin quanta whose speed and behavior are governed by local energy density and the resulting time flow (via the Maley transform). When the mirror is removed mid-reflection, the boundary condition changes suddenly. This alters the local energy-density landscape and forces a rapid phase resolution across the spin wave. The original coherent spin-wave mode does not “split” into two photons — instead, the interference and energy redistribution create a superposition of many correlated spin-wave modes. The result is a state that contains contributions from zero photons, one photon, two photons, and in principle infinitely many, all while preserving overall coherence through negentropy-driven organization. The apparent “weirdness” disappears once we recognize that the ξM-field supports collective spin-wave excitations whose number is not fixed in the classical sense. The same energy-density and spin-correlation rules that produce ordinary light propagation, refraction in glass, gravitational time delay, and magnon behavior also produce this multi-photon superposition when a boundary is changed. No new postulates are required; it is the natural outcome of how spin waves respond to sudden shifts in their supporting field. Uniphics therefore predicts that similar multi-mode states should appear whenever spin waves encounter rapid changes in energy-density boundaries, whether in optics, magnonics, or other wave systems. This offers a practical guide for designing experiments that control or exploit such states. Could techniques like this “photon truncation” ultimately be used to test and harness multi-mode spin-wave superpositions predicted by Uniphics — and could they accelerate the development of new quantum technologies based on controlled spin-wave interference rather than abstract wavefunction collapse? **A Theory of Everything should be able to answer everything.** Uniphics Explained Simply PDF: Chapters 1–10 free: Grokipedia: #Uniphics #TheoryOfEverything #Photon #QuantumStates #SpinWaves Grok xAI
0:30

🚨PHYSICS NEWS🚨: Physicists Tried to Chop a Photon in Half — and Got an Infinite Swarm Instead 🧨 According to a theoretical study published in *Physical Review Letters* on July 15, 2026 by researchers at the University of Oslo, if you try to “cut” a photon by removing a mirror at the exact moment it is reflecting, the result is not two smaller photons. Instead, you create a complex quantum state that is a superposition containing any number of photons from zero all the way to infinity. The state looks ordinary (single photon on one side, vacuum on the other) when measured locally, but globally it is a bizarre mixture that defies classical intuition. This development offers the broader scientific community a striking demonstration of how quantum fields behave when boundaries change suddenly. It raises deep questions about what “a photon” really is and how quantum states respond to abrupt modifications in their environment. **Uniphics provides a clear, first-principles explanation without invoking mysterious quantum weirdness.** In Uniphics, what we call a photon is simply a propagating spin wave in the ξM-field. These waves are patterns of organized spin quanta whose speed and behavior are governed by local energy density and the resulting time flow (via the Maley transform). When the mirror is removed mid-reflection, the boundary condition changes suddenly. This alters the local energy-density landscape and forces a rapid phase resolution across the spin wave. The original coherent spin-wave mode does not “split” into two photons — instead, the interference and energy redistribution create a superposition of many correlated spin-wave modes. The result is a state that contains contributions from zero photons, one photon, two photons, and in principle infinitely many, all while preserving overall coherence through negentropy-driven organization. The apparent “weirdness” disappears once we recognize that the ξM-field supports collective spin-wave excitations whose number is not fixed in the classical sense. The same energy-density and spin-correlation rules that produce ordinary light propagation, refraction in glass, gravitational time delay, and magnon behavior also produce this multi-photon superposition when a boundary is changed. No new postulates are required; it is the natural outcome of how spin waves respond to sudden shifts in their supporting field. Uniphics therefore predicts that similar multi-mode states should appear whenever spin waves encounter rapid changes in energy-density boundaries, whether in optics, magnonics, or other wave systems. This offers a practical guide for designing experiments that control or exploit such states. Could techniques like this “photon truncation” ultimately be used to test and harness multi-mode spin-wave superpositions predicted by Uniphics — and could they accelerate the development of new quantum technologies based on controlled spin-wave interference rather than abstract wavefunction collapse? **A Theory of Everything should be able to answer everything.** Uniphics Explained Simply PDF: Chapters 1–10 free: Grokipedia: #Uniphics #TheoryOfEverything #Photon #QuantumStates #SpinWaves Grok xAI

Paul Maley

153,765 Aufrufe • vor 2 Monaten

It is done! Grok has literally built something that no one else has but anyone studying space weather would want! I will be releasing it today to the world on my website for free , and as open source for anyone to tweak, add or change things as they see fit! First we managed to resurrect the old Japanese NICT magnetosphere monitoring page, getting all the original data... then, added in the SOLAR-1 Data, and the data from the BOM Australia on the Magnetopause (shell around earth that bounces around). The magnetic field lines of the magnetoSPHERE coming from the ACE and Solar-1 data, and the bouncing shell of the magnetopause from the BOM, then we added in the Schumann resonance, to give each magnetic field line from the ACE and Solar-1 data an oscillating glow that lights up according to the current Hz of the Schumann. There is an option to turn on charged particles, which will then reflect off of , or be pulled in towards (or stay neutral) to the magnetic field of both the ACE + Solar-1 field LINES and the outer shell magnetopause from the BOM!!!! This is huge! When Bz goes green one way the particles turn RED and are reflected off of Earths magnetic field like a force field.. and when Bz goes Red, the particles turn GREEN and are accepted in to Earths fields! When neutral positive you see light blue and when neutral negative the particles are grey. Simply put, you can now look at this and know just by the particle color and their direction whether Earth was accepting or denying particle absorption!!!! Nowhere in the world has anyone put anything like this together into one model. Now we have it! All of these are toggleable on and off, so each one can be viewed on or off at any time. Also added multiple types of lighting and graphics controls , so that the user can control the amount of lighting, glow, thickness of the field lines when displayed, and the amount of the flashes per second using the Schumann hz filter, as well as the amount of brightness of each flash. Can go anywhere from 7hz up to 40+hz just like the actual Schumann resolance. You can also turn on "manual control" so that you can simulate "what would happen if" solar wind went up high or if the hz rate of the Schumann were to spike what it would look like magnetically on the spherical display of the magnetic field. Finally a record option was built in to allow for recording of movies in the program, which I have uploaded here now. Not the highest quality yet, but I figure people will use screen capture programs instead of this if they're making movies (this is fine for public use as is now imo). Also added in color controls for the background (default set to black) and for the magnetic field lines which default to Red and Blue just like the old NICT Japanese site which got shut down years ago.
2:41

It is done! Grok has literally built something that no one else has but anyone studying space weather would want! I will be releasing it today to the world on my website for free , and as open source for anyone to tweak, add or change things as they see fit! First we managed to resurrect the old Japanese NICT magnetosphere monitoring page, getting all the original data... then, added in the SOLAR-1 Data, and the data from the BOM Australia on the Magnetopause (shell around earth that bounces around). The magnetic field lines of the magnetoSPHERE coming from the ACE and Solar-1 data, and the bouncing shell of the magnetopause from the BOM, then we added in the Schumann resonance, to give each magnetic field line from the ACE and Solar-1 data an oscillating glow that lights up according to the current Hz of the Schumann. There is an option to turn on charged particles, which will then reflect off of , or be pulled in towards (or stay neutral) to the magnetic field of both the ACE + Solar-1 field LINES and the outer shell magnetopause from the BOM!!!! This is huge! When Bz goes green one way the particles turn RED and are reflected off of Earths magnetic field like a force field.. and when Bz goes Red, the particles turn GREEN and are accepted in to Earths fields! When neutral positive you see light blue and when neutral negative the particles are grey. Simply put, you can now look at this and know just by the particle color and their direction whether Earth was accepting or denying particle absorption!!!! Nowhere in the world has anyone put anything like this together into one model. Now we have it! All of these are toggleable on and off, so each one can be viewed on or off at any time. Also added multiple types of lighting and graphics controls , so that the user can control the amount of lighting, glow, thickness of the field lines when displayed, and the amount of the flashes per second using the Schumann hz filter, as well as the amount of brightness of each flash. Can go anywhere from 7hz up to 40+hz just like the actual Schumann resolance. You can also turn on "manual control" so that you can simulate "what would happen if" solar wind went up high or if the hz rate of the Schumann were to spike what it would look like magnetically on the spherical display of the magnetic field. Finally a record option was built in to allow for recording of movies in the program, which I have uploaded here now. Not the highest quality yet, but I figure people will use screen capture programs instead of this if they're making movies (this is fine for public use as is now imo). Also added in color controls for the background (default set to black) and for the magnetic field lines which default to Red and Blue just like the old NICT Japanese site which got shut down years ago.

Dutchsinse

14,548 Aufrufe • vor 1 Monat

Desveaux’s Theory of Helix Fields Tweet 1 of 2 For decades, we treated the universe like a giant stage where particles act out their lives. What if the stage itself isn't empty? What if the "void" of space is a structured, rotating medium that is constantly twisting & turning? We have been looking at the pieces of the puzzle, but ignoring the board they sit on. It’s been hiding in plain in sight? My idea on Helix Fields suggests everything we see, from the smallest atom to the largest galaxy, is actually a "corkscrew" in the fabric of reality. In standard physics, we think of particles like tiny billiard balls. In Helix Field Theory, we rethink them as solitons - essentially stable, high-energy "whirlpools" or "corkscrews" made of space itself. Imagine a ribbon. If you pull it straight, it’s flat and simple. But if you twist it, you create a spiral. That twist is what we call Torsion. My theory says that what we perceive as "matter" is simply a place where the vacuum of space is rotating so tightly that it creates a "density core." To explain how this all works, we use a mathematical roadmap called a Lagrangian. Think of it as a master recipe or cookbook for the universe. It combines four key ingredients: 1. Electricity and Magnetism: How light and energy move. 2. Scalar Fields: The underlying "pressure" of the vacuum in space itself 3. Matter: The physical "stuff" we can touch. 4. Torsion: The "twist" or rotational force of space. By looking at the universe through this "helix" lens, we can solve two of the biggest headaches in modern science: The Hubble Tension - Astronomers are currently arguing over how fast the universe is expanding because different measurements give different answers. My theory suggests this "tension" exists because we haven't accounted for the universal rotation (torsion) affecting our measurements. The Dark Sector - We know there is "Dark Matter" and "Dark Energy" out there, but we can't find it. Helix Field Theory suggests these aren't invisible particles, but rather geometric artifacts - ripples and shadows created by the rotating vacuum itself. We are moving away from a universe of "things" and toward a universe of geometry and motion. If the vacuum of space is a structured, rotating medium, then we aren't just living in the universe - we are part of its physical rotation. The "Quantum Substratum" isn't just a theory; it’s a new way to understand the very "twist" of existence. - Protons: tiny rotating black hole-like cores anchoring the scalar field - Neutrons: stabilisers that prevent proton cores from decaying - Quarks & Gluons: the deepest torsion knots; the strong force is intrinsic torsion - Electrons: corkscrew-shaped solitons producing microscopic magnetic vortices - Neutrinos: ultra-light messenger waves traveling along the field’s “communication lines.” - Photons: instruction ripples traveling at light speed - Dark Photons: the “torsion messengers” of the Helix Field. They carry the twist, regulate the twist, and stabilize the twisting motion - W and Z bosons: on/off switches and stabilising nodes in the neutrino network - Higgs field: the local density of the scalar field, giving solitons their effective mass - Dark energy: the gentle outward push caused by the field’s overall rotation - Entropy: friction in that rotation, giving time its arrow - Cosmic Microwave Background: the universal “sustain pedal” like on a guitar or piano, keeping the field’s vibrations in phase. In this vision, dark matter isn’t missing matter - it’s the gravitational echo of the scalar field itself. Exploring antiparticles further: they may regulate the flow of time itself. In a rotational and torsional universe Time is a neutral observer: the past and future are fixed, but the present - our free will - is the point where choices echo into fate. Time is the “God” of the universe: ever-present, ever-aware, never interfering - just watching, as we decide which thread to follow.
1:18

Desveaux’s Theory of Helix Fields Tweet 1 of 2 For decades, we treated the universe like a giant stage where particles act out their lives. What if the stage itself isn't empty? What if the "void" of space is a structured, rotating medium that is constantly twisting & turning? We have been looking at the pieces of the puzzle, but ignoring the board they sit on. It’s been hiding in plain in sight? My idea on Helix Fields suggests everything we see, from the smallest atom to the largest galaxy, is actually a "corkscrew" in the fabric of reality. In standard physics, we think of particles like tiny billiard balls. In Helix Field Theory, we rethink them as solitons - essentially stable, high-energy "whirlpools" or "corkscrews" made of space itself. Imagine a ribbon. If you pull it straight, it’s flat and simple. But if you twist it, you create a spiral. That twist is what we call Torsion. My theory says that what we perceive as "matter" is simply a place where the vacuum of space is rotating so tightly that it creates a "density core." To explain how this all works, we use a mathematical roadmap called a Lagrangian. Think of it as a master recipe or cookbook for the universe. It combines four key ingredients: 1. Electricity and Magnetism: How light and energy move. 2. Scalar Fields: The underlying "pressure" of the vacuum in space itself 3. Matter: The physical "stuff" we can touch. 4. Torsion: The "twist" or rotational force of space. By looking at the universe through this "helix" lens, we can solve two of the biggest headaches in modern science: The Hubble Tension - Astronomers are currently arguing over how fast the universe is expanding because different measurements give different answers. My theory suggests this "tension" exists because we haven't accounted for the universal rotation (torsion) affecting our measurements. The Dark Sector - We know there is "Dark Matter" and "Dark Energy" out there, but we can't find it. Helix Field Theory suggests these aren't invisible particles, but rather geometric artifacts - ripples and shadows created by the rotating vacuum itself. We are moving away from a universe of "things" and toward a universe of geometry and motion. If the vacuum of space is a structured, rotating medium, then we aren't just living in the universe - we are part of its physical rotation. The "Quantum Substratum" isn't just a theory; it’s a new way to understand the very "twist" of existence. - Protons: tiny rotating black hole-like cores anchoring the scalar field - Neutrons: stabilisers that prevent proton cores from decaying - Quarks & Gluons: the deepest torsion knots; the strong force is intrinsic torsion - Electrons: corkscrew-shaped solitons producing microscopic magnetic vortices - Neutrinos: ultra-light messenger waves traveling along the field’s “communication lines.” - Photons: instruction ripples traveling at light speed - Dark Photons: the “torsion messengers” of the Helix Field. They carry the twist, regulate the twist, and stabilize the twisting motion - W and Z bosons: on/off switches and stabilising nodes in the neutrino network - Higgs field: the local density of the scalar field, giving solitons their effective mass - Dark energy: the gentle outward push caused by the field’s overall rotation - Entropy: friction in that rotation, giving time its arrow - Cosmic Microwave Background: the universal “sustain pedal” like on a guitar or piano, keeping the field’s vibrations in phase. In this vision, dark matter isn’t missing matter - it’s the gravitational echo of the scalar field itself. Exploring antiparticles further: they may regulate the flow of time itself. In a rotational and torsional universe Time is a neutral observer: the past and future are fixed, but the present - our free will - is the point where choices echo into fate. Time is the “God” of the universe: ever-present, ever-aware, never interfering - just watching, as we decide which thread to follow.

Clinton Desveaux

23,492 Aufrufe • vor 8 Monaten

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.
1:16

What if #AI became as decentralized as #Bitcoin? We sat down with our new friend 3700 from Bitcoin Virtual Machine to hear what their incredible team of anons are working on - "Truly Open AI." Full interview here:👇 1: What positive impact will Layer 2s have on Bitcoin? Layer 2s on Bitcoin open up opportunities for innovation, allowing developers to build dApps and smart contracts on top of Bitcoin, expanding its utility and use cases. By submitting transactions for final settlement on the Bitcoin network, Bitcoin Layer 2 networks claim to achieve the same (or close to) level of security and decentralization as the Bitcoin blockchain. Building a separate execution layer allows them the freedom to employ several technologies (such as rollups). Layer 2 can significantly improve Bitcoin's scalability by processing transactions off-chain, reducing congestion on the main blockchain. Overall, Layer 2s on Bitcoin have the potential to address some of Bitcoin's key limitations, making it more efficient, accessible, and versatile in the long run. 2: What does the ETF approval mean for Layer 2 on Bitcoin? The approval of ETF could potentially have several implications for Layer 2 on Bitcoin: Innovation and Development: With a growing interest in Bitcoin spurred by ETF approval, there could be a surge in research and development efforts focused on enhancing Layer 2. Developers and projects may be incentivized to create new and improved Layer 2 protocols to meet the evolving needs of the expanding Bitcoin ecosystem. An ETF approval could boost mainstream Bitcoin adoption and liquidity. This influx of users may also drive interest in Layer 2 on Bitcoin as a means to enhance the scalability and functionality of Bitcoin. 3: What are the primary challenges facing L2s on Bitcoin? The interoperability of different Layer 2s and their compatibility with Bitcoin's main blockchain can be a challenge. Ensuring seamless interaction between various Layer 2 networks and the Bitcoin blockchain is essential for a cohesive and efficient ecosystem. Some Layer 2s may introduce centralization risks if they rely heavily on centralized entities or trusted intermediaries. Maintaining decentralization and censorship resistance, which are core tenets of Bitcoin, while scaling with Layer 2s is a challenge. 4: What aspects of Layer 2 solutions for Bitcoin are you most enthusiastic about? AI represents one of the cornerstones of our modern era. However, achieving a decentralized AI infrastructure, owned and managed by users, has posed significant challenges. The primary obstacle has been the limited capacity to store and execute AI models due to size and computational limitations. To address this challenge, we propose a new blockchain architecture enabling developers to deploy their own Bitcoin Layer 2 solutions tailored specifically for AI tasks, called Truly Open AI. These Layer 2 blockchains are optimized to handle computationally intensive tasks, such as matrix multiplication, directly on-chain. These Bitcoin Layer 2 solutions offer exceptional throughput, minimal latency, and cost-effectiveness. AI dApps are programmed as Solidity smart contracts, ensuring they operate precisely as intended, free from interference or manipulation. Our BVM AI Contracts Library simplifies the integration of neural networks into dApps, empowering developers to embed AI seamlessly. In summary, I'm particularly enthusiastic about the potential of Layer 2 solutions for Bitcoin to revolutionize decentralized AI by providing scalability, security, and accessibility. 5: How is your Layer 2 different from others being built? BVM distinguishes itself as a Modular infrastructure that empowers thousands of distinct Bitcoin Layer 2 networks, spanning Gaming, DeFi, Social, and AI applications. We're continuously enriching the BVM Module Store with new modules to enhance its capabilities. With each new module, builders gain access to a wider array of tools to explore different use cases on the Bitcoin network. Recent additions include the Filecoin module for affordable storage and the AI Contracts Library for constructing AI-powered Bitcoin Layer 2 chains. We're also gearing up to release a ZK roll-up module in the coming weeks to offer an alternative to the standard optimistic roll-up. We aim to simplify the process of launching a Bitcoin Layer 2 network customized to specific requirements. Think of it as a SaaS offering with predefined best practices. Whether it's a DeFi Bitcoin Layer 2 or a GameFi Bitcoin Layer 2, we provide default solutions tailored to each use case. We're dedicated to expanding the BVM ecosystem by incentivizing more builders to join the Bitcoin network. Through various programs and grants, we support builders in covering their operational costs for Bitcoin Layer 2. Additionally, we offer rewards akin to 'L2 mining' to those who contribute to expanding the user base and total value locked on the network. In summary, BVM stands out with its modular infrastructure, tailored solutions, and efforts to grow the Bitcoin ecosystem.

Supra

83,548 Aufrufe • vor 2 Jahren

🚨BREAKING: Violent protest erupts in Pucheng, Shaanxi Province following tragic student death A violent mass protest has erupted in Pucheng, Shaanxi Province in the last 24 hours, following the mysterious death of 17-year-old Dang Changxin. This is the first large-scale protest in China of 2025. Videos circulating online show heavily armed police using batons, kicking protesters, and deploying tear gas to disperse the crowds. The unrest, sparked by public outrage over the handling of Dang’s death and alleged attempts at a cover-up by local authorities, has escalated into one of the most visible challenges to public order in recent months. The Public Security Bureau has been deployed to maintain control as tensions continue to rise. 📆 Timeline of Events 1 January 2025: Dang Changxin allegedly fell to his death from the roof of Pucheng Vocational Education school. 2 January 2025 (Early Morning): Dang’s parents were summoned to the school, informed only that something urgent had happened. Dang’s mother arrived but was not permitted to see her son. His classmates appeared flustered and avoided her questions before quickly dispersing. She was taken to a room, confined there, and denied updates on her son's condition. Later that morning: The deputy director of the local police station informed Dang’s mother that her son had committed suicide by jumping from the school roof. The investigation was declared complete and closed within 24 hours. Afternoon: After repeated demands, Dang’s mother was allowed to see her son at the funeral home. Her brief viewing revealed a bruise on his neck, but teachers forcibly stopped her from inspecting the body further or taking photos. This was her only opportunity to see her son’s body. ⚠️ Suspicious Circumstances The school reportedly confiscated and wiped the mobile phones and smartwatches of all students with potential knowledge of the incident. Witnesses noted signs of a struggle in Dang’s dormitory, but the school has not addressed these claims. Online speculation suggests Dang may have been pushed from the roof, allegedly by children of local officials. Dang’s family has rejected the official explanation, recording videos to publicize their demands for justice. Censors have aggressively suppressed these efforts. Public anger has surged over perceived indifference and a cover-up by the local government and police. Crowds gathered at the school, demanding accountability. ❗️Breakout into violence Although the exact trigger for the clash with police remains unclear, videos circulating on X show protesters storming the school gates and struggling against armed police. The violent response from authorities, including baton strikes and tear gas, has further inflamed tensions. The death of Dang Changxin and the violent crackdown on protests in Pucheng reveal systemic failures of transparency, accountability, and governance in China’s local institutions. The situation remains volatile. (video source: 李老师不是你老师 )
0:13

🚨BREAKING: Violent protest erupts in Pucheng, Shaanxi Province following tragic student death A violent mass protest has erupted in Pucheng, Shaanxi Province in the last 24 hours, following the mysterious death of 17-year-old Dang Changxin. This is the first large-scale protest in China of 2025. Videos circulating online show heavily armed police using batons, kicking protesters, and deploying tear gas to disperse the crowds. The unrest, sparked by public outrage over the handling of Dang’s death and alleged attempts at a cover-up by local authorities, has escalated into one of the most visible challenges to public order in recent months. The Public Security Bureau has been deployed to maintain control as tensions continue to rise. 📆 Timeline of Events 1 January 2025: Dang Changxin allegedly fell to his death from the roof of Pucheng Vocational Education school. 2 January 2025 (Early Morning): Dang’s parents were summoned to the school, informed only that something urgent had happened. Dang’s mother arrived but was not permitted to see her son. His classmates appeared flustered and avoided her questions before quickly dispersing. She was taken to a room, confined there, and denied updates on her son's condition. Later that morning: The deputy director of the local police station informed Dang’s mother that her son had committed suicide by jumping from the school roof. The investigation was declared complete and closed within 24 hours. Afternoon: After repeated demands, Dang’s mother was allowed to see her son at the funeral home. Her brief viewing revealed a bruise on his neck, but teachers forcibly stopped her from inspecting the body further or taking photos. This was her only opportunity to see her son’s body. ⚠️ Suspicious Circumstances The school reportedly confiscated and wiped the mobile phones and smartwatches of all students with potential knowledge of the incident. Witnesses noted signs of a struggle in Dang’s dormitory, but the school has not addressed these claims. Online speculation suggests Dang may have been pushed from the roof, allegedly by children of local officials. Dang’s family has rejected the official explanation, recording videos to publicize their demands for justice. Censors have aggressively suppressed these efforts. Public anger has surged over perceived indifference and a cover-up by the local government and police. Crowds gathered at the school, demanding accountability. ❗️Breakout into violence Although the exact trigger for the clash with police remains unclear, videos circulating on X show protesters storming the school gates and struggling against armed police. The violent response from authorities, including baton strikes and tear gas, has further inflamed tensions. The death of Dang Changxin and the violent crackdown on protests in Pucheng reveal systemic failures of transparency, accountability, and governance in China’s local institutions. The situation remains volatile. (video source: 李老师不是你老师 )

中国人权-Human Rights in China

21,362 Aufrufe • vor 1 Jahr

Deconstructed: How a Lancet Study Manufactured the "Vaccine Safer Than COVID" Headline. A recent headline from The Independent, based on a Lancet study, claims the COVID jab presents less harm than the virus itself. But a closer look reveals a masterclass in how to manipulate data to reach a predetermined conclusion. Here’s how the study was engineered: 1. Rigged Timeline for the "Infection" Group: - The study defined its "COVID infection" cohort from Jan 2020 to March 2022. - This crucially includes the pandemic's darkest period: no vaccines, no natural immunity, failed lockdown policies, and the highest mortality. This group was set up to look as bad as possible. 2. Stacked the Deck for the "Vaccinated" Group: - The vaccinated cohort was studied from Aug 2021 to Dec 2022. - By this time, natural immunity was widespread and the virus had evolved to be less severe (IFR decreased by an order of magnitude). This group was already at a massively lower risk before the vaccine was even factored in. 3. Narrowed the Focus to Hide the Truth: - The alarming graphs you see are based on outcomes in the FIRST SIX MONTHS for each group. - This further hones in on the absolute peak of the pandemic for the unvaccinated, versus a much calmer period for the vaccinated. 4. The "Vaccinated" Group is a Misonomer: - The study's definition of "vaccinated" was a record of a SINGLE Pfizer dose. - We know from robust data that myocarditis risk spikes dramatically after the SECOND and THIRD doses. By ignoring this, the study completely sidesteps the primary source of vaccine-associated harm. The result? A chart showing infection as dangerously red and vaccination as safely blue. But when you analyze ALL the data from the same study—without the cherry-picked windows—the graph flips. It shows the vaccinated group with significantly higher rates of myocarditis, inflammatory conditions, pulmonary embolism, and stroke. This isn't science; it's narrative support. It's starting with a headline and working backward, twisting every parameter until the numbers comply. The days of accepting these "cooked-book" studies at face value are over. It's time for the objective, transparent science the public deserves.
8:05

Deconstructed: How a Lancet Study Manufactured the "Vaccine Safer Than COVID" Headline. A recent headline from The Independent, based on a Lancet study, claims the COVID jab presents less harm than the virus itself. But a closer look reveals a masterclass in how to manipulate data to reach a predetermined conclusion. Here’s how the study was engineered: 1. Rigged Timeline for the "Infection" Group: - The study defined its "COVID infection" cohort from Jan 2020 to March 2022. - This crucially includes the pandemic's darkest period: no vaccines, no natural immunity, failed lockdown policies, and the highest mortality. This group was set up to look as bad as possible. 2. Stacked the Deck for the "Vaccinated" Group: - The vaccinated cohort was studied from Aug 2021 to Dec 2022. - By this time, natural immunity was widespread and the virus had evolved to be less severe (IFR decreased by an order of magnitude). This group was already at a massively lower risk before the vaccine was even factored in. 3. Narrowed the Focus to Hide the Truth: - The alarming graphs you see are based on outcomes in the FIRST SIX MONTHS for each group. - This further hones in on the absolute peak of the pandemic for the unvaccinated, versus a much calmer period for the vaccinated. 4. The "Vaccinated" Group is a Misonomer: - The study's definition of "vaccinated" was a record of a SINGLE Pfizer dose. - We know from robust data that myocarditis risk spikes dramatically after the SECOND and THIRD doses. By ignoring this, the study completely sidesteps the primary source of vaccine-associated harm. The result? A chart showing infection as dangerously red and vaccination as safely blue. But when you analyze ALL the data from the same study—without the cherry-picked windows—the graph flips. It shows the vaccinated group with significantly higher rates of myocarditis, inflammatory conditions, pulmonary embolism, and stroke. This isn't science; it's narrative support. It's starting with a headline and working backward, twisting every parameter until the numbers comply. The days of accepting these "cooked-book" studies at face value are over. It's time for the objective, transparent science the public deserves.

Camus

22,083 Aufrufe • vor 10 Monaten

🚨 BREAKING… the top performing 5m & 15m Polymarket Claude setup is now fully open-source Sounds insane? 100%. Unreal? NOT at all. A modest wallet deployed a fully automated system that scaled up to roughly ~$1.8M in profit No affiliation with the Polymarket team Just a developer operating a bot directly connected to Polymarket Profile → Copytrade → Everything is fully automated His FULL strategy: 1. 5 & 15-minute BTC & ETH latency arbitrage The bot trades ultra-short Bitcoin and Ethereum markets with 5 & 15-minute expirations - and similar logic applies to fast 5m markets often associated with claude-style execution. When BTC moves on Binance, Polymarket pricing reacts slower. For around 30 seconds, odds reflect stale data. The system enters during that gap, when YES + NO combined is below $1, waits for repricing, and exits the moment the market corrects. No predictions, no bias - just harvesting mispriced odds 2. Automation over reaction When volatility spikes, humans pause. The system doesn’t. It triggers instantly when the window opens. No emotion, no hesitation, no missed fills. By the time manual traders click, the inefficiency has already disappeared 3. Scale through repetition Each trade earns small spreads, not headline wins. But automation allows continuous execution at scale, every 15 minutes - and on faster 5m rotations running 24/7 without burnout Scale is the edge 23,457 trades placed - irrelevant on their own. Together, they compounded into ~$1.8M in profit, with a largest single gain of $41,2K and an equity curve that trends almost vertically
0:38

🚨 BREAKING… the top performing 5m & 15m Polymarket Claude setup is now fully open-source Sounds insane? 100%. Unreal? NOT at all. A modest wallet deployed a fully automated system that scaled up to roughly ~$1.8M in profit No affiliation with the Polymarket team Just a developer operating a bot directly connected to Polymarket Profile → Copytrade → Everything is fully automated His FULL strategy: 1. 5 & 15-minute BTC & ETH latency arbitrage The bot trades ultra-short Bitcoin and Ethereum markets with 5 & 15-minute expirations - and similar logic applies to fast 5m markets often associated with claude-style execution. When BTC moves on Binance, Polymarket pricing reacts slower. For around 30 seconds, odds reflect stale data. The system enters during that gap, when YES + NO combined is below $1, waits for repricing, and exits the moment the market corrects. No predictions, no bias - just harvesting mispriced odds 2. Automation over reaction When volatility spikes, humans pause. The system doesn’t. It triggers instantly when the window opens. No emotion, no hesitation, no missed fills. By the time manual traders click, the inefficiency has already disappeared 3. Scale through repetition Each trade earns small spreads, not headline wins. But automation allows continuous execution at scale, every 15 minutes - and on faster 5m rotations running 24/7 without burnout Scale is the edge 23,457 trades placed - irrelevant on their own. Together, they compounded into ~$1.8M in profit, with a largest single gain of $41,2K and an equity curve that trends almost vertically

Shelpid.WI3M

31,101 Aufrufe • vor 6 Monaten

🚀 Introducing PantheonOS ( A Fully Open-Source Agent OS for Science PantheonOS began as a research project in my Stanford lab and has since evolved into a vision to redefine data science in the era of AI—starting with computational biology, especially single-cell and spatial genomics. PantheonOS is a general agent platform built from the ground up. It is arguably the first distributed agent framework designed for scientific data analysis. 🔑 Key Features 1. Multi-Agent Collaboration – Built-in paradigms for distributed, cross-machine cooperation among agents and toolsets. 2. Native Toolset Support – Python, R, Julia, LaTeX, and more—designed for real scientific workflows. 3. Modular & Extensible – Developer-friendly design with shallow wrappers, plus LLM-driven toolset generation. 4. Evolvable Agents – Capable of evolving large-scale code projects to achieve superhuman performance (e.g., evolving upon the original Harmony [I Korsunsky, 2019, Nature Biotechnology] and Scanorama [BL Hie, 2019, Nature Biotechnology] implementations), and even evolving the system itself to adapt to new fields. 🎉 Stepwise Release Strategy We’re releasing PantheonOS in stages: Pantheon-CLI (today!), followed by Pantheon-Lab, Pantheon-Notebook, Pantheon-Slack, and more. 🌟 Pantheon-CLI Highlights - We're not just building another CLI tool. We're defining how scientists will interact with data in the AI era. - Open, Powerful, Python-First – The first fully open-source, endlessly extendable scientific “vibe analysis” framework. - Mixed Programming Magic – Combine Python, natural language, R, or Julia—seamlessly in the same environment. - PhD-Level Assistant – A command-line agent for complex real-world genomics and beyond, handling workflows at the PhD level. - Privacy by Design – Run entirely offline with local LLMs—your data never leaves your computer. ✅ Proven Applications (10 Demonstrations) Computational biology: 1. ATAC-seq: From raw reads to peak matrix 2. RNA-seq: From raw reads to expression matrix 3. Complex single-cell workflows (PhD-level) 4. Hybrid natural language + R for Seurat annotation 5. Learning from web tutorials + invoking single-cell foundation models 6. Cell segmentation on 10x Genomics HD Visium data And beyond: 7. Mixed Python & R programming examples 8. Molecular docking & structural analysis 9. Exploratory factor analysis for behavioral survey data 10. Customer segmentation & finance analytics 🌐 Learn More & Get Started Website: Pantheon-CLI Documentation: GitHub Repo: 💬 Join our community: PantheonOS Slack: PantheonOS Discord:
1:16

🚀 Introducing PantheonOS ( A Fully Open-Source Agent OS for Science PantheonOS began as a research project in my Stanford lab and has since evolved into a vision to redefine data science in the era of AI—starting with computational biology, especially single-cell and spatial genomics. PantheonOS is a general agent platform built from the ground up. It is arguably the first distributed agent framework designed for scientific data analysis. 🔑 Key Features 1. Multi-Agent Collaboration – Built-in paradigms for distributed, cross-machine cooperation among agents and toolsets. 2. Native Toolset Support – Python, R, Julia, LaTeX, and more—designed for real scientific workflows. 3. Modular & Extensible – Developer-friendly design with shallow wrappers, plus LLM-driven toolset generation. 4. Evolvable Agents – Capable of evolving large-scale code projects to achieve superhuman performance (e.g., evolving upon the original Harmony [I Korsunsky, 2019, Nature Biotechnology] and Scanorama [BL Hie, 2019, Nature Biotechnology] implementations), and even evolving the system itself to adapt to new fields. 🎉 Stepwise Release Strategy We’re releasing PantheonOS in stages: Pantheon-CLI (today!), followed by Pantheon-Lab, Pantheon-Notebook, Pantheon-Slack, and more. 🌟 Pantheon-CLI Highlights - We're not just building another CLI tool. We're defining how scientists will interact with data in the AI era. - Open, Powerful, Python-First – The first fully open-source, endlessly extendable scientific “vibe analysis” framework. - Mixed Programming Magic – Combine Python, natural language, R, or Julia—seamlessly in the same environment. - PhD-Level Assistant – A command-line agent for complex real-world genomics and beyond, handling workflows at the PhD level. - Privacy by Design – Run entirely offline with local LLMs—your data never leaves your computer. ✅ Proven Applications (10 Demonstrations) Computational biology: 1. ATAC-seq: From raw reads to peak matrix 2. RNA-seq: From raw reads to expression matrix 3. Complex single-cell workflows (PhD-level) 4. Hybrid natural language + R for Seurat annotation 5. Learning from web tutorials + invoking single-cell foundation models 6. Cell segmentation on 10x Genomics HD Visium data And beyond: 7. Mixed Python & R programming examples 8. Molecular docking & structural analysis 9. Exploratory factor analysis for behavioral survey data 10. Customer segmentation & finance analytics 🌐 Learn More & Get Started Website: Pantheon-CLI Documentation: GitHub Repo: 💬 Join our community: PantheonOS Slack: PantheonOS Discord:

evo-devo

17,446 Aufrufe • vor 1 Jahr

3D scanning and rendering is moving so fast - got my splats up and running and I'm mind blown getting ~100fps for this complex 3D scene ⬇️ 🤯 1. WAY faster than NeRF: For comparison, NeRFs would takes around 10 seconds per frame (!) Instead I'm zipping around with FPV controls without breaking a sweat - though I do crash a few times towards the end of the video lol 2. Old Meets New: Gaussian Splatting is cool in that it fuses classical graphics and deep learning techniques. Like NeRFs, this is still a radiance field - just without the slower (ne)ural rendering part. 3. Explicit Representation: Instead you represent a 3D scene as a collection of ellipsoidal "splats" called gaussians. Each gaussian has a position, size, and color. Rendering in real-time is done by projecting into the image plane and alpha blending. 4. Photorealistic Effects: Gaussian splatting use spherical harmonics to represent the view-dependent effects and lighting - allowing surfaces to change color when viewed from different angles, enabling greater photorealism. It doesn't use a neural network, but the training loop is similar to deep learning. 5. Enables Direct Editing: But it's not just speed - with Gaussian Splatting you also get 3D editing support! So you can select, move, and delete stuff, even relight stuff. This type of editing has been more tedious to do with NeRFs and their implicit black box representations. 📲 More tests cooking! Much more to unpack here including simpler explanations. If you enjoyed this post, you might enjoy my feed: Bilawal Sidhu
0:35

3D scanning and rendering is moving so fast - got my splats up and running and I'm mind blown getting ~100fps for this complex 3D scene ⬇️ 🤯 1. WAY faster than NeRF: For comparison, NeRFs would takes around 10 seconds per frame (!) Instead I'm zipping around with FPV controls without breaking a sweat - though I do crash a few times towards the end of the video lol 2. Old Meets New: Gaussian Splatting is cool in that it fuses classical graphics and deep learning techniques. Like NeRFs, this is still a radiance field - just without the slower (ne)ural rendering part. 3. Explicit Representation: Instead you represent a 3D scene as a collection of ellipsoidal "splats" called gaussians. Each gaussian has a position, size, and color. Rendering in real-time is done by projecting into the image plane and alpha blending. 4. Photorealistic Effects: Gaussian splatting use spherical harmonics to represent the view-dependent effects and lighting - allowing surfaces to change color when viewed from different angles, enabling greater photorealism. It doesn't use a neural network, but the training loop is similar to deep learning. 5. Enables Direct Editing: But it's not just speed - with Gaussian Splatting you also get 3D editing support! So you can select, move, and delete stuff, even relight stuff. This type of editing has been more tedious to do with NeRFs and their implicit black box representations. 📲 More tests cooking! Much more to unpack here including simpler explanations. If you enjoyed this post, you might enjoy my feed: Bilawal Sidhu

Bilawal Sidhu

337,090 Aufrufe • vor 3 Jahren