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I built MatmulFlow ( — an interactive tool that makes matrix multiplication dimensions visual, part of my AI by Hand ✍️ series. Matrix multiplication dimensions are confusing. Which is the inner dimension? Columns of the first or rows of the second? And when you chain five multiplications together, it...

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Tom Yeh

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26,269 просмотров • 7 месяцев назад •via X (Twitter)

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Going beyond the 3rd dimension in Blender. Here the hypercubes for 4D-5D-6D, with some rotation and projection down to 3D. The shameful bit is having matrix multiplication implemented in GN via nested "Repeat Zone". #b3d #geometrynodes #hypercube
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Going beyond the 3rd dimension in Blender. Here the hypercubes for 4D-5D-6D, with some rotation and projection down to 3D. The shameful bit is having matrix multiplication implemented in GN via nested "Repeat Zone". #b3d #geometrynodes #hypercube

Alex Martinelli

11,781 просмотров • 2 лет назад

i'm trying to master matrix multiplication. help me out here. column vector notation was just a mistake right? why do i have to flip it over and put it on top when multiplying? why not just write it horizontally and on top by default?
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i'm trying to master matrix multiplication. help me out here. column vector notation was just a mistake right? why do i have to flip it over and put it on top when multiplying? why not just write it horizontally and on top by default?

Louis Arge

185,721 просмотров • 1 год назад

Singular Value Decomposition is a matrix factorization that expresses a matrix M as UΣVᵀ, where U and V are orthogonal matrices, and Σ is diagonal. It can interpreted as the action of a rotation, a scaling and a rotation/reflection.
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Singular Value Decomposition is a matrix factorization that expresses a matrix M as UΣVᵀ, where U and V are orthogonal matrices, and Σ is diagonal. It can interpreted as the action of a rotation, a scaling and a rotation/reflection.

Samuel Vaiter

31,385 просмотров • 1 год назад

Transformer by hand ✍️ ~ 6 steps walkthrough below Open the hood of a transformer and the parts list is overwhelming: embeddings, positional encoding, attention weighting, self-attention, cross-attention, multi-head attention, layer norm, skip connections, softmax, linear, Nx, shifted right, query, key, value, masking. Which of those actually make the car run? Two of them. Attention weighting and the feed-forward network. Everything else is an enhancement to make it run faster and longer, which is how we got from a car to a truck, and to the word "large" in large language model. So I drew and calculated those two parts entirely by hand. Goal: push five features through one transformer block, filling in every cell yourself. 1. Given Five positions of input features, arriving from the previous block. 2. Attention matrix Let us feed all five features to a query-key module (QK) and read back an attention weight matrix, A. The details of that module are a post of their own. 3. Attention weighting We multiply the input features by A to get the attention weighted features, Z. Still five positions. The effect is to combine features *across positions*, horizontally: X1 becomes X1 + X2, X2 becomes X2 + X3, and so on. 4. First layer Let us feed all five weighted features into the first layer of the FFN. Multiply by the weights and biases. This time the combining happens *across feature dimensions*, vertically, and each feature grows from 3 numbers to 4. Note that every position goes through the same weight matrix. That is what "position-wise" means. 5. ReLU We cross out the negatives. They become zeros. 6. Second layer Let us bring it back down: 4 dimensions to 3. The output feeds the next block, which has a completely separate set of parameters, and the whole thing runs again. You have just calculated a transformer block by hand. ✍️ The takeaway: the two parts are doing two different jobs, and neither one alone is enough. Attention mixes *across positions*, so a feature can see its neighbours. The FFN mixes *across feature dimensions*, so each position can think about itself. Horizontal, then vertical. Then that pattern repeats N times, each block with its own separate set of weights. That is the Nx from the list up top, and that is what makes the transformer run. 💾 Save this post! #AIbyHand #Transformers #DeepLearning
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Transformer by hand ✍️ ~ 6 steps walkthrough below Open the hood of a transformer and the parts list is overwhelming: embeddings, positional encoding, attention weighting, self-attention, cross-attention, multi-head attention, layer norm, skip connections, softmax, linear, Nx, shifted right, query, key, value, masking. Which of those actually make the car run? Two of them. Attention weighting and the feed-forward network. Everything else is an enhancement to make it run faster and longer, which is how we got from a car to a truck, and to the word "large" in large language model. So I drew and calculated those two parts entirely by hand. Goal: push five features through one transformer block, filling in every cell yourself. 1. Given Five positions of input features, arriving from the previous block. 2. Attention matrix Let us feed all five features to a query-key module (QK) and read back an attention weight matrix, A. The details of that module are a post of their own. 3. Attention weighting We multiply the input features by A to get the attention weighted features, Z. Still five positions. The effect is to combine features *across positions*, horizontally: X1 becomes X1 + X2, X2 becomes X2 + X3, and so on. 4. First layer Let us feed all five weighted features into the first layer of the FFN. Multiply by the weights and biases. This time the combining happens *across feature dimensions*, vertically, and each feature grows from 3 numbers to 4. Note that every position goes through the same weight matrix. That is what "position-wise" means. 5. ReLU We cross out the negatives. They become zeros. 6. Second layer Let us bring it back down: 4 dimensions to 3. The output feeds the next block, which has a completely separate set of parameters, and the whole thing runs again. You have just calculated a transformer block by hand. ✍️ The takeaway: the two parts are doing two different jobs, and neither one alone is enough. Attention mixes *across positions*, so a feature can see its neighbours. The FFN mixes *across feature dimensions*, so each position can think about itself. Horizontal, then vertical. Then that pattern repeats N times, each block with its own separate set of weights. That is the Nx from the list up top, and that is what makes the transformer run. 💾 Save this post! #AIbyHand #Transformers #DeepLearning

Tom Yeh

26,279 просмотров • 3 месяцев назад

[Discrete Fourier Transform] by Hand ✍️ In signal processing, the Discrete Fourier Transform (DFT) is no doubt the most important method. But the math involved is extremely complex, literally, involving a summation over a complex number term e^(-iwt). I developed this exercise to demonstrate that underneath such complexity, DFT is just a series of matrix multiplications you can calculate by hand. ✍️ Once you see that, it should not surprise you that a deep neural network, which is also a series of matrix multiplications, with activation functions in-between, can learn to perform DFT to process and analyze signals so effectively. How does DFT work? [1] Given ↳ Signals A, B, and C in the 🟧 frequency domain: ◦ A = cos(w) + 2cos(2w) ◦ B = cos(w) + cos(3w) + cos(4w) ◦ C = -cos(2w) + cos(3w) ◦ Each signal is a weighed sum of four cosine waves at frequencies 1w, 2w, 3w, and 4w. ◦ We will apply Inverse DFT to convert the signals to time domain representations, and then demonstrate DFT can convert back to their original frequency domain representations. ↳ Signal X in the 🟩 time domain. X is sampled at 10 time points 1t, 2t, …, 10t: ◦ X = [-2.5, -1.8, 3, -0.7, -1.0, -0.7, 3, -1.8, -2.5, 5] ◦ Suppose X is also a weighted sum of the same four cosine waves, but we don’t already know their weights. We will apply DFT to discover them. [2] 🟧 Frequency Matrix (F) ↳ Write the coefficients of A, B, C as a matrix F. Each signal is a row. Each frequency is a column. ↳ A → [1, 2, 0, 0] ↳ B → [1, 0, 1, 1] ↳ C → [0, 1-, 1, 0] [3] Cosine → Discrete ↳ Sample from the continuous cosine waves at discrete time points 1t, 2t, 3t, to 10t. [4] Cosine Matrix (W) ↳ Write the samples as a matrix, Each frequency is a row. Each time point is a column. [5] Inverse DFT: 🟧 Frequency → 🟩 Time ↳ Multiply the frequency matrix F and the cosine matrix W. ↳ The meaning of this multiplication is to linearly combine the four cosine waves (rows in W) into time-domain signals (rows in T) using the weights specified in F. ↳ The result is matrix T, which are signals A, B, C converted to the time domain. Each signal is a row. Each time point is a column. [6] Transpose ↳ Transpose T, converting each signal’s time domain representation from a row to a column. [7] DFT: 🟩 Time → 🟧 Frequency ↳ Multiply the cosine matrix W with the transpose of matrix T. ↳ The purpose of this multiplication is to take a dot-product between each time-domain signal (columns in the transpose of T) and each cosine wave (rows in W), which has the effect of projecting the signal onto a cosine wave to determine how much they are correlated. Zero means not correlated at all. ↳ The result is an intermediate version of the “recovered” frequency matrix where each column corresponds to a signal and each row corresponds to a frequency. ↳ Compared to the original frequency matrix F, this intermediate matrix has non-zero weights in the correct places, but scaled up by a factor of 5 (n/2, n=10). For example, signal A, originally [1,2,0,0], is recovered at [5,10,0,0]. [8] Scale ↳ Multiply each value by 2/n = 1/5 to scale down the intermediate matrix to match the magnitude of the original frequency matrix F. [9] Transpose ↳ Transpose the recovered frequency matrix back to the same orientation of the original frequency matrix F. ↳ Like magic 🪄, the result is identical to the original F, which means DFT successfully recovered the frequency components of signals A, B, C. [10] Apply DFT to X: 🟩 Time → 🟧 Frequency ↳ Now that we have some confidence in DFT’s ability to recover frequency components, we apply DFT to X’s time-domain representation by multiplying W with X. ↳ The result is the an intermediate matrix. [11] Scale ↳ Similarly, we scale down by a factor of 5 to obtain the recovered frequency components of X (a column). [12] Transpose ↳ Similarly, we transpose the recovered column to row to match the orientation of the frequency matrix. ↳ Using the coefficients [0,0,3,2], we can write the equation of X as 3cos(3w) + 2cos(4w). Notes: I hope this by hand exercise helps you understand the essence of DFT. But there is more technical details, such as: • Sine: The complete DFT math also includes sine waves that follow a similar calculation process. • Phase: Here, we assume all the cosine waves are aligned at the origin, namely, phase is 0. If a phase p is added, for example, cos(w+p), we will need to calculate the sine component and use their ratio to figure out what p is. • Magnitude: If phase is not zero, the magnitude will need to be calculated by combining both cosine and sine terms.
0:12

[Discrete Fourier Transform] by Hand ✍️ In signal processing, the Discrete Fourier Transform (DFT) is no doubt the most important method. But the math involved is extremely complex, literally, involving a summation over a complex number term e^(-iwt). I developed this exercise to demonstrate that underneath such complexity, DFT is just a series of matrix multiplications you can calculate by hand. ✍️ Once you see that, it should not surprise you that a deep neural network, which is also a series of matrix multiplications, with activation functions in-between, can learn to perform DFT to process and analyze signals so effectively. How does DFT work? [1] Given ↳ Signals A, B, and C in the 🟧 frequency domain: ◦ A = cos(w) + 2cos(2w) ◦ B = cos(w) + cos(3w) + cos(4w) ◦ C = -cos(2w) + cos(3w) ◦ Each signal is a weighed sum of four cosine waves at frequencies 1w, 2w, 3w, and 4w. ◦ We will apply Inverse DFT to convert the signals to time domain representations, and then demonstrate DFT can convert back to their original frequency domain representations. ↳ Signal X in the 🟩 time domain. X is sampled at 10 time points 1t, 2t, …, 10t: ◦ X = [-2.5, -1.8, 3, -0.7, -1.0, -0.7, 3, -1.8, -2.5, 5] ◦ Suppose X is also a weighted sum of the same four cosine waves, but we don’t already know their weights. We will apply DFT to discover them. [2] 🟧 Frequency Matrix (F) ↳ Write the coefficients of A, B, C as a matrix F. Each signal is a row. Each frequency is a column. ↳ A → [1, 2, 0, 0] ↳ B → [1, 0, 1, 1] ↳ C → [0, 1-, 1, 0] [3] Cosine → Discrete ↳ Sample from the continuous cosine waves at discrete time points 1t, 2t, 3t, to 10t. [4] Cosine Matrix (W) ↳ Write the samples as a matrix, Each frequency is a row. Each time point is a column. [5] Inverse DFT: 🟧 Frequency → 🟩 Time ↳ Multiply the frequency matrix F and the cosine matrix W. ↳ The meaning of this multiplication is to linearly combine the four cosine waves (rows in W) into time-domain signals (rows in T) using the weights specified in F. ↳ The result is matrix T, which are signals A, B, C converted to the time domain. Each signal is a row. Each time point is a column. [6] Transpose ↳ Transpose T, converting each signal’s time domain representation from a row to a column. [7] DFT: 🟩 Time → 🟧 Frequency ↳ Multiply the cosine matrix W with the transpose of matrix T. ↳ The purpose of this multiplication is to take a dot-product between each time-domain signal (columns in the transpose of T) and each cosine wave (rows in W), which has the effect of projecting the signal onto a cosine wave to determine how much they are correlated. Zero means not correlated at all. ↳ The result is an intermediate version of the “recovered” frequency matrix where each column corresponds to a signal and each row corresponds to a frequency. ↳ Compared to the original frequency matrix F, this intermediate matrix has non-zero weights in the correct places, but scaled up by a factor of 5 (n/2, n=10). For example, signal A, originally [1,2,0,0], is recovered at [5,10,0,0]. [8] Scale ↳ Multiply each value by 2/n = 1/5 to scale down the intermediate matrix to match the magnitude of the original frequency matrix F. [9] Transpose ↳ Transpose the recovered frequency matrix back to the same orientation of the original frequency matrix F. ↳ Like magic 🪄, the result is identical to the original F, which means DFT successfully recovered the frequency components of signals A, B, C. [10] Apply DFT to X: 🟩 Time → 🟧 Frequency ↳ Now that we have some confidence in DFT’s ability to recover frequency components, we apply DFT to X’s time-domain representation by multiplying W with X. ↳ The result is the an intermediate matrix. [11] Scale ↳ Similarly, we scale down by a factor of 5 to obtain the recovered frequency components of X (a column). [12] Transpose ↳ Similarly, we transpose the recovered column to row to match the orientation of the frequency matrix. ↳ Using the coefficients [0,0,3,2], we can write the equation of X as 3cos(3w) + 2cos(4w). Notes: I hope this by hand exercise helps you understand the essence of DFT. But there is more technical details, such as: • Sine: The complete DFT math also includes sine waves that follow a similar calculation process. • Phase: Here, we assume all the cosine waves are aligned at the origin, namely, phase is 0. If a phase p is added, for example, cos(w+p), we will need to calculate the sine component and use their ratio to figure out what p is. • Magnitude: If phase is not zero, the magnitude will need to be calculated by combining both cosine and sine terms.

Tom Yeh

116,622 просмотров • 2 лет назад

No one can be told what the Matrix is. You have to see it for yourself.
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No one can be told what the Matrix is. You have to see it for yourself.

Mecha BREAK

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A Madlad right next to me brought his 86 Matrix and opened it up at THAT moment. Love the spirit for the 86 Movie Re-release
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A Madlad right next to me brought his 86 Matrix and opened it up at THAT moment. Love the spirit for the 86 Movie Re-release

The Fire Brothers

59,860 просмотров • 1 месяц назад

unfortunately, no one can be told what the matrix is. you have to see it for yourself
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unfortunately, no one can be told what the matrix is. you have to see it for yourself

tetsuo

23,661 просмотров • 3 месяцев назад

There's no need to speak. You must only - concentrate and recall all your past life. When a man thinks of the past, he becomes kinder. ~ Stalker (1979) Tarkovsky A déjà vu is usually a glitch in the Matrix. It happens when they change something. ~ The Matrix (1999) Wachowski
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There's no need to speak. You must only - concentrate and recall all your past life. When a man thinks of the past, he becomes kinder. ~ Stalker (1979) Tarkovsky A déjà vu is usually a glitch in the Matrix. It happens when they change something. ~ The Matrix (1999) Wachowski

Reconsidering Cinema

68,358 просмотров • 3 лет назад

It was 1999, and a seismic cultural shift was happening. You could feel it in Fight Club & The Matrix. The hope and sincerity the 80s & early 90s were falling out of fashion. A spiritual “meaning crisis” was moving through culture. And it came for men first.
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It was 1999, and a seismic cultural shift was happening. You could feel it in Fight Club & The Matrix. The hope and sincerity the 80s & early 90s were falling out of fashion. A spiritual “meaning crisis” was moving through culture. And it came for men first.

Paul Anleitner

143,871 просмотров • 5 месяцев назад

When you have an angel and a demon sitting on your right and left shoulders, this is what it means... The Tree of Life is within; the emotions and urges come from the higher dimensions of the Sefirot/Chakras/Egyptian parts of the soul. When energy flows from the left side, especially through Gevurah, that raw energy—the energy of the lion—brings anger that cannot be easily controlled. In the Egyptian system, that part of the soul is called Sekhem. From the right side, or from Chokmah and Chesed, a flow of energy brings kindness and love. There are people who receive energy only from one side, without balance. A lot of what you receive depends on the influence of the stars when you are born, and that is by design. Some are here to bring evil, some good—you need to find the balance and purify your heart.
0:10

When you have an angel and a demon sitting on your right and left shoulders, this is what it means... The Tree of Life is within; the emotions and urges come from the higher dimensions of the Sefirot/Chakras/Egyptian parts of the soul. When energy flows from the left side, especially through Gevurah, that raw energy—the energy of the lion—brings anger that cannot be easily controlled. In the Egyptian system, that part of the soul is called Sekhem. From the right side, or from Chokmah and Chesed, a flow of energy brings kindness and love. There are people who receive energy only from one side, without balance. A lot of what you receive depends on the influence of the stars when you are born, and that is by design. Some are here to bring evil, some good—you need to find the balance and purify your heart.

Open Minded Approach

167,832 просмотров • 5 месяцев назад

Self Attention vs Cross Attention by hand ✍️ Resize the matrices yourself 👉 Two attention mechanisms, side by side. Both project X into queries; both compute attention via S = Kᵀ × Q and F = V × A. The only difference is the source of K and V. Self attention uses X for everything. Q, K, and V all come from projecting X. Each X token attends to every other X token. The score matrix S is square — 128 × 128. Cross attention uses X for queries and a second sequence E for keys and values. Each X token attends to every E token instead. The score matrix S is rectangular — 64 × 128. Notice what's shared and what's not: X is the same in both — same 36 × 128 input. Q and K share the 16 dimension — that's what makes the dot product Kᵀ × Q valid in either case. V dimensions are independent: self-attention uses 12, cross-attention uses 12. The choice doesn't depend on which mechanism you're using; it depends on what output dimension your downstream layer expects.
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Self Attention vs Cross Attention by hand ✍️ Resize the matrices yourself 👉 Two attention mechanisms, side by side. Both project X into queries; both compute attention via S = Kᵀ × Q and F = V × A. The only difference is the source of K and V. Self attention uses X for everything. Q, K, and V all come from projecting X. Each X token attends to every other X token. The score matrix S is square — 128 × 128. Cross attention uses X for queries and a second sequence E for keys and values. Each X token attends to every E token instead. The score matrix S is rectangular — 64 × 128. Notice what's shared and what's not: X is the same in both — same 36 × 128 input. Q and K share the 16 dimension — that's what makes the dot product Kᵀ × Q valid in either case. V dimensions are independent: self-attention uses 12, cross-attention uses 12. The choice doesn't depend on which mechanism you're using; it depends on what output dimension your downstream layer expects.

Tom Yeh

62,508 просмотров • 6 месяцев назад

POV: How it felt when I won my first trade and realized that escaping the matrix was possible.
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POV: How it felt when I won my first trade and realized that escaping the matrix was possible.

lynk

19,692 просмотров • 5 месяцев назад

Humans are not at the top of the food chain or the evolutionary scale. Material existence is the lowest dimension. We can’t perceive more than 95% of reality, even with the most recent technology. But as we unlock the hidden potential of DNA, the higher dimensions will begin to appear. The book The Kybalion teaches that there are multiple states of existence, and the ones we call ‘gods’ exist in the spiritual dimension. The evolution of our soul moves toward the higher mental and spiritual dimensions.
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Humans are not at the top of the food chain or the evolutionary scale. Material existence is the lowest dimension. We can’t perceive more than 95% of reality, even with the most recent technology. But as we unlock the hidden potential of DNA, the higher dimensions will begin to appear. The book The Kybalion teaches that there are multiple states of existence, and the ones we call ‘gods’ exist in the spiritual dimension. The evolution of our soul moves toward the higher mental and spiritual dimensions.

Open Minded Approach

42,190 просмотров • 5 месяцев назад

Elon Musk: Make sure you accept reality for what it is. “When I said take the red pill, I didn't realize that was some politically charged phrase. I was referring to The Matrix. Make sure you accept reality for what it is. And it turns out it's also accepting right wing dogma or something. I didn't realize that at the time.” TIME Person of the Year Interview, December 13, 2021
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Elon Musk: Make sure you accept reality for what it is. “When I said take the red pill, I didn't realize that was some politically charged phrase. I was referring to The Matrix. Make sure you accept reality for what it is. And it turns out it's also accepting right wing dogma or something. I didn't realize that at the time.” TIME Person of the Year Interview, December 13, 2021

ELON CLIPS

57,343 просмотров • 1 год назад

We are truly living in the Matrix, which comes from the Latin word mātrix, meaning "mother" or "womb." The material world is also connected to the words matrix and matter, as we all enter the material world through the wombs of our mothers. The illusion is born from the divine feminine energy. In Hinduism and Buddhism, this is known as the Maya illusion, born of Samsara or the Bhavachakra (Wheel of Life). There is no material existence; everything is an illusion created from electromagnetic waves, just as you create an entire material illusion when you dream through the electromagnetic activity of the brain. The Great Mother is known as the Shekinah, and her consort is the divine masculine energy known as Yesod. Together, they are part of the Creator. This knowledge is present in every religion.
0:16

We are truly living in the Matrix, which comes from the Latin word mātrix, meaning "mother" or "womb." The material world is also connected to the words matrix and matter, as we all enter the material world through the wombs of our mothers. The illusion is born from the divine feminine energy. In Hinduism and Buddhism, this is known as the Maya illusion, born of Samsara or the Bhavachakra (Wheel of Life). There is no material existence; everything is an illusion created from electromagnetic waves, just as you create an entire material illusion when you dream through the electromagnetic activity of the brain. The Great Mother is known as the Shekinah, and her consort is the divine masculine energy known as Yesod. Together, they are part of the Creator. This knowledge is present in every religion.

Open Minded Approach

15,917 просмотров • 5 месяцев назад

U.S. company Powerus has unveiled the Matrix Fold, a modular and foldable FPV drone reportedly selected for the Pentagon’s Drone Dominance program. The 12-inch platform is designed to fold into a compact form that can reportedly fit into a backpack and be deployed within seconds. Its 3D-printed construction is intended to allow individual components to be replaced in the field, with printing files reportedly supplied for manufacturing replacement parts. According to Powerus, the Matrix Fold has a maximum speed of 242 km/h, a payload capacity of up to 6.8 kg and an endurance of up to 35 minutes. The system features a modular architecture compatible with the company’s Matrix 5, 7, 10 and F platforms. Tool-free maintenance and rapid payload changes are also reportedly supported. Powerus states that the Matrix Fold is NDAA-compliant and Blue UAS certified, with its supply chain under company control. In May 2026, the Matrix Fold was reportedly selected for the second phase of the Pentagon’s Drone Dominance program, described as having a value of approximately $1 billion. The program is intended to support the procurement of tens of thousands of low-cost, expendable drones and expand U.S. domestic production capacity. Serial production is reportedly planned to follow the program’s next phase. #USA #Powerus #MatrixFold #FPV #FPVDrone #DroneDominance #Pentagon #UAV #DroneWarfare #3DPrinting #BlueUAS #NDAA
0:15

U.S. company Powerus has unveiled the Matrix Fold, a modular and foldable FPV drone reportedly selected for the Pentagon’s Drone Dominance program. The 12-inch platform is designed to fold into a compact form that can reportedly fit into a backpack and be deployed within seconds. Its 3D-printed construction is intended to allow individual components to be replaced in the field, with printing files reportedly supplied for manufacturing replacement parts. According to Powerus, the Matrix Fold has a maximum speed of 242 km/h, a payload capacity of up to 6.8 kg and an endurance of up to 35 minutes. The system features a modular architecture compatible with the company’s Matrix 5, 7, 10 and F platforms. Tool-free maintenance and rapid payload changes are also reportedly supported. Powerus states that the Matrix Fold is NDAA-compliant and Blue UAS certified, with its supply chain under company control. In May 2026, the Matrix Fold was reportedly selected for the second phase of the Pentagon’s Drone Dominance program, described as having a value of approximately $1 billion. The program is intended to support the procurement of tens of thousands of low-cost, expendable drones and expand U.S. domestic production capacity. Serial production is reportedly planned to follow the program’s next phase. #USA #Powerus #MatrixFold #FPV #FPVDrone #DroneDominance #Pentagon #UAV #DroneWarfare #3DPrinting #BlueUAS #NDAA

Drone Wars

52,981 просмотров • 1 месяц назад

The Matrix launch video lands on one line; in Matrix, anyone can become a CEO. That’s the real claim. Not one assistant doing one task. A company that keeps running after the first prompt, routing intent through a CEO Office, into OKRs, down to departments that do the work and send back proof to review. Connect Codex, point your leftover credits at it, and the first agent company is live.
0:17

The Matrix launch video lands on one line; in Matrix, anyone can become a CEO. That’s the real claim. Not one assistant doing one task. A company that keeps running after the first prompt, routing intent through a CEO Office, into OKRs, down to departments that do the work and send back proof to review. Connect Codex, point your leftover credits at it, and the first agent company is live.

Iseunife The First

19,414 просмотров • 4 месяцев назад

The UFO/UAP phenomenon is ancient. It has been here long before us, and it is connected to a grand hierarchy created by God. They are coming from higher dimensions, something that religions would describe as the Tree of Life. The phenomenon is part of us, and our soul is part of this grand system. We are currently in the lowest dimension, chakra, sefirot, or part of the soul, known as Malkhut in Kabbalah, Muladhara in Hinduism, and Khat in ancient Egypt. We are made of matter, but our soul is made of fire, and it is time for an upgrade. The Geophysical Event is connected to this transformation.
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The UFO/UAP phenomenon is ancient. It has been here long before us, and it is connected to a grand hierarchy created by God. They are coming from higher dimensions, something that religions would describe as the Tree of Life. The phenomenon is part of us, and our soul is part of this grand system. We are currently in the lowest dimension, chakra, sefirot, or part of the soul, known as Malkhut in Kabbalah, Muladhara in Hinduism, and Khat in ancient Egypt. We are made of matter, but our soul is made of fire, and it is time for an upgrade. The Geophysical Event is connected to this transformation.

Open Minded Approach

136,661 просмотров • 5 месяцев назад