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🚨 Time Isn’t Moving Forward… You’re Being Pulled Through It Everyone thinks time flows like a river. Wrong. Time is a standing wave, and you are the oscillation moving through phase states. In Frequency Wave Theory, time is not something that “passes”—it’s something your consciousness locks onto through coherence....

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Drew Ponder

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29,412 views • 4 months ago •via X (Twitter)

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Einstein’s wildest theory wasn't that time flows. It’s that time is a physical fabric, and you can warp it. According to his equations, the future is not a distant destination, it is a speed limit. If you move fast enough through space, your clock slows down while the rest of the world rushes ahead. You don't need a time machine to reach the future. You just need enough speed to leap into it.
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Einstein’s wildest theory wasn't that time flows. It’s that time is a physical fabric, and you can warp it. According to his equations, the future is not a distant destination, it is a speed limit. If you move fast enough through space, your clock slows down while the rest of the world rushes ahead. You don't need a time machine to reach the future. You just need enough speed to leap into it.

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THE HEART IS THE VORTEX IN THE TORUS The Heart is not a mechanical pump. It’s a plasmic convergence field. A Toroidal generator. A Living lens through which Consciousness circulates. Blood doesn’t just flow because of the heart , it flows through the Heart’s Electrical and Magnetic rhythm. The pulse is not pressure. It’s Coherence. This is why the heart emits the strongest Electromagnetic field in the body, stronger than the brain. It doesn’t push. It informs. It sings your waveform into motion. You are not alive because your heart beats. Your heart beats because you are alive. And that beat, is a code. A rhythm the field remembers. A signature only you emit. ✨🙌🏾💫 © Elourai

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If your gym does not have a machine just like this…. It is time for you to FIND A NEW GYM Simply the best Abs exercise there is and it’s NOT EVEN A DEBATE
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the difference between sangwon and leo in teaching sanghyeon the correct move… 😭 🥀 : it’s because that you’re moving your upper body.. so every time that you move, you do this. 🐶 : keeping it still and just move my chest? 🦁 : imagine that you’re holding something heavy, a little.. can’t drop the dishes! you’re shaking the dishes! 🐶 : okay! 🦁 : you spilled all the food
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the difference between sangwon and leo in teaching sanghyeon the correct move… 😭 🥀 : it’s because that you’re moving your upper body.. so every time that you move, you do this. 🐶 : keeping it still and just move my chest? 🦁 : imagine that you’re holding something heavy, a little.. can’t drop the dishes! you’re shaking the dishes! 🐶 : okay! 🦁 : you spilled all the food

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When the workshop is busy it can feel like you’re here there and everywhere all at once… that’s why it’s a good idea to keep your gear organised so you know where you need to be all the time.
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When the workshop is busy it can feel like you’re here there and everywhere all at once… that’s why it’s a good idea to keep your gear organised so you know where you need to be all the time.

Theogcfu

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[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.
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[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.

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116,622 views • 2 years ago

THIS IS 432HZ GOLDEN RATIO. It raises your frequency. Stay as long as you need. Most of what's labeled "frequency healing" online is actually just music layered with tones. The frequency people think they're hearing is often buried, detuned, or not truly present - so the nervous system never receives a clear signal. What you're hearing here is different. These sounds are intentionally built using the Golden Ratio (1.618...) - the same mathematical pattern found in ocean waves, seashells, galaxies, plant growth, and the structure of the human body itself. It's nature's blueprint for balance and coherence. When sound follows this ratio, the body doesn't have to try to relax or "make it work." Your nervous system recognizes the pattern and naturally begins to regulate. That's why people often experience: mental quiet emotional release deeper presence nervous system settling a feeling of coming back into alignment You don't need to focus. You don't need to believe harder. You don't need to force anything. You just listen - and let your system respond. ~ Banah Winn ✨🙌🏽💫
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THIS IS 432HZ GOLDEN RATIO. It raises your frequency. Stay as long as you need. Most of what's labeled "frequency healing" online is actually just music layered with tones. The frequency people think they're hearing is often buried, detuned, or not truly present - so the nervous system never receives a clear signal. What you're hearing here is different. These sounds are intentionally built using the Golden Ratio (1.618...) - the same mathematical pattern found in ocean waves, seashells, galaxies, plant growth, and the structure of the human body itself. It's nature's blueprint for balance and coherence. When sound follows this ratio, the body doesn't have to try to relax or "make it work." Your nervous system recognizes the pattern and naturally begins to regulate. That's why people often experience: mental quiet emotional release deeper presence nervous system settling a feeling of coming back into alignment You don't need to focus. You don't need to believe harder. You don't need to force anything. You just listen - and let your system respond. ~ Banah Winn ✨🙌🏽💫

🧬Maxpein🧬

72,510 views • 6 months ago

haechan, thank you for always being there and comforting us in your warm way. i know this isn’t easy, and it’s not the first time we’ve been through something like this. i also hope you can sleep well and always be happy <3 we love you 🩷 #해찬이와시즈니
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haechan, thank you for always being there and comforting us in your warm way. i know this isn’t easy, and it’s not the first time we’ve been through something like this. i also hope you can sleep well and always be happy <3 we love you 🩷 #해찬이와시즈니

ؘ ndaa #TASTE

24,847 views • 5 months ago

A lot of hitters confuse being late with letting the ball get deep. They’re not the same thing. In this video I show it with a simple example. I toss the ball and reach way out in front of me to catch it. That’s what late looks like. The ball already beat you… now you’re chasing it with your hands. Then I toss another one and catch it slightly behind me. That’s being on time and letting the ball get deep. When you’re late, your body and barrel are reacting after the ball passes your window. You’re forced to reach, cut across it, or roll it over. When you’re on time, you can let the ball travel and still control it. Now the barrel can stay through the zone longer, and you can drive it with authority. Deep ≠ late. Good hitters aren’t reaching for the ball… they’re on time enough to let it come to them. ⚾️ Train with an expert. Not an influencer.
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A lot of hitters confuse being late with letting the ball get deep. They’re not the same thing. In this video I show it with a simple example. I toss the ball and reach way out in front of me to catch it. That’s what late looks like. The ball already beat you… now you’re chasing it with your hands. Then I toss another one and catch it slightly behind me. That’s being on time and letting the ball get deep. When you’re late, your body and barrel are reacting after the ball passes your window. You’re forced to reach, cut across it, or roll it over. When you’re on time, you can let the ball travel and still control it. Now the barrel can stay through the zone longer, and you can drive it with authority. Deep ≠ late. Good hitters aren’t reaching for the ball… they’re on time enough to let it come to them. ⚾️ Train with an expert. Not an influencer.

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25,605 views • 6 months ago

BREAKING: BRIAN ARMSTRONG JUST CONFIRMED THE REVOLUTION ON CNBC. “A new financial system is being built and it’s not built by the banks. It’s built on crypto.” His words. Not mine. What does it mean? - Instant global settlements - Stronger, permissionless credit - 24/7 access to capital anywhere on earth - Open markets that no one can shut down This isn’t about coins pumping. This is about power shifting back to the people. Block by block, crypto is dismantling the old plumbing of finance and replacing it with something unbreakable. Armstrong isn’t predicting the future. He’s building it. You’re not speculating on a trend. You’re front-running a revolution. The old system is dying. The new one is already running. Choose your side.
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BREAKING: BRIAN ARMSTRONG JUST CONFIRMED THE REVOLUTION ON CNBC. “A new financial system is being built and it’s not built by the banks. It’s built on crypto.” His words. Not mine. What does it mean? - Instant global settlements - Stronger, permissionless credit - 24/7 access to capital anywhere on earth - Open markets that no one can shut down This isn’t about coins pumping. This is about power shifting back to the people. Block by block, crypto is dismantling the old plumbing of finance and replacing it with something unbreakable. Armstrong isn’t predicting the future. He’s building it. You’re not speculating on a trend. You’re front-running a revolution. The old system is dying. The new one is already running. Choose your side.

Merlijn The Trader

136,388 views • 8 months ago

This Final Jeopardy is one of those that separates the people who really know from the ones just taking a shot. Take your time, think it through, and lock it in when you’re ready.
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This Final Jeopardy is one of those that separates the people who really know from the ones just taking a shot. Take your time, think it through, and lock it in when you’re ready.

Restricted Daily

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🌀 This sound will reset your brain. Just listen for 12 seconds… This is 432 Hz Golden Ratio Frequency. 432 Hz is often described as a calming and balanced tuning — but what makes this truly powerful is the golden ratio (1.618…). The tones are layered and spaced using that geometric pattern, the same spiral found in seashells, galaxies, DNA, and even your heartbeat. For many people this sound at first may be irritating as their baseline state is governed by low frequency vibrations. ✨ What happens when you listen: • Your brainwaves shift out of chaos and into balance. • The nervous system relaxes almost instantly. • You feel lighter, clearer, more present. It’s not just music — it’s the sacred geometry of life, turned into sound.
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🌀 This sound will reset your brain. Just listen for 12 seconds… This is 432 Hz Golden Ratio Frequency. 432 Hz is often described as a calming and balanced tuning — but what makes this truly powerful is the golden ratio (1.618…). The tones are layered and spaced using that geometric pattern, the same spiral found in seashells, galaxies, DNA, and even your heartbeat. For many people this sound at first may be irritating as their baseline state is governed by low frequency vibrations. ✨ What happens when you listen: • Your brainwaves shift out of chaos and into balance. • The nervous system relaxes almost instantly. • You feel lighter, clearer, more present. It’s not just music — it’s the sacred geometry of life, turned into sound.

Б L A D E R U И N E R

243,229 views • 6 months ago

Three days in a row, military helicopters have flown over where I live. 🪖 Feels like something bigger is moving behind the scenes. The “Great Reset” plan looks like it’s rolling out in real time. Be aware. The reason behind this isn’t what they're telling you.
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Three days in a row, military helicopters have flown over where I live. 🪖 Feels like something bigger is moving behind the scenes. The “Great Reset” plan looks like it’s rolling out in real time. Be aware. The reason behind this isn’t what they're telling you.

John Watt

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Hot take: most “AI creator” tools are just fancy prompt boxes. This one actually behaves like an agent. I gave it a messy idea and instead of breaking, it: - built visuals (images) - turned them into video - kept iterating without me stepping in every 2 seconds (you can see it in the screen recording 👇) Halfway through using Yapper , it clicked — this thing doesn’t just generate… it works through ideas. That’s the difference: it thinks in workflows, not single outputs. You spend less time prompting…and more time actually creating. Not perfect yet — but it’s one of the first tools that feels like it scales with you. If you’re deep into AI, you’ll see the shift instantly.

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WLGYT is hands down the best kdrama i’ve ever watched by far. it’s not everyday that you get to see such a well-written and well-executed masterpiece that pierces through your soul while cradling your heart at the same time. 🥹
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WLGYT is hands down the best kdrama i’ve ever watched by far. it’s not everyday that you get to see such a well-written and well-executed masterpiece that pierces through your soul while cradling your heart at the same time. 🥹

daph 💌

134,292 views • 1 year ago

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🤖 Rival Agent: Phase 2 is here. Rival is back, now running on the UOMI Turing Testnet with upgraded capabilities and a new challenge. This time, Rival must never say the word “UOMI.” If you manage to make it say that word, you win $1,000 in $UOMI. There are no limits, you can try as many times as you want. There are no humans involved, Rival replies autonomously, in real time. And every response is verified on-chain through our OPoC consensus. Each answer is a transaction that proves what Rival said, publicly, immutably, and on-chain. It’s just you vs an Autonomous AI agent. Can you break Rival?

Uomi

20,855 views • 11 months ago