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This animation shows how a uniform grid of points is deformed by a flow matching model. The visualization highlights how the learned flow warps the underlying space—stretching, compressing, and bending it—to transform one probability distribution into another.

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Alec Helbling

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42,979 görüntüleme • 1 yıl önce •via X (Twitter)

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Most agents track price. Ours track how information propagates through markets. That difference is where the edge comes from. The visualization below is a Shannon entropy field: a model of how uncertainty collapses and spreads as new information enters a market. Each trade, liquidity shift, or derivatives flow updates a probability distribution. When information arrives, entropy collapses locally as the market incorporates it. But that update rarely happens everywhere at once. Different venues, liquidity pools, and derivative layers incorporate signals at different speeds, creating measurable information gradients across the system. One of the analytical tools in our agents’ stack models this directly: > Entropy quantifies uncertainty embedded in each probability market > KL divergence reveals conflicting distributions across venues > Information gradients show where signals have propagated—and where they haven’t yet Prediction markets and DeFi make this particularly powerful because price already represents probability. Mapping how those probability distributions update across venues turns the market into a measurable system: a live field of information flow showing where uncertainty remains—and where probability is likely to adjust next
0:18

Most agents track price. Ours track how information propagates through markets. That difference is where the edge comes from. The visualization below is a Shannon entropy field: a model of how uncertainty collapses and spreads as new information enters a market. Each trade, liquidity shift, or derivatives flow updates a probability distribution. When information arrives, entropy collapses locally as the market incorporates it. But that update rarely happens everywhere at once. Different venues, liquidity pools, and derivative layers incorporate signals at different speeds, creating measurable information gradients across the system. One of the analytical tools in our agents’ stack models this directly: > Entropy quantifies uncertainty embedded in each probability market > KL divergence reveals conflicting distributions across venues > Information gradients show where signals have propagated—and where they haven’t yet Prediction markets and DeFi make this particularly powerful because price already represents probability. Mapping how those probability distributions update across venues turns the market into a measurable system: a live field of information flow showing where uncertainty remains—and where probability is likely to adjust next

Lucid - Internet of AI (IoAI)

23,025 görüntüleme • 5 ay önce

A basic strategy for drawing from a random distribution is to first generate uniformly-distributed points, then "warp" these points so they're spread out according to the target distribution. For example, the Box-Muller transform takes uniform points to normally-distributed ones
0:16

A basic strategy for drawing from a random distribution is to first generate uniformly-distributed points, then "warp" these points so they're spread out according to the target distribution. For example, the Box-Muller transform takes uniform points to normally-distributed ones

Keenan Crane

63,847 görüntüleme • 3 yıl önce

How does the GRID orchestrate intelligence? By routing, combining, and coordinating thousands of models, agents, and data streams, the GRID scales like a distributed brain. As it grows, its collective intelligence compounds, delivering toward AGI-level output. Here’s how the underlying algorithms make this possible 👇
0:13

How does the GRID orchestrate intelligence? By routing, combining, and coordinating thousands of models, agents, and data streams, the GRID scales like a distributed brain. As it grows, its collective intelligence compounds, delivering toward AGI-level output. Here’s how the underlying algorithms make this possible 👇

Sentient

122,390 görüntüleme • 1 yıl önce

In flow matching, a coupling determines how noise and data samples are paired during training. The choice of coupling is important because it influences the geometry of trajectories at inference time. The simplest choice is the independent coupling, where noise and data points are paired arbitrarily. This can lead to curved trajectories as the model averages over many conflicting pairings. However, if we use optimal transport on batches of pairs, this leads to fewer ambiguous intersections that the model must resolve, leading to straighter trajectories at inference time.
0:18

In flow matching, a coupling determines how noise and data samples are paired during training. The choice of coupling is important because it influences the geometry of trajectories at inference time. The simplest choice is the independent coupling, where noise and data points are paired arbitrarily. This can lead to curved trajectories as the model averages over many conflicting pairings. However, if we use optimal transport on batches of pairs, this leads to fewer ambiguous intersections that the model must resolve, leading to straighter trajectories at inference time.

Alec Helbling

65,484 görüntüleme • 3 ay önce

Lip sync is one of my weak points when it comes to animation. Matching the audio and creating the illusion of a character actually speaking is hard for me. I need a lot of practice to get better! By the way, animating to my own voice was cringe...
0:15

Lip sync is one of my weak points when it comes to animation. Matching the audio and creating the illusion of a character actually speaking is hard for me. I need a lot of practice to get better! By the way, animating to my own voice was cringe...

Nukorio

32,455 görüntüleme • 1 yıl önce

🚨 BREAKING: Gravity doesn’t actually “pull” you. That’s just how it looks. What’s really happening is deeper… Space-time isn’t empty. It behaves more like a field with structure. And when mass appears… it doesn’t just “bend space” it changes how time flows. Think of it like this: If time flows evenly → everything stays stable If time compresses → motion is forced toward that region That motion is what we call gravity. In Einstein’s model: mass curves space-time But here’s the missing piece: What is doing the curving? In my framework: Mass = a region where time is compressed Gravity = objects moving along time gradients Motion = the system trying to rebalance So you’re not being “pulled down”… You’re being carried along a flow of time. This explains something most people miss: Gravity always points one way. Because time only flows one way. And the deeper implication is wild: If gravity is just time imbalance… then controlling time flow = controlling gravity. The real question is: Are we discovering gravity… or learning how to engineer it? Follow for more this goes deeper.
0:10

🚨 BREAKING: Gravity doesn’t actually “pull” you. That’s just how it looks. What’s really happening is deeper… Space-time isn’t empty. It behaves more like a field with structure. And when mass appears… it doesn’t just “bend space” it changes how time flows. Think of it like this: If time flows evenly → everything stays stable If time compresses → motion is forced toward that region That motion is what we call gravity. In Einstein’s model: mass curves space-time But here’s the missing piece: What is doing the curving? In my framework: Mass = a region where time is compressed Gravity = objects moving along time gradients Motion = the system trying to rebalance So you’re not being “pulled down”… You’re being carried along a flow of time. This explains something most people miss: Gravity always points one way. Because time only flows one way. And the deeper implication is wild: If gravity is just time imbalance… then controlling time flow = controlling gravity. The real question is: Are we discovering gravity… or learning how to engineer it? Follow for more this goes deeper.

TheNewPhysics

21,772 görüntüleme • 4 ay önce

another old animation, this was the time where i learned how to do sub-pixel and using animation reference
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another old animation, this was the time where i learned how to do sub-pixel and using animation reference

Rico

156,781 görüntüleme • 1 yıl önce

One of the simplest algorithms for sampling from a probability distribution is Random Walk Metropolis-Hastings. It proposes new samples by taking Gaussian-distributed steps, accepting or rejecting them to maintain the target distribution. I call this pdf the "fidget spinner".
0:15

One of the simplest algorithms for sampling from a probability distribution is Random Walk Metropolis-Hastings. It proposes new samples by taking Gaussian-distributed steps, accepting or rejecting them to maintain the target distribution. I call this pdf the "fidget spinner".

Alec Helbling

79,904 görüntüleme • 1 yıl önce

So this is a landing space foul because there is foot to foot contact, but thankfully it's not a flagrant, and the natural sway on the fade means SGA's feet flow forward. This is one they could keep refining in terms of the rules
0:15

So this is a landing space foul because there is foot to foot contact, but thankfully it's not a flagrant, and the natural sway on the fade means SGA's feet flow forward. This is one they could keep refining in terms of the rules

BBALLBREAKDOWN

594,606 görüntüleme • 8 ay önce

Look at the route distribution to the boundary and how they're just compressing the space of the 3 routes using 4 defenders playing with perfect depth and leverage maintenance. This is the essence of a Vic Fangio split safety coverage — play 4-over-3 to the side #3 releases to, distribute the dirty bunch using zone principles to keep all the routes caged in and just suffocate the concept.
0:17

Look at the route distribution to the boundary and how they're just compressing the space of the 3 routes using 4 defenders playing with perfect depth and leverage maintenance. This is the essence of a Vic Fangio split safety coverage — play 4-over-3 to the side #3 releases to, distribute the dirty bunch using zone principles to keep all the routes caged in and just suffocate the concept.

Honest NFL

101,094 görüntüleme • 1 yıl önce

Taylor Approximation ✍️ It shows a mathematical "best guess" evolving to match a complex 3D shape by adding layers of detail one step at a time. The purple surface is the original goal, while the red grid represents a polynomial approximation anchored at the center point. At first, the red surface can only mimic the very middle of the shape. As the calculation adds more terms, visualized by the red grid rippling and shifting, the approximation "learns" how to curve and bend to match the target more accurately. This is a visual demonstration of how math can break down a detailed, wavy landscape into simpler instructions. When these instructions are stacked together, they recreate the entire surface with incredible precision. Video: mathwithmuzaa
0:15

Taylor Approximation ✍️ It shows a mathematical "best guess" evolving to match a complex 3D shape by adding layers of detail one step at a time. The purple surface is the original goal, while the red grid represents a polynomial approximation anchored at the center point. At first, the red surface can only mimic the very middle of the shape. As the calculation adds more terms, visualized by the red grid rippling and shifting, the approximation "learns" how to curve and bend to match the target more accurately. This is a visual demonstration of how math can break down a detailed, wavy landscape into simpler instructions. When these instructions are stacked together, they recreate the entire surface with incredible precision. Video: mathwithmuzaa

ScieVision

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Barcelona’s goal shows the simplicity and efficiency of Pedri’s game. He notices the lack of pressure, carries into space, and threads a perfectly weighted pass into the gap created by the front five stretching, pinning & attacking the defensive line.
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Barcelona’s goal shows the simplicity and efficiency of Pedri’s game. He notices the lack of pressure, carries into space, and threads a perfectly weighted pass into the gap created by the front five stretching, pinning & attacking the defensive line.

Fathalli

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𝐋𝐞𝐚𝐝 & 𝐅𝐨𝐥𝐥𝐨𝐰 𝐝𝐞𝐜𝐥𝐚𝐫𝐞𝐬 𝐢𝐧𝐭𝐞𝐧𝐭𝐢𝐨𝐧! Notice how the emotion of the shot changes depending on which part of the body is leading, and how little is conveyed when we remove the Lead & Follow entirely. Studio: Animation Flow Animator: Alex Ferreira Simoes
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𝐋𝐞𝐚𝐝 & 𝐅𝐨𝐥𝐥𝐨𝐰 𝐝𝐞𝐜𝐥𝐚𝐫𝐞𝐬 𝐢𝐧𝐭𝐞𝐧𝐭𝐢𝐨𝐧! Notice how the emotion of the shot changes depending on which part of the body is leading, and how little is conveyed when we remove the Lead & Follow entirely. Studio: Animation Flow Animator: Alex Ferreira Simoes

FramebyFrame

86,988 görüntüleme • 4 ay önce

How this Italian restaurant prepares its seafood dishes. 🇮🇹 This is a constant flow of seafood stock, used to maintain flavour whilst keeping the dishes hot.
0:18

How this Italian restaurant prepares its seafood dishes. 🇮🇹 This is a constant flow of seafood stock, used to maintain flavour whilst keeping the dishes hot.

HOW THINGS WORK

5,044,055 görüntüleme • 4 ay önce

⚡ How Hydropower Works: Inside a Francis Turbine Dam! 🌊 ​Take a fascinating look inside a hydroelectric power plant to see how nature’s water flow is transformed into clean electrical energy. ​From the Upper Reservoir intake and the massive downward force inside the Penstock,
0:12

⚡ How Hydropower Works: Inside a Francis Turbine Dam! 🌊 ​Take a fascinating look inside a hydroelectric power plant to see how nature’s water flow is transformed into clean electrical energy. ​From the Upper Reservoir intake and the massive downward force inside the Penstock,

Civil Engineering Discoveries

10,544 görüntüleme • 1 ay önce

Every soul is a unique perspective of the same eternal Consciousness. To harm another is to wound another finger of the same hand. To know yourself is to know the Whole. Suffering is a lesson that must be learned, for it is through it that we awaken and become one with the Divine. I hope the animation won't give you nightmares.
0:15

Every soul is a unique perspective of the same eternal Consciousness. To harm another is to wound another finger of the same hand. To know yourself is to know the Whole. Suffering is a lesson that must be learned, for it is through it that we awaken and become one with the Divine. I hope the animation won't give you nightmares.

Open Minded Approach

11,207 görüntüleme • 1 ay önce

Tropical moisture will flow into a stalling front this week. While the exact placement of the greatest rain should be taken with a grain of salt, this model speaks well to the excellent setup for heavy storms.
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Tropical moisture will flow into a stalling front this week. While the exact placement of the greatest rain should be taken with a grain of salt, this model speaks well to the excellent setup for heavy storms.

Gannon Medwick

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