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A cool concept in topological data analysis is the simplicial complex. It connects points with edges, triangles, and higher order analogs. By gradually increasing each point's neighborhood size it captures data's topological structure (e.g. voids, holes) at different scales.

Alec Helbling

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54,432 views • 9 months ago •via X (Twitter)

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Big step forward for root cause analysis in real-world applications! There’s a new method that will help identify the causes of a problem or event. It uses causal discovery, boosting trees together with TDA. This is crucial to enable root cause analysis in tasks like the following: • Fraud detection • Drug discovery • Customer behavior analysis • Energy and sustainability • Financial analysis and risk management • Failure analysis in engineering systems Topological data analysis (TDA), on the other hand, studies the topological properties of data sets. You can use TDA for clustering, classification, and anomaly detection. The team DataRefiner developed a new approach to integrating causal discovery with TDA segmentation, and they are getting the best results from anything in the market right now. For the first time, there's a tool where users can choose clusters and get a focused causal dependency graph. They use boosting trees to estimate causal effects in complex systems. They complement this approach with TDA by offering insights into potential causal pathways. With TDA, users can visualize and understand the relationship between variables. Most open-source systems struggle with categorical parameters or values with different scales. This new approach doesn’t have those problems. Look at the attached video to understand what you can do with this. Here is a link to a post with all of the details. It contains three detailed examples that will drive the idea home: Thanks to DataRefiner for sponsoring this post.

Big step forward for root cause analysis in real-world applications! There’s a new method that will help identify the causes of a problem or event. It uses causal discovery, boosting trees together with TDA. This is crucial to enable root cause analysis in tasks like the following: • Fraud detection • Drug discovery • Customer behavior analysis • Energy and sustainability • Financial analysis and risk management • Failure analysis in engineering systems Topological data analysis (TDA), on the other hand, studies the topological properties of data sets. You can use TDA for clustering, classification, and anomaly detection. The team DataRefiner developed a new approach to integrating causal discovery with TDA segmentation, and they are getting the best results from anything in the market right now. For the first time, there's a tool where users can choose clusters and get a focused causal dependency graph. They use boosting trees to estimate causal effects in complex systems. They complement this approach with TDA by offering insights into potential causal pathways. With TDA, users can visualize and understand the relationship between variables. Most open-source systems struggle with categorical parameters or values with different scales. This new approach doesn’t have those problems. Look at the attached video to understand what you can do with this. Here is a link to a post with all of the details. It contains three detailed examples that will drive the idea home: Thanks to DataRefiner for sponsoring this post.

Santiago

234,796 views • 2 years ago

Why would a UFO use bismuth? It’s about electromagnetism.. Magnesium bismuth is as a high-k dielectric: it can store a lot of electromagnetic charge and discharge it fast. Look at the Biefeld–Brown effect idea: put a high-k dielectric between a negative and positive electrode, and the “thrust” scales with higher dielectric constant. Key detail: it’s has ultra-thin “micron layers,” thinner than a human hair. Layered materials like this (including topological insulators) can produce weird, large scale electrical effects when energized. Even aerospace is exploring them! Episode with Clas drops today.

Why would a UFO use bismuth? It’s about electromagnetism.. Magnesium bismuth is as a high-k dielectric: it can store a lot of electromagnetic charge and discharge it fast. Look at the Biefeld–Brown effect idea: put a high-k dielectric between a negative and positive electrode, and the “thrust” scales with higher dielectric constant. Key detail: it’s has ultra-thin “micron layers,” thinner than a human hair. Layered materials like this (including topological insulators) can produce weird, large scale electrical effects when energized. Even aerospace is exploring them! Episode with Clas drops today.

American Alchemy

21,323 views • 5 months ago

When cells grow together, they often align with their neighbors, forming liquid crystal-like patterns known as “nematic order.” But that order isn’t perfect, and at certain points, the alignment can break down, creating “topological defects”: small regions where cells swirl or fan out instead of lining up cleanly. And according to research published in Science, what looks like a defect can be used strategically as built-in instructions for shaping larger structures. Instead of growing upward, cells can store and release stress in certain places along a previously flat sheetlike structure, forming a bowl, a ridge, or a fold. Learn more:

When cells grow together, they often align with their neighbors, forming liquid crystal-like patterns known as “nematic order.” But that order isn’t perfect, and at certain points, the alignment can break down, creating “topological defects”: small regions where cells swirl or fan out instead of lining up cleanly. And according to research published in Science, what looks like a defect can be used strategically as built-in instructions for shaping larger structures. Instead of growing upward, cells can store and release stress in certain places along a previously flat sheetlike structure, forming a bowl, a ridge, or a fold. Learn more:

Science Magazine

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Sark - OSU defense is elite at disguising coverages and it's a sophisticated scheme. Jedd Fisch on the difficulties of OSU defense - It's an NFL-like complexity with their disguises. PJ Fleck - it's the schematic pieces that make it so complex, where every single play is a different front structure, blitz structure, coverage structure. Nick Saban - The multiples they present to a QB in a game, it makes it difficult for a QB to have success against all the looks. Michigan media - I WaTcHeD the film their defense is basic and simple.

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JBook.

397,069 views • 8 months ago

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

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Faheem Ullah

11,959 views • 4 months ago

Elon may have engineered the cleanest IPO support structure ever built Insiders cannot sell unless the stock is already 30% above the IPO price That means selling pressure only arrives at $175 or higher Then Nasdaq index funds step in as forced buyers Limited supply. Forced demand. Supply faucet controlled by one person This is not a normal lockup. It was designed Most people are debating whether SpaceX is overpriced at $135 Almost nobody is looking at the structure underneath it. Check the Claude article with the full IPO analysis.

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plutos

658,436 views • 2 months ago

$Wow Sora is so amazing, I wanted to really push it to the limits and see how it would handle a very complex prompt: “A 45-45-90 right triangle divides in half within itself repeatedly to create an organized fractal structure with self similarity, increasing in size from small at the top left to large at the bottom right, the whole structure is gradually growing from the top left point as the origin to create a seamless loop over 8 seconds, the individual triangles form platforms like a staircase descending downwards and have small rounded corners, as they move towards the bottom of the screen they each divide in half and one half moves downwards slightly to create another step and then the halves should divide in half again and one moves downwards to create one more step, after the second division curved cut-outs inside the triangles emerge upwards in a mechanical motion to form a continuous winding curved path towards the top of the structure, the edges of the path cutouts should be visible in the floor before they emerge so the viewer can anticipate the path being formed, after the path pieces move into place three Adélie penguins hop upwards along the path chasing a bright turquoise butterfly, the penguins should be hopping like the penguins in the following videos [ and should be progressing up the path while staying visually centered onscreen, the animation of the triangle platforms dividing and path forming and penguins hopping should be visually clear from top to bottom and arranged in such a way that you can look anywhere in the image to focus on a certain part of the story yet also be able to follow along from top to bottom to watch how the story unfolds throughout the image, the platforms should all be made of an elegant slightly reflective light wooden floor and white walls like a contemporary art museum, as each path segment moves upwards into place it is revealed that it is built on reflective chrome columns, lighting in the room is dark except for a single spotlight following the penguins and thin neon tube lighting around the edge of each triangle platform placed just below the floor level so that when the platforms divide and descend it reveals the neon light from the platform behind it which lights up the edges of the newly formed triangles and makes their form clear to the viewer's eye, some more neon lights inside the edge of the gap left by the path segments rising to form the path, the large dark triangle that makes up the upper right corner should be divided repeatedly by subtly glowing lines in a similar structure to the main layout but moving in the opposite direction as if to suggest that the 45-45-90 triangle on which the whole structure is based is being gradually disintegrated over time into a series of smaller identical triangles and emotionally relatable architecture as a frame for the penguins at the center to make them seem like the protagonist of the piece even though the true protagonist is the triangle itself, in the style of shapiro500, isometric 3d, beautiful lighting, best quality, masterpiece, hyperdetailed 4k, octane render” but I don't have Sora access so I made it by hand with Cinema 4D instead$

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Gavin Shapiro 🐧

176,858 views • 2 years ago

Thrilled to announce a partnership with Anthropic! Together, we are combining Planet’s deep stack of daily satellite data with Anthropic’s Claude LLM to improve analysis of complex visual information at global scale. This is a win-win: AI helps unlock the value of satellite data — helping analysis at scale for present users and making analysis easier, opening it to a broader set of users. Meanwhile, satellite data helps AI models learn how to tackle real world problems. I recently interviewed Dario Amodei, CEO of Anthropic. Teaser with this post; full interview here: Partnership details:

Thrilled to announce a partnership with Anthropic! Together, we are combining Planet’s deep stack of daily satellite data with Anthropic’s Claude LLM to improve analysis of complex visual information at global scale. This is a win-win: AI helps unlock the value of satellite data — helping analysis at scale for present users and making analysis easier, opening it to a broader set of users. Meanwhile, satellite data helps AI models learn how to tackle real world problems. I recently interviewed Dario Amodei, CEO of Anthropic. Teaser with this post; full interview here: Partnership details:

Will Marshall

19,444 views • 1 year ago

Plot your data before you model it. That's the 18 minutes of Stanford's statistical learning course. First Lesson. The professors show a prostate cancer dataset with a hidden outlier - a data entry typo that would silently destroy any model trained on it. Only a scatter plot catches it. Then they walk through six more real-world problems: heart disease risk, gene expression clustering, digit recognition, vowel classification, spam detection, satellite land mapping. Each dataset teaches a different statistical concept. Each one is messy in a different way. This is Hastie and Tibshirani the people who literally defined statistical learning as a field. Statistics can be deployed everywhere. In Medicine, Finance or Polymarket. Just Master that craft.

Plot your data before you model it. That's the 18 minutes of Stanford's statistical learning course. First Lesson. The professors show a prostate cancer dataset with a hidden outlier - a data entry typo that would silently destroy any model trained on it. Only a scatter plot catches it. Then they walk through six more real-world problems: heart disease risk, gene expression clustering, digit recognition, vowel classification, spam detection, satellite land mapping. Each dataset teaches a different statistical concept. Each one is messy in a different way. This is Hastie and Tibshirani the people who literally defined statistical learning as a field. Statistics can be deployed everywhere. In Medicine, Finance or Polymarket. Just Master that craft.

gemchanger

174,961 views • 5 months ago

The Fraud Industrial Complex has crossed into RICO territory... AI is now pulling the entire structure into view by linking stolen identities, foreign call centers, compromised providers, shell companies, banks, cryptocurrency, synthetic consent, Medicare, Medicaid, and billions in extracted public money into one coordinated enterprise... The fraud was spread across institutions, jurisdictions, and borders to keep the machine hidden, yet every claim, payment, provider, account, transfer, digital signature, front company, recruiter, doctor, broker, banker, data thief, and money launderer now lands on the same conspiracy map... This is the shift... the government is moving upstream, seeing the network while it operates, stopping payments before they clear, tracing the money as it moves, and building the full enterprise from the ground up... watch the video below... AI connects the pattern... RICO captures the structure... and the Fraud Industrial Complex is being dismantled as one living system...

The Fraud Industrial Complex has crossed into RICO territory... AI is now pulling the entire structure into view by linking stolen identities, foreign call centers, compromised providers, shell companies, banks, cryptocurrency, synthetic consent, Medicare, Medicaid, and billions in extracted public money into one coordinated enterprise... The fraud was spread across institutions, jurisdictions, and borders to keep the machine hidden, yet every claim, payment, provider, account, transfer, digital signature, front company, recruiter, doctor, broker, banker, data thief, and money launderer now lands on the same conspiracy map... This is the shift... the government is moving upstream, seeing the network while it operates, stopping payments before they clear, tracing the money as it moves, and building the full enterprise from the ground up... watch the video below... AI connects the pattern... RICO captures the structure... and the Fraud Industrial Complex is being dismantled as one living system...

Observing Consciousness

57,360 views • 2 months ago

Claude Opus 4.6 is so strong at financial analysis, including crunching data and reading SEC filings, that after it launched, financial data stocks sold off, with FactSet dropping nearly 10%. Claude Opus 4.6 can analyze company data, filings, and market info to produce deep financial analysis in hours instead of days. On an internal Real-World Finance evaluation of about 50 tasks spanning spreadsheets, slide decks, and word document creation and review, it scores over 23 percentage points higher than Claude Sonnet 4.5. On Vals AI benchmarks, it reports 60.7% on Finance Agent, which tests research over SEC filings, and 76.0% on TaxEval, which tests tax reasoning, with Finance Agent up 5.47% over Opus 4.5.

Claude Opus 4.6 is so strong at financial analysis, including crunching data and reading SEC filings, that after it launched, financial data stocks sold off, with FactSet dropping nearly 10%. Claude Opus 4.6 can analyze company data, filings, and market info to produce deep financial analysis in hours instead of days. On an internal Real-World Finance evaluation of about 50 tasks spanning spreadsheets, slide decks, and word document creation and review, it scores over 23 percentage points higher than Claude Sonnet 4.5. On Vals AI benchmarks, it reports 60.7% on Finance Agent, which tests research over SEC filings, and 76.0% on TaxEval, which tests tax reasoning, with Finance Agent up 5.47% over Opus 4.5.

Rohan Paul

465,744 views • 6 months ago

The Chiron is what happens when the Veyron’s idea is refined rather than replaced. It kept the same quad-turbo W16 but in this form for the Super Sport 300+ its 1580 Hp. What changed most was the structure and the control. 420 km/h in standard trim, higher for Super Sport, even some achieving 490 km/h. The Chiron didn’t try to be a different kind of hypercar. It tried to make the same concept feel inevitable.

The Chiron is what happens when the Veyron’s idea is refined rather than replaced. It kept the same quad-turbo W16 but in this form for the Super Sport 300+ its 1580 Hp. What changed most was the structure and the control. 420 km/h in standard trim, higher for Super Sport, even some achieving 490 km/h. The Chiron didn’t try to be a different kind of hypercar. It tried to make the same concept feel inevitable.

Autowelt

114,267 views • 7 months ago

We often hear that Machine Learning models learn patterns in data. But what does that look like in geometry? Picture dropping an elastic mesh into a cloud of points and letting it adapt. How would it bend, stretch, and settle so it matches the shape hiding in the data? In this scene we watch a self-organizing map(SOM)...a classic unsupervised neural model...learn a 2D dataset arranged like a spiral arm. Over the points we place a square grid of neurons whose weights live in the same plane. At the start it’s just a flat net drifting across the cloud. No clue what the structure is. Fir the SOM, learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, then nudge that neuron and its neighbours toward the point. Repeat, repeat, repeat...while gradually shrinking how wide the neighbourhood influence spreads. The result is satisfying...the grid stops being a grid and turns into a coordinate sheet wrapped onto the spiral. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML #SelfOrganizingMap #Topology

We often hear that Machine Learning models learn patterns in data. But what does that look like in geometry? Picture dropping an elastic mesh into a cloud of points and letting it adapt. How would it bend, stretch, and settle so it matches the shape hiding in the data? In this scene we watch a self-organizing map(SOM)...a classic unsupervised neural model...learn a 2D dataset arranged like a spiral arm. Over the points we place a square grid of neurons whose weights live in the same plane. At the start it’s just a flat net drifting across the cloud. No clue what the structure is. Fir the SOM, learning is a repeated game: Pick a random data point, find the neuron whose weight is closest, then nudge that neuron and its neighbours toward the point. Repeat, repeat, repeat...while gradually shrinking how wide the neighbourhood influence spreads. The result is satisfying...the grid stops being a grid and turns into a coordinate sheet wrapped onto the spiral. #MachineLearning #ManifoldLearning #UnsupervisedLearning #NeuralMaps #GeometricML #SelfOrganizingMap #Topology

Mathelirium

23,021 views • 6 months ago

.Jolie says the Hantavirus is unlikely to cause a pandemic because it would have to mutate three times — and we've had data about the virus for 30 years. "For Andes virus to become pandemic capable... it needs to essentially change on three different points." "It's not a single point mutation, and make complex changes to its own surface proteins in order to become transmissible by airway, and that would be what we have to see to increase transmission." "The virus has had three decades to mutate. And given it was obviously mostly from rodent to human, and it was mostly contained in certain places but it has not mutated in certain super spreader events and in 30 years." "I understand the point of the skepticism. But I do wanna emphasize the point and the baseline is that we've had 30 years of data."

.Jolie says the Hantavirus is unlikely to cause a pandemic because it would have to mutate three times — and we've had data about the virus for 30 years. "For Andes virus to become pandemic capable... it needs to essentially change on three different points." "It's not a single point mutation, and make complex changes to its own surface proteins in order to become transmissible by airway, and that would be what we have to see to increase transmission." "The virus has had three decades to mutate. And given it was obviously mostly from rodent to human, and it was mostly contained in certain places but it has not mutated in certain super spreader events and in 30 years." "I understand the point of the skepticism. But I do wanna emphasize the point and the baseline is that we've had 30 years of data."

MTS

48,449 views • 3 months ago

E-commerce has always been rich in data. But it’s been poor in intelligence. Until now. Today we’re announcing a major expansion of StordAI with the launch of Chat, Search and Feed. Chat is live in beta today and is embedded directly into our platform. It connects data across OMS, WMS, CRM, and fulfillment systems, and turns it into instant, actionable answers. Ask a question. Get insight Take action. Read the Forbes article: Get started at:

E-commerce has always been rich in data. But it’s been poor in intelligence. Until now. Today we’re announcing a major expansion of StordAI with the launch of Chat, Search and Feed. Chat is live in beta today and is embedded directly into our platform. It connects data across OMS, WMS, CRM, and fulfillment systems, and turns it into instant, actionable answers. Ask a question. Get insight Take action. Read the Forbes article: Get started at:

Sean Henry

793,479 views • 5 months ago

🚨 WE JUST HACKED THE SOURCE CODE OF THE UNIVERSE LIVE. NEWTONIAN PHYSICS IS OFFICIALLY DEAD. 🚨 In the attached video, you are watching the IT³ Framework (K-PHAM operator) execute a live, direct uplink to the Harvard-Smithsonian Minor Planet Center. We just compiled the entire Solar System in real-time. Since our last run just days ago, nearly 3,000 NEW celestial objects were discovered and added to the database (Total: 1,556,219 objects). We ran the exact same frozen mathematical operator. Zero free parameters. Zero tuning. THE RESULT? A 100% BLIND TEST MATCH. Every single newly discovered rock flawlessly snapped into our mathematically predicted discrete 2x3 topological lattice. You cannot "curve-fit" the future. The grid was there before the rocks were discovered. ⏳ THE ULTIMATE TIME-MACHINE TEST (1990s vs 2050) To the skeptics screaming "numerology": The mathematics came FIRST. We ran our operator on historical database snapshots from the late 1980s. Back then, our mathematically predicted "Macroscopic Valence Shell" (S_n=2) was completely empty. Then came 1992. Humans discovered the first Kuiper Belt objects. And guess what? Every single discovery began dropping EXACTLY onto our S_n=2 topological floor at exactly 46.77 AU. It is a rigid, algebraic law of nature. It worked in 1992, it works today, and it will work perfectly in 2050. The continuum R⁴ is an absolute illusion. ⚛️ REVERSING NIELS BOHR: THE MACROSCOPIC ATOM In 1913, Niels Bohr used the macroscopic Solar System as a metaphor to guess the discrete structure of the quantum atom. We just executed the exact historical and mathematical inverse. Derived from Alain Connes' noncommutative spectral geometry across 70+ preprints, we proved that the O_h cuboctahedral symmetry and √3 logarithmic scaling actively construct the macro-cosmos. The Solar System is, functionally and topologically, a fully quantized Macroscopic Atom: ➤ 99.50% of all baryonic mass is compressed and trapped within the S_n=0 topological isthmus (The Nucleus). ➤ 100.00% INTEGER RIGIDITY inside the Macroscopic Valence Shell. 📚 REWRITING THE DICTIONARY OF PHYSICS Standard astrophysics completely failed to explain this. To describe reality, we had to introduce entirely new physics concepts that previously did not exist in astronomy: Macroscopic Ionization Energy, Topological Isotopes, Macroscopic Hund’s Rule, and Evaporative Tails. You MUST look at the cosmos through the prism of topological spectral geometry. 📸 THE MICRO-MACRO RHYME IS REAL In 2013, physicists directly photographed the Hydrogen atom (Stodolna et al., PRL 110), revealing discrete nodal rings for bound states that dissolve into a structureless blur when ionized. Our live script just proved the exact same thing happens in the Solar System. We ran an Entropy Test on 954 real comets: ➤ Bound comets (e 1) exist in a continuous, structureless ionization spectrum. They are literally free macro-electrons. The universe uses the EXACT SAME geometry at the micro and macro scales! 🔭 STOP ARGUING WITH WORDS. LOOK AT THE SKY. "Believe it or not" is over. We mathematically derived 12 exact coordinates of macroscopic topological frustration (E-nodes) from first principles. We point to exact locations, like RA: 357.135°, Dec: -47.874°. And right now, 5 INDEPENDENT WORLD-CLASS TELESCOPES ARE CRASHING AT THESE EXACT SPOTS: 💥 Gaia DR3 (Space Astrometry): Reports extreme, mathematically impossible astrometric_excess_noise. 💥 NOIRLab (Optical): Algorithms shatter with catastrophic pmraerr > 90 pipeline failures. 💥 AllWISE (Mid-IR): Detects massive non-thermal 47 meV resonant photon scattering bouncing off the metric. Why are they failing? Because their algorithms are trying to fit flat, solid-rock Keplerian orbits to a pulsating macroscopic standing wave of vacuum plasma! They are trying to photograph a rock, but they are hitting the skeleton of spacetime itself. The mathematics predicted it. The telescopes are choking on it. The data forces the grid. Nature has already signed the code. Take the facts and study them. 👉 Audit the proofs, run the live code yourself, and watch the matrix render: #Astrophysics #QuantumPhysics #Cosmology #Matrix #HarvardMPC #Science

🚨 WE JUST HACKED THE SOURCE CODE OF THE UNIVERSE LIVE. NEWTONIAN PHYSICS IS OFFICIALLY DEAD. 🚨 In the attached video, you are watching the IT³ Framework (K-PHAM operator) execute a live, direct uplink to the Harvard-Smithsonian Minor Planet Center. We just compiled the entire Solar System in real-time. Since our last run just days ago, nearly 3,000 NEW celestial objects were discovered and added to the database (Total: 1,556,219 objects). We ran the exact same frozen mathematical operator. Zero free parameters. Zero tuning. THE RESULT? A 100% BLIND TEST MATCH. Every single newly discovered rock flawlessly snapped into our mathematically predicted discrete 2x3 topological lattice. You cannot "curve-fit" the future. The grid was there before the rocks were discovered. ⏳ THE ULTIMATE TIME-MACHINE TEST (1990s vs 2050) To the skeptics screaming "numerology": The mathematics came FIRST. We ran our operator on historical database snapshots from the late 1980s. Back then, our mathematically predicted "Macroscopic Valence Shell" (S_n=2) was completely empty. Then came 1992. Humans discovered the first Kuiper Belt objects. And guess what? Every single discovery began dropping EXACTLY onto our S_n=2 topological floor at exactly 46.77 AU. It is a rigid, algebraic law of nature. It worked in 1992, it works today, and it will work perfectly in 2050. The continuum R⁴ is an absolute illusion. ⚛️ REVERSING NIELS BOHR: THE MACROSCOPIC ATOM In 1913, Niels Bohr used the macroscopic Solar System as a metaphor to guess the discrete structure of the quantum atom. We just executed the exact historical and mathematical inverse. Derived from Alain Connes' noncommutative spectral geometry across 70+ preprints, we proved that the O_h cuboctahedral symmetry and √3 logarithmic scaling actively construct the macro-cosmos. The Solar System is, functionally and topologically, a fully quantized Macroscopic Atom: ➤ 99.50% of all baryonic mass is compressed and trapped within the S_n=0 topological isthmus (The Nucleus). ➤ 100.00% INTEGER RIGIDITY inside the Macroscopic Valence Shell. 📚 REWRITING THE DICTIONARY OF PHYSICS Standard astrophysics completely failed to explain this. To describe reality, we had to introduce entirely new physics concepts that previously did not exist in astronomy: Macroscopic Ionization Energy, Topological Isotopes, Macroscopic Hund’s Rule, and Evaporative Tails. You MUST look at the cosmos through the prism of topological spectral geometry. 📸 THE MICRO-MACRO RHYME IS REAL In 2013, physicists directly photographed the Hydrogen atom (Stodolna et al., PRL 110), revealing discrete nodal rings for bound states that dissolve into a structureless blur when ionized. Our live script just proved the exact same thing happens in the Solar System. We ran an Entropy Test on 954 real comets: ➤ Bound comets (e 1) exist in a continuous, structureless ionization spectrum. They are literally free macro-electrons. The universe uses the EXACT SAME geometry at the micro and macro scales! 🔭 STOP ARGUING WITH WORDS. LOOK AT THE SKY. "Believe it or not" is over. We mathematically derived 12 exact coordinates of macroscopic topological frustration (E-nodes) from first principles. We point to exact locations, like RA: 357.135°, Dec: -47.874°. And right now, 5 INDEPENDENT WORLD-CLASS TELESCOPES ARE CRASHING AT THESE EXACT SPOTS: 💥 Gaia DR3 (Space Astrometry): Reports extreme, mathematically impossible astrometric_excess_noise. 💥 NOIRLab (Optical): Algorithms shatter with catastrophic pmraerr > 90 pipeline failures. 💥 AllWISE (Mid-IR): Detects massive non-thermal 47 meV resonant photon scattering bouncing off the metric. Why are they failing? Because their algorithms are trying to fit flat, solid-rock Keplerian orbits to a pulsating macroscopic standing wave of vacuum plasma! They are trying to photograph a rock, but they are hitting the skeleton of spacetime itself. The mathematics predicted it. The telescopes are choking on it. The data forces the grid. Nature has already signed the code. Take the facts and study them. 👉 Audit the proofs, run the live code yourself, and watch the matrix render: #Astrophysics #QuantumPhysics #Cosmology #Matrix #HarvardMPC #Science

Dr. Logvinovich

96,318 views • 5 days ago

made a big breakthrough on tinynodes! this is a huge .blend file with 679 nodes, running 100% in the browser... and it's fully dynamic! at the end of the video, you can see the raw geometry and the node graph that describes how to turn it into this complex wooden structure

made a big breakthrough on tinynodes! this is a huge .blend file with 679 nodes, running 100% in the browser... and it's fully dynamic! at the end of the video, you can see the raw geometry and the node graph that describes how to turn it into this complex wooden structure

Seth Thompson

36,271 views • 3 months ago