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Pretty face, killer confidence, giant problem. Shockwave vs THS is exactly the kind of sexy mismatch fans lock the door for. #IndieWrestling #Wrestling #MensWrestling #Muscle #CombatSports #Streaming #hunkpunishment #mismatch #davidvsgoliath

WeekendWrestling

10,092 subscribers

10,289 просмотров • 4 месяцев назад •via X (Twitter)

#IndieWrestling #Wrestling #MensWrestling #Muscle #CombatSports #Streaming #hunkpunishment #mismatch #davidvsgoliath

Anya Rossi• Live Now

Private livecam show

Комментарии: 1

Фото профиля 형님🔞

형님🔞4 месяцев назад

gonna need more of THS!!

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“My Lady Jane” is a historical romance show loosely based on the life of Lady Jane Grey, who was Queen of England and Ireland for exactly nine days in the mid-1500s. Despite critical acclaim and a devoted fanbase, Amazon Prime canceled the show after just one season, and fans are now rallying to convince the streaming giant to bring it back. To read more or join their calls for more “My Lady Jane,” go to #amazonprime #primevideo #myladyjane #tv

“My Lady Jane” is a historical romance show loosely based on the life of Lady Jane Grey, who was Queen of England and Ireland for exactly nine days in the mid-1500s. Despite critical acclaim and a devoted fanbase, Amazon Prime canceled the show after just one season, and fans are now rallying to convince the streaming giant to bring it back. To read more or join their calls for more “My Lady Jane,” go to #amazonprime #primevideo #myladyjane #tv

Change.org

107,214 просмотров • 2 лет назад

Deadtime Stories (2012) is the kids’ horror show that nobody talks about, and that’s a shame. Jennifer Stone narrates this fun and creepy series through some REALLY wild moments. The episode ‘Along Came A Spider’ is a great example, with a giant killer spider that they first try to crush with the garage door Ghostface vs Tatum style, then just decide to pump it full of air until it blows away into the sky. It’s wacky but also just scary enough for kids dipping their toes into the genre!

Deadtime Stories (2012) is the kids’ horror show that nobody talks about, and that’s a shame. Jennifer Stone narrates this fun and creepy series through some REALLY wild moments. The episode ‘Along Came A Spider’ is a great example, with a giant killer spider that they first try to crush with the garage door Ghostface vs Tatum style, then just decide to pump it full of air until it blows away into the sky. It’s wacky but also just scary enough for kids dipping their toes into the genre!

Horror4Kids

34,334 просмотров • 6 месяцев назад

Arsenal vs Manchester City felt like a mix of individual issues and a game plan that didn’t quite match the problem in front of them. In a match where City are always going to dominate possession, it didn’t really make sense to have Eze used out wide. It reduced Arsenal’s ability to both stretch the pitch and attack transitions effectively. That also left Mosquera exposed in repeated 1v1 situations against Doku, which is exactly the kind of matchup City want to create. There were positives though. Kai Havertz was the right call in terms of pressing and structure he helped set the tone for the equaliser. And defensively, Piero Hincapié looked much more secure in his duel compared to earlier games, especially in isolated defensive moments. Ultimately though, it felt like a combination of small individual mistakes and a broader mismatch in structure. Against City, you can’t afford to lose both width protection and vertical threat at the same time and that’s where Arsenal fell just short.

Arsenal vs Manchester City felt like a mix of individual issues and a game plan that didn’t quite match the problem in front of them. In a match where City are always going to dominate possession, it didn’t really make sense to have Eze used out wide. It reduced Arsenal’s ability to both stretch the pitch and attack transitions effectively. That also left Mosquera exposed in repeated 1v1 situations against Doku, which is exactly the kind of matchup City want to create. There were positives though. Kai Havertz was the right call in terms of pressing and structure he helped set the tone for the equaliser. And defensively, Piero Hincapié looked much more secure in his duel compared to earlier games, especially in isolated defensive moments. Ultimately though, it felt like a combination of small individual mistakes and a broader mismatch in structure. Against City, you can’t afford to lose both width protection and vertical threat at the same time and that’s where Arsenal fell just short.

Pythagoras In Boots ⚽️

38,542 просмотров • 5 месяцев назад

“They need to go sit the Heisman on his dresser. He was definitely the best player in college football” - Andre Johnson on Travis Hunter. The Hall of Fame receiver doesn’t know if Hunter can play both sides of the ball exactly as he did in college, but knows the Heisman front runner is a one of a kind talent. One that Johnson was tapped into the entire year. He wanted to see what Travis Hunter had in store for fans each week! Pivot Podcast on YouTube & all streaming platforms. #ThePivot #AndreJohnson #TravisHunter #NFL #HeismanTrophy

“They need to go sit the Heisman on his dresser. He was definitely the best player in college football” - Andre Johnson on Travis Hunter. The Hall of Fame receiver doesn’t know if Hunter can play both sides of the ball exactly as he did in college, but knows the Heisman front runner is a one of a kind talent. One that Johnson was tapped into the entire year. He wanted to see what Travis Hunter had in store for fans each week! Pivot Podcast on YouTube & all streaming platforms. #ThePivot #AndreJohnson #TravisHunter #NFL #HeismanTrophy

Ryan Clark

116,642 просмотров • 1 год назад

This lady bursts into a busy laundromat 🧺 instead of physically looking for her daughter’s “$500 tracksuit” she never makes it past the washing machine section and starts throwing around accusations, screaming, cursing and making threats to call police and demanding to see the cameras only to find the treasured garments in the dryer exactly where her daughter left them wet with the Dryer door hanging open.😡 In the second part of the clip there is no self reflection, no shame, humility instead she pretty much Pat herself in the back for “being at the ready behind her kids”🙄 I am puzzled by this mindset, cannot wrap my head around this kind of behavior, it’s almost psychopathic wouldn’t you say?🤯

This lady bursts into a busy laundromat 🧺 instead of physically looking for her daughter’s “$500 tracksuit” she never makes it past the washing machine section and starts throwing around accusations, screaming, cursing and making threats to call police and demanding to see the cameras only to find the treasured garments in the dryer exactly where her daughter left them wet with the Dryer door hanging open.😡 In the second part of the clip there is no self reflection, no shame, humility instead she pretty much Pat herself in the back for “being at the ready behind her kids”🙄 I am puzzled by this mindset, cannot wrap my head around this kind of behavior, it’s almost psychopathic wouldn’t you say?🤯

ℳ𝒜𝒢𝒜_𝒳_𝒯𝒾𝓂𝑒𝓈 𝒟𝒶𝒾𝓁𝓎 𝒩𝑒𝓌𝓈🇺🇸

43,779 просмотров • 2 месяцев назад

This is a pretty good example of the kind of tankie argument you see used to defend North Korea. On its face, it should be ridiculous enough to dismiss, but more people seem to be buying into the basic framing that America is the real evil, therefore North Korea must somehow be good. It feels like critical theory turned up to 11, where everything gets flattened into oppressor vs. oppressed and America’s sins become a kind of universal defense for anyone positioned against it. The argument is a mess of cherry-picking, whataboutism, and non sequiturs. Even the slavery point is framed in an absurdly selective way. Korea historically had slavery, just not the specifically racialized system of slavery she is invoking. But there is a claim that is simply false. “They’ve [North Korea] never invaded another country.” North Korea invaded South Korea in 1950. It literally started the Korean War.

This is a pretty good example of the kind of tankie argument you see used to defend North Korea. On its face, it should be ridiculous enough to dismiss, but more people seem to be buying into the basic framing that America is the real evil, therefore North Korea must somehow be good. It feels like critical theory turned up to 11, where everything gets flattened into oppressor vs. oppressed and America’s sins become a kind of universal defense for anyone positioned against it. The argument is a mess of cherry-picking, whataboutism, and non sequiturs. Even the slavery point is framed in an absurdly selective way. Korea historically had slavery, just not the specifically racialized system of slavery she is invoking. But there is a claim that is simply false. “They’ve [North Korea] never invaded another country.” North Korea invaded South Korea in 1950. It literally started the Korean War.

Stu Smith

22,596 просмотров • 2 дней назад

the best AD FORMAT i've seen in months came from scrolling past dave ramsey on instagram a call-in advice show. authoritative host behind a desk takes a live call from a woman who can't sleep, diagnoses her problem on air, and recommends the fix with the kind of quiet confidence that makes you forget you're watching an ad quick workflow: - generated a single studio shot of the host at his desk and used it as the identity lock for all six clips - the caller is audio-only through his headset, so there's no second face to keep consistent across cuts - wrote full performance direction into every Omni prompt: the southern drawl, the listening beats where his mouth stays shut, the product pickup - rendered all six clips and stitched in CapCut, nothing reshot the format works because the host sounds like he's run this show for twenty years, and the caller's exhaustion is relatable RT + reply 'DAVE' and i'll send you the full workflow (must follow so i can dm)

the best AD FORMAT i've seen in months came from scrolling past dave ramsey on instagram a call-in advice show. authoritative host behind a desk takes a live call from a woman who can't sleep, diagnoses her problem on air, and recommends the fix with the kind of quiet confidence that makes you forget you're watching an ad quick workflow: - generated a single studio shot of the host at his desk and used it as the identity lock for all six clips - the caller is audio-only through his headset, so there's no second face to keep consistent across cuts - wrote full performance direction into every Omni prompt: the southern drawl, the listening beats where his mouth stays shut, the product pickup - rendered all six clips and stitched in CapCut, nothing reshot the format works because the host sounds like he's run this show for twenty years, and the caller's exhaustion is relatable RT + reply 'DAVE' and i'll send you the full workflow (must follow so i can dm)

Lad

104,598 просмотров • 26 дней назад

The man who turned $225 million into $13.7 billion said something two years ago that the entire AI industry is still catching up to (Save this). Everyone is talking about AI safety but almost nobody is actually working on it. That is not a hot take, that is Leopold Aschenbrenner doing the math out loud after going inside the actual labs and looking at the actual research happening. "There's barely anybody working on it. You could basically just talk to a couple few dozen people who are actually kind of good on the issue. And that's maybe even overstating it." He ran the numbers directly, and the ratio was staggering even then. Approximately 300 people were working on AI alignment globally against roughly 100,000 ML researchers, a ratio of 300 to 100,000. At the time, OpenAI had 400 employees and exactly seven people on its scalable alignment team, the group responsible for solving the long-term safety problem. DeepMind had thousands of researchers and roughly 20 working on alignment, and as Aschenbrenner pointedly noted, they were not exactly deploying their strongest scientists to the job. Two years later, it has only gotten worse. In February 2026, OpenAI dissolved its Mission Alignment unit entirely, a six-person team that had existed for just 16 months and redistributed the staff into product lines. The Future of Life Institute's AI Safety Index called out OpenAI by name this year, urging the company to rebuild lost safety team capacity. Stanford's 2026 AI Index confirmed that responsible AI is simply not keeping pace with AI capability, with safety benchmarks lagging while incidents rise sharply. The International AI Safety Report 2026, backed by 29 nations and over 100 experts, explicitly warned that capabilities are scaling faster than any mechanism we have to verify they are safe. What makes this clip so important is where Aschenbrenner started from. When he first encountered the AI safety discourse, he assumed the problem was covered, everyone was talking about it, so surely everyone was working on it. Then he actually looked at the research, sat with the systems, and came face to face with what was really there. "I was like, oh my god, this sucks. Nobody's actually doing anything. Like you've got to actually do things, people." The gap between the volume of online discourse and the actual number of people doing hard technical alignment work was not a minor mismatch two years ago and the labs have spent the time since making it larger, not smaller. We need more safety!

The man who turned $225 million into $13.7 billion said something two years ago that the entire AI industry is still catching up to (Save this). Everyone is talking about AI safety but almost nobody is actually working on it. That is not a hot take, that is Leopold Aschenbrenner doing the math out loud after going inside the actual labs and looking at the actual research happening. "There's barely anybody working on it. You could basically just talk to a couple few dozen people who are actually kind of good on the issue. And that's maybe even overstating it." He ran the numbers directly, and the ratio was staggering even then. Approximately 300 people were working on AI alignment globally against roughly 100,000 ML researchers, a ratio of 300 to 100,000. At the time, OpenAI had 400 employees and exactly seven people on its scalable alignment team, the group responsible for solving the long-term safety problem. DeepMind had thousands of researchers and roughly 20 working on alignment, and as Aschenbrenner pointedly noted, they were not exactly deploying their strongest scientists to the job. Two years later, it has only gotten worse. In February 2026, OpenAI dissolved its Mission Alignment unit entirely, a six-person team that had existed for just 16 months and redistributed the staff into product lines. The Future of Life Institute's AI Safety Index called out OpenAI by name this year, urging the company to rebuild lost safety team capacity. Stanford's 2026 AI Index confirmed that responsible AI is simply not keeping pace with AI capability, with safety benchmarks lagging while incidents rise sharply. The International AI Safety Report 2026, backed by 29 nations and over 100 experts, explicitly warned that capabilities are scaling faster than any mechanism we have to verify they are safe. What makes this clip so important is where Aschenbrenner started from. When he first encountered the AI safety discourse, he assumed the problem was covered, everyone was talking about it, so surely everyone was working on it. Then he actually looked at the research, sat with the systems, and came face to face with what was really there. "I was like, oh my god, this sucks. Nobody's actually doing anything. Like you've got to actually do things, people." The gap between the volume of online discourse and the actual number of people doing hard technical alignment work was not a minor mismatch two years ago and the labs have spent the time since making it larger, not smaller. We need more safety!

Milk Road AI

162,438 просмотров • 3 месяцев назад

🚨 WARNING: THIS IS HOW 2008 CRASH STARTS AGAIN!! The US housing market is now at one of the most UNAFFORDABLE points in history. This is a $47 TRILLION market, and it is now breaking affordability. Real US home prices just hit about 420. The 2006 bubble peak was about 266. And if you think this is just another scary chart YOU ARE COMPLETELY WRONG. From 2000 to 2026, median home prices rose about 217%, while income rose about 153%. And rates are the killer. The 30-year fixed mortgage rate is still about 6.09%. That is HIGH enough to break demand. At 6%, the monthly payment is the real problem. Prices can go sideways and buyers still tap out. And a small move in rates matters way more than people think. Another 0.50% from here is not noise. It is a payment shock. Rates do NOT need to go to 8% to freeze housing. 6% is already enough to cap buyers and kill volume. Builders are saying the same thing. They keep warning that elevated mortgage rates are the biggest problem, and many expect that problem to stay in 2026. Builder confidence is still weak too. THIS IS EXACTLY HOW 2006 STARTS. Payment stress stays HIGH. And it does NOT matter if prices stop going up, because the monthly bill is still heavy enough to push buyers out. So demand does NOT collapse in one headline. It just quietly disappears. Then the sequence always looks the same. - Transactions die first, because people cannot qualify or they do not want to lock in a brutal payment. - Then confidence dies, because everyone sees listings sit longer and concessions start showing up. - Then the real economy feels it, because housing is not just housing, it is moving, renovations, furniture, credit creation, fees, and jobs. That is why 2006 did NOT crash in one day. It froze. Then it cracked. Then it broke. And most people only noticed when the damage was already everywhere. I’ve studied macro for 10 years and I called almost every major market top, including the October BTC ATH. Follow and turn notifications on. I’ll post the warning BEFORE it hits the headlines.

🚨 WARNING: THIS IS HOW 2008 CRASH STARTS AGAIN!! The US housing market is now at one of the most UNAFFORDABLE points in history. This is a $47 TRILLION market, and it is now breaking affordability. Real US home prices just hit about 420. The 2006 bubble peak was about 266. And if you think this is just another scary chart YOU ARE COMPLETELY WRONG. From 2000 to 2026, median home prices rose about 217%, while income rose about 153%. And rates are the killer. The 30-year fixed mortgage rate is still about 6.09%. That is HIGH enough to break demand. At 6%, the monthly payment is the real problem. Prices can go sideways and buyers still tap out. And a small move in rates matters way more than people think. Another 0.50% from here is not noise. It is a payment shock. Rates do NOT need to go to 8% to freeze housing. 6% is already enough to cap buyers and kill volume. Builders are saying the same thing. They keep warning that elevated mortgage rates are the biggest problem, and many expect that problem to stay in 2026. Builder confidence is still weak too. THIS IS EXACTLY HOW 2006 STARTS. Payment stress stays HIGH. And it does NOT matter if prices stop going up, because the monthly bill is still heavy enough to push buyers out. So demand does NOT collapse in one headline. It just quietly disappears. Then the sequence always looks the same. - Transactions die first, because people cannot qualify or they do not want to lock in a brutal payment. - Then confidence dies, because everyone sees listings sit longer and concessions start showing up. - Then the real economy feels it, because housing is not just housing, it is moving, renovations, furniture, credit creation, fees, and jobs. That is why 2006 did NOT crash in one day. It froze. Then it cracked. Then it broke. And most people only noticed when the damage was already everywhere. I’ve studied macro for 10 years and I called almost every major market top, including the October BTC ATH. Follow and turn notifications on. I’ll post the warning BEFORE it hits the headlines.

Wimar.X

184,343 просмотров • 4 месяцев назад

The Most Powerful Part of “The Sound of Silence” Isn’t the Singing. It’s What Happens Between the Words. Dana Winner’s voice is the first thing you notice. Clean. Warm. Held in place. For a minute I listened the way you listen to a good singer: waiting for the next note to land. Then I stopped doing that. The interesting part was not the note. It was the gap after it. That is the odd job this song has always had. It is packed with language and still keeps pointing at the place language does not reach. People talk. People look up. People sit in the same room. Nothing crosses. Winner does not act that idea out. She does not darken the vowel to prove she understands loneliness. She leaves the line where it is and lets it cool. The melody has air around it. The words sit. You can walk into them if you want to. You can also look away. I looked away and landed somewhere private. A message I drafted and never sent. A talk I kept moving to next week. A moment that did come, and I still said the safer thing. That is a quieter kind of isolation than an empty house. You can have company and still fail to get across the few feet between two chairs. Her voice matters because of where it stands. She does not sound swallowed by the quiet. She sounds just outside it, looking in. The sound is beautiful. The thing it is looking at is not. That mismatch is what makes the performance slightly hard to sit with. You come for the tone. The song keeps turning your face toward the cheaper fact: how often we speak and how rarely we arrive. The world around the song has changed. Distance collapsed. A voice can travel farther in a second than it used to travel in a week. None of that fixed the smaller problem. Being reached is not the same as being heard. Being heard is not the same as being taken in. So the song does not survive because it predicted a mute planet. It survives because it still describes a noisy one, full of people missing each other by inches. Sometimes the silence between two people is not empty. It is the sentence neither of them would risk.

The Most Powerful Part of “The Sound of Silence” Isn’t the Singing. It’s What Happens Between the Words. Dana Winner’s voice is the first thing you notice. Clean. Warm. Held in place. For a minute I listened the way you listen to a good singer: waiting for the next note to land. Then I stopped doing that. The interesting part was not the note. It was the gap after it. That is the odd job this song has always had. It is packed with language and still keeps pointing at the place language does not reach. People talk. People look up. People sit in the same room. Nothing crosses. Winner does not act that idea out. She does not darken the vowel to prove she understands loneliness. She leaves the line where it is and lets it cool. The melody has air around it. The words sit. You can walk into them if you want to. You can also look away. I looked away and landed somewhere private. A message I drafted and never sent. A talk I kept moving to next week. A moment that did come, and I still said the safer thing. That is a quieter kind of isolation than an empty house. You can have company and still fail to get across the few feet between two chairs. Her voice matters because of where it stands. She does not sound swallowed by the quiet. She sounds just outside it, looking in. The sound is beautiful. The thing it is looking at is not. That mismatch is what makes the performance slightly hard to sit with. You come for the tone. The song keeps turning your face toward the cheaper fact: how often we speak and how rarely we arrive. The world around the song has changed. Distance collapsed. A voice can travel farther in a second than it used to travel in a week. None of that fixed the smaller problem. Being reached is not the same as being heard. Being heard is not the same as being taken in. So the song does not survive because it predicted a mute planet. It survives because it still describes a noisy one, full of people missing each other by inches. Sometimes the silence between two people is not empty. It is the sentence neither of them would risk.

Emotion & Music

15,311 просмотров • 21 дней назад

Prime Intellect's will brown says continual learning could be solved in the first half of 2026: "Continual learning is going to fall pretty quickly, I think. It's more of an engineering problem. No one's actually trying." "OpenAI and Anthropic don't want to continuously train their models for each user. It's expensive and annoying and hard to serve at scale. But from a research perspective, we do continue learning where they just keep training the model more and it knows more stuff because they put more of the Internet in it." "I think there's a lot of experimentation around exactly the recipe that's going to be the most reliable. But we kind of have a grab bag of six or seven tricks that kind of work, or they work in different ways, and you can mix and match them. And it's just going to be like whatever's the best combination of these tricks." "People are going to experiment with it and find the versions that work the best. And there doesn't seem to be any big wall inside that prevents that from being practical."

Prime Intellect's will brown says continual learning could be solved in the first half of 2026: "Continual learning is going to fall pretty quickly, I think. It's more of an engineering problem. No one's actually trying." "OpenAI and Anthropic don't want to continuously train their models for each user. It's expensive and annoying and hard to serve at scale. But from a research perspective, we do continue learning where they just keep training the model more and it knows more stuff because they put more of the Internet in it." "I think there's a lot of experimentation around exactly the recipe that's going to be the most reliable. But we kind of have a grab bag of six or seven tricks that kind of work, or they work in different ways, and you can mix and match them. And it's just going to be like whatever's the best combination of these tricks." "People are going to experiment with it and find the versions that work the best. And there doesn't seem to be any big wall inside that prevents that from being practical."

TBPN

72,357 просмотров • 6 месяцев назад

I built KumoMoments because I am the creator I was trying to help🫠 I work full-time with a lot of overtime. Streaming is basically how I unwind and spend time with my viewers. By the time I’m done, I just wanna sleep. 😭 Outside of work and streams, I spend time with my husband and cats, explore a new country that we just relocated to, and just… live my life. I don’t have and shouldn’t be wasting time scrubbing through six hours of my VOD, finding highlights, editing them, and scheduling content for YT/Tiktok before work the next day. So I thought… why can’t I just use AI to help me find the moments worth looking at? I still choose what gets clipped, what gets sent to my editor, and what gets posted. Maybe this is exactly the kind of problem AI should be solving🕶️ maybe this is why I decided to share the tool😊💙 maybe this is also why I’m willing to pour my heart and soul into this pet project🥺 Shameless product placement:

I built KumoMoments because I am the creator I was trying to help🫠 I work full-time with a lot of overtime. Streaming is basically how I unwind and spend time with my viewers. By the time I’m done, I just wanna sleep. 😭 Outside of work and streams, I spend time with my husband and cats, explore a new country that we just relocated to, and just… live my life. I don’t have and shouldn’t be wasting time scrubbing through six hours of my VOD, finding highlights, editing them, and scheduling content for YT/Tiktok before work the next day. So I thought… why can’t I just use AI to help me find the moments worth looking at? I still choose what gets clipped, what gets sent to my editor, and what gets posted. Maybe this is exactly the kind of problem AI should be solving🕶️ maybe this is why I decided to share the tool😊💙 maybe this is also why I’m willing to pour my heart and soul into this pet project🥺 Shameless product placement:

Kumomo Rin/クモモリン Living in 🇯🇵

15,046 просмотров • 2 месяцев назад

What if Your Neural Network Was Forced to Obey Physics? Physics-Informed Neural Networks (PINNs) are neural networks trained to satisfy a differential equation by building the PDE residual directly into the loss. They emerged from a very practical problem...classical PDE pipelines can be brilliant, but they often demand heavy discretization work (meshes, stencils, stability tuning), and the method you build is usually tied to one geometry and one solver setup. A PINN flips the workflow by representing the solution itself as a smooth function uᵩ(x,t) and enforcing the physics everywhere you choose to sample the domain. People often meet PINNs in the least helpful way...via a flashy solution plot, and almost no explanation of what was enforced to get it. In this series we keep the enforcement visible. We pick a differential equation, represent the unknown solution as a flexible function, measure how well that function satisfies the equation across the domain, and train it to reduce that mismatch everywhere we sample. A normal neural net learns from labels...you give it inputs and target outputs. A PINN learns from a differential equation...you give it inputs (x,t) and it gets punished whenever its output fails the PDE. By punish we mean that the loss increases when the mismatch is large we reward it if the loss decreases as the mismatch gets smaller. The network isn’t replacing physics, it’s becoming a flexible function that is forced to satisfy the same calculus you’d impose on any candidate solution. The math breakdown: We start with a PDE we want to solve on a domain Ω. Write it as uₜ(x,t) + N(u(x,t), uₓ(x,t), uₓₓ(x,t), …) = 0 for (x,t) in Ω A PINN replaces the unknown function u with a neural network output uᵩ(x,t) Now define the physics residual by plugging uᵩ into the PDE rᵩ(x,t) = ∂uᵩ/∂t + N(uᵩ, ∂uᵩ/∂x, ∂²uᵩ/∂x², …) If uᵩ were an exact solution, we would have rᵩ(x,t) = 0 everywhere. We may also have data points (xᵢ,tᵢ,uᵢ) from measurements or a known initial condition. The training objective is just a weighted sum of squared errors L(ᵩ) = L_data(ᵩ) + λ L_phys(ᵩ) + L_bc/ic(ᵩ) with L_data(ᵩ) = meanᵢ |uᵩ(xᵢ,tᵢ) − uᵢ|² L_phys(ᵩ) = meanⱼ |rᵩ(xⱼ,tⱼ)|² where (xⱼ,tⱼ) are the collocation points in Ω L_bc/ic(ᵩ) = penalties enforcing boundary conditions and initial conditions The key technical step is that the derivatives inside rᵩ are computed by automatic differentiation ∂uᵩ/∂t, ∂uᵩ/∂x, ∂²uᵩ/∂x², … So we can differentiate the total loss L(ᵩ) with respect to ᵩ and train with gradient descent. This is the whole idea behind PINNs. Learn a function, but make the PDE part of the loss, so the network is trained to be a solution, not just a curve-fitter. In the render, the main 3D surface is the network’s current guess uᵩ(x,t), drawn as a living sheet over the (x,t) plane. Hovering above is the neural scaffold...a visible graph of feature nodes and connections. The bright tension threads are the physics residual rᵩ(x,t): each thread tethers a collocation bead on the sheet up to the scaffold, and it thickens and brightens exactly where |rᵩ| is large (color encodes the sign). As training runs, those threads go slack across the domain not because we hid the error, but because the network has actually been pushed toward rᵩ(x,t) ≈ 0. #PINNs #PhysicsInformedNeuralNetworks #ScientificMachineLearning #PDE #DifferentialEquations #Optimization #MachineLearning #AppliedMath #ComputationalPhysics

What if Your Neural Network Was Forced to Obey Physics? Physics-Informed Neural Networks (PINNs) are neural networks trained to satisfy a differential equation by building the PDE residual directly into the loss. They emerged from a very practical problem...classical PDE pipelines can be brilliant, but they often demand heavy discretization work (meshes, stencils, stability tuning), and the method you build is usually tied to one geometry and one solver setup. A PINN flips the workflow by representing the solution itself as a smooth function uᵩ(x,t) and enforcing the physics everywhere you choose to sample the domain. People often meet PINNs in the least helpful way...via a flashy solution plot, and almost no explanation of what was enforced to get it. In this series we keep the enforcement visible. We pick a differential equation, represent the unknown solution as a flexible function, measure how well that function satisfies the equation across the domain, and train it to reduce that mismatch everywhere we sample. A normal neural net learns from labels...you give it inputs and target outputs. A PINN learns from a differential equation...you give it inputs (x,t) and it gets punished whenever its output fails the PDE. By punish we mean that the loss increases when the mismatch is large we reward it if the loss decreases as the mismatch gets smaller. The network isn’t replacing physics, it’s becoming a flexible function that is forced to satisfy the same calculus you’d impose on any candidate solution. The math breakdown: We start with a PDE we want to solve on a domain Ω. Write it as uₜ(x,t) + N(u(x,t), uₓ(x,t), uₓₓ(x,t), …) = 0 for (x,t) in Ω A PINN replaces the unknown function u with a neural network output uᵩ(x,t) Now define the physics residual by plugging uᵩ into the PDE rᵩ(x,t) = ∂uᵩ/∂t + N(uᵩ, ∂uᵩ/∂x, ∂²uᵩ/∂x², …) If uᵩ were an exact solution, we would have rᵩ(x,t) = 0 everywhere. We may also have data points (xᵢ,tᵢ,uᵢ) from measurements or a known initial condition. The training objective is just a weighted sum of squared errors L(ᵩ) = L_data(ᵩ) + λ L_phys(ᵩ) + L_bc/ic(ᵩ) with L_data(ᵩ) = meanᵢ |uᵩ(xᵢ,tᵢ) − uᵢ|² L_phys(ᵩ) = meanⱼ |rᵩ(xⱼ,tⱼ)|² where (xⱼ,tⱼ) are the collocation points in Ω L_bc/ic(ᵩ) = penalties enforcing boundary conditions and initial conditions The key technical step is that the derivatives inside rᵩ are computed by automatic differentiation ∂uᵩ/∂t, ∂uᵩ/∂x, ∂²uᵩ/∂x², … So we can differentiate the total loss L(ᵩ) with respect to ᵩ and train with gradient descent. This is the whole idea behind PINNs. Learn a function, but make the PDE part of the loss, so the network is trained to be a solution, not just a curve-fitter. In the render, the main 3D surface is the network’s current guess uᵩ(x,t), drawn as a living sheet over the (x,t) plane. Hovering above is the neural scaffold...a visible graph of feature nodes and connections. The bright tension threads are the physics residual rᵩ(x,t): each thread tethers a collocation bead on the sheet up to the scaffold, and it thickens and brightens exactly where |rᵩ| is large (color encodes the sign). As training runs, those threads go slack across the domain not because we hid the error, but because the network has actually been pushed toward rᵩ(x,t) ≈ 0. #PINNs #PhysicsInformedNeuralNetworks #ScientificMachineLearning #PDE #DifferentialEquations #Optimization #MachineLearning #AppliedMath #ComputationalPhysics

Mathelirium

17,459 просмотров • 4 месяцев назад

The original Little House on the Prairie went off the air in 1983. In 2024, Nielsen measured 13.25 billion minutes of streaming viewership for it. That's more than any other legacy title on any platform. A show that's been off the air for 41 years is outperforming series that launched last quarter. Netflix saw that number and made a very specific bet. They renewed Season 2 before a single viewer had seen the first episode. Netflix rarely makes that move for a drama. The confidence tells you everything about how they read the data: 90 years of compound interest on a book franchise that's sold 73 million copies in 100+ countries. The showrunner choice tells you how seriously they're taking it. Rebecca Sonnenshine ran The Boys, one of the most tonally aggressive shows on television. You don't hand her a cozy reboot. You hand her a property where you need someone who knows how to build tension inside warmth, which is exactly what the original books did. Laura Ingalls Wilder wrote survival stories disguised as children's literature. The producer angle is even better. Trip Friendly is running this. His father, Ed Friendly, produced the original NBC series. That's a 50-year generational handoff on the same property. Netflix is essentially paying for inherited institutional knowledge of what makes this franchise work. And here's the strategic layer: Netflix has been quietly building a comfort programming pipeline. Virgin River. Sweet Magnolias. Ransom Canyon. These shows never trend on Film Twitter. They don't win Emmys. They generate the kind of steady, low-churn viewership that subscription businesses are actually built on. Every streamer is chasing the next prestige hit. Netflix looked at the data and realized the most-watched legacy title on all of streaming is a family drama about a log cabin in Kansas. Sometimes the smartest content bet is the one nobody's bragging about at dinner parties.

The original Little House on the Prairie went off the air in 1983. In 2024, Nielsen measured 13.25 billion minutes of streaming viewership for it. That's more than any other legacy title on any platform. A show that's been off the air for 41 years is outperforming series that launched last quarter. Netflix saw that number and made a very specific bet. They renewed Season 2 before a single viewer had seen the first episode. Netflix rarely makes that move for a drama. The confidence tells you everything about how they read the data: 90 years of compound interest on a book franchise that's sold 73 million copies in 100+ countries. The showrunner choice tells you how seriously they're taking it. Rebecca Sonnenshine ran The Boys, one of the most tonally aggressive shows on television. You don't hand her a cozy reboot. You hand her a property where you need someone who knows how to build tension inside warmth, which is exactly what the original books did. Laura Ingalls Wilder wrote survival stories disguised as children's literature. The producer angle is even better. Trip Friendly is running this. His father, Ed Friendly, produced the original NBC series. That's a 50-year generational handoff on the same property. Netflix is essentially paying for inherited institutional knowledge of what makes this franchise work. And here's the strategic layer: Netflix has been quietly building a comfort programming pipeline. Virgin River. Sweet Magnolias. Ransom Canyon. These shows never trend on Film Twitter. They don't win Emmys. They generate the kind of steady, low-churn viewership that subscription businesses are actually built on. Every streamer is chasing the next prestige hit. Netflix looked at the data and realized the most-watched legacy title on all of streaming is a family drama about a log cabin in Kansas. Sometimes the smartest content bet is the one nobody's bragging about at dinner parties.

Aakash Gupta

59,686 просмотров • 5 месяцев назад

‘The US has a problem where only people who are a problem for the deep state are ASSASSINATED by lone gunmen.’ —Tucker Carlson on Going Underground ‘Charlie Kirk was murdered a year ago, and I don’t know exactly what that was. But I’ll say that anyone telling you he does know exactly what it was is deluded or lying. I I don’t know why in this world where so many things that we thought were true have turned out to be false. I don’t know why anyone would have full confidence in the incomplete version of events the federal government has given us… I would be very happy if it turns out that Charlie Kirk was murdered by some crazed lone gunman who was upset about trans rights or something. But as a good friend of mine once said to me, the United States has this situation where we have a lot of lone gunmen, a lot of crazy lone gunmen, but none of them ever kill anyone who opposes institutional or who supports institutional power. They only kill people who are a problem for the deep state. It’s kind of weird, isn’t it? Like, what are the odds of that? We had a president [JFK] who talks about limiting the power of CIA and preventing Israel from getting a nuclear weapon at Dimona, and that guy gets killed by a lone gunman. And then you have his brother who’s about to become the Democratic nominee in 1968, who’s like, “Wait, we’re going to get to the bottom of what happened to my brother.” And then he gets killed by another lone gunman, a Christian Palestinian, kind of weird. And then you have a civil rights leader who all of a sudden pivots and starts talking about economic inequality and he gets killed by whom? A lone gunman. And then you have Charlie Kirk, the single most important organiser on the American right with the largest base of young supporters, who says, “Wait a second, why should we be following Israel into another regime change war?” And he gets killed by another lone gunman. I mean, it’s just kind of a pattern that I notice, and maybe it’s all real. Maybe I’m the crazy one. Maybe I’m a conspiracy theorist. But I do think it’s worth pushing a little bit and asking, like, what do we know? And the fastest way to find out what we know is by declassifying the government documents that tell us more. But they never have, and they never will. So don’t expect me, in the face of that kind of unjustifiable secrecy, to buy your story, because I don’t.’ Watch the full interview in the quoted post below 👇

‘The US has a problem where only people who are a problem for the deep state are ASSASSINATED by lone gunmen.’ —Tucker Carlson on Going Underground ‘Charlie Kirk was murdered a year ago, and I don’t know exactly what that was. But I’ll say that anyone telling you he does know exactly what it was is deluded or lying. I I don’t know why in this world where so many things that we thought were true have turned out to be false. I don’t know why anyone would have full confidence in the incomplete version of events the federal government has given us… I would be very happy if it turns out that Charlie Kirk was murdered by some crazed lone gunman who was upset about trans rights or something. But as a good friend of mine once said to me, the United States has this situation where we have a lot of lone gunmen, a lot of crazy lone gunmen, but none of them ever kill anyone who opposes institutional or who supports institutional power. They only kill people who are a problem for the deep state. It’s kind of weird, isn’t it? Like, what are the odds of that? We had a president [JFK] who talks about limiting the power of CIA and preventing Israel from getting a nuclear weapon at Dimona, and that guy gets killed by a lone gunman. And then you have his brother who’s about to become the Democratic nominee in 1968, who’s like, “Wait, we’re going to get to the bottom of what happened to my brother.” And then he gets killed by another lone gunman, a Christian Palestinian, kind of weird. And then you have a civil rights leader who all of a sudden pivots and starts talking about economic inequality and he gets killed by whom? A lone gunman. And then you have Charlie Kirk, the single most important organiser on the American right with the largest base of young supporters, who says, “Wait a second, why should we be following Israel into another regime change war?” And he gets killed by another lone gunman. I mean, it’s just kind of a pattern that I notice, and maybe it’s all real. Maybe I’m the crazy one. Maybe I’m a conspiracy theorist. But I do think it’s worth pushing a little bit and asking, like, what do we know? And the fastest way to find out what we know is by declassifying the government documents that tell us more. But they never have, and they never will. So don’t expect me, in the face of that kind of unjustifiable secrecy, to buy your story, because I don’t.’ Watch the full interview in the quoted post below 👇

Going Underground

64,327 просмотров • 5 дней назад

Pilot cancels flight due to safety concerns: "im not feeling it" A pilot canceled an American Airlines flight for one simple, brutally honest reason: “I’m not feeling it.” No dramatic engine failure. No weather chaos. No cryptic announcement full of airline jargon. Just a human being in the cockpit recognizing that something felt off—and choosing safety over schedule, pressure, or convenience. In an industry where delays spark outrage and cancellations trigger viral meltdowns, this moment is a reminder of what we actually want from the person flying a metal tube at 35,000 feet: self-awareness, caution, and the confidence to say no. Because “not feeling it” isn’t laziness—it’s intuition, experience, and responsibility speaking up before a problem has a chance to exist. We praise pilots for handling emergencies, but maybe we should praise them just as much for preventing one. So yeah, the flight didn’t take off. Plans were delayed. People were annoyed. But everyone walked away safe. And honestly? That’s exactly the kind of energy you want in the cockpit. ✈️ 📸 aviationmp4

Pilot cancels flight due to safety concerns: "im not feeling it" A pilot canceled an American Airlines flight for one simple, brutally honest reason: “I’m not feeling it.” No dramatic engine failure. No weather chaos. No cryptic announcement full of airline jargon. Just a human being in the cockpit recognizing that something felt off—and choosing safety over schedule, pressure, or convenience. In an industry where delays spark outrage and cancellations trigger viral meltdowns, this moment is a reminder of what we actually want from the person flying a metal tube at 35,000 feet: self-awareness, caution, and the confidence to say no. Because “not feeling it” isn’t laziness—it’s intuition, experience, and responsibility speaking up before a problem has a chance to exist. We praise pilots for handling emergencies, but maybe we should praise them just as much for preventing one. So yeah, the flight didn’t take off. Plans were delayed. People were annoyed. But everyone walked away safe. And honestly? That’s exactly the kind of energy you want in the cockpit. ✈️ 📸 aviationmp4

aircraftmaintenancengineer

2,874,108 просмотров • 8 месяцев назад

Well we had ourselves a year, didn’t we folks? Didn’t really know where i would be at this point. Every thought crossed my mind from being black balled to being signed (brother). Seems like I fell somewhere in the middle of all that. 2025 was certainly the best and busiest year of my wrestling career so for. 78 matches in 19 different states. Wrestling against people from the FED, AEW, TNA, NWA, MLW, AAA and other important 3 letter acronyms! Also - The got dam BRAND exploded. Gained more followers as the year went on and it helped me secure a WWE tryout in which I failed for the 2nd time! You can call me a 2x WWE Tryout finalist. I swear I was the last one cut. Would have made it if Coach didn’t hate me (Politics, brother)…. But it was an incredible experience with some talented people. Most of all, I had fun. And even though I haven’t “made it” (“yet”, as my pals say), I have been able to organically create a lane for myself in the business of independent wrestling. Am I Officially an indie darling? Maybe not but I’m pretty got dam closer than I’ll ever be! So for 2026? Idk what the hell is going to happen. So don’t ask! 😁. I’ll probably continue to piss people off on the internet for “not respecting the business” or some other dumb reason they make up in their head but a wise man and close personal friend, Sir Mix A Lot, once told me - “When you get haters, you actually feel like a success”. And Success is subjective. Everyone has an opinion on what it is. My opinion is that I am a success and I will always be proud of what I’ve been able to do. With all that said, I’m accepting bookings in 2026. Here’s to more laughs, sidewalk slams, GOOZLE Gimmicks, and unrelenting self confidence that inflates my already giant head And never forget… WHO’S THE MAN?!? BEN! 😃👍🏻

Big Trouble Ben Bishop

17,709 просмотров • 8 месяцев назад

🏹CHILD OF THE JUNGLE🏹 A gaze you simply cannot look away from. Touching innocence and an angelic face unexpectedly found in the harsh jungle. Two and a half minutes of absolute, primal beauty, where the untamed wilderness becomes a stage for a brave young hero. Through mud, sweat, and blinding sun rays, he moves with feline grace. Every line of his body, every tense muscle, and the burning gaze of a predator command your attention. He blurs the line between human and wild nature - from the dizzying tops of giant trees to mysterious swamps, this fearless young warrior walks his path with chilling ease and absolute confidence. This film is pure visual ecstasy on the cutting edge of advanced neuro-technologies. Flawless pixel-perfect quality, incredible detailing, and a tribute to the National Geographic aesthetic. We are exploring the very physics of reality here: how light hits wet skin, how mud smears tribal paint, how life pulses in every single frame. Mind-blowing generation, hypnotic beauty, and a flawless picture. Just watch and immerse yourself in this perfect visual meditation. 🤩🤗 #cinematicart #bbart #mowgli #visualart #survival #amazonjungle #nativeamerican #cinematic #digitalart #индеец #выживание #амазонкаation

🏹CHILD OF THE JUNGLE🏹 A gaze you simply cannot look away from. Touching innocence and an angelic face unexpectedly found in the harsh jungle. Two and a half minutes of absolute, primal beauty, where the untamed wilderness becomes a stage for a brave young hero. Through mud, sweat, and blinding sun rays, he moves with feline grace. Every line of his body, every tense muscle, and the burning gaze of a predator command your attention. He blurs the line between human and wild nature - from the dizzying tops of giant trees to mysterious swamps, this fearless young warrior walks his path with chilling ease and absolute confidence. This film is pure visual ecstasy on the cutting edge of advanced neuro-technologies. Flawless pixel-perfect quality, incredible detailing, and a tribute to the National Geographic aesthetic. We are exploring the very physics of reality here: how light hits wet skin, how mud smears tribal paint, how life pulses in every single frame. Mind-blowing generation, hypnotic beauty, and a flawless picture. Just watch and immerse yourself in this perfect visual meditation. 🤩🤗 #cinematicart #bbart #mowgli #visualart #survival #amazonjungle #nativeamerican #cinematic #digitalart #индеец #выживание #амазонкаation

BB Art

30,368 просмотров • 1 месяц назад

Case File - The Train Track Killer Exhibit No - 612 Jackson was a 21-year-old cocky Iowa jock, the kind of guy who knew exactly how good he looked and loved making sure everyone else did too. Tall, broad-shouldered, with sun-kissed skin stretched tight over hard-earned muscle. That night he showed up to the house party in tight, frayed denim shorts that barely reached mid-thigh, showing off his powerful legs, and an open flannel shirt that hung loose over his sculpted chest and abs. Every time he moved, the fabric shifted, giving teasing glimpses of his toned body. He drank hard, flirted harder, and soaked up the attention as girls (and a few guys) openly stared, complimented, and touched his arms, chest, and abs. Jackson just smirked, flexing subtly, loving every second. As the hours passed, the drinks kept coming. By the end of the night, Jackson was heavily intoxicated—laughing too loud, stumbling slightly, that cocky confidence now sloppy and unguarded. He realized too late that he’d lost his phone. No Uber. So he started walking home along the quiet rural road, the cool night air doing little to sober him up. He weaved unsteadily, muttering to himself, his muscular legs struggling to stay straight. Unbeknownst to him, the Train Track Killer was out driving that night. He wasn’t actively hunting, but when his headlights caught the stumbling, barely-dressed jock with that perfect body on full display, he couldn’t resist. Opportunity like this didn’t come often. The killer pulled up slowly beside him, window down, voice full of fake concern: “You okay, man? Looking a little rough. Need a ride?” Drunk and arrogant, Jackson laughed. “What, you just wanna stare at this body too?” he slurred, flexing his chest playfully. He climbed in anyway, completely unaware how true his joke was. During the ride, Jackson wouldn’t shut up—bragging about how the girls at the party were all over him, how they couldn’t keep their hands off his “sexy fucking abs and legs.” He kept touching himself absentmindedly as he talked, completely oblivious. Then, with a cocky grin, he joked, “You’re not some serial killer, are you?” The driver glanced over calmly. “Actually… I am.” Jackson burst out laughing. “Good one, dude. You’re a joker.” Without warning, the killer plunged a syringe deep into Jackson’s neck. The jock’s eyes widened in shock, his strong body jerking once before going limp as the drug took hold. The killer smiled, one hand sliding over Jackson’s warm, firm chest and abs as he drove the rest of the way home, savoring his prize. Back at his isolated house, the killer dragged the unconscious hunk inside, stripped him down to just his tight underwear, and tightly restrained him to a sturdy chair—arms behind his back, legs spread. When Jackson finally woke up groggy and disoriented, the taunting began. The killer circled him slowly, hands roaming freely over every inch of that perfect athletic body. “All that cockiness… and now you’re mine.” Jackson struggled, panicked, but it was useless. The strangling was slow and intimate. The killer’s hands tightened around the jock’s thick neck while continuing to touch and fondle him. Jackson’s muscular body bucked and flexed desperately against the ropes until his strength finally faded. Even after death, the killer wasn’t done—fondling, kissing, fucking, and using the still-warm body before finally disposing of it. Jackson’s corpse was dumped near the railroad tracks, left to decompose for six long days under the summer sun before anyone found what remained. #serialkiller #aigenerated #fiction

Case File - The Train Track Killer Exhibit No - 612 Jackson was a 21-year-old cocky Iowa jock, the kind of guy who knew exactly how good he looked and loved making sure everyone else did too. Tall, broad-shouldered, with sun-kissed skin stretched tight over hard-earned muscle. That night he showed up to the house party in tight, frayed denim shorts that barely reached mid-thigh, showing off his powerful legs, and an open flannel shirt that hung loose over his sculpted chest and abs. Every time he moved, the fabric shifted, giving teasing glimpses of his toned body. He drank hard, flirted harder, and soaked up the attention as girls (and a few guys) openly stared, complimented, and touched his arms, chest, and abs. Jackson just smirked, flexing subtly, loving every second. As the hours passed, the drinks kept coming. By the end of the night, Jackson was heavily intoxicated—laughing too loud, stumbling slightly, that cocky confidence now sloppy and unguarded. He realized too late that he’d lost his phone. No Uber. So he started walking home along the quiet rural road, the cool night air doing little to sober him up. He weaved unsteadily, muttering to himself, his muscular legs struggling to stay straight. Unbeknownst to him, the Train Track Killer was out driving that night. He wasn’t actively hunting, but when his headlights caught the stumbling, barely-dressed jock with that perfect body on full display, he couldn’t resist. Opportunity like this didn’t come often. The killer pulled up slowly beside him, window down, voice full of fake concern: “You okay, man? Looking a little rough. Need a ride?” Drunk and arrogant, Jackson laughed. “What, you just wanna stare at this body too?” he slurred, flexing his chest playfully. He climbed in anyway, completely unaware how true his joke was. During the ride, Jackson wouldn’t shut up—bragging about how the girls at the party were all over him, how they couldn’t keep their hands off his “sexy fucking abs and legs.” He kept touching himself absentmindedly as he talked, completely oblivious. Then, with a cocky grin, he joked, “You’re not some serial killer, are you?” The driver glanced over calmly. “Actually… I am.” Jackson burst out laughing. “Good one, dude. You’re a joker.” Without warning, the killer plunged a syringe deep into Jackson’s neck. The jock’s eyes widened in shock, his strong body jerking once before going limp as the drug took hold. The killer smiled, one hand sliding over Jackson’s warm, firm chest and abs as he drove the rest of the way home, savoring his prize. Back at his isolated house, the killer dragged the unconscious hunk inside, stripped him down to just his tight underwear, and tightly restrained him to a sturdy chair—arms behind his back, legs spread. When Jackson finally woke up groggy and disoriented, the taunting began. The killer circled him slowly, hands roaming freely over every inch of that perfect athletic body. “All that cockiness… and now you’re mine.” Jackson struggled, panicked, but it was useless. The strangling was slow and intimate. The killer’s hands tightened around the jock’s thick neck while continuing to touch and fondle him. Jackson’s muscular body bucked and flexed desperately against the ropes until his strength finally faded. Even after death, the killer wasn’t done—fondling, kissing, fucking, and using the still-warm body before finally disposing of it. Jackson’s corpse was dumped near the railroad tracks, left to decompose for six long days under the summer sun before anyone found what remained. #serialkiller #aigenerated #fiction

The Serial Killer Files Ai

15,206 просмотров • 2 месяцев назад