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field lecture

Cristina Méndez

59,016 subscribers

54,848 次观看 • 11 天前 •via X (Twitter)

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field lecture

field lecture

Cristina Méndez

40,825 次观看 • 1 个月前

Field Marshal Sam Manekshaw's Lecture at St. Xavier's College, Bombay on Leadership.

Field Marshal Sam Manekshaw's Lecture at St. Xavier's College, Bombay on Leadership.

𝐒𝐓𝐀𝐕𝐊𝐀

42,792 次观看 • 3 年前

New lecture! This one is a recap of a bunch of history of preferences, the nature of rewards, how RLHF is formulated, which were once seen as central problems in the field. How much as changed. Still... super interesting to understand our optimization tools today. Books coming soon :D 00:00 Intro & context 07:34 A short history of preferences (from Aristotle to the VNM Utility Theorem) 20:17 A brief overview of preference data (from the last two years of my practice) 31:11 Open questions in RLHF data Lecture 8, covering Chapters 10 & 11 of my book.

New lecture! This one is a recap of a bunch of history of preferences, the nature of rewards, how RLHF is formulated, which were once seen as central problems in the field. How much as changed. Still... super interesting to understand our optimization tools today. Books coming soon :D 00:00 Intro & context 07:34 A short history of preferences (from Aristotle to the VNM Utility Theorem) 20:17 A brief overview of preference data (from the last two years of my practice) 31:11 Open questions in RLHF data Lecture 8, covering Chapters 10 & 11 of my book.

Nathan Lambert

28,579 次观看 • 1 个月前

Introducing the 18th S R Nathan Fellow, Dr Shawn Lum, Senior Lecturer at The Asian School of the Environment, Nanyang Technological University. Through his work in research, teaching and conservation, Dr Lum has spent decades exploring the relationship between people and the natural world. Beyond the lecture hall and the field, he also has a lighter side — shaped by the experiences and stories that sparked his lifelong interest in nature. In his upcoming lecture series, “Singapore, Nature and a World Reimagined,” Dr Lum explores Singapore’s journey from the bold vision of a “Garden City” to the evolving ambition of becoming a “City in Nature.” The lectures examine how this transformation took shape over decades through careful planning, ecological understanding, and a shift in how society views nature. They also make a case for why biodiversity matters — not only for ecosystems, but for the well-being of people and communities, and how Singapore can better protect and value nature at home and beyond. 🎟️Sign up for his first lecture on 9 March 2026 here: 📺Watch his lecture series live on IPS’ Facebook page: 🌳Learn more about Dr Lum’s lectures:

Introducing the 18th S R Nathan Fellow, Dr Shawn Lum, Senior Lecturer at The Asian School of the Environment, Nanyang Technological University. Through his work in research, teaching and conservation, Dr Lum has spent decades exploring the relationship between people and the natural world. Beyond the lecture hall and the field, he also has a lighter side — shaped by the experiences and stories that sparked his lifelong interest in nature. In his upcoming lecture series, “Singapore, Nature and a World Reimagined,” Dr Lum explores Singapore’s journey from the bold vision of a “Garden City” to the evolving ambition of becoming a “City in Nature.” The lectures examine how this transformation took shape over decades through careful planning, ecological understanding, and a shift in how society views nature. They also make a case for why biodiversity matters — not only for ecosystems, but for the well-being of people and communities, and how Singapore can better protect and value nature at home and beyond. 🎟️Sign up for his first lecture on 9 March 2026 here: 📺Watch his lecture series live on IPS’ Facebook page: 🌳Learn more about Dr Lum’s lectures:

Institute of Policy Studies - IPS

237,200 次观看 • 6 个月前

MODI’S ONE-SIDED PREACHING GOT DESTROYED SANDEEP: Is sacrifice always the common citizen’s responsibility? SHEETAL 🎯: Railways cancelled 3 crore waiting tickets. Labourers were forced back home. And you lecture people on public transport? SANDEEP: Mahamanav is also saying do organic farming. SHEETAL ⚡️: It takes at least 3 years to prepare a field for organic farming. Pure impracticality. SANDEEP: And his transport advice? SHEETAL 🔥: He doesn’t even know the condition of public transport. Poor people are travelling packed like sacks.

MODI’S ONE-SIDED PREACHING GOT DESTROYED SANDEEP: Is sacrifice always the common citizen’s responsibility? SHEETAL 🎯: Railways cancelled 3 crore waiting tickets. Labourers were forced back home. And you lecture people on public transport? SANDEEP: Mahamanav is also saying do organic farming. SHEETAL ⚡️: It takes at least 3 years to prepare a field for organic farming. Pure impracticality. SANDEEP: And his transport advice? SHEETAL 🔥: He doesn’t even know the condition of public transport. Poor people are travelling packed like sacks.

Mr Sharma

38,359 次观看 • 3 个月前

This is a lecture by 33rd degree Mason Manly P Hall discussing the AETHER, MAGNETISM and the manipulation of ENERGY. He discusses the absolute IMPORTANCE of protecting one's ENERGY FIELD and what happens WHEN this MAGNETIC FIELD - that holds together ALL of existence FAILS... How and WHY it fails and how YOU each and every one of us IMPACT it in a POSITIVE or NEGATIVE way. Over the years l've read a lot of books and watched numerous videos, many, on SECRET TEACHINGS from 'occultists' and Masons. Imparting 'hidden knowledge' and at times not everything made sense. Only much later l began to understand. Remembering, l will piece together bits of the PUZZLES and l post for YOUR opinion and INPUT....

This is a lecture by 33rd degree Mason Manly P Hall discussing the AETHER, MAGNETISM and the manipulation of ENERGY. He discusses the absolute IMPORTANCE of protecting one's ENERGY FIELD and what happens WHEN this MAGNETIC FIELD - that holds together ALL of existence FAILS... How and WHY it fails and how YOU each and every one of us IMPACT it in a POSITIVE or NEGATIVE way. Over the years l've read a lot of books and watched numerous videos, many, on SECRET TEACHINGS from 'occultists' and Masons. Imparting 'hidden knowledge' and at times not everything made sense. Only much later l began to understand. Remembering, l will piece together bits of the PUZZLES and l post for YOUR opinion and INPUT....

WhatdoIknow

269,251 次观看 • 2 年前

🚨 BIDEN ADMIN LOST TRACK OF 450,000 UNACCOMPANIED MINORS, NOW DEMS ACT SHOCKED? Secretary dropping hard truth: Half a million kids crossed during Biden’s open border disaster. Hundreds of thousands lost track of. Some ended up trafficked, sexually assaulted, working in slaughterhouses. Cartels and smugglers had a field day while Democrats stayed silent. Now they want to lecture everyone? The hypocrisy is insane. Trump’s team is actually finding them and fixing the mess. No more turning a blind eye to child trafficking at the border.

🚨 BIDEN ADMIN LOST TRACK OF 450,000 UNACCOMPANIED MINORS, NOW DEMS ACT SHOCKED? Secretary dropping hard truth: Half a million kids crossed during Biden’s open border disaster. Hundreds of thousands lost track of. Some ended up trafficked, sexually assaulted, working in slaughterhouses. Cartels and smugglers had a field day while Democrats stayed silent. Now they want to lecture everyone? The hypocrisy is insane. Trump’s team is actually finding them and fixing the mess. No more turning a blind eye to child trafficking at the border.

Gunther Eagleman™

21,464 次观看 • 2 个月前

There's a physics lecture with over a million views' worth of value and almost no views. No slides. No notes. Just a green chalkboard and a man who clearly doesn't need one. The professor is V. Balakrishnan, IIT Madras. The course is Classical Physics, recorded for NPTEL — India's free national lecture archive — and quietly sitting on YouTube since 2009. What makes lecture one worth your time isn't production value. It's the opposite. It's a full subject held in one person's head, delivered in order, with nothing between him and the material. He opens with the actual boundary of the field: what classical mechanics can explain, and the precise point where it hands off to quantum theory. No hedging, no filler — every sentence finished before the next one starts. A friend who works in physics told me he went through the entire series and ranks it above courses he paid thousands of dollars for. It's been online for 17 years. Low resolution, zero marketing, still one of the best physics courses on the internet. Sometimes the best teaching in the world was never behind a paywall — it just wasn't in English-language algorithms' way of finding it.

There's a physics lecture with over a million views' worth of value and almost no views. No slides. No notes. Just a green chalkboard and a man who clearly doesn't need one. The professor is V. Balakrishnan, IIT Madras. The course is Classical Physics, recorded for NPTEL — India's free national lecture archive — and quietly sitting on YouTube since 2009. What makes lecture one worth your time isn't production value. It's the opposite. It's a full subject held in one person's head, delivered in order, with nothing between him and the material. He opens with the actual boundary of the field: what classical mechanics can explain, and the precise point where it hands off to quantum theory. No hedging, no filler — every sentence finished before the next one starts. A friend who works in physics told me he went through the entire series and ranks it above courses he paid thousands of dollars for. It's been online for 17 years. Low resolution, zero marketing, still one of the best physics courses on the internet. Sometimes the best teaching in the world was never behind a paywall — it just wasn't in English-language algorithms' way of finding it.

Nainsi Dwivedi

12,346 次观看 • 1 个月前

There is insatiable demand for super-premium experiences. It doesn’t seem like there’s any real limit to what people will pay for something that feels singular. "Rich people are asking for more and more because they can get everything, so they want something even more special. In the Super Bowl, go to Tiffany's, get a blue football, get a lecture from Peyton Manning, walk on the field pregame and come out with a team. And for that I'm going to pay $300,000. In every category there's premiums and people pay crazy."

There is insatiable demand for super-premium experiences. It doesn’t seem like there’s any real limit to what people will pay for something that feels singular. "Rich people are asking for more and more because they can get everything, so they want something even more special. In the Super Bowl, go to Tiffany's, get a blue football, get a lecture from Peyton Manning, walk on the field pregame and come out with a team. And for that I'm going to pay $300,000. In every category there's premiums and people pay crazy."

Patrick OShaughnessy

222,863 次观看 • 9 个月前

We just watched Professor Arthur Mattuck kick off MIT’s ODE course with the one interpretation that most differential equations classes somehow postpone: An ordinary differential equation isn’t primarily a method hunt. It’s a geometric rule. You write dy/dx = f(x,y), and that right-hand side is literally telling you the slope your solution curve must have at each point (x,y). So I built this animation as a visual companion to that first lecture. It draws the direction field (little line elements whose slope is f(x,y)) and then shows integral curves sliding through it...curves that are tangent to the field everywhere they go. Two quick examples from the animation: For dy/dx = −x/y, the slope field steers you onto circles x² + y² = R². You also see a subtle point that gets missed when everything is taught as y(x)...even when the curve exists smoothly, the graph y(x) may only exist on a limited interval (|x|<R for the upper semicircle). For dy/dx = 1 + x − y, the isoclines (curves where the slope is constant) make the global behavior obvious...trajectories get funneled into a corridor and become asymptotic to the special solution y=x. You learn qualitative behavior without solving it the traditional way. #DifferentialEquations #ODEs #MITOCW #VectorFields #MathAnimation #Mathematics

We just watched Professor Arthur Mattuck kick off MIT’s ODE course with the one interpretation that most differential equations classes somehow postpone: An ordinary differential equation isn’t primarily a method hunt. It’s a geometric rule. You write dy/dx = f(x,y), and that right-hand side is literally telling you the slope your solution curve must have at each point (x,y). So I built this animation as a visual companion to that first lecture. It draws the direction field (little line elements whose slope is f(x,y)) and then shows integral curves sliding through it...curves that are tangent to the field everywhere they go. Two quick examples from the animation: For dy/dx = −x/y, the slope field steers you onto circles x² + y² = R². You also see a subtle point that gets missed when everything is taught as y(x)...even when the curve exists smoothly, the graph y(x) may only exist on a limited interval (|x|<R for the upper semicircle). For dy/dx = 1 + x − y, the isoclines (curves where the slope is constant) make the global behavior obvious...trajectories get funneled into a corridor and become asymptotic to the special solution y=x. You learn qualitative behavior without solving it the traditional way. #DifferentialEquations #ODEs #MITOCW #VectorFields #MathAnimation #Mathematics

Mathelirium

22,461 次观看 • 7 个月前

This is what California Democrats don’t want you to see This is Kern County. It’s right above Los Angeles and it’s home to one of the largest concentrations of oil fields in the United States “It's the largest oil field in California, and it's produced more than three billion barrels” These oil fields are still producing oil today while California Democrats lecture everyone on going electric, they’re still making millions with oil - Kern County produces 177,000 barrels every day. 5.5 million barrels in May 2026 - California statewide produces 260,000 barrels per day. Thats 94 million barrels in 2025 - Kern county oil sells for $6.5 billion per year - California statewide is selling for $8.7 billion per year gross Democrats are hypocrites

This is what California Democrats don’t want you to see This is Kern County. It’s right above Los Angeles and it’s home to one of the largest concentrations of oil fields in the United States “It's the largest oil field in California, and it's produced more than three billion barrels” These oil fields are still producing oil today while California Democrats lecture everyone on going electric, they’re still making millions with oil - Kern County produces 177,000 barrels every day. 5.5 million barrels in May 2026 - California statewide produces 260,000 barrels per day. Thats 94 million barrels in 2025 - Kern county oil sells for $6.5 billion per year - California statewide is selling for $8.7 billion per year gross Democrats are hypocrites

Wall Street Apes

756,446 次观看 • 3 天前

When I first took ordinary differential equations, the pattern was always the same. Week 1 turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations… and by Week 2 or 3 the course has quietly degenerated into hammer-picking. Spot the form, apply the recipe, move on. Mechanical! Fuuuuck!😫😫😫😫 The problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. And that’s a big deal, because in real modeling the equations you meet are rarely nice enough to reward memorized recipes. So you end up trained to solve toy forms, while the actual subject...the behavior, the flow, the shape of solutions stays blurry. This is why I’m biased toward the old-timers. Their old-school way of doing things always surprises me:...they’ll spend time on one idea until it sticks, instead of sprinting through a syllabus checklist. One lecture from them and you start noticing a contrast. A lot of modern teaching feels like "finish the content,". You get marched through techniques, but you’re not left with a single thought that keeps bothering you later...the kind of thought that actually pushes you toward research-level curiosity. MIT OpenCourseWare’s Professor Arthur Mattuck did that to me in his very first ODE lecture. One lecture, and your whole relationship with dy/dx = f(x,y) changes. In this segment, Prof. Mattuck is basically saying: A first-order ODE is a slope field, and a solution is a curve that moves everywhere tangent to that field. The math breakdown Write the ODE as dy/dx = f(x,y). At each point (x,y) you attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes:. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the single line that unifies both viewpoints: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages!👌🏻 Once you see that, you can stop obsessing over whether you can write y(x) in closed form. You can start asking the questions that matter: where do solutions flow, where do they get trapped, where do they blow up, and where does existence/uniqueness fail just because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early and it’s exactly why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #MathAnimation #Mathematics

When I first took ordinary differential equations, the pattern was always the same. Week 1 turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations… and by Week 2 or 3 the course has quietly degenerated into hammer-picking. Spot the form, apply the recipe, move on. Mechanical! Fuuuuck!😫😫😫😫 The problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. And that’s a big deal, because in real modeling the equations you meet are rarely nice enough to reward memorized recipes. So you end up trained to solve toy forms, while the actual subject...the behavior, the flow, the shape of solutions stays blurry. This is why I’m biased toward the old-timers. Their old-school way of doing things always surprises me:...they’ll spend time on one idea until it sticks, instead of sprinting through a syllabus checklist. One lecture from them and you start noticing a contrast. A lot of modern teaching feels like "finish the content,". You get marched through techniques, but you’re not left with a single thought that keeps bothering you later...the kind of thought that actually pushes you toward research-level curiosity. MIT OpenCourseWare’s Professor Arthur Mattuck did that to me in his very first ODE lecture. One lecture, and your whole relationship with dy/dx = f(x,y) changes. In this segment, Prof. Mattuck is basically saying: A first-order ODE is a slope field, and a solution is a curve that moves everywhere tangent to that field. The math breakdown Write the ODE as dy/dx = f(x,y). At each point (x,y) you attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes:. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the single line that unifies both viewpoints: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages!👌🏻 Once you see that, you can stop obsessing over whether you can write y(x) in closed form. You can start asking the questions that matter: where do solutions flow, where do they get trapped, where do they blow up, and where does existence/uniqueness fail just because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early and it’s exactly why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #MathAnimation #Mathematics

Mathelirium

53,338 次观看 • 7 个月前

A woman who spent nine years gluing paper models in a print shop just told a room of physicists their whole field stands on a mistake, one line, no hedging: "Every theory we have takes space and time for granted, like a bagel that forgot it was once a flat sheet of paper." That's Bianca Dittrich. She has a free lecture course that asks one question: what is left of geometry once you quantize it? The answer is: far less than you can picture. Quantum gravity looks like one more field theory. Buried inside is something stranger. Every quantum theory we have puts fields on a fixed stage, flat or curved, and does the physics on top of it. Here the stage itself is the thing being quantized. Your gut reads an atom of spacetime as a tiny grain sitting somewhere. Wrong. It cannot sit anywhere, because it is the somewhere. The move is invisible to human intuition, which is exactly why the people who get anywhere stop asking where the pieces are and start asking what a measurement even means. In 3D the whole thing collapses in a way that should scare you. No matter, no cosmological constant, and gravity is locally flat everywhere. Six degrees of freedom per point, all of them eaten by diffeomorphism symmetry. A field theory that ends up with finitely many real degrees of freedom, sometimes zero. None of it is hidden. Cut a parallelogram out of paper, glue the edges, and you have a torus. Flat everywhere, and yet two numbers survive that no local measurement can see. The lecture is free. Here is the trap: you feel every equation you can solve, every geometry you can draw, as progress. What you cannot feel is the gap you have to cross. Ten to the forty-one, from the Planck scale back up to the world you live in. And crossing it is the only thing that pays. Almost every approach still cannot show it recovers ordinary physics at the far end, and almost everyone quits long before then. The math is free to learn. The nerve to stop trusting your picture of space, that part you still have to bring yourself.

A woman who spent nine years gluing paper models in a print shop just told a room of physicists their whole field stands on a mistake, one line, no hedging: "Every theory we have takes space and time for granted, like a bagel that forgot it was once a flat sheet of paper." That's Bianca Dittrich. She has a free lecture course that asks one question: what is left of geometry once you quantize it? The answer is: far less than you can picture. Quantum gravity looks like one more field theory. Buried inside is something stranger. Every quantum theory we have puts fields on a fixed stage, flat or curved, and does the physics on top of it. Here the stage itself is the thing being quantized. Your gut reads an atom of spacetime as a tiny grain sitting somewhere. Wrong. It cannot sit anywhere, because it is the somewhere. The move is invisible to human intuition, which is exactly why the people who get anywhere stop asking where the pieces are and start asking what a measurement even means. In 3D the whole thing collapses in a way that should scare you. No matter, no cosmological constant, and gravity is locally flat everywhere. Six degrees of freedom per point, all of them eaten by diffeomorphism symmetry. A field theory that ends up with finitely many real degrees of freedom, sometimes zero. None of it is hidden. Cut a parallelogram out of paper, glue the edges, and you have a torus. Flat everywhere, and yet two numbers survive that no local measurement can see. The lecture is free. Here is the trap: you feel every equation you can solve, every geometry you can draw, as progress. What you cannot feel is the gap you have to cross. Ten to the forty-one, from the Planck scale back up to the world you live in. And crossing it is the only thing that pays. Almost every approach still cannot show it recovers ordinary physics at the far end, and almost everyone quits long before then. The math is free to learn. The nerve to stop trusting your picture of space, that part you still have to bring yourself.

Zyron

110,850 次观看 • 1 个月前

The final lecture of my course is an intro to character training! This is a topic that I've been quietly very invested in for ~18 months, as it: * Has potential for high real world impact * Clearly used extensively at frontier labs * Almost no empirical literature exists * More accessible on academic compute This lecture covers what character training is, reviews model specs, constitutions, the differences, the motivations in real world events, some example research papers I like, and open questions in how it relates to post-training/model use generally. Hopefully this brings more people into the field (and reach out if you have questions). It is one of the more research-y chapters in my book, but one that I felt needed the reference. There is still so little, educational content on the topic online. 0:00 Intro 6:22 Part 1: Fundamentals — character, constitutions, and model specs 19:21 Part 2: Character training in practice 23:23 Part 3: Character elicitation without gradient steps 28:03 Part 4: Open questions (and the end of the course) 32:27 The course, complete Thanks for watching. No need to like and subscribe now that the course is done, you definitely wouldn't! h/t to Sharan for leading the technical work I got to do in the space, and Zafir Stojanovski for investing a lot of attention at this book chapter.

The final lecture of my course is an intro to character training! This is a topic that I've been quietly very invested in for ~18 months, as it: * Has potential for high real world impact * Clearly used extensively at frontier labs * Almost no empirical literature exists * More accessible on academic compute This lecture covers what character training is, reviews model specs, constitutions, the differences, the motivations in real world events, some example research papers I like, and open questions in how it relates to post-training/model use generally. Hopefully this brings more people into the field (and reach out if you have questions). It is one of the more research-y chapters in my book, but one that I felt needed the reference. There is still so little, educational content on the topic online. 0:00 Intro 6:22 Part 1: Fundamentals — character, constitutions, and model specs 19:21 Part 2: Character training in practice 23:23 Part 3: Character elicitation without gradient steps 28:03 Part 4: Open questions (and the end of the course) 32:27 The course, complete Thanks for watching. No need to like and subscribe now that the course is done, you definitely wouldn't! h/t to Sharan for leading the technical work I got to do in the space, and Zafir Stojanovski for investing a lot of attention at this book chapter.

Nathan Lambert

56,783 次观看 • 27 天前

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.

Voltex

27,976 次观看 • 1 个月前

CHAOS ERUPTS during German Soccer Match! Riot Police TAKE THE FIELD!! Mannheim vs Kaiserslautern Look at this. Full stadium. German Cup. And before extra time even starts the place turns into a battlefield. Rockets flying into the stands. Fans storming the pitch. Smoke so thick you can’t see the goal. Then the cavalry rolls in: horses, dogs, hundreds of riot cops, just so two teams can keep playing. 35 officers hurt. Dozens more people needing medical help. Almost 1,400 cops on duty for one soccer game. And the media still calls this “passionate support.” This isn’t football culture. This is what happens when a society decides consequences are optional and “ultras” get treated like folklore instead of a public safety problem. Grown men in masks throwing fireworks at other people’s faces and charging the field because the score was 0-0. You want to know why a lot of regular folks are done with this soft, excuse-making culture? Because the same people who lecture American dads about “toxic masculinity” will look at mounted police clearing a soccer pitch and call it tradition. Kaiserslautern still found a way to win it late. Good for them. But the real scoreboard is the one that shows 35 injured cops and a stadium that needed a full security operation just to finish 120 minutes. That’s not sport. That’s a warning.

CHAOS ERUPTS during German Soccer Match! Riot Police TAKE THE FIELD!! Mannheim vs Kaiserslautern Look at this. Full stadium. German Cup. And before extra time even starts the place turns into a battlefield. Rockets flying into the stands. Fans storming the pitch. Smoke so thick you can’t see the goal. Then the cavalry rolls in: horses, dogs, hundreds of riot cops, just so two teams can keep playing. 35 officers hurt. Dozens more people needing medical help. Almost 1,400 cops on duty for one soccer game. And the media still calls this “passionate support.” This isn’t football culture. This is what happens when a society decides consequences are optional and “ultras” get treated like folklore instead of a public safety problem. Grown men in masks throwing fireworks at other people’s faces and charging the field because the score was 0-0. You want to know why a lot of regular folks are done with this soft, excuse-making culture? Because the same people who lecture American dads about “toxic masculinity” will look at mounted police clearing a soccer pitch and call it tradition. Kaiserslautern still found a way to win it late. Good for them. But the real scoreboard is the one that shows 35 injured cops and a stadium that needed a full security operation just to finish 120 minutes. That’s not sport. That’s a warning.

AmericanPapaBear™

18,299 次观看 • 11 天前

$Today we introduce Stochastic Differential Equations (SDEs). I find that the best way to introduce these complex concepts is to look at an application. This is part I of the lecture🙂 We look at the theory behind electromagnetic scattering/radar clutter which leads to anomaly detection on scattering statistics. When a narrowband wave scatters off a messy cloud of particles, the complex field at your receiver is a random phasor sum...at time t you can write the electric field as E_N(t) = Σⱼ₌₁ᴺ e^{iθⱼ(t)}, each term a unit arrow in the complex plane from scatterer j. This is exactly where the magic of Brownian motion appears naturally and in the most reasonable way. Think of all the microscopic chaos...tiny motions, index fluctuations, path jitters, Doppler shifts that shows up as small random kicks to the phases θⱼ(t) over very short times. If you just made θⱼ(t) random in an ad-hoc way (say, resampling independent angles at each time), the field would jump around unrealistically with no temporal structure. Brownian motion is what you get when you let each phase take the continuous-time limit of many tiny, independent kicks...it’s continuous in t, it has the right cumulative variance growth, and it remembers just enough of its past to look physical. So we model each phase as a Brownian walk, θⱼ(t) = θⱼ⁰ + σ_θ Bⱼ(t), with independent Brownian motions Bⱼ(t) and a phase-diffusion rate σ_θ. Brownian motion here isn’t window dressing...it’s the clean way to compress all the small random stuff into a single process that actually matches how the phases wander in time. #StochasticProcesses #BrownianMotion #ItoCalculus #RadarClutter #RayleighScattering #SignalProcessing$

Today we introduce Stochastic Differential Equations (SDEs). I find that the best way to introduce these complex concepts is to look at an application. This is part I of the lecture🙂 We look at the theory behind electromagnetic scattering/radar clutter which leads to anomaly detection on scattering statistics. When a narrowband wave scatters off a messy cloud of particles, the complex field at your receiver is a random phasor sum...at time t you can write the electric field as E_N(t) = Σⱼ₌₁ᴺ e^{iθⱼ(t)}, each term a unit arrow in the complex plane from scatterer j. This is exactly where the magic of Brownian motion appears naturally and in the most reasonable way. Think of all the microscopic chaos...tiny motions, index fluctuations, path jitters, Doppler shifts that shows up as small random kicks to the phases θⱼ(t) over very short times. If you just made θⱼ(t) random in an ad-hoc way (say, resampling independent angles at each time), the field would jump around unrealistically with no temporal structure. Brownian motion is what you get when you let each phase take the continuous-time limit of many tiny, independent kicks...it’s continuous in t, it has the right cumulative variance growth, and it remembers just enough of its past to look physical. So we model each phase as a Brownian walk, θⱼ(t) = θⱼ⁰ + σ_θ Bⱼ(t), with independent Brownian motions Bⱼ(t) and a phase-diffusion rate σ_θ. Brownian motion here isn’t window dressing...it’s the clean way to compress all the small random stuff into a single process that actually matches how the phases wander in time. #StochasticProcesses #BrownianMotion #ItoCalculus #RadarClutter #RayleighScattering #SignalProcessing

Mathelirium

55,319 次观看 • 8 个月前

ANTHROPIC JUST DROPPED THE OBSIDIAN VAULT THAT RUNS THEIR ENTIRE COMPANY. AND SHIPPED A FABLE 5 PROMPTING GUIDE INSIDE IT 8,893 nodes. 4,729 links. A Chinese-English translation magazine founded in 1973 opens the graph. Master Index in the center. Marginalia Collection, Glossary Backbone, and Comparative Grammar MOC branching off. Field Notes Archive links to Oral History Transcripts translation Studies Index carries 419 cross-references on its own. Survey Data pool feeds 212 coded entries back to the middle. 9,000+ documents. Every node connected this is not a company wiki. This is a research civilization in a $10 app. The team training the most powerful model on Earth manages its brain in the same markdown editor a student uses for lecture notes Fable 5 came back 5 days ago still runs on default Obsidian graph view. no plugins, no in-house tool, no custom infrastructure you're reading this on a device that could pull the whole vault tonight and be running the prompting guide by morning

ANTHROPIC JUST DROPPED THE OBSIDIAN VAULT THAT RUNS THEIR ENTIRE COMPANY. AND SHIPPED A FABLE 5 PROMPTING GUIDE INSIDE IT 8,893 nodes. 4,729 links. A Chinese-English translation magazine founded in 1973 opens the graph. Master Index in the center. Marginalia Collection, Glossary Backbone, and Comparative Grammar MOC branching off. Field Notes Archive links to Oral History Transcripts translation Studies Index carries 419 cross-references on its own. Survey Data pool feeds 212 coded entries back to the middle. 9,000+ documents. Every node connected this is not a company wiki. This is a research civilization in a $10 app. The team training the most powerful model on Earth manages its brain in the same markdown editor a student uses for lecture notes Fable 5 came back 5 days ago still runs on default Obsidian graph view. no plugins, no in-house tool, no custom infrastructure you're reading this on a device that could pull the whole vault tonight and be running the prompting guide by morning

chewa.

78,064 次观看 • 1 个月前

Ask anyone who’s taken a course in Ordinary Differential Equations (ODEs) what a solution to an ODE represents geometrically, and most of them won’t have a clean answer. When I first took ordinary differential equations, the pattern was always the same. Early on it turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations. Then pretty quickly the course slides into hammer-picking. Spot the form, apply the recipe, move on. Too mechanical! And the real problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. That matters because in real modeling the equations you meet are rarely nice enough to reward memorised recipes. So you get trained to solve toy forms, while the actual subject stays blurry. The behavior. The flow. The shape of solutions. It wasn't until I watched the first lecture of Professor Arthur Mattuck that I realized I didn’t actually know what a solution to a differential equation represents geometrically. His point is almost embarrassingly simple. A first-order ODE is a slope field, and a solution is a curve that stays tangent to that field everywhere. The math breakdown: Write the ODE as dy/dx = f(x,y). At each point (x,y), attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the one line that ties both viewpoints together: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages. Once you see that, you stop obsessing over whether you can write y(x) in closed form. You start asking the questions that actually matter. Where do solutions flow. Where do they get trapped. Where do they blow up. Where does existence or uniqueness fail because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early. It’s also why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #AppliedMathematics #Mathematics #

Ask anyone who’s taken a course in Ordinary Differential Equations (ODEs) what a solution to an ODE represents geometrically, and most of them won’t have a clean answer. When I first took ordinary differential equations, the pattern was always the same. Early on it turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations. Then pretty quickly the course slides into hammer-picking. Spot the form, apply the recipe, move on. Too mechanical! And the real problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. That matters because in real modeling the equations you meet are rarely nice enough to reward memorised recipes. So you get trained to solve toy forms, while the actual subject stays blurry. The behavior. The flow. The shape of solutions. It wasn't until I watched the first lecture of Professor Arthur Mattuck that I realized I didn’t actually know what a solution to a differential equation represents geometrically. His point is almost embarrassingly simple. A first-order ODE is a slope field, and a solution is a curve that stays tangent to that field everywhere. The math breakdown: Write the ODE as dy/dx = f(x,y). At each point (x,y), attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the one line that ties both viewpoints together: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages. Once you see that, you stop obsessing over whether you can write y(x) in closed form. You start asking the questions that actually matter. Where do solutions flow. Where do they get trapped. Where do they blow up. Where does existence or uniqueness fail because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early. It’s also why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #AppliedMathematics #Mathematics #

Mathelirium

40,841 次观看 • 6 个月前

MIT filmed a mathematician proving the theorem behind every equation Maxwell ever wrote. Most engineers use it daily without understanding why any surface gives the same answer - and the ones who can explain it earn $350K at aerospace firms. Stokes' theorem is the reason Maxwell could write four equations instead of forty. Every result in electromagnetism, fluid mechanics, and differential geometry sits on top of it. Understanding it changes how you see every circulation and flux problem in physics. This is Denis Auroux. MIT, 18.02, Multivariable Calculus, Fall 2007. Lecture 31. It covers Stokes' theorem - the result that unifies line integrals, surface integrals, and curl into one statement. He opens with one confession. Then the statement. The work done by a vector field along any closed curve equals the flux of its curl through any surface bounded by that curve. Not a specific surface. Any surface you want. You pick whichever one makes the calculation easier. Then Green's theorem disappears. The result from the plane that converts line integrals to double integrals turns out to be Stokes' theorem in disguise. Same formula, restricted to a flat surface in the xy-plane. What looked like two separate theorems is one. Then orientation. Walk along the curve with the surface to your left and the normal vector points up. This rule is not chosen for elegance. It is the price of having a consistent coordinate system in three dimensions. Then the proof. Cut your surface into tiny flat tiles. Apply Green's theorem to each one. Sum everything up. All interior boundary edges cancel. Only the outer edge survives. The sum of tiny Green's theorems gives you Stokes' theorem for any surface. Watch the moment he computes the same line integral two ways. First directly - answer is pi. Then through a paraboloid instead of the obvious flat disk. Longer calculation, stranger setup, same answer. The theorem is not an abstraction. You can watch it work. A fluid dynamics engineer I know kept this lecture open during their first week modeling atmospheric circulation. Said it was the first time Stokes felt like a tool rather than a formula to verify on exams. Free on YouTube, MIT OpenCourseWare, 18.02. bookmark this and watch later - after this lecture every circulation, every flux, and every curl you encounter will feel like three views of the same object

MIT filmed a mathematician proving the theorem behind every equation Maxwell ever wrote. Most engineers use it daily without understanding why any surface gives the same answer - and the ones who can explain it earn $350K at aerospace firms. Stokes' theorem is the reason Maxwell could write four equations instead of forty. Every result in electromagnetism, fluid mechanics, and differential geometry sits on top of it. Understanding it changes how you see every circulation and flux problem in physics. This is Denis Auroux. MIT, 18.02, Multivariable Calculus, Fall 2007. Lecture 31. It covers Stokes' theorem - the result that unifies line integrals, surface integrals, and curl into one statement. He opens with one confession. Then the statement. The work done by a vector field along any closed curve equals the flux of its curl through any surface bounded by that curve. Not a specific surface. Any surface you want. You pick whichever one makes the calculation easier. Then Green's theorem disappears. The result from the plane that converts line integrals to double integrals turns out to be Stokes' theorem in disguise. Same formula, restricted to a flat surface in the xy-plane. What looked like two separate theorems is one. Then orientation. Walk along the curve with the surface to your left and the normal vector points up. This rule is not chosen for elegance. It is the price of having a consistent coordinate system in three dimensions. Then the proof. Cut your surface into tiny flat tiles. Apply Green's theorem to each one. Sum everything up. All interior boundary edges cancel. Only the outer edge survives. The sum of tiny Green's theorems gives you Stokes' theorem for any surface. Watch the moment he computes the same line integral two ways. First directly - answer is pi. Then through a paraboloid instead of the obvious flat disk. Longer calculation, stranger setup, same answer. The theorem is not an abstraction. You can watch it work. A fluid dynamics engineer I know kept this lecture open during their first week modeling atmospheric circulation. Said it was the first time Stokes felt like a tool rather than a formula to verify on exams. Free on YouTube, MIT OpenCourseWare, 18.02. bookmark this and watch later - after this lecture every circulation, every flux, and every curl you encounter will feel like three views of the same object

Zyphor

10,999 次观看 • 6 天前