Warren Buffett disowned his granddaughter in 2006, writing that he had never accepted her as his grandchild, legally or emotionally. Buffett has largely avoided publicly discussing what happened between them. In the video below, Nicole Buffett tells her side of the story. Nicole had grown up as part of the Buffett family after Warren’s son Peter adopted her. She visited Warren in Omaha, called him Grandpa, and Warren’s wife Susan even referred to Nicole as one of her “adored grandchildren.” But being the granddaughter of one of the richest men in the world never meant living like one. Warren paid for her education, but Nicole didn’t have access to his fortune. She worked with children at Family Human Services in San Francisco and built her own life while carrying one of the most famous surnames in American business. Then their relationship fell apart after Nicole appeared in The One Percent, a documentary about wealth inequality. She later received a letter from Buffett telling her that he had not emotionally or legally adopted her as a grandchild, and that the rest of his family had not adopted her as a niece or cousin either. One detail made the letter even more painful. Buffett signed it simply “Warren.” Nicole still had a card from only a year earlier signed “Grandpa.” One of the richest men in the world had spent years as her grandfather. Then a letter told her he no longer considered himself one. Do you think Buffett had the right to draw that line, or did he take it too far? Save the video to finish Nicole’s story later, and follow for more heartbreaking stories from behind the lives of the world’s most famous billionaires.

David Bowie sold over 140 million records. He invented three different musical personas that each defined an entire era. Rolling Stone called him the most influential artist of the 20th century. He turned down a knighthood from the Queen of England because he didn't think he deserved it. In the mid-1970s he was also consuming so much cocaine that he later said he remembers almost nothing from that entire period. Not the albums. Not the tours. Not the interviews. This is one of those interviews. In the video below, the most creative mind in rock history sits across from a journalist and explains, completely unprompted, how to destroy a city. Black noise, he says. A specific frequency that shatters everything it hits. Available in the French Patent Office until recently for about three or four dollars. A small device could kill four people in a room. A large one could level an entire city. The interviewer stares at him. Bowie moves on to Machiavelli. Then Diane Arbus. Then Frank Zappa naming his son Moon Unit. Then his own son Zowie. Then his wife Angela's thesis on everything. Then a book about the Trans-Siberian Express he may or may not be writing. At one point he offers the interviewer a part in his next show. This is the same man who wrote Heroes in a single afternoon. Who created Ziggy Stardust, Aladdin Sane, and the Thin White Duke from scratch. Who produced Iggy Pop's two greatest albums while barely sleeping. He survived the decade. Cleaned up. Made ten more classic records. The cocaine almost took all of it. The talent was just bigger than the damage. Was he a genius who almost destroyed himself, or did the destruction make him the genius he became?

A 40 point jump in your credit score can cut your real default odds nearly in half andrew ng, stanford cs229, machine learning, logistic regression. then the technique. one formula turns raw financial history into a single number, the probability of default. then the real data. borrowers scoring 600 to 639 defaulted at 15.8 percent within two years, federal reserve data on new loans. then the cutoff. borrowers scoring just above that band, 640 to 679, defaulted at 8.9 percent, nearly half the rate, for a 40 point difference. then the top. borrowers at 720 or higher defaulted at just 1.0 percent, 41 times lower than the bottom band under 520, at 41.0 percent. watch the moment he writes the probability as a smooth curve instead of a hard yes or no line. the score was never a grade on character. it was a probability wearing a three digit number.

Steve Jobs literally predicted the iPhone, the Internet, AI and the next 50 years of technology in a single speech from 1983:

two gas stations across the street can't stop destroying their own margins ben polak, yale university, econ 159 game theory, lecture 1. then the baseline. quebec city, over 300 gas stations, margins averaging 5.64 cents per liter in the late 1990s, about 9 percent of the price. then the collapse. a price war starting january 2000 drove those margins from 5.64 cents to nearly zero, and it lasted almost a full year. then the reason nobody blinks first. pricing just 2 cents above a neighboring station could cost a station 36 percent of its volume. then the transfer. both stations do better holding price high together, but each is individually better off undercutting, so undercutting is what happens. watch the moment he calls undercutting 'bad for the firm, bad for industry profit.' the price war was never bad strategy. it was the dilemma working exactly as predicted.

A bbb credit rating is a coin that only remembers its last flip john tsitsiklis, mit 6.041sc, probabilistic systems analysis, lecture 16, markov chains. then the matrix. s&p's own data, 1981 to 1996, a bbb company stays bbb 82.49 percent of the time in one year. nothing further back matters. then the slide. 5.81 percent of bbb companies get downgraded within that year, to bb, b, or ccc. real risk, ignored by most bond buyers. then the default. outright default inside that year hits only 0.17 percent of bbb companies, 34 times rarer than a plain downgrade. then the transfer. a markov chain predicts next year from this year's grade alone. the real risk in a bbb bond is the slide, not the cliff. watch the moment he shows the next state depends only on where it stands now, not how it got there. the rating was never a cliff edge. it was always a slope, graded in percent per year.

Your revenue forecast runs on an equation that has zero exact solutions gilbert strang, mit 18.06 linear algebra, lecture 16, projection matrices and least squares. then the problem. three months of data, reaching 1, 2, 2. no straight line passes through all three, zero exact solutions exist. then the fix. solve the normal equations, 3c plus 6d equals 5, 6c plus 14d equals 11. answer, d is one half, c is two thirds, one best line. then the leftover. that line predicts 7 sixths, 5 thirds, 13 sixths. actual was 1, 2, 2. the miss never hits zero, only its smallest size. then the transfer. that equation, a transpose a times x equals a transpose b, runs every model forecasting revenue, pricing risk, or reading gps. watch the moment he calls this equation the most important one in statistics. the line was never going to hit every point. the math was how close it could still get.

MIT FILMED THE LECTURE WHERE THEIR MOST LEGENDARY LINEAR ALGEBRA PROFESSOR EXPLAINS WHY EVERY GPS SIGNAL EVERY MEDICAL SCAN AND EVERY AI MODEL ON EARTH IS SECRETLY SOLVING A PROBLEM THAT HAS NO SOLUTION This is Gilbert Strang, MIT, 18.06 Linear Algebra, lecture 15. The professor whose textbooks sit in over 3,000 universities across 135 countries and whose course has been watched by millions of engineers and mathematicians worldwide. He opens with one claim - when equations have no solution, you do not give up. You solve the closest problem that can be solved. He starts with a vector b and a line through a. The projection of b onto that line is the point closest to b that actually lives on the line. The key is a right angle. The error - the gap between b and its projection - must be perpendicular to a. That one geometric fact produces the entire formula. The projection is a times a transpose b divided by a transpose a. One fraction. The numerator is a matrix. The denominator is a number. Then the projection matrix. Every projection is carried out by a matrix P that multiplies b and lands on the line. That matrix has two properties that define all projections everywhere. First, P transpose equals P - the matrix is symmetric. Second, P squared equals P - project twice and you stay where you are. The first projection moves you onto the line. The second leaves you there. Then higher dimensions. Instead of projecting onto a line, project onto a plane. Instead of one vector a, you have a matrix A with two columns spanning the plane. The error vector must now be perpendicular to both columns - perpendicular to the entire plane. That condition produces one matrix equation. A transpose times the error equals zero. The error lives in the null space of A transpose, which is exactly perpendicular to the column space of A. The four fundamental subspaces appear without being called. Then the formula for n dimensions. The projection matrix is A times A transpose A inverse times A transpose. Strang warns the room not to simplify it by canceling A inverse against A - A is not square, it has no inverse, and the formula cannot be pulled apart. The only case where it simplifies to the identity is when A spans the whole space and the projection is b itself. Watch the moment he applies the formula to fitting a line through three points that no line can pass through. Three equations, two unknowns, no solution. He writes the matrix, writes the right-hand side, and shows that multiplying both sides by A transpose produces an equation that always has a solution. That equation is the foundation of least squares, GPS triangulation, and every regression model trained in 2026. A data scientist I know rewatched this lecture before building a recommendation system from scratch. Said it was the first time the normal equations felt like geometry rather than a formula someone handed down to memorize.