Matthew RC Albano

Get your copy today and help reform the education system. amazon.com/Learning-Isnt-… @realDonaldTrump @elonmusk @JDVance #math

@miniapeur You never use omnicron.

Mathematician’s training

@nypost I thought that was Cher.

Demi Moore’s toned arms take center stage on Cannes Film Festival 2026 red carpet trib.al/DYJYRNx

@nypost “Liberated Jews show off toned physiques from time in Nazi fitness camps!” - New York Post probably…

@MathMath901 I have not proven it, but by the basic problem solving strategy of computing a few specific examples to observe a pattern we have x_n = 1/4^n, for all n = 0, 1 , 2, ...

#math problem 13-05-2026 Calculus: Recursive equations with nested radicals. Find x(n) ( "x" as a function of "n")

Suicidal & Toxic Empathy cost this man his life. GRAPHIC WARNING: Horrifying video shows 76-year-old being shoved down su... youtube.com/shorts/0uVvytz… via @YouTube

When a scientist or an engineer earns a Ph.D., it is usually based on reproducible, verifiable outcomes drawn from the laws of the natural world. When a social "scientist" earns a Ph.D. in a "social science," it is based on collating the written opinions of other "social scientists" into a heavily footnoted thesis, and those people who were footnoted earned THEIR Ph.D. based on collating the written opinions of other "social scientists" into a heavily footnoted thesis, and those people who were footnoted earned THEIR Ph.D. based on collating the written opinions of other "social scientists" into a heavily footnoted thesis, and so on, and so on. A Ph.D. in science or engineering is based on the laws of the natural world. A Ph.D. in any "social science" is based on regurgitating the writings of other people who never had to prove anything. A "social science" Ph.D. is an intellectual Ponzi scheme--one based largely on restating the (often mistaken) opinions of those who came before you as if their opinions were fact, but arranging those opinions in such a way as to create your own novel and equally untrustworthy opinion. We are at a point in society where anyone with a Ph.D. in a non-scientific or non-engineering field is more untrustworthy than random people on the street.

Arthur Brooks: "There shouldn't be a classroom in America from kindergarten to PhD where you're allowed to use your personal devices" "We're rewiring their brains to become lonely and depressed."

@Math_files What is Poch(n) ?

Ramanujan said that he received his formulas from God

My heritage.

@GadSaad @GadSaad @elonmusk Wasn't there already a book published by Allie Beth Stuckey (@conservmillen) titled "Toxic Empathy" in 2024? How is this book different? It is secular?

It’s book release day!!! amazon.com/gp/aw/d/006344…

Policies do not ultimately change people. But we need to get them right.

@MathMath901 @MathMath901 These are good problems. Where do you get your problems?

@MatthewAlbano8 yes

#math problem 12-05-2026 Differential Calculus / Analytic Geometry.

@MathMath901 ... the incenter can be found using basic geometry, thus the radius.

@MathMath901 My intuition: (1) By geometric symmetry the circle is in the "center" of the curve, and thus the center, P, of the circle also (i.e. (0,0), P, B are collinear). (2) Find the tangent line at point B, draw, creating a right triangle. The circle is inscribed in the right triangle ..

@elonmusk #teacher #parent #school #math We can do better.

@elonmusk Right now to churn the gears of the Learning Revolution, available is Learning Isn't Linear on amazon. learningisntlinear.com

@elonmusk @GadSaad @elonmusk Wasn't there already a book published by Allie Beth Stuckey called "Toxic Empathy" in 2024? How is this book different? It is secular?

Worth reading

One theorem every ML engineer should know: The Johnson–Lindenstrauss Lemma. It states that high-dimensional data can be projected into a much lower-dimensional space while approximately preserving pairwise distances. Why it matters: • Explains why random projections work • Enables scalable learning in high dimensions • Used in embeddings, compressed learning, and ANN search • Helps fight the curse of dimensionality The surprising part: You can reduce dimensions dramatically without destroying the geometry of the data. That’s why many ML systems can operate efficiently even with massive feature spaces. Modern representation learning is deeply connected to this idea: Good embeddings preserve structure while compressing information. In ML, compression is often not loss of intelligence — it’s removal of redundancy.

@atulit_gaur Like I have said before, "All is geometry."

what else did you all think neural networks think in if not geometry? chocolate cookies? why is everyone freaking out over this

#money #stocks