Washed Visual

Grok is actually better than I thought...wtf

Over the course of my 14 year crusade for justice in the PSU/Jerry Sandusky case, I think this is the first time I’ve ever told the story, on video, about the moment I finally (in 2014) concluded that Sandusky is innocent. It was when I interviewed him for a 2nd time in prison.😱

When Micah Parsons went one-on-one with a sumo wrestler 👀

There Will Be Oil

Sonny Styles (LB, Ohio State) Height: 6'5" Weight: 244 Arm Length: 32 7/8" Hand Size: 10"

Bitcoin.

these models are crazy man

I left xAI today. Not because of the pay. Not because of the internal politics. Not even because of the SpaceX and xAI merger. But because, in my manager’s words: “No matter what feedback I give you, you never change your direction.” At first, I thought he was just calling me stubborn. Then my inner math brain clicked… He was literally describing an eigenvector. See, in math, when you apply a transformation (matrix A) to a vector (v), most vectors get spun around, twisted, and thrown somewhere else. They change direction and magnitude. But an eigenvector is different, it keeps the same direction. The only thing that changes is its scale, given by something called an eigenvalue (λ). If λ = 2 → The vector doubles in size. If λ = 0.5 → It shrinks. If λ = −1 → It flips direction. If λ = 1 → It stays the same size. Apparently… in his eyes, I was λ = 1. Always the same size. Always the same direction. Now the math part (because unlike my manager, I actually explain things): Here’s how you find eigenvalues and eigenvectors using a 2×2 matrix example: Let’s say the transformation matrix was: A = [ 2 1 ] [ 1 2 ] Step 1: Find eigenvalues (λ) We solve: A·v = λ·v → (A − λI)·v = 0 → det(A − λI) = 0 Subtract λ from each diagonal entry of A: A − λI = [ 2−λ 1 ] [ 1 2−λ ] Set the determinant equal to 0 and solve for λ: Determinant: (2−λ)(2−λ) − 1 = 0 (2−λ)² − 1 = 0 (2−λ)² = 1 2 − λ = ±1 λ = 2 ± 1 Case 1: λ = 2 − 1 → λ = 1 Case 2: λ = 2 + 1 → λ = 3 So, the eigenvalues are: λ₁ = 1, λ₂ = 3 Step 2: Find eigenvectors (v) For λ = 1: (A − λI)·v = 0 [ 2−λ 1 ] [ x ] = [ 0 ] [ 1 2−λ ] [ y ] [ 0 ] [ 2−1 1 ] [ x ] = [ 0 ] [ 1 2−1 ] [ y ] [ 0 ] [ 1 1 ] [ x ] = [ 0 ] [ 1 1 ] [ y ] [ 0 ] From the first row: x + y = 0 y = −x From the second row: x + y = 0 y = −x So, the eigenvector is any scalar multiple of [ 1, −1 ]ᵀ. For λ = 3: (A − λI)·v = 0 [ 2−λ 1 ] [ x ] = [ 0 ] [ 1 2−λ ] [ y ] [ 0 ] [ 2−3 1 ] [ x ] = [ 0 ] [ 1 2−3 ] [ y ] [ 0 ] [ −1 1 ] [ x ] = [ 0 ] [ 1 −1 ] [ y ] [ 0 ] From the first row: −x + y = 0 y = x From the second row: x − y = 0 x = y So, the eigenvector is any scalar multiple of [ 1, 1 ]ᵀ. Final result: λ = 1 → v = [ 1, −1 ] λ = 3 → v = [ 1, 1 ] Congratulations 🎉, You have just learned how to find the eigenvectors and eigenvalues of a matrix. Bonus: Why does AI/ML care? Eigenvalues and eigenvectors are everywhere in AI/ML: PCA → Reduces dimensions by keeping the top eigenvectors of the covariance matrix (largest eigenvalues = most variance). Spectral clustering → Graph Laplacian eigenvalues help find clusters. Neural stability → Eigenvalues of weight matrices can indicate exploding or vanishing gradients. Markov chains → Long-term behavior is the eigenvector with eigenvalue 1. In short: Eigenvectors tell you the “unchangeable direction” under a transformation. Eigenvalues tell you “how much” that direction is stretched.