https://www.peter-keep.com
̶K̶r̶i̶s̶t̶i̶ ̶N̶o̶e̶m̶ ̶i̶s̶ ̶t̶h̶r̶e̶a̶t̶e̶n̶i̶n̶g̶ ̶t̶h̶e̶ ̶w̶a̶t̶c̶h̶d̶o̶g̶ ̶t̶h̶a̶t̶’̶s̶ ̶s̶u̶p̶p̶o̶s̶e̶d̶ ̶t̶o̶ ̶i̶n̶v̶e̶s̶t̶i̶g̶a̶t̶e̶ ̶t̶h̶i̶s̶ ̶A̶d̶m̶i̶n̶i̶s̶t̶r̶a̶t̶i̶o̶n̶'̶s̶ ̶a̶b̶u̶s̶e̶
Things need to change. Fast.
𝘈𝘳𝘦 Democrats ̶a̶r̶e̶ fighting to rein in ̶T̶r̶u̶m̶p̶'̶s̶ ̶D̶H̶S̶ 𝘈𝘮𝘦𝘳𝘪𝘤𝘢𝘯 𝘣𝘳𝘶𝘵𝘢𝘭𝘪𝘵𝘺?
Kristi Noem is threatening the watchdog that’s supposed to investigate this Administration's abuse.
Things need to change. Fast.
Democrats are fighting to rein in Trump's DHS.
̶K̶r̶i̶s̶t̶i̶ ̶N̶o̶e̶m̶ ̶i̶s̶ ̶t̶h̶r̶e̶a̶t̶e̶n̶i̶n̶g̶ ̶t̶h̶e̶ ̶w̶a̶t̶c̶h̶d̶o̶g̶ ̶t̶h̶a̶t̶’̶s̶ ̶s̶u̶p̶p̶o̶s̶e̶d̶ ̶t̶o̶ ̶i̶n̶v̶e̶s̶t̶i̶g̶a̶t̶e̶ ̶t̶h̶i̶s̶ ̶A̶d̶m̶i̶n̶i̶s̶t̶r̶a̶t̶i̶o̶n̶'̶s̶ ̶a̶b̶u̶s̶e̶
Things need to change. Fast.
𝘈𝘳𝘦 Democrats ̶a̶r̶e̶ fighting to rein in ̶T̶r̶u̶m̶p̶'̶s̶ ̶D̶H̶S̶ 𝘈𝘮𝘦𝘳𝘪𝘤𝘢𝘯 𝘣𝘳𝘶𝘵𝘢𝘭𝘪𝘵𝘺?
rhinopotamus.github.io/pdf/derivati...
rhinopotamus.github.io/pdf/derivati...
Mamdani: [rolls natural 20]
GM: that’s a d6 how did you
Mamdani: [direct to camera] Did you know you can check out board games at your local public library? 😊
Mamdani: [rolls natural 20]
GM: that’s a d6 how did you
Mamdani: [direct to camera] Did you know you can check out board games at your local public library? 😊
@tempobasketball.bsky.social make it happen, please!!
@tempobasketball.bsky.social make it happen, please!!
www.peter-keep.com/2026-02-06-g...
www.peter-keep.com/2026-02-06-g...
www.peter-keep.com/2026-02-06-g...
My submission for a presentation: "GenAI ∉ (Teaching ∪ Learning)"
Abstract: "There are no ethical use cases for generative AI in higher education. In this presentation, we'll discuss..."
Fyi, a lot of what I'll go over today is covered in Chapter 4 of "Stochastic Methods" by Crispin Gardiner, but this is a pretty standard derivation too, and I explained it in a way I think makes more sense. For today I am going to derive Ito's Lemma for you. (1/n)
Last week we found Brownian motion is not continuously differentiable, and has nonzero quadratic variation.
In a SUPER similar proof scheme that I won't go through here (bc it's v similar), you can prove it has unbounded total variation as well. (1/n)
Fyi, a lot of what I'll go over today is covered in Chapter 4 of "Stochastic Methods" by Crispin Gardiner, but this is a pretty standard derivation too, and I explained it in a way I think makes more sense. For today I am going to derive Ito's Lemma for you. (1/n)
Anyways.
Again, I'm so happy Yale hired David Brooks to restore trust.
yaledailynews.com/articles/gel...
Anyways.