Follow Tokens on Solana for 24/7 Breaking News.

You’ve heard of Albert Einstein, but do you know the woman he called a genius in a world that tried to silence her? She reshaped the laws of the universe. She defied prejudice. She revolutionized physics without ever being allowed to teach under her own name. Mathematics,

Tensor networks crack open topology in systems with hundreds of millions of sites Topological phases of matter are usually characterized by global invariants like the Chern number, computed in momentum space for translationally invariant crystals. But some of the most exciting

"Mathematics: Its Content, Methods and Meaning" By Aleksandrov, Kolmogorov, and Lavrent'ev. A pretty good overview of many subject areas of math. Over 1000 pages. Archive: archive.org/details/Mathem…

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this

#キャルちゃんのquantphチェック カリフォルニア大学サンタクルーズ校にて開講された、学部性向けの量子計算の講義テキスト。線形代数を履修済みの物理系の学生を対象としたもの。Shorアルゴリズムや量子誤り訂正の入門にまで触れた、200ページ超のテキスト。読みたい！ arxiv.org/abs/2604.10396

How to respect yourself:

𝗖𝗵𝗮𝗼𝘁𝗶𝗰 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝘀 "Prediction of chaotic dynamics from data: An introduction" Looks like an excellent tutorial (with code!) to learn some of the basics of dynamical systems analysis together with RNNs, echo state machines, LSTM, etc. arxiv.org/abs/2604.11624…

Best Probability Courses- mltut.com/best-resources…

Machine learning notes drive.google.com/file/d/1HSDp8x…

approaching a completely new domain is simple. most people make it complicated and stay stuck. how to actually do it: • map the field fast → what are the core concepts, tools, and problems. don’t go deep yet, just see the landscape • pick one small problem → not “learn

3D visualization of Euler’s formula: e^{jθ} = cos(θ) + j sin(θ). A point moves on the unit circle, with projections forming cosine, sine, and exponential curves along the θ-axis.

In 2024, a 2-hour Stanford lecture quietly revealed how top engineers actually build AI systems. Most people will scroll past it. But inside Stanford University, this is how they train engineers not with theory, but with real, practical workflows. It’s more useful than most