boolean_filter @boolean_filter

In 1986 a guy got kicked out of every casino in Vegas for counting cards. So he flew to Hong Kong with $180,000 and started betting on horses instead. He walked away with almost $900 million. It's Bill Benter. He figured horse racing was just another counting problem. Same math, more moving parts. He and a partner showed up with $180k and a computer. Benter spent years teaching that computer to guess one thing, the real chance each horse had to win. If his number was better than the odds the bookies gave, he bet. If not, he skipped it. That's the whole trick. Expected value. EV = p · b − (1 − p) Only bet when your win chance p, at odds b, is worth more than your chance of losing. This recording was never meant to be some hidden gem. Nobody expected Professor Tsitsiklis to hand the whole foundation away in 45 minutes, but that's exactly what happens on the board. Students in that room pay over $80,000 a year to sit through it. It's free right here. It's free right here. Every quant, every professional bettor, every hedge fund analyst started with this exact hour. Benter just watched it and actually did the homework. Almost nobody knows this lecture even exists. Watch it before it gets taken down. The answer is in this video.

MIT professor Patrick Winston literally gave a 1-hour masterclass on communication that teaches you how to speak better than most schools ever could.

Creative Puzzles to Ignite Your Mind (Problem Books in Mathematics) Cool book at amzn.to/4bevJHB

"Partial Differential Equations" is a free textbook of more than 400 pages intended for students encountering partial differential equations for the first time, after studying multivariable calculus and ordinary differential equations. The book begins with the motivation and classification of PDEs, initial and boundary-value problems, and first-order equations. It then covers the wave, heat, and Laplace equations, Fourier series and transforms, separation of variables, variational methods, distributions and weak solutions, nonlinear equations, eigenvalues, eigenfunctions, and much more, with clear and rigorous explanations throughout. The book also includes numerous problems and applications that make advanced theoretical concepts, especially those requiring a greater degree of abstraction, easier to understand. math.utoronto.ca/ivrii/PDE-text…

As someone who builds institutional-level quant trading systems, this Stanford Market Making paper is the closest thing to quant desk I've ever seen publicly shared Feed this MM algorithm pdf to grok bot & ask to build AI hedge fund bot that prints alpha 24/7 (full guide below)

Roan @RohOnChain

3 weeks ago

Grok Bot is the best AI agent right now It gives you an army of agents that can do work for you 24/7 If you set it up correctly, you gain super powers In this article, I cover how to use Grok Bot to make a Market Making Bot like Hedge Funds. x.com/i/article/2093…

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I came across a PDF that MIT students compiled, and it's genuinely one of the best quant interview resources I've seen. They call it the "Quant Bible" a single document that reads less like a study sheet and more like an operational playbook. Full version in replies

gemchanger @gemchange_ltd

7 months ago

x.com/i/article/2028…

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"Transformers" by Daniel Jurafsky and James H. Martin is one of the clearest and most mathematically grounded introductions to the Transformer architecture I have ever read. Chapter 8 introduces the Transformer as the standard architecture behind modern large language models. What makes this chapter particularly interesting is its step-by-step presentation of the underlying mechanisms: contextual embeddings, self-attention, query, key and value vectors, scaled dot-product attention, multi-head attention, residual streams, feedforward layers, layer normalization, masking, and the parallel matrix formulation of attention. In particular, the treatment of attention as a weighted sum of contextual representations is especially valuable. The chapter first develops an intuitive, simplified view of attention and then gradually derives the full formulation using the Q, K, and V matrices. This approach makes it easier to understand what is actually happening inside the architecture from an algebraic and matrix-based perspective, rather than simply viewing the usual block diagrams. I think it is an excellent resource for anyone interested in understanding how Transformers work from linguistic, mathematical, and computational perspectives. web.stanford.edu/~jurafsky/slp3…

The University of Michigan put their entire robotics degree on GitHub. Not one course. The whole curriculum. ROB 101 — Computational Linear Algebra for Robotics ROB 311 — How to Build Robots and Make Them Move ROB 501 — Mathematics for Robotics ROB 530 — Mobile Robotics Every lecture video on YouTube. Every textbook on GitHub. Every problem set, every exam, every line of code. Professor Jessy Grizzle said it best when they launched it: "Linear algebra has become the language of computer vision, machine learning, robotics, and autonomy." So instead of making students wait four semesters of calculus before touching a robot... they built a curriculum that starts with the math that actually matters, applied to real robotics problems from day one. This is what open education looks like when a top-10 engineering school decides to mean it. Free. GitHub. YouTube. 📌 [github.com/michiganroboti…] Follow for more robotics resources! —— Weekly robotics and AI insights. Subscribe free: 22astronauts.com

Python is wild for time series. Introducing Orbit. A Python library for Bayesian probabilistic time series forecasting by Uber.

Jane Street pays $750K/year for quants who master matrix calculations holistically that can be used to get alpha from space satellites. 57-minutes. free. By MIT Applied Math team. "If you are hand-computing derivatives on complicated matrix factorizations, it is incredibly error-prone. Modern automatic differentiation is more computer science than calculus." here's the breakdown: • The math behind large-scale engineering simulations • How backpropagation relies entirely on reverse-mode adjoint differentiation • Thinking in matrices instead of scalar arrays Watch this now. This will change the way you optimize networks forever and then read below article.

venus @RitOnchain

3 months ago

x.com/i/article/2064…

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An engineering professor who failed math her entire childhood spent years figuring out exactly what had been sabotaging her, and the answer was not low intelligence. It was a hidden mode her brain kept switching into that nobody had ever told her existed. Her name is Barbara Oakley. The book is called A Mind for Numbers. She failed math and science from grade school to the end of high school. Numbers felt like a language everyone else had been taught in secret. So she ran toward the thing she was good at. She enlisted in the Army right after graduation, and the Army paid her to learn Russian at the Defense Language Institute in Monterey. She got very good at Russian. Good enough to earn a degree in Slavic Languages, serve four years in Germany as a Signal Officer, and rise to Captain. Then the wall appeared. She watched her career options shrink because she could not handle the technical side of her own job. The people with math moved up and moved out. The people without it stayed stuck. So at 26 she did something that sounds insane. She left the Army and enrolled in engineering, starting from remedial math, sitting in classrooms with teenagers. In between, she worked as a Russian translator on Soviet trawlers in the Bering Sea and as a radio operator in Antarctica. Today she is a professor of engineering at Oakland University with a doctorate in systems engineering. The question that drove her for years was simple. What changed? She was the same brain that failed algebra. Why did it suddenly start working? The clue was hiding in the one subject she had mastered. She noticed she had never learned Russian by staring at it. She practiced a little every day, walked away, came back, and the language quietly assembled itself between sessions. Math she had attacked the opposite way. Lock eyes with the problem. Push harder. Refuse to look away until it cracks. It never cracked. And neuroscience explains why. Your brain has two modes. The focused mode is the one you know. Tight attention, prefrontal cortex engaged, grinding through familiar steps. The diffuse mode is the one nobody teaches you. It runs in the background when you relax. It is loose, wide, and wired for connecting ideas that sit far apart from each other. Oakley uses a pinball machine to explain the difference. In focused mode, the bumpers are packed tight. Your thought bounces in the same small circle, over the same ground, again and again. In diffuse mode, the bumpers spread out. The thought travels. It reaches parts of the brain the tight loop could never touch. The trap has a name. The Einstellung effect. The first approach that comes to mind blocks every better approach behind it. The harder you focus, the tighter the loop, the more locked in you become. The grinding feels virtuous. It is actually the cage. And every time her mind wandered off a math problem as a kid, she dragged it back, believing the wandering was laziness. The wandering was her brain trying to switch into the mode that solves things. She spent ten years fighting the half of her brain that wanted to help her. You cannot run both modes at once. The diffuse mode only takes over when you genuinely let go. Which is why answers ambush you in the shower, on a walk, at the edge of sleep. Salvador Dali knew this. He napped in a chair holding a key over a plate, and the instant he drifted off, the key dropped, woke him, and he carried the half-formed ideas straight back into focused work. Edison did the same trick with ball bearings. Two of the most inventive minds in history were deliberately farming the mode the rest of us treat as slacking off. The practical version fits in two sentences. Focus hard on the problem until you stall. Then stop completely, and let the other mode take the shift. The break is not a reward for the work. The break is the work. It is also why cramming fails and procrastination is fatal. Diffuse mode needs hours and nights between focused sessions to build anything, and procrastination burns that time before the first session even starts. Oakley failed math for ten years using one mode at full strength. She became an engineering professor the day she started using both.

I just spent months handwriting a 200 page guide on the entirety of ML foundations and math from scratch. The guide features: - Neural Nets (Backprop, Adam, SGD, Batch Norm) - ML Algorithms (SVM, Grad Boosting, K-means, PCA) - Hardware (Tensor Cores, Systolic Arrays, CUDA) - Transformers (Multi-Head Attn, KV Cache, LoRA) - Vision (ViT, Convolutions, MAE, IoU, NMS, VLM) - Agents (OpenClaw, ReAct, Memory, Orchestration) Everything I wish I had years ago, for free.

A DEVELOPER TAUGHT GIT WITH A BOX OF CHILDREN'S TOYS AND ENGINEERS WITH TEN YEARS IN SAY IT'S THE FIRST TIME THE THING EVER ACTUALLY MADE SENSE 90 minutes, one table, a pile of Tinkertoys. No wall of jargon -- he builds a real Git repo out of plastic rods right in front of you. -> The moment he snaps the first pieces together, Git stops being scary command-line magic and becomes what it really is: a chain of tiny objects pointing at each other. Branches, merges, rebase, the staging area -- every concept that's ever burned you at 2am -- he rebuilds with toys until a four year old could follow. He calls Git a two-trick pony. After this you'll see exactly why. Memorizing commands was never the skill -> holding the graph in your head is. And with an AI agent now committing and rebasing on your machine all day, that mental model is the only thing between you and a history you can't read. Scroll the comments and you'll see the same thing over and over: this is the talk that finally made Git click and made people the one their whole team comes to when it breaks. Bookmark & watch it today. It's the 1.5 hours that pays you back for the rest of your career ↓

slash1s @slash1sol

4 months ago

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room

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You don’t need 10 years of experience to stand out in tech. You just need to master the fundamentals that 90% of people skip. Here are 7 fundamentals that will level up your career 👇

in 1988 Jack Crenshaw wrote Let's Build a Compiler because most compiler books were impossible for beginners to follow in 2017 Nora Sandler did something similar for C while working through Abdulaziz Ghuloum's paper on incremental compiler construction she adapted the ideas from Scheme to C the series starts with a compiler that can do exactly one thing return an integer from main then adds one feature at a time until it becomes a real compiler generating x86 64 assembly the entire series is free online it eventually became a 750 page No Starch Press book in 2024 the original blog posts are still available and still work as a step by step guide

Terence Tao says the math behind today’s LLMs is actually simple. Training and running them mostly uses linear algebra, matrix multiplication, and a bit of calculus, material an undergraduate can handle. We understand how to build and operate these models. The real mystery is why they work so well on some tasks and fail on others, and why we cannot predict that in advance. We lack good rules for forecasting performance across tasks, so progress is largely empirical. A key reason is the nature of real-world data. Pure noise is well understood, perfectly structured data is well understood, but natural text sits in between, partly structured and partly random. Mathematics for that middle regime is thin, similar to how physics struggles at meso-scales between atoms and continua. Because of this gap, we can describe the mechanisms but cannot yet explain capability jumps or give reliable task-level predictions. That mismatch, simple machinery versus hard-to-predict behavior, is the core puzzle. ---- Video from 'Dr Brian Keating' YT Channel (Link in comment)

Hanson's C Interfaces and Implementations remains undefeated for learning proper abstraction in C

9. Most people underestimate the power of doing something for decades. Compounding is not just financial. Skills compound. Relationships compound. Reputation compounds. Confidence compounds. The person who stays obsessed longest usually wins. Stick to something for at least 10 years.

4. Fear is why people stay stuck. Not logic. Not circumstances. Not lack of talent. Fear. And fear disguises itself beautifully: • “I’m being realistic” • “Timing isn’t right” • “Maybe later” • “I should play it safe” Fear rarely sounds dramatic.

In 2023, Stanford professor Graham Weaver gave his last lecture on how to destroy fear & live a wildly ambitious life. His frameworks: - Suffering is inevitable - Signup for "10 years" test - "Not me" & "Not now" traps 13 lessons on how to build an asymmetric life: