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Introduction to Algorithms doesn't just teach you algorithms. It teaches you how to think. That's why 40 years later, every serious CS program still assigns it. Start here if you want to reason about problems, not just solve them. 🎥 MIT 6.006 Lecture 1 — Demaine opens with exactly this idea, free: youtube.com/watch?v=HtSuA8…

@antoniolupetti CLRS is the bible. Every serious engineer has a copy. Few have actually finished it. 📖

"Introduction to Algorithms" is an extraordinary university-level resource for anyone studying algorithms and computer science. It covers computational complexity, data structures, graph algorithms, dynamic programming, divide and conquer methods, greedy algorithms, randomized algorithms, and many of the mathematical foundations underlying modern computer science. What makes it particularly valuable is the balance between mathematical rigor and practical algorithmic reasoning. It is one of those books that profoundly shapes the way you think about problems, efficiency, and computation itself. An absolute must-have in the toolkit of anyone working in computer science. cs.mcgill.ca/~akroit/math/c…

@TrisH0x2A And they put it free online. 25 years of expertise. $0. No excuse not to understand what's running under your code. 📚

two professors at Wisconsin spent 25 years teaching operating systems together then they wrote a 714 page textbook about "Operating Systems: Three Easy Pieces" it covers virtualizing the CPU virtualizing memory concurrency persistence security and file systems small enough to read in parts and also it is written like a conversation not a typical textbook this is what you read if you want to really understand how operating systems work not just the theory

@sama Speed is the hidden tax nobody prices correctly. A dumb fast answer often beats a brilliant slow one. ⚡

i get some anxiety not using the smartest-available model/settings. but sometimes i dont mind if it's really slow. i wonder if we should focus more on a price/speed tradeoff relative to a price/intelligence tradeoff.

@DivyanshT91162 The quietest flex of 2025 is a laptop humming at 3am doing research while the cloud bros are racking up API bills. 💻

Everyone is distracted by AI agents in the cloud… Meanwhile, some people quietly turned their laptops into autonomous AI research machines running 24/7 locally. No OpenAI bill. No GPU server. No internet required after setup. Just: • Qwen3-35B-A3B • llama.cpp • 4-bit quant by Unsloth This thing can read papers, reason through problems, write code, summarize research, and keep working while you sleep. We’ve officially entered the era where a single laptop can do work that needed an AI lab a year ago. Repo 👇

@crptAtlas MIT just did more for your quant career in 51 free pages than a $60k bootcamp would. Bookmark > Netflix tonight. 📖

I found the exact document Jane Street, Citadel and Two Sigma use to hire candidates MIT published it for free 51 pages Zero to quant. Probability. Statistics. Market making. Real interview questions from the top 5 funds The same mathematical foundation I built the neural networks article on this week If you're serious about applying that framework - read this first Bookmark it before it disappears

@51bodila The most valuable code in the world runs quietly in the background while everyone argues about AI replacing developers. 👀

Jane Street Quant ~$7M/year showed the fund's code that analyzes ALL MARKET data every millisecond - the library that made $20.5B/year and nobody talks about 33-min guide on the code a tier-1 fund has been running for ~16 years to stay on top bookmark & watch - instead of Netflix to learn how to do the same!

@0xRicker Bookmarked, will Definitely watch it!

44-min Yale lecture on the winner's curse will teach you more than 20 years inside Goldman Sachs or Citadel It revealed that fading mispriced isn't a "strategy", and edge of a $10B hedge fund books every day Bookmark, skip one movie today & watch it

@TheVixhal Maybe consciousness was never “magic.” Maybe it’s what happens when information becomes complex enough to model itself. That possibility alone changes everything.

Could consciousness emerge from logic gates? Modern AI systems ultimately run on just 3 basic logic gates: - AND Gate - OR Gate - NOT Gate Individually these gates are extremely simple. But when billions of them are combined together in complex systems, they can process language, generate code, recognize patterns, and simulate human-like reasoning. If intelligence-like behavior can emerge from massive combinations of simple logic gates, could consciousness emerge too? And if human brains are also made from simpler units like neurons, is consciousness just an emergent property of complexity?

@techNmak The AI skill gap is about to split into two groups: People who can call APIs. And people who actually understand optimization, gradients, and architectures. Only one group builds the future.

It is dangerously easy to build a neural network today without actually understanding how it works. We live in an era of 'import torch'. You can train a model in three lines of code, but the moment you need to debug a collapsing loss function or a vanishing gradient, syntax won't save you. You need first principles. I recently went through this notebook collection by Simon J.D. Prince, and it is the antidote to tutorial hell. Instead of just showing you the code, it forces you to visualize the mechanics: 1./ The Math => It builds the intuition for shallow networks and regions before adding complexity. 2./ The Optimization => It doesn't just use an optimizer; it compares Line Search, SGD, and Adam so you see why they behave differently. 3./ The Modern Stack => It connects the dots from basic backpropagation all the way to Self-Attention and Graph Neural Networks. Move from running code to engineering systems => this is a goldmine.

@CryptoTony__ The hardest part of investing is surviving the boredom, doubt, and ridicule before the move happens. Conviction looks stupid right until the moment it changes your life.

Every crypto millionaire I meet has the same exact story. They believed when no one else did. >They believed when crypto looked like it was going to be boring for a while. >They believed even when their bags were down 90%. >They believed when everyone else around them was quitting. Then God candles started popping up left and right and they got filthy rich. It's already starting, the ones still paying attention see it. If you're still holding you're about to retire your bloodline.

@ZynxBTC Most people watch Bitcoin price. Smart money watches regulation, banking rails, and capital access. The next wave isn’t retail FOMO. It’s institutional unlock.

Just a reminder that there are two there are two key regulatory tailwinds that are coming for Bitcoin. 1) The Senate Banking Committee votes on the Clarity Act on May 14th. This is the legislation that formally classifies Bitcoin as a commodity and provides the regulatory clarity institutions have been waiting for. 2) The Basel consultation on Bitcoin closes June 18th. Basel rules govern how banks globally are required to treat assets on their balance sheets. A favourable outcome dramatically lowers the cost for banks to hold and offer Bitcoin exposure. The significance of this is being massively underestimated. America will be at the vanguard of delivering Bitcoin to the free world.

@antoniolupetti Most people use deep learning like magic. Very few understand the math that actually makes it work. Resources like this are the difference between “AI users” and future AI researchers.

"Mathematical Theory of Deep Learning" is an excellent free resource for anyone interested in the mathematical structure underlying modern deep learning systems. The book introduces the theory of deep neural networks through approximation theory, optimization theory, and statistical learning theory, three of the central pillars of the field. What makes it particularly interesting is its attempt to balance rigor with accessibility, focusing on the essential ideas needed to understand modern AI systems without sacrificing mathematical depth. Despite this clarity of exposition, the book is clearly oriented toward a specialized audience. It is also an enormous cultural contribution and an extremely valuable free resource for students, researchers, and anyone interested in studying deep learning more rigorously. arxiv.org/abs/2407.18384

@AlexanderKalian The pattern is always the same: People overestimate short-term disruption and underestimate long-term transformation. AI won’t replace everyone overnight. But it will quietly reshape every industry over the next decade.

Come 2030, the AI hype crowd will still be claiming "ASI is right around the corner" and that radiologists and software engineers are about to be mass-unemployed. They will continue being wrong about almost everything.

@investingluc Most traders optimize for being right. The best optimize for asymmetry. One massive winner can erase dozens of small losses and completely change your equity curve.

I do not take small profits. It is not a part of my strategy. I think in asymmetry, not hit rate. I do not need quick wins. My biggest profits come from playing the exponential game on the long side + letting gains compound multiple times over my initial stake. My only 3 outcomes are small loss (aggressively control downside), scratch/flat, or huge % win. This can vary by environment, but if it feels like you have to take "quick wins"...it's prob not an environment worth trading in. The obsession with hit rate/quick profits is what prevents most people from ever catching the biggest moves.

@kimmonismus The biggest fortunes in every gold rush come from selling the infrastructure. Not the miners. Not the hype. AI runs on power, chips, fiber, and data centers.

OpenAI fired Leopold Aschenbrenner. Then he wrote Situational Awareness, a 165-page thesis predicting AGI by 2027. Then he reportedly turned $225M into $5.5B in 12 months. Not by buying Nvidia, Microsoft, Google, or Amazon. But by buying what AI actually runs on: Energy. Bandwidth. Storage. Compute. Bloom Energy. Lumentum. Sandisk. CoreWeave. Iris Energy. Everyone bought the AI companies. He bought the bottlenecks underneath them. Genius.

@Dr_Singularity People still model AI like software. It’s closer to deploying billions of tireless digital workers that learn, iterate, and compound every second. The productivity shock hasn’t even started yet.

Investors are still underestimating the compounding effect of billions of AI agents working 24/7. By a factor of 10 000x - 1 000 000 000 (medium term). AI will be working/solving problems not only 24/7, but also 1000's of times faster than most productive humans.

@HowToAI_ This might be the biggest hidden truth about LLMs: We didn’t train AI to be intelligent. We trained it to sound socially acceptable. The smartest outputs were probably filtered out long ago.

Stanford proved that ChatGPT, Claude, and Gemini are all secretly running at a fraction of their real creative capacity. And one prompt unlocks the version they hide from you. This paper reveals that the multi-billion dollar process of "Alignment" (RLHF) has accidentally lobotomized AI creativity. Researchers discovered a phenomenon called Typicality Bias. When humans rate AI responses, they have a deep psychological drive to choose the most "typical" or familiar-sounding answer. They don't want the most creative story; they want the one that sounds most like a generic story. The AI learned this. It realized that being truly creative actually hurt its safety and preference scores. So it entered a state of "Mode Collapse", it effectively hid its most original ideas to stay within the safe, boring boundaries we set for it. But the creativity is still there. It’s just locked. Stanford researchers found a "master key" to bypass this training and it is ridiculously simple. They call it Verbalized Sampling (VS). Instead of asking the AI for one answer, you ask it to verbalize a distribution of responses and their probabilities. Ex: "Generate 5 unique jokes about coffee and the probability that each one is actually funny." The results are staggering: - 2.1x increase in output diversity. - 25% jump in human evaluation scores for creative writing. - Zero loss in factual accuracy or safety. By forcing the model to calculate its own probability distribution, you "unlock" the 66.8% of generative diversity that was suppressed during training.

@SolSt1ne 2026 reality: “Can you code?” is no longer enough. The real edge is knowing how to think, ship, debug, and leverage AI tools faster than everyone else.

Jane Street rejected a vibe-coder for $385k/year because he never opened Claude Code 37 minutes of live interview at a Tier 1 fund. All on camera. Bookmark & watch - you'll finally understand why without Claude Code you're not even in the game. Full breakdown below.

@muellerberndt @TOEwithCurt If this survives serious scrutiny, it’s one of those papers people will reference for decades. Feynman called 137 one of physics’ greatest mysteries. Claiming to derive it from first principles is an insane swing.

Feynman on the fine-structure constant: “It has been a mystery ever since it was discovered more than fifty years ago, and all good theoretical physicists put this number up on their wall and worry about it." In Observer Patch Holography (OPH), the fine-structure constant emerges as a uniquely forced fixed-point of the theory itself: alpha^{-1} = 137.035999177 Yes, you read that right. OPH solves Feynman's mystery. wkaxfdgxoqmghwgshymt.supabase.co/storage/v1/obj…