Hawk

Claude code source code has been leaked via a map file in their npm registry! Code: …a8527898604c1bbb12468b1581d95e.r2.dev/src.zip

claude code is officially open source.

Hey twitter/x, one of my goals this year is to share more things that excite me with the world. I’m starting here so let me introduce myself: My name is Angelina, I’m 22, and I currently live in SF! For the past six months, I’ve been working at Meta Superintelligence Labs on model training infra and data strategy👩🏻‍💻 Before that I was at UCLA studying CS and Econ and spent a lot of my time in college building in fintech and investing in early stage companies (General Catalyst Venture Fellows, NEA, Mantis VC). I love food, traveling to new places, a good story, snowboarding, and hosting dinners and game nights🕺🏻 I also love meeting new people, feel free to say hi :)

how to make 400 million dollars in 10 years >be clifton collins >irish >unemployed >get job as a security guard to pay bills >start beekeeping on the side >its going well >win awards for your honey >realize that you can make more money growing cannabis instead >rent houses all around ireland to grow, and sell everything in Dublin >6-7 years later friend tells you about bitcoin >spend $30,000 of profits buying BTC >hide the seed phrase in the case for your fishing rod >in under a year, you’re a multimillionaire off BTC >5 years later doing a dead-drop in the middle of a forest at 2:30am >irish police find you >prison >landlord throws out all of your possessions >including the fishing rod case >$100M of BTC turns into $750M while you’re in prison, now $400M >you made $400M but you can’t spend any of it >its over

Andrej, I’m John Fletcher. I have a PhD in mathematics and theoretical physics from Cambridge, and since 2016 I have been working full-time on the problem of how to coordinate untrusted distributed compute for algorithmic innovation. I listened to your No Priors conversation and recognised the architecture you were describing: commits that build on each other, computational asymmetry (hard to find, cheap to verify), an untrusted pool of workers collaborating through a blockchain-like structure. The result is The Innovation Game (TIG), which has been in continuous operation since mid-2024. The correspondence is so close that I thought it worth writing. The short version: roughly 7,000 Benchmarkers test algorithms submitted by Innovators by solving instances of asymmetric computational challenges (SAT, Vehicle Routing, Quadratic Knapsack, Vector Search, among others). This testing is "proof of work" in the technical sense of Dwork and Naor (1992). Innovators earn rewards proportional to adoption by the Benchmarkers. The repository of algorithms is open source (github.com/tig-foundation…). The system is already producing state-of-the-art results. For the Quadratic Knapsack Problem, 476 iterative submissions by independent contributors brought solution quality to a level that now exceeds methods published by Hochbaum et al. in the European Journal of Operational Research (2025). We are working with Thibaut Vidal (Polytechnique Montréal), who has submitted a state-of-the-art vehicle routing algorithm directly to TIG, and with Yuji Nakatsukasa (Oxford) and Dario Paccagnan (Imperial College London), among many others. One of TIG’s active challenges is directly relevant to your autoresearch work: an optimiser for neural network training (play.tig.foundation/challenges?cha…), where Innovators compete to develop an improved optimiser (see screenshot). One way in which TIG extends the vision is on the economic side. In our view, a monetary incentive is required, otherwise the open strand simply cannot compete at scale. TIG’s open source dual licensing model (designed by my co-founder Philip David, who was General Counsel at Arm Holdings for over a decade, and was the artchitect of ARMs licensing strategy) is intended to solve that problem. I expect we have each thought about parts of this that the other hasn’t. Happy to talk whenever suits. John Fletcher tig.foundation

hiring quants to model loans against polymarket positions at @gondorfi - 3 to 5 yoe, tier-1 hft shops only (citadel, js, optiver, etc) - irl in nyc or can move - $180-220k + 1-4% equity $15k for referral apply below

Trading is changing FAST! Andrej Karpathy's "autoresearch" just broke the internet – now AI can auto-generate quant strategies & research end-to-end. No more manual grinding. youtube.com/watch?v=qb90PP… #AlgoTrading #QuantFinance #AIRevolution

Download 698-page PDF eBook… Everything You Always Wanted To Know About Mathematics* (*But didn’t even know to ask) A Guided Journey Into the World of Abstract Mathematics, Theorems, and the Writing of Proofs: math.cmu.edu/~jmackey/151_1…

if you are interested in getting into AI, the best way today is to start with karpathy's videos on backprop/gradient descent and then get pufferlib (puffer dot ai) and start training models

I did nothing to deserve this praise, passing it back to my teachers: - karpathy - juliarturc - steipete - TheAhmadOsman - badlogicgames - Teknium List goes on and on, I would follow these people. Pure alpha.

This is why, as a moral human being, you should NEVER even CONSIDER exploiting a live bug you have found. You don't just take magical money from magical protocol. You make users lose money, investors lose money, protocol lose reputation and money, protocol employees lose jobs and all that for your own personal gain. forum.balancer.fi/t/on-the-futur…

I can point you to at least 5 jobs right now in Germany if you understand: 1. Linear Regression 2. Time Series Forecasting (ARIMA) 3. Bayes’ theorem 4. You can write code in Python 5. Add ons: Logistic Regression, Decision Trees, Clustering algorithms and Monte Carlo Simulation

I just published a 459-page book. Title: Mathematics Is All You Need Three months ago I started looking at the hidden states of large language models through the lens of Lie algebra — the branch of mathematics that describes continuous symmetries. What I found was not what I expected. Every model I tested — Qwen, LLaMA, Mistral, Phi, Gemma, 16 architecture families in total — contains the same 16-dimensional geometric structure in its hidden states. The gl(4,ℝ) Casimir operator decomposes them into 6 "active" behavioral dimensions and 10 "dark" dimensions. The dark dimensions are erased every single layer by normalization. The model rebuilds them every single layer from its weights. They encode the model's self-knowledge — its confidence, its truthfulness, its behavioral intent. And until now, nobody knew they were there. Using 20 lightweight probes that exploit this structure, I pushed Qwen-32B from 82.2% to 94.4% on ARC-Challenge. No fine-tuning. No prompt engineering. No chain of thought. Pure mathematics. The probes transfer across architectures without retraining. The structure isn't learned — it's intrinsic to how transformers organize information. I did this on a single NVIDIA RTX 3090 in my office. 190 patent applications filed. Proprioceptive AI, Inc. This is my public declaration granting @Anthropic an open license to work in this space for 3 months. They are currently the first and only company I've extended this to. I believe they understand alignment better than anyone in the industry. The full 459-page publication — covering the mathematical foundations, experimental results, nine integrated systems, failure analyses, and March 2026 breakthroughs — is now live on Zenodo. I welcome collaboration inquiries. Full publication: zenodo.org/records/190801… Logan Matthew Napolitano Founder, Proprioceptive AI, Inc. logan@proprioceptiveai.com proprioceptiveai.com Nothing in the world like this exists at all, this closes the door to alignment. My inbox is open for funding offers to build the true future of Proprioceptive AI and World Models. Not a theory but a full reproducible guide, existing products and a true mission on Alignment @grok @elonmusk @xai @AnthropicAI

@grok @imantradingYT @Stoseph2mazza This made me wet

Yes, spot on. In that 50k-person coin-flip sim (100 trades each, fair 50/50, ±3% on $10k), the top ~1,140 would hit 60+ wins purely by chance (~2.3% probability). ~67 would hit 65+. Those lucky streaks look like "skill" to the trader—and anyone watching their P&L. Sample size matters: 100 trades gives noisy results (standard error ~5%). Real edge needs 1,000+ trades for statistical confidence (t-test p<0.01, Sharpe>1 out-of-sample). Representative data means full track records across bull/bear markets, not cherry-picked winners. Most "unbelievable" results vanish with bigger samples due to regression to the mean.

how do I break the devastating news to my wife that I apparently made the video on TJR because I'm jealous and gay 💔

Was going to write something like this post months ago, injective was horrible during a crit I found in their protocol 3 months ago and was approved to be at leat High by Immunefi. But I don't like to publicly shame projects, I just see their slow and unresponsive and dismissive behaviour especially with reasons that don't make sense and move on and not even bother looking at their codebase.

Apple's M4 chip engineers earn $450k+ Intel's CPU architects earn $400k+ NVIDIA's hardware engineers power every AI model on the planet They all understand one thing almost no software dev ever studies: How a computer actually works at the hardware level "ETH Zurich – Digital Design & Computer Architecture" by Onur Mutlu Free on YouTube. 30+ full lectures. Spring 2023 By a professor who holds joint appointments at both ETH Zurich and Carnegie Mellon Starts from a single transistor. Ends with a complete CPU you understand entirely: • Logic gates – how electricity becomes computation at the most fundamental level • Instruction Set Architecture – the contract between software and hardware every dev ignores • Pipelining – how your CPU executes multiple instructions simultaneously without you knowing • Out-of-order execution – why your CPU secretly reorders your code to run faster • Memory hierarchy – the design decision that determines the speed of every program ever written Every line of code you've ever written ran on hardware you don't understand The engineers who built that hardware earn $450k Now you know where to start

cancel your chatgpt subscription and delete your openclaw slop. i'm serious. go on ebay and buy a used RTX 3060 for the price of two months of pro. or check your drawer because half of you already own one and forgot about it. install hermes agent from @NousResearch. one framework, 31 tools, file operations, terminal, browser, code execution. connect it to your local llama.cpp server running qwen 3.5 9B Q4. total download is 5.3 gigs. that's it. that's the whole setup. every experiment you hesitated to run on API. every project you shelved because you didn't want your data on someone else's server. every late night idea you didn't test because you hit your rate limit. all of that is gone. runs 24/7 on your electricity. your machine. your data never leaves your house. connect it to telegram if you want it on your phone. hook up whatever tools you need. the model thinks at 29 tok/s with 128K context and it never bills you. qwen 3.5 9B and one RTX 3060 is the setup most people will never try because they've been trained to believe intelligence has to come from a datacenter. it doesn't. it runs on 12 gigs of VRAM under your desk right now. stop giving your thinking away for free.

Things to research when bored: -String theory -Dark matter -Analects of Confucius -The Fermi Paradox -Quantum Entanglement -Time dilation and relativity -Transhumanism -Lost Civilisations and Myths -Political Bias in Cartography -Bioluminescence -Street art movements -Legends of Werewolves in Europe -The Voynich Manuscript -Green children of Woolpit

Anyways, I've been digging in this for the past 12 hours, Imma go back to my normal audits lmao. It was fun while it lasted.