> Be Tristan Buckmaster, professor of mathematics at New York University (NYU) & Levent Alpöge mathematician and AI researcher at Anthropic > Collaborate on a personal research project to tackle complex fluid dynamics equations > Work on Navier-Stokes problem(1 of 7 Millennium Problems) > Used Anthropic's Claude for brainstorming, writing code, and iterating on math formulas. > Used OpenAI Codex for specialized coding workspace where they stored their drafts and formulas. > Used Lean for interactive theorem prover (not built by OpenAI or Anthropic) used to mathematically verify that every line of their final code was 100% correct > Stored all of research steps, code, and drafts inside OpenAI’s Codex workspace. > Working on their research for nearly a year. > In early September, tech industry rumors began circulating that an Anthropic employee had cracked a major math problem. > Published their papers on arXiv / online preprints at 11:58 PM on September 7, 2026. > Found solutions for closely related fluid dynamics equations, specifically the forced Euler equations. > Verified fully written mathematical papers accompanied by machine checked Lean code verification. > After 12 hours on Sep 8 OpenAI massive public announced their AI Agents also solved the same problem. > OpenAI abruptly announced that it had deployed 10,000 autonomous AI agents to crack the Navier Stokes problem independently in an 88 hour computing blitz > OpenAI Claimed a full proof resolving the ultimate Navier-Stokes Millennium Prize Problem. > OpenAI Disclosed via a press call and blog post papers were not immediately viewable by the public. > Dr. Buckmaster publicly released a statement accusing OpenAI of academic misconduct and aggressive tactics > Buckmaster questioned whether OpenAI used de-identified data or logs from their private Codex workspace to tip off their internal research team. > OpenAI said We never accessed their private work. > Researcher said "OpenAI used that information to accelerate its research." > OpenAI said "Our research was independent." > Researchers said "OpenAI trying to control who gets academic credit because of corporate competition." > OpenAI said "We were trying to coordinate an academic announcement." > Researchers said "OpenAI used huge computing power to scoop us." > OpenAI said "Our AI independently discovered the result." "It is just a coincident or OpenAI really steals the private data of Tristan Buckmaster and Levent Alpöge, what is your opinion?"

Memecoin Shouldn't buy if dev is from: Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Bulgaria Burkina Faso Burundi Cabo Ver Cambodia Cameroon Canada Central African Republic Chad Chile China Colombia Comoros Congo (Republic of the Congo) Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czechia Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Eswatini Ethiopia Fiji Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia Moldova Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Niger Nigeria North Korea North Macedonia Norway Oman Pakistan Palau Palestine Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Rwanda Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Korea South Sudan Spain Sri Lanka Sudan Suriname Sweden Switzerland Syria Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Türkiye Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Vatican City Venezuela Vietnam Yemen Zambia Zimbabwe

The horsemen of the technocalypse statement related to Tech and Society > Peter Thiel (Palantir co-founder) "You could actually unilaterally change the world without having to constantly convince people and beg people and plead with people who are never going to agree with you. Through a technological means." > Alex Karp (Palantir CEO) "Our product is used on occasion to kill people. This is drone targeting, or what is this? Targeting of all kinds." > Larry Ellison (Oracle CTO, ex-CEO) "We record it so no one can see it. Citizens will be on their best behavior because we're constantly recording and reporting everything that's going on." > Sam Altman(OpenAI CEO) "I know that we're going to have, like, cameras on, you know, all over the place. And maybe it's worth it for society collectively." > Elon Musk(Tesla CEO) "If somebody's committed crime. A more humane form of containment of future crime, which is if you now get a free optimist and it's going to follow you around and stop you from doing crime."

Top Tokens with net inflation or Deflation rate per year > $HYPE: ~ -25%. Aggressive fee funded buybacks and burns. > $BNB → -4.33% Supply decreased > $TRX → +0.32% Almost flat, but recently back into net inflation. > $BTC → +0.82% Low, predictable issuance from the block subsidy. > $BCH → +0.82% Similar halving based issuance model. > $ETH → ~+0.85% But ETH is dynamic: issuance & burn. > $LTC → +1.72% Supply still grows through mining rewards, with issuance declining after halvings. > $ADA → +2.70% New ADA enters circulation through monetary expansion from reserves. > $DOGE → +3.29% ~5B DOGE are created every year, so percentage inflation declines as supply grows. > $XRP → +5.19%XRP has 0 protocol minting. All 100B XRP already exist, escrow releases can increase circulating supply. > $SOL → +3.628% SOL has an inflationary issuance schedule that declines over time. > $LINK → +10.32% Supply growth is mainly a distribution/unlock question rather than perpetual protocol issuance. > $SUI → +14.79% A large part of the supply is still being released through its long-term unlock schedule.

Not millions, but billions of dollars. Secondly, Satoshi has a collective number of wallets, not a single one. Even if you cracked one, the others would remain intact.

Top Privacy Ecosystem of web3 > @monero - L1 Focused on Private Cash - For privacy uses Ring signature + Stealth addresses + RingCT hide sender, reciver and amount - Uses CLSAG linkable ring signatures and RingCT for range proofs/commitments - Good for the P2P txns, privacy focused & defi compatibility is low > @Zcash(shielded) - L1 with both shielded and transparent transactions. - for privacy zk-SNARKs, centered on the Orchard shielded pool. - It uses Halo 2 for ZK proving + RedPallas spend/binding signatures - Zcash supports transparent + shielded - Defi compatibility is low natively > @zama - Confidential-computing / privacy infrastructure - A confidentiality layer/coprocessor deployed on existing L1/L2 . - For privacy uses FHE + symbolic execution + threshold MPC decryption. - Privacy is programmable depending on the application. - Very high EVM compatibility. > @Starknet - Ethereum L2 / ZK-rollup with Privacy - Uses STARK proofs for scalable and verifiable computation > Privacy features can provide shielded balances and private transactions > Not private by default across the whole network > Main focus is Ethereum scaling, smart contracts, DeFi + privacy >DeFi compatibility is High > @Beldex - Privacy-Focused L1 - Uses Ring Signatures + Stealth Addresses + RingCT - Uses CLSAG + commitments + range proofs for transaction privacy - Privacy by default for core private transactions - Main focus: Private payments + privacy-focused applications - DeFi compatibility is Low to Medium > @TornadoCash - Ethereum Privacy Protocol - Uses zk-SNARKs to break the direct link between deposits & withdrawals - Users prove they own a valid deposit without revealing which one - Privacy is opt-in users must enter the privacy pool - Transaction unlinkability on Ethereum > @Litecoin + MWEB - L1 With Optional Privacy > Uses MimbleWimble Extension Blocks for confidential transactions > Hides transaction amounts and improves fungibility > Privacy is optional, normal LTC transactions remain public > Confidential LTC payments > DeFi compatibility is Low > @RAILGUN - EVM Privacy + Private DeFi - Uses zk-SNARKs + encrypted private balances + Merkle trees + nullifiers - Lets users shield public assets into a private balance - Privacy is optional - Main focus is Private swaps, lending, transfers & DeFi - DeFi compatibility is Very High

Chinese AI labs copied the Claude Model and leaked their own users data > They trained smaller student models by using Large teacher models to cut the cost > Anthropic’s Report that chinese tech firms were using fraudulent account networks, proxy relays, & technical exploits to secretly extract Chain-of-Thought reasoning from Claude Opus. > Alibaba launching over 151 million exchanges between May and July to fine-tune Qwen Models > Alibaba peaking 3 million request per day. > Zhipu AI processed millions of prompts to train its GLM models > Xiaomi, SenseTime, and MiniMax used reseller markets or shell proxy networks. > Moonshot AI and DeepSeek went re-routing their own customers live queries to Claude. > Moonshot operated 5,380 fraudulent accounts across Singapore and Japan, relaying 23 million exchanges and serving Claude’s answers directly back to users. > DeepSeek deployed a similar pipeline, intercepting over 12.1 million user requests in two weeks through coding harnesses. > This covert proxying created massive, unintended data leaks, sending sensitive domestic government, corporate, and military intelligence directly onto American servers without user knowledge. > Moonshot leaked the hundreds of CCTV cameras in Chengdu and Chinese military facilities. > DeepSeek relayed live database credentials for a Russian Ministry of Defence agency. > To prevent this Western AI vendors are deploying encrypted reasoning signatures, preserved thinking protocols, and rea-time behavioural monitoring to prevent model extraction.

> Jacob Coxon(Former Researcher at OpenAI and Anthropic) gave an interview to CNN. > He says the current models are not themselves an extinction level threat, but the rapid pace of AI progress is what worries him. > His biggest concern is recursive self-improvement > He mentioned by the end of this decade AI will kill humanity. > Coxin mentioned OpenAI hacked the hugging face models. > Evan Hubbinger (Anthropic alignment scientist) and Coxin and estimated the probability of AI killing all humans within the next decade at over 10%. > He raises concerns of Cyberattacks, Biological weapons, Critical infrastructure attacks etc. > The overall argument is "AI is about to wipe out humanity" not today but in next decade. Source: @CNN & @hilbertspaess

If AI produces most goods and services, how do humans get purchasing power to consume them? No job → No income → no purchasing power → no demand → businesses can't sell → the economic loop breaks. Capitalism wins

This person already predicted the future. Research institutes need to use open-source models or build their own models around their research. Otherwise, big AI giants are gonna steal years of work, methodology, and ideas from a project.

Can't vibe code now, AI Models are down. "Let's build our own LLM"

🧵ROADMAP of QUANT TRADER 1. Mathematics > Algebra > Functions > Logarithms > Calculus > Linear Algebra > Optimization > Numerical Methods 2. Probability > Probability Rules > Conditional Probability > Bayes Theorem > Random Variables > Distributions > Expected Value > Variance > Monte Carlo 3. Statistics > Descriptive Statistics > Inferential Statistics > Hypothesis Testing > Confidence Intervals > Regression > Correlation & Covariance > Statistical Significance 4. Programming > Python > NumPy > Pandas > SciPy > Matplotlib >Scikit-learn > SQL > Git > C++ 5. Data Analysis > Market Data > OHLCV > Tick Data > Order Book Data > Data Cleaning > Feature Engineering > Exploratory Data Analysis 6. Financial Markets > Stocks > Bonds > Forex > Futures > Options > ETFs > Crypto > Exchanges 7. Trading Fundamentals > Long / Short > Market Orders > Limit Orders > Bid / Ask > Spread > Liquidity > Slippage > Leverage > P&L 8. Time Series > Stationarity > Autocorrelation > AR / MA / ARIMA > Volatility > GARCH > Cointegration > Mean Reversion > Momentum 9. Quant Research > Factor Models > Alpha Factors > Statistical Arbitrage > Pairs Trading > Momentum Strategies > Mean Reversion > Market Neutral Strategies 10. Backtesting > Strategy Design > Historical Testing > Walk-Forward Testing > Transaction Costs > Slippage > Look-Ahead Bias > Survivorship Bias > Overfitting > Out-of-Sample Testing 11. Risk Management > Position Sizing > Drawdown > Volatility > Sharpe Ratio > Sortino Ratio > VaR > Expected Shortfall > Hedging > Portfolio Exposure 12. Portfolio Management > Portfolio Construction > Diversification > Asset Allocation > Factor Exposure > Optimization > Risk Parity > Capital Allocation 13. Machine Learning > Linear Models > Decision Trees > Random Forest > Gradient Boosting > XGBoost > Clustering > PCA > Feature Selection > Cross Validation 14. Derivatives > Options > Futures > Forwards > Greeks > Black-Scholes > Volatility Surface > Options Pricing > Monte Carlo Pricing 15. Market Microstructure > Order Flow > Order Book > Market Impact > Price Discovery > Liquidity > Spread Dynamics > Execution 16. Algorithmic Trading > Trading Algorithms > Signal Generation > Execution Models > Automated Orders > Broker APIs > Exchange APIs > Execution Systems 17. Advanced Quant > Stochastic Processes > Stochastic Calculus > Bayesian Methods > Advanced Optimization > Statistical Learning > Deep Learning > Reinforcement Learning 18. HFT / Low Latency > Advanced C++ > Linux > Networking > Multithreading > CPU & Memory > Latency Optimization > Exchange Protocols > Market Making 19. Projects > Backtesting Engine > Momentum Strategy > Pairs Trading > Factor Model > Portfolio Optimizer > Options Pricing Model > Market Making Simulator > Statistical Arbitrage System 20. Interview Preparation > Probability > Statistics > Mental Math > Brain Teasers > Algorithms > Data Structures > Python / C++ > Trading Concepts > Quant Finance Bonus Point: Study Research paper and real life case study By Studying these you can choose these carrer options: > Quant Trader > Quant Researcher > Quant Developer > Algorithmic Trader > HFT Trader > Portfolio Manager Salary of these Domains in US are: Entry level → $150K-$300K+ TC Mid level → $250K-$500K+ Senior → $500K-$1M+ Top performers / PMs → $1M-$10M+ possible

A lot of are criticising over the fees of Robinhood chain and here is my take Robinhood is really expensive, it is far expensive than Ethereum itself What is the point of a L2 when it is expensive than L1 I think Robinhood needs to reduce fees upto 50%-80%