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50 Sites That Show What Billionaires Buy and Read for Free🤯 1.) → Current portfolios of famous investors 2.) → Quarterly fund trades 3.) → Stock transactions of US politicians 4.) → Executives' own company stocks 5.) → Making fund filings readable 6.) → Companies' official reports 7.) → Buffett's letters since 1977 8.) → Video archive of Buffett meetings 9.) → Howard Marks' investor memos 10.) → Research from Ray Dalio's firm 11.) → Dalio's principles for free 12.) → Jeremy Grantham's market notes 13.) → Quantitative fund research 14.) → Damodaran's valuation data 15.) → Valuation professor's blog 16.) → Morgan Housel's money writings 17.) → Mental models for decision-making 18.) → JPMorgan's free market guide 19.) → Innovation-focused fund research 20.) → Venture capital essays 21.) → Value investing lecture notes 22.) → Columbia's value investing archive 23.) → Fed's economic data archive 24.) → Fed decisions and statements 25.) → European Central Bank data 26.) → Reports from the central bank of central banks 27.) → Countries' economic indicators 28.) → Companies' long-term charts 29.) → Company financials for free 30.) → Companies' market cap rankings 31.) → Stock screener and heat map 32.) → Portfolio backtesting 33.) → Charts and market analysis 34.) → Professional market screener 35.) → Company activity reports archive 36.) → SEC's investor education site 37.) → Passive investing encyclopedia 38.) → Financial terms dictionary 39.) → Free finance guides 40.) → CFA research publications 41.) → Fund and stock evaluations 42.) → Companies' visual analysis 43.) → Archive of selected investment theses 44.) → Seeing what's inside funds 45.) → European fund backtests 46.) → Investment writings with data 47.) → Market history notes 48.) → Investor psychology writings 49.) → Explaining the economy with charts 50.) ourfiniteworld? → (see note) The rich don't hoard information. They just don't tell you where it is. Save it, follow @Liyana_Coder you'll need it. Not investment advice. @Liyana_Coder

Leopold Aschenbrenner’s complete Q2 performance before his newest trades release Monday Top tier: • SanDisk $SNDK: -62% • Micron $MU: -34% Second tier: • Bloom Energy $BE: -38% • Taiwan Semiconductor $TSM: -18% • Nebius $NBIS: -59% Third tier: • CoreWeave $CRWV: -49% • Core Scientific $CORZ: -45% • STMicroelectronics $STM: -39% • Applied Digital $APLD: -49% • Riot Platforms $RIOT: -37% • SharonAI $SHAZ: -74% • IREN $IREN: -61% Smaller positions: • T1 Energy $TE: -82% • WhiteFiber $WYFI: -76% • Babcock & Wilcox $BW: -69% • Keel Infrastructure $KEEL: -69% • Bitdeer $BTDR: -66% • HIVE Digital $HIVE: -58% • CleanSpark $CLSK: -56% • Cerebras $CBRS: -53% • Vishay $VSH: -52% • ProPetro $PUMP: -49% • Solaris Energy $SEI: -23% Options: • Bloom Energy $BE [Calls]: $44,100,000 • Taiwan Semiconductor $TSM [Calls]: $23,900,000 • Infosys $INFY [Puts]: $5,200,000 Summary: Every common stock position is red. His entire portfolio is down 46.8% from Q2 release to present day. His newest trades release Monday, November 16th, showing what he owned on September 30th, after he sold most of this to Citadel in late July

🧵 BNB Chain Recap Last 24H 1/ DeFi & Trading • @Aster_DEX → added Spot trading support to Aster Vault alongside Perps. • @lista_dao → added 5 new bStocks as collateral to Lista Lending, alongside new USDC and fixed-rate lending markets. • @pancakeswap → launched a MAG7Xon Syrup Pool with Ondo, distributed Ondo Intelligent Portfolio tokens to eligible traders, and expanded to 8 additional chains with an 88% LP fee share. • @VenusProtocol → enabled stablecoin borrowing against 20 tokenized stocks and ETFs through Venus Spoke. 2/ AI & Infrastructure • @flapdotsh → integrated Binance Account Bound (BAB) Token into its Agent ID Launch bonding curve. • @pieverse_io → introduced Confidential Agents, protecting agent memory and credentials using Intel TDX and Google Confidential Space. 3/ Rewards & Community • @predictdotfun → opened Season 1 $PREDICT airdrop claims, offering 35% instant unlock or 100% vesting over 12 months, starting November 18.

Multi-agent workflows have a scaling problem! The agents inside the same workflow rarely need the same amount of capacity at the same time. Say you have a planner, multiple research agents, and a final synthesis agent. The planner might finish quickly while requests start piling up behind one of the research agents. Scaling the entire application would add capacity everywhere, even though only one part of the workflow actually needs it. CanyonOS is an open-source, workflow-aware platform for multi-agent Python applications and the harness between your agents. Each agent runs as a separate service with its own queue and replica limits, so the busy research agent can get additional capacity while the planner and synthesis agents stay where they are. It also handles communication between the agents and gives you visibility into what happens across the workflow after a request comes in. CanyonOS records the path a request took, including the agents it called, parallel branches, model usage, execution time, and estimated model costs. And you don't need to add tracing decorators or a separate instrumentation SDK throughout your agent code. You still integrate the workflow with CanyonOS, but tracing is handled at the platform layer. I also ran a multi-agent portfolio analysis workflow through it with separate agents working across three stocks. A single request produced 10 traceable steps across the workflow, and I could see which agents ran, where the LLM calls happened, how long each stage took, and the estimated model cost. For multi-agent applications, being able to operate each agent independently while still seeing the complete execution path becomes much more useful as the workflow grows. CanyonOS is open source. Link to the GitHub repo in the comments!

JUST IN: Investment bank giant TD Cowen says "Bitcoin is increasingly being evaluated within a portfolio construction framework" 👀 "Conversation around Bitcoin has evolved from WILL institutions participate, to HOW will institutions participate." 🚀

What Menlo has done over the last 24 months is exceptional. Yes, they did Anthropic’s round at $4BN that will make them $50BN+ as a firm. NICE! But they then took that and went f****** hard. They do Lovable, Higgsfield, Legora. They compounded the brand win of Anthropic to cement their position from respectfully a slightly ageing firm to being a dominant venture leader at the forefront with Thrive, a16z, Benchmark etc. It is incredible to see and I sat down with @venkyganesan to shoot the s*** on this. 1. Every Seed Investment Is an Option Bet Seed-stage deals function as financial options to discover true outlier potential before committing major capital. VCs must construct a broad enough portfolio to secure sufficient at-bats, only sizing up their position when quantifiable, revenue-based metrics prove an outlier has emerged. 2. What Is the First Sign of the AI Wave Cracking? Market cycles rarely collapse due to equity write-downs. They fracture when levered debt defaults occur. When market participants take on heavy leverage during an AI boom, precise timing becomes as critical as being directionally correct, making debt obligations the primary failure vector when cycles turn. 3. Why Every VC Must Now Prioritize IRR Every startup in the current landscape writes a structural tax to the Mag 7 and hyperscalers for compute and foundation models. Because LPs can buy index exposure to those same public tech giants without fees or carry, private VCs must generate outsized IRR performance, beating public markets by at least 1,000 basis points, to justify private capital allocation. 4. Why LPs Wanting Smaller Funds and Slower Deployment Is BS LPs cannot demand both smaller fund sizes and multi-year deployment paces in the middle of a generational platform shift. Because AI startups face immense capital requirements to scale compute, VC firms that refuse to deploy capital rapidly will watch competitors step in and capture the category winners. 5. The Theory of Reflexivity and How It's Causing Markups Reflexivity creates self-reinforcing loops where fast revenue growth triggers rapid valuation markups, attracting elite human and financial capital that further accelerates growth. However, when copycat investors chase markups for momentum rather than underlying fundamentals, reflexive valuation bubbles inflate until market realities force an inevitable correction. 6. Sometimes You Pay Up Because You Have Unique Insight Premium entry prices are not always the result of irrational bidding. They often stem from an investor's ability to recognize a fundamentally larger total addressable market than the consensus. When a VC possesses unique conviction around an expanding market opportunity, paying above current rates reflects calculated foresight rather than price-taking behavior. 7. There's No Limit to What a Person Can Do as Long as They Don't Care Who Gets the Credit Personal ego remains one of the most expensive liabilities in venture capital and company building. True long-term success requires setting aside individual notoriety, focusing ruthlessly on generating LP returns, and empowering teammates and founders without worrying about who receives the public credit. (links in comments)

Huawei and Qualcomm have announced a multi-year, broad patent license agreement that includes cross licenses to the companies' patent portfolios across a range of fields, including 5G, compute, AI, and networking, together with Qualcomm's purchase of certain Huawei U.S. patents.

What stood out to me is how Jumper brings swaps and portfolio tracking together, cutting out the need to jump between different apps solana:Aybt5BWuW6kMNt7Jk1UmSgLzpEKev5pEnh82TA5rpump

🎙️ The Sujal Show Ep. 21: @EowynChen "What Happens If Binance Gets Hacked Tomorrow" @binance CMO & former @TrustWallet CEO Eowyn Chen reveals 3 things to check before joining an exchange, the hardest scams for exchanges to stop, why self-custody isn't for 90% of beginners, & Binance's roadmap to 1 billion users We discussed on @TheSujalShow: - Her journey: joining crypto in 2018, at the bear market bottom - Why Binance is betting big on India 🇮🇳 - India’s 30% crypto tax problem - India vs PAK vs UAE vs Nigeria: Biggest crypto superpower? - What happens to your money if Binance ever gets hacked - How Binance responded to its own 2019 hack - Lessons from the FTX collapse - The hardest scams for any exchange to actually stop - Inside the Gujarat case: $24M traced, 14 arrests made - $1 test that can save your entire portfolio - Exchange or self-custody? Her honest answer - Binance’s vision for 3 billion users - Gold vs Bitcoin, what Indians will hold in 10 years - 1 life advice for making it big in crypto Timestamps: 00:00 Intro 02:11 Who Is Eowyn Chen? 03:09 Why Is Binance Betting Big on India? 06:24 Binance’s Future in India 10:20 3 Things to Check Before Investing 14:06 What Happens If Binance Fails? 16:00 How Does Binance Stop Crypto Scams? 21:37 How to Avoid Crypto Scams 24:30 Exchange vs Self-Custody: What’s Safer? 29:42 Why Should Indians Care About Tokenization? 35:45 Can AI Make Crypto Traders Richer? 41:39 How to Protect Yourself From AI Deepfakes 45:34 Rapid Fire 48:35 Outro She's led growth at both Binance and Trust Wallet. Here's her real playbook for staying safe in crypto. Watch Now 👇