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朋友们 回来了 ​ ​32G + 1t ​ ​除了贵没什么其他特别之处，我买的是翻新的版本 上一台翻新 MacBook Pro 还是 2021 年买的，买了后做了个外包挣了 6 万人民币，​现在送给我老婆用，她每天的工作就是聊聊微信，改下PPT，可以用很久 这一台计划用五年，真正让我感到兴奋了还是七八万Mac Studio Ultra啊 可惜太贵了

Why Tokenization Could Be the Most Important Economic Innovation of Our Lifetime | Bruce Fenton @BruceFenton is a longtime Bitcoin advocate since 2012, securities professional, and one of the early pioneers of tokenized securities. He is the founder and CEO of Chainstone Labs and founder of Atlantic Financial. Alongside Overstock founder @PatrickByrne, Bruce was involved in some of the earliest efforts to bring securities and financial-market infrastructure onto blockchain rails. In 2018, he tokenized equity in his own company, Chainstone Labs, on the Ravencoin blockchain—years before tokenization became one of Wall Street’s biggest narratives. In this episode, Bruce breaks down naked short selling, the plumbing behind traditional securities markets, and why he believes much of the problem is ultimately a ledger problem. We dive into questions such as: Who actually owns the stocks sitting in your brokerage account? How can shares be lent and rehypothecated? What role do brokers, clearing firms, DTCC, and Cede & Co. play in determining who owns what? And could blockchain replace an opaque system of claims and intermediaries with a transparent ledger showing exactly where an asset is and who owns it? We unpack what “tokenized stocks” actually means—and the important distinction between a token that simply gives you exposure to a stock’s price and a security issued natively onchain, where the token itself represents the actual share and the ownership rights that come with it. Bruce discusses how tokenization could allow smaller businesses to tokenize ownership and have their stock traded globally without needing to be listed on a traditional exchange like the NYSE, as well as what a future without KYC could look like. Finally, we get into Wall Street’s embrace of tokenization, the SEC’s evolving approach to tokenized securities, and one of the biggest questions surrounding the future of this technology: who ultimately controls these new financial rails? Remember to subscribe and hit the bell “🔔” icon to get notifications 0:00 Bruce Fenton & Patrick Byrne: Pioneers of Tokenized Securities 3:36 Naked Short Selling Explained 6:28 Who Actually Owns the Stocks in Your Brokerage Account? 12:00 Tokenization Solves Wall Street’s “Ledger Problem” 15:48 What Are You Actually Buying With Robinhood’s Tokenized Stocks? 16:48 Real Tokenized Stocks: When the Token IS the Share 21:46 Tokenization Could Open U.S. Markets to the Entire World 22:52 Why Companies May No Longer Need the NYSE 26:29 Could Tokenized Markets Exist Without KYC? 29:31 Bruce Tokenized Equity on Ravencoin in 2018 30:53 Will Blockchain Make Clearing Houses & DTCC Obsolete? 33:17 Could Tokenization Have Prevented the GameStop Crisis? 38:19 Wall Street Is Embracing Tokenization — Who Gets the Power? 40:20 The SEC’s New Tokenized Securities “Innovation Exemption” 42:57 Why Robinhood’s Tokenized Stocks Don’t Qualify 47:01 Could Everything Eventually Be Tokenized? 51:02 The Dystopian Side of Tokenization: Surveillance & Asset Seizure

Kling 4.0 is coming this October. Kling 4.0 Flash is live now for Ultra Yearly subscribers. Kling 4.0 brings video creation to a new level of visual realism, creative control, and narrative completeness. 🎞️ Upgraded Audio & Visuals: Stable dynamic motion, high-quality stereo audio, more accurate lip sync, up to 4K resolution, and 10-bit HDR output. 🔮 Omni Reference: Richer reference options with up to 15 multi-modal references, more consistent results, and enhanced video editing. 🎥 Seamless Storytelling: Multi-keyframe control supporting up to 10 keyframes, native 30-second generation. 🌍 Diverse Possibilities: Video extension, support for multiple languages, accents, and dialects. The stage is set. You call the shots!

Higgsfield is the most untold story in tech. $1BN in ARR in 18 months. Faster than everyone other than OpenAI and Anthropic. They spend $4M a month on models. They expect this to be $100K per person per month. They have 150 people working in a content machine. They will breed more millionaires than any other company in Kazakh history. For the first time, @alexmashrabov on the journey to $1BN in ARR. (below) 1. The Power of the Immigrant Founder Coming from Uzbekistan, Alex was pushed into competitive programming at age eight as his single path to reach the United States. For international founders, placing top in global competitions serves as the ultimate social elevator, instilling the relentless work ethic required to build breakout companies. 2. My Biggest Lessons in the Journey to Finding Product-Market Fit @higgsfield burned over $10 million of its $16 million seed round chasing hype and narrative rather than product quality. With under $5 million left, the team pivoted to product-led growth, solving camera control for creative directors, which immediately triggered organic hypergrowth without paid ads. 3. The 150-Person Content Team Powering Higgsfield's Billion in ARR Nearly half of Higgsfield's workforce consists of 150 in-house creative professionals producing tutorials, ads, and cinematic projects. Generating 90 minutes of TV-quality AI video requires 100 hours of raw output, proving human taste and curation remain the primary drivers of distribution. 4. We Spend $4 Million per Month on Models Higgsfield spends $4 million monthly on internal model usage, averaging $10,000 per employee so teams can freely vibe code and test workflows. Uncapped inference compute acts as a force multiplier, allowing top talent to discover breakthroughs at maximum velocity. 5. Why Chasing Benchmarks Is Bullshit and the Corporate Misalignment Occurring Public benchmarks have devolved into corporate psyops where lab researchers overfit test data to secure bonuses before job-hopping. Text-to-video benchmarks ignore real production workflows requiring 3,000-word prompts, proving direct customer iteration beats artificial leaderboards. 6. Why Team Sizes Won't Be Impacted as Much as People Think While AI handles over 60% of basic support requests, complex B2B environments cannot eliminate human teams. High product velocity constantly shifts rules and context, requiring smart, coordinated operators across legal and customer success. 7. Americans Are Way More Promiscuous When It Comes to Leaving Companies Silicon Valley workers routinely jump jobs every two years, prioritizing short-term trends over deep commitment. This transactional market gives international hubs an advantage, where cultural loyalty and team stability build compounding technical moats. (links in comments)

C Ye @C_Ye__ & Britney’s Drama reminds me another story back in 2023. It’s kinda like Deja Vu. Britney leveraged her fame and connections to bully an unknown small-time poker player. I’ve seen many hustler players backing Britney. A lot of them were coerced into supporting her, yet none stepped up to speak up for C Ye. To me, this is grossly unfair and amounts to collective bullying. Most of them don’t even know what really happened. They may simply fear that if they fail to back her, she will get them banned from Hustler @HCLPokerShow since she is the “Queen” of hustler. XD This happened in 2023 at the Bicycle Casino in Los Angeles. Britney set up a game with Peter. She personally brought in two players, Hank and Jimmy, and asked another host, L, to bring five more. (L is just a codename — he is a very well-known Chinese host, but he prefers not to have his name mentioned here.) Most importantly, Britney bought action in all of the players — every player’s wins and losses ultimately ran through her. Charles the Prince also played in the game for about an hour and had action in Hank as well. That means both L and Charles were financially exposed to Hank’s play that night. To this day, L still believes that Hank and Britney were together at the time. The players on L’s side ended up losing. But when it came time to settle up, Britney refused to pay, claiming that someone had stolen chips from the table. The game itself balanced, though — every number was accounted for. Since Britney had action in all of the players, the “stolen chips” story looked less like an explanation and more like an excuse to avoid settling. And when people pushed back, she firmly denied owing the debt. Alan, an Australian regular who used to play at the Bike, happened to be there that night, and he took the matter into his own hands. He spent 36 hours on it. For the first 20-plus hours, he negotiated with the casino and obtained everyone’s deposit and withdrawal records along with the surveillance footage. The review showed that the person who had actually taken the chips was Hank — one of Britney’s own players, and reportedly someone very close to her at the time. Alan handed all of the evidence to Britney. She still refused to pay — and even after all of this came out, she kept up her good relationship with Hank. Only after their relationship later ended did she start distancing herself from him and publicly criticizing him, as if she had never had anything to do with him. Later, L — who was in Vegas at the time — called Britney and warned her that the police would be involved if the payment wasn’t made. Meanwhile, Alan and several other pros were right there at the Bike. Alan never left — he stayed the entire time for one purpose: to get his money. He was relentless. In the end, Britney had no choice but to pay up. Start to finish, the whole thing took 36 hours.

"Everything I touch with my keyboard and mouse, I try to delegate to my bots." Here's my new episode with @poteto and @pengzheng_, the eng and design leads for Grok @bot, where they showed me the 14 bots they use for work and life, including: → A design bot that turns one keyframe into a full user flow → An eng lead bot that manages a team of eng bots → How to trust your bots with more of your work Some quotes from both: "I like to call it the Michelin kitchen…when you say software factory, it has this connotation of mass manufactured slop." "Sometimes I actually don't even look at the PR until after it's landed and then I'm like, 'Oh, okay. Yeah, that looks good.'" "I think it ultimately comes back to trust. First, watch your bot work and correct it. Turn what worked into a skill. Once it nails the task in one shot, make it a routine." 📌 Watch now: Thanks to our sponsors: @meetgranola: AI meeting notes that don’t suck @RiversidedotFM: All-in-one AI studio for podcasts and video