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📍大井町@lesss37 また素敵なプライベートサウナみっけ🧖‍♀️ ロウリュウできるし、 体感だけど割と高温好きにはたまらない室温。 上から浴びれるシャワーも最高🚿 ポカリと水も頂けるし、手ぶらで🆗だから 仕事終わりまた行きたくなるなぁ〜 #サウナイキタイ# #サウナ# #lesss#

Get ready for @askar_dex "Part 2" 😎 Built on the @CantonNetwork Non-custodial wallet & DEX on Canton Network. Atomic swaps, private by design, global composability. Your Keys. Your Assets. One Synchronized Network. 🟡 We always hear the words "Atomic Settlement" but what exactly is that? 🤔 Atomic: Transaction Finality = No partial fills & No counterparty Risk! Private: Data shared strictly on a need-to-know basis Composable: One wallet across every app on the Global Synchronizer 1) Features: 👇 Everything a Canton wallet should be...which is😉 "An independent wallet built to run on the Canton Network...only now in your pocket!" Non-Custodial by Design: Your private keys are generated and stored only on your device. Nobody — including us — can touch your funds. Atomic Settlement: Trades either complete fully or not at all. Real-time finality with zero counterparty risk on the Canton ledger. Global Composability: Interact with any application connected to the Global Synchronizer — no bridges, no silos, one wallet. Trustless Swaps: Swap Canton Coin and tokenized assets peer-to-peer, settled instantly and atomically on-ledger. Sub-Transaction Privacy: Canton shares transaction data strictly on a need-to-know basis. Your activity is invisible to everyone else. Real-World Assets: Hold and manage tokenized RWAs alongside crypto — built for the network where institutional finance settles. *** @WalletConnect Support *** Connect @askar_dex Wallet to dApps on the Canton Network with WalletConnect! Simply scan, approve, and sign every session securely from your phone...😎🔥 2) Runs on the Canton Network 👇 The network where real finance goes on-chain... The Canton Network is a privacy-enabled, interoperable blockchain network designed for institutional-grade assets. @askar_dex Wallet is an independent, non-custodial wallet built to run on this network. 01 Privacy with compliance... canton-network:native is the only public network with sub-transaction privacy. Participants see only the data they are entitled to, while remaining fully auditable. 02 Institutional-Grade Rails... Built by @digitalasset and operated by a decentralized ecosystem of banks, exchanges, and market infrastructure providers moving real value on-chain. 03 The Global Synchronizer... A decentralized interoperability backbone that lets independent applications transact atomically with each other. No Wrapped Assets & No Bridges! 3) The App 👇 Designed to feel effortless... A clean and Very Fast interface for everything you do on the cc rails...from your first Canton Coin, to bridging and swapping tokenized assets, Askar DEX has you covered! *Portfolio - Track Crypto & RWA holdings at a glance *Swap - Trustless Atomic Swaps settled on-ledger *Bridge - Move Assets in & out of Canton *Activity - Full Transaction history with live status *Profile - Manage your identity & security settings 4) Get Started 👇 On the network in three steps... From download to your first atomic transaction in minutes. 1: Download & Install Get Askardex Wallet on your Android device. Setup takes less than a minute. No account or email required 2: Create or Import a Wallet Generate a new wallet with a 12-word recovery phrase, or import an existing one. Keys never leave your device. 3) Transact on Canton! Send, receive, swap, and bridge assets across the Canton Network. Every transaction signed by you, settled atomically...😉 Website: Wallet:

Salesforce acquires Listen Labs for ~$2b. But who gets the 💰? My usual breakdown below 👇 The company was founded in Sep-23 and had raised less than $100m. Investors will share ~$850m of profits on $96m invested, a ~10x blended. From a huge pivot to a unicorn valuation term sheet they refused, this is truly a wild story ✍️. In the end an incredible outcome for all involved. So let's dive in! 1) The boldest move: walking away from $1.5b 🎲 Listen Labs had a signed $125m Series C term sheet from Menlo at a $1.5b valuation... and walked away from it to sell to Salesforce at ~$2b. This takes a huge amount of courage: few founders turn down a signed unicorn-plus round. @itsalfredw and @florian_jue did it 2.5 years after founding. Huge congratulations are in order for the discipline and execution of the founders here. 2) Founders and team: a life-changing outcome in under 3 years 🥳 By my best estimates, founders and team still own over half the company. At $2b, that's ~$1.1b for them to share. Specifically, assuming a 15% option pool, that is ~$300m for the team and ~$750m for the two co-founders. And, as was the case with Hugging Face, this is all from a pivot. They originally built an AI customer-interview tool to understand why their viral app BeFake was growing, then realised the tool was the business! 3) Sequoia's Bryan Schreier did it again 👑 What few people know is that Bryan was an early backer of Qualtrics... the category Listen Labs is disrupting. Now he led both the seed and the Series A here. By my estimates, those two rounds will return ~$670m combined, or ~25x on ~$27m invested, in under three years. Pattern recognition and industry knowledge have their perks, it would seem! 4) Ribbit: 4x in 8 months ⚡ Ribbit led the $69m Series B in Jan-26 at ~$500m. At $2b, that's ~4x in 8 months. Unbelievable IRR and a great return on a meaningful cheque. 5) Neo does it again, congrats @apartovi 🎯 Listen Labs went through the Neo accelerator early on, which came with a $600k SAFE. On my estimates, that cheque is worth ~$25m+ today. This comes just three months after Cursor's acquisition (a cool >1,000x for Neo). What a hit rate! This one is very straightforward: it is a massive win for everyone. And so congratulations to all involved: Sequoia, Ribbit, Conviction, Pear, Neo and the team!

I am technologically illiterate, but in less than 24 hours, Grok Bot has transformed my productivity & personal business outlook. I have agents that are experts / "employees" in real estate development, DTC fashion e-commerce, niche private equity, and HPC/GPU customer acquisition & deployment.

7/12｜Halo: Product Launch：虚构产品发布片 用代码做视觉鲜明的产品发布视频，白色 UI，适合发 X；允许生成音效，并要求加快画面节奏。 需要：代码动画、UI 渲染与导出；音效可用素材或生成工具。注意原 Prompt 说的是“虚构产品”，若改成真实产品，应提供真实 UI 和功能资料。 完整 Prompt： Use code to create a cool, visually striking launch video for a fictional product, with a beautiful white UI, suitable for sharing on X. Generated sound effects are welcome. Speed up the visuals so the pacing feels less slow.

Introducing Claude Sonnet 5.5, the second model in the Claude 5.5 family. It’s a clear upgrade over Sonnet 5, runs more than 30% faster, and costs up to 30% less for most work.

Many have asked me to give my 2 cents on the (newest) HCL controversy. Probably a bad idea, so I’ll do it anyway: I don’t know C Ye or Britney or any of the “newer” HCL regs. And I have no insider info on this dispute. But I do know a lil’ somethin’ somethin’ about Nik Airball and Ryan Feldman. In my opinion, this is all par for the course for them: I believe they will do and say whatever they need to protect their financial interests. If you haven’t read Beneath the Cards, I break it all down there. As I see it, Airball has long been committed to doing whatever it took to become a mainstay on HCL. Give him credit: mission accomplished. And I think Ryan did and said numerous things in an attempt to destroy my reputation the moment I publicly stated that something shady went down on his show. Four years and a bestselling book later, mission definitely not accomplished. Here’s the takeaway, as is sadly too often the case in the pokersphere: Follow the money. It will so often explain the why behind the actions of the less-savory characters in our industry. In short, if Britney is good for the game, I suspect Ryan and Airball will go to great lengths to protect her.

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)

Researchers proved AI has deleted every reason universities exist. Harvard University ran a controlled experiment pitting a custom AI against their own top-tier classrooms. And the results are going to collapse the higher education bubble. They took 194 undergraduates and split them up. One group learned physics in one of Harvard’s best hands-on, active-learning physical classrooms. Group work. Instructor support. The premium university experience. The other group went home and learned the exact same material with an AI tutor. The AI didn't just win. It embarrassed the institution. Students using the AI learned more than twice as much as the students in the elite Harvard classroom. They scored 30% higher on the final assessment. And they did it in less time. Let that sink in. A piece of software sitting on a laptop outperformed a world-class faculty in one of the most elite learning environments on Earth. Universities have always justified their exorbitant tuition with two things: access to elite knowledge and the physical classroom experience. This study just proved both of those moats are gone. When software can teach you complex physics twice as well as a $60,000-a-year institution, the math of higher education breaks permanently. The AI didn't just give the students answers. It used strict pedagogical guardrails. It guided. It questioned. It forced the students to do the cognitive work. It offered perfect, one-to-one tutoring, personalized to the exact moment a student misunderstood a concept. That level of attention is mathematically impossible to scale in a physical lecture hall. For a thousand years, the university was the only place to get a premium education. Now, it’s the bottleneck. If AI can double your learning speed for a fraction of the cost, what exactly are students taking on decades of debt to pay for?