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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)

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Manual yield farming is a headache. you deposit into a pool, check back two days later, the apy dropped by half, rewards are sitting unharvested, and gas fees eat whatever you made. Introducing HelixMind. An autonomous ai agent that manages and compounds your defi positions while you sleep what it does: 1. Auto compounding on autopilot: No more manual harvesting. it tracks your rewards, claims them, and re stakes them back into your principal automatically to maximize compounding 2. smart rebalancing: when pool yields drop or risk spikes, the agent rotates your funds into better opportunities without you lifting a finger 3. manage with prompts: just type what you want: "move 30% of my rewards into stables". "find the best low-risk usdc yield". PUT YOUR PORTFOLIO ON AUTOPILOT.

Crypto scams are getting smarter. 🔒 Never share your seed phrase 🎁 Real giveaways never ask for funds first 📱 Use official sources only ⚠️ Guaranteed returns are a red flag Stay informed. Stay skeptical. 👇

马斯克曾经为了争取到奔驰母公司的订单， 直接买了一辆对方的Smart，然后把车给拆了🤣 这一拆，直接帮当时的特斯拉走出了困境，还得到了对方5000万美元的投资。 2007年底，特斯拉还在为第一款电动跑车 Roadster 的生产问题发愁，研发和生产又不断花钱。 马斯克想给公司找一项新收入，把电池技术卖给大车企，于是找上了正在推进电动 Smart 的奔驰母公司戴姆勒。 但戴姆勒的高管听完介绍，对这家小公司的技术很有顾虑，他们提出想来特斯拉考察。 于是，马斯克决定提前改出一辆电动 Smart，让他们亲自开一开。 他让团队买来一辆 Smart。新车一到，当天就把它拆了。 他们取下燃油发动机，准备装进特斯拉的电池和电机。 为了把这些东西放进小车里，团队重新制作了电池包，又改装了特斯拉跑车上的电机，等车重新装好，外观和内饰几乎没变，开起来却完全不同。 马斯克后来说，这辆改装后的 Smart 动力大到能在停车场里把前轮翘起来。 戴姆勒的高管来考察时，马斯克先介绍了技术，对方反应十分冷淡。 随后他把人带到车前，告诉对方，这辆 Smart 已经换上了特斯拉的电池和电机，可以直接试驾。 对方坐进驾驶座，马斯克坐在旁边，一踩加速踏板，车就冲出了车库。 大约15分钟后，原本不苟言笑的高管把车开回来，已经有些藏不住笑意，主动提出，可以谈谈合作。 随后，特斯拉拿到了电动 Smart 的电池包和充电系统开发合同，收到的开发款缓解了公司的资金压力。 合作继续推进，到2009年5月，戴姆勒又投资5000万美元，买下特斯拉近一成股份。 马斯克后来回忆，如果没有当时拿下的开发合同，特斯拉就撑不下去了。

Regarding who is behind the hack: I present to you THE LAZARUS GROUP. just linked this hack to the AFX hack, which stole $24M in July and was specifically attributed to TraderTraitor. The stolen XRP from Bitget was bridged and can be directly linked to the funds stolen in the AFX hack. Stay smart.

🦔Someone is buying Japanese used books by the ton. Not novels or comics. Philosophy, history, medical law, Edo-period cultural texts. Multiple buyer accounts all ship to one logistics center in Okayama Prefecture that won't answer questions. Export records show over 50 tons, roughly 100,000 volumes, shipped to the US since last year. No buyer has been identified. Separately, court documents confirmed Anthropic's "Project Panama" bought millions of books in the US, cut the spines off, scanned them, and shredded the originals. An internal memo said the goal was to "destructively scan all the books in the world." Similar operations have been reported across Europe. My Take Anthropic's US book operation came out in court filings earlier this year. Project Panama. Millions of books, cut the spines off, scanned, shredded. Their VP chose the codename so nobody outside the company would find out. That story broke and apparently the same operation just moved to Japan. Anonymous buyer accounts, a warehouse in Okayama that won't take questions, and 100,000 volumes of specialized academic texts that nobody buys to resell. I don't know how you build a trillion-dollar industry on material you had to acquire in secret through middlemen because you knew the public would object. Authors spent years on those books and now they're fed through a scanner and thrown away so a chatbot can sound smarter. If the data was free to use, they wouldn't need codenames and anonymous warehouses. The lawsuits over who owns this material have barely begun and I think the copyright exposure across the whole AI industry is enormous. Anthropic's internal memo said the goal was every book in the world, which describes the foundation of the product. Hedgie🤗