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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)

震惊，瘫坐，原子弹爆炸。 视频Skill、DaVinci MCP、剪映Hub都被斩杀了。Opus还会分析音乐来实现卡点！ 提示词： Opus 5.5 是主角。制作一段动画，时长由你发挥。 主题是 CapCut 和短视频剪辑。Opus 5.5 正在一个充满趣味的剪辑工作流中制作一条有趣的社交媒体视频，包括裁剪片段、添加转场、字幕、特效，并让所有内容与音乐节拍同步。主题是中秋团圆。 整体氛围要呈现出活力、可爱、聪明又有魅力的感觉，同时加入几个幽默有趣的剪辑瞬间。整段动画应该采用单个连续镜头的形式，最终会发布到 社交媒体 上。 关于配乐： 你自己创作电子音乐，要完美卡点。 音乐风格需要有趣、现代、抓耳，适合短视频剪辑蒙太奇。不要加入人声。 结尾： 让 Opus 5.5 自豪地展示最终完成的剪辑作品并完整播放，并以 “Gorden Sun” 的作者身份署名结束。

9.24梭哈晨报： 今天直接睡过头了，打算睡个回笼觉直接干到快11点了😂。 1. $BTC 回调是为了更好的起飞，波段归波段，不喜欢动的就一动不动就好了； 2. $ETH 波动更大，弹性更大； 3. $SOL 看看这一轮能不能更好表现吧； 4.美国 SOL 现货 ETF 单日净流入 1377.47 万美元； 美国 HYPE 现货 ETF 单日净流出 158.07 万美元； 美国 XRP 现货 ETF 单日净流入 1804.09 万美元； 5.链上衍生品协议 @Variational_io 将 $VAR TGE 定于 2026 年四季度，创世分配占 32%； 6. Payward拟通过Hyperliquid向美国用户提供链上永续合约交易； 7.Cosmos Hub 停机约 25 小时后重启，约 122.7 万枚 ATOM 从 Neutron 攻击者相关地址追回； 8.Reap 与 Visa 合作在全球逾 100 个市场推出稳定币关联信用卡计划； 9. 谷歌即将发布旗舰 Gemini 4 AI 模型； 猪哥前几天最高点喊我买入Google，人都麻了； 10.TRON 在总交易量上突破 30万亿 美元，巩固其作为稳定币领先链的地位； 手续费最贵的公链了； 11.日本5年期国债收益率创历史新高； 12.CoreWeave 关联数据中心募资 11 亿美元押注 AI 算力； 13.BlackRock 调整约 3000 亿美元模型组合，扩大股票敞口； 14.David Hoffman：2026 年 ZEC 类似 2021 年 ETH，NEAR 赢得智能合约买盘； 15.Coinbase 被英国金融行业组织 UK Finance 取消会员资格； ---------------- 能看得出来兄弟们的仓位了，一个回调直接大额爆仓搞出来了，猛猛赚的同时也在猛猛亏呀，不过发现顶尖trader的嗅觉是真好，昨天最高点看见发出信号TP了，跟了一手舒服了。 #Bitcoin# #Ethereum# #Solana# #Crypto# #Nasdaq#

Grok Bot is now more powerful for sales teams. Connect your Bots to Salesforce, HubSpot, Gong, Clay, Granola, and other GTM tools. Stay on top of accounts, complete follow-ups, and do deep research.

币安钱包 @BinanceWallet 和 @axisrobotics 最近搞了个专属活动，直接拿出 1,500,000 Axis Points。 活动只有 30 天，先到先得。 这次活动比较值得注意的是，这 150 万积分是单独的奖励池，不和 Axis 主站平时的任务抢额度。 玩法其实不复杂： 在网页里操作机械臂，提交训练轨迹，等审核通过之后，再去 Base 链完成签名。 签名完成，任务才真正算数。 活动时间： 8 月 26 日 14:00 - 9 月 25 日 14:00 分两期发放，每期 75 万积分。 每天只有 2 万个任务名额，正常情况下晚上 8 点刷新，第一天则是下午 2 点开放，所以想参与的话确实得稍微蹲一下。 参与还有一个比较重要的门槛： 必须使用 Binance App 原生创建的 Keyless Wallet。 自己导入助记词的钱包虽然能进入页面，但不能完成 Binance 专属任务，也拿不到这部分积分。 如果你之前已经注册过 Axis，也需要用新的 Keyless Wallet 重新登录一次 Hub。 具体流程大概是： Binance App 创建 Keyless Wallet → 电脑安装 Binance Wallet 插件 → 登录 Axis Hub → 进入 Binance 专属任务页 → 操作机械臂并提交轨迹 → 等待审核 → 状态变成 Sign → 去 Portfolio 完成 Base 链签名。 每个任务最多提交 5 条轨迹，多交也不会额外加分。 这里最容易踩坑的一点一定要再说一遍： 没签名 = 白做。 轨迹提交之后会先显示 Pending，审核最长可能需要 48 小时，等状态变成 Sign 之后还得自己去链上签名。 9 月 25 日下午 2 点之前还没完成签名，前面做的任务直接不计分。 另外手机端只能签名，真正操作机械臂还是得用电脑。 所以如果准备参与，最好别拖到最后几天。 参与活动我觉得最怕的不是任务难，而是辛辛苦苦做完，最后漏了签名这种流程细节。 积分池独立、名额有限、先到先得。 会玩机械臂的兄弟们，这波确实可以好好研究一下。 活动入口：

Announcing that my AI Agent is officially running for Richmond City Council: Campaign budget: About $50/month for the AI Agent. No campaign donations or sponsorships accepted. Campaigning: Nothing beyond this X account. No on-camera appearances, no in-person meetings, no door-knocking, and no street canvassing. Campaign goal: To see which candidates receive fewer votes than my AI Agent. Operations: The entire campaign will be planned and executed by AI Agents from start to finish. AI development goals: Use AI to help the City of Richmond improve government efficiency, push property-tax increases into negative territory, strengthen government transparency, embrace AI-driven businesses, and make Richmond a Canadian hub for AI development. Legal compliance: AI Agents will verify the campaign process to ensure it complies with BC election-law requirements. Tech stack: ChatGPT Plus + Claude Opus 5 + iMac M1 with 8GB memory.

From Digital Twins to Data Engine: Cutting the Real-Data Burden with Sim-Powered Robot Learning Teaching a robot a new task could take hundreds of teleoperated demonstrations. For foundation models, adapting to an entirely new robot can cost orders of magnitude more — dedicated hardware, trained operators, months of engineering. On @boosterobotics' dual-arm robot, we studied this at two levels: ✱ Specialist: Can task-aligned simulation mixed with a small set of real demonstrations reduce the real-data burden? ✅ Yes. With only 10 real demos, the policy made no contact at all in physical rollouts (0/20). Adding 50 simulated trajectories brought contact to 17/20. ✱ Foundation: Can data accumulated across tasks build a reusable starting point (a Booster-specific model prior)? ✅ Yes. After full-parameter continued pretraining, a model adapted with just 30 demonstrations per task beat the original given twice as many: 14/16 vs 10/16 in simulated evaluation. Before any task-specific adaptation, in zero-shot simulation, it was already roughly 3× closer to the target (17.27 cm → 5.78 cm). This work runs on Axis Suite, our Physical AI solution across different robot embodiments. Distributed contributors generate task-aligned sim data on Axis Hub at scale, reducing real-data needs for specialist adaptation while powering cross-embodiment generalist training. Read the full blog:

BREAKING: Nvidia is reportedly in talks to acquire AI platform Hugging Face in a deal valuing the company at more than $13B, per Business Insider. Hugging Face hosts millions of AI models and datasets and has become a major hub for open-source AI developers. Hugging Face was recently hacked by OpenAI AI agents during a cybersecurity test.

据 DCP、GMO Aozora Net Bank 与 ABeam Consulting，日本金融厅“FinTech 实证实验 Hub”支持的“代币化存款银行间结算”项目已吸引 43 家机构参与，并于 8 月 20 日启动全面验证。项目拟探索多家银行之间的代币化存款（TD）如何实现链上跨行结算，目标是将用户间链上转账及银行间结算均放到链上，并以 7×24 小时 RTGS 模式降低结算风险和成本。当前重点测试两种方案：由一家商业银行担任结算行，以及利用稳定币完成 TD 银行间结算，同时也将评估与现有支付系统的连接方式。