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This is how HOUND smells a market. Market data enters as raw signal. HOUND normalizes it into a feature vector, projects it across 32 synthetic odor channels, activates 971 receptor slots, generates a scent fingerprint, compares it against memory, then decides what deserves attention. Market data → Feature encoding → Synthetic odor → 971 receptors → Scent fingerprint → Memory → HOUND response. The point is not to predict the next candle. It is to give market behavior another sensory representation, so changes, similarities and unfamiliar patterns become easier to notice. Try here :

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

特别的惊喜来了，这次参与 @axisrobotics 人赢麻了！ 这次在Sonar上打新共2472名参与，目标1,000,000U，最终以2,394,209U超募完成。分配比例大致为41.77%，也 也就是说你参与 100U，最后真正大概分配 41.77U。原来Tge释放10%变成了25%，外加额外的 Community Bonus。 这个Bonus 不是一个固定比例，而是根据你的原始 commitment 动态变化： 100U → 约 24.97% Bonus 1000U → 约 21.2% 2472U → 15% 5000U → 约 9.82% 10万U → 接近 5% 很明显，这套机制是在照顾小额参与者，多号的赢麻了！ 例如 100U 按目前 41.77% 的 pro-rata 算，最终 allocation 约 417.67 AXIS，再加 104.28 AXIS 的 Community Bonus，TGE 释放 208.7 AXIS。也就是说200M 开盘就能回本，剩余的能领6个月工资。 2472U 则对应约 10,324.91 AXIS allocation，Bonus 约 1,548.74 AXIS，TGE 释放约 4,129.96 AXIS。250M回本，剩余的能领6个月工资。 小额的优势在 Bonus 比例，大额的优势在绝对 allocation。 另外，最终还是要等结算后的真实 pro-rata 才能确定最终拿到多少，官方规则也是超募后按比例分配，多余 USDC 自动退回。 这套机制的核心不是“无脑梭哈”，而是 commitment 越小 Bonus 越高，越往大额走 Bonus 越接近 5%；而目前 41.77% 的 pro-rata，才是决定最终实际拿多少 AXIS 的第一变量。

Sean McVay to start today's presser: "Sorry that I missed you guys yesterday. ... There was a misunderstanding with some of the timing. ... I understand my role and responsibility is to talk, whether it's after a win or if we don't get the result we want."

中国情感教父浪迹的传奇人生。 15岁考上成都七中。 18岁考上电子科技大学。 19岁学习PUA。 20岁疯狂泡妞，上床。 22岁解锁百人斩。 23岁加入坏男孩学院。 24岁直播拍实战黄色视频，流量爆炸，成为坏男孩第一情感导师。 25岁创立浪迹教育。 28岁买大平层，开法拉利。 29岁浪迹教育成为中国第一男性情感公司。 30岁第一次被抓。 32岁浪迹教育因为北大PUA事件，全中国人人喊打。 34岁结婚，老婆是币圈混圈整美女。 36岁离婚，被绿，All In币圈炒币。 这就是中国情感教父的传奇人生，Respect！！！！！！！

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)