Twitter · 股票

People really need to stop thinking about 5% yields over 10 years or 30 years. Even the inflation of my $SUBWAY sandwich has gone up like 12% a year. Today it was like $20 after tax, when it used to be like $5… 12 years ago. The best way to keep up with inflation of the things that matter, like Sandwiches, is equities like $SPY.

@yingbinance 真理

😅😅😅 就算你说的对，但 彭女士也不是国家元首啊 也不是民选出来的结果啊 退一万步说的话，也应该拿高市早苗的老公去来比 你说呢？ https://t.co/27LJlF7sQ3

特朗普盘后宣布谈判破裂，下周看戏。 你说他们家不炒股，我是不信的 😅😅😅 https://t.co/HeAvA5L5Fw

昨天才说Muse Agent会越狱，今天就出事了？？

既然反正要大涨，既然AI马上成功，那就直接All In得了 我也懒得逆流而上，和你们扯皮了 反正既不用看国债，也不用看选举 直接All in 一年后，继续翻倍！！！

@Zvodka47 冬天能砍树？ Full Time算的都是Compensation 😅😅😅

@y0f2j 🤡🤡😳😳😳

半导体，Ai的发展，真的是超过了所有人的想象 我现在有一种不真实感 等这一波结束了 人类文明，真的还会有未来吗？

Fine, QQQ will double in 12 months, Are you happy now?

半导体的最后阶段，台湾疯了

按这个速度计算，收益率爆炸最多也就只要2年。

@Nemodedt 一个月一次

@Nemodedt 一年就一单

现在还在用每小时工资算蓝领收入， 你以为他们每天能砍8小时，一周砍5天，一年能砍365天？ 这是在哪个监狱发的帖子！ 😅😅

@Zvodka47 现在还在用小时工资算蓝领收入，你以为他们每天能砍8小时树，一周砍5天？？ 你是在哪个监狱发的帖子？ 😅😅

@nobita_3D 这是哪里？

如果这张图是真的，我就承认我们生活在Simulation中 😅😅😅 https://t.co/OzRm9Pc5Ex

投资者运行 1 万个 AI 工作负载，测试 Meta 免费 Muse Agent 的成本压力 投资者 Lee Roach 透露，在其团队将 Meta 的个人 AI Agent Muse 扩展到 1 万个持续运行、满负载的工作实例之后，他建立了规模较大的 Meta Platforms 空头仓位。 Muse 于 9 月 8 日推出，可执行发送邮件、预订旅行等任务，目前在美国通过 iOS、Android、网页端和 WhatsApp 免费提供。为了吸引用户，Meta 承担 Muse 每次查询所产生的计算成本。 Roach 表示，每部署一个 Muse 实例，他这边只需要承担大约 300 美元的一次性前期成本，但此后持续运行产生的、理论上没有上限的经常性计算费用则由 Meta 承担。 基于这一点，他认为，如果大量用户以类似方式高强度使用 Muse，可能会给 Meta 的利润率带来压力，甚至导致未来盈利低于市场预期。 这一做法也引发了争议：一些人认为，这是对 Meta AI 产品商业模式和单位经济性的一个聪明的“压力测试”；另一些人则认为，这更像是为了从做空获利而有意制造高额资源消耗，尤其是在 Meta 正投入巨资建设 AI 数据中心的背景下。

美国银行下调评级后，耐克股价跌至 2014 年以来最低水平 美国银行将耐克（Nike）的股票评级从 “中性”（Neutral）下调至“跑输大盘”（Underperform），并将目标价从 47 美元大幅下调至 30 美元。该行认为，耐克经典产品线业务的疲软已经盖过了新品创新带来的积极影响，同时预计公司销售额将持续下滑至 2027 财年。 耐克股价在盘前交易中一度跌至 35.40 美元附近，此前 9 月 24 日收盘价为 35.99 美元。该股今年以来累计下跌约 44%，较历史最高点更是下跌约 80%。目前公司还面临多重压力，包括批发渠道需求走弱以及市场竞争加剧。 不过，市场对耐克当前估值存在明显分歧。部分投资者认为，耐克目前约为 21 倍预期市盈率，同时股息收益率接近 4%，因此已经具备一定的抄底吸引力；但也有人担心，在耐克 10 月 1 日公布 2027 财年第一季度财报之前，股价仍可能进一步下跌。

第一目标7%，第二目标10%，最后目标15%

@hi_penguin 必须在10%的高点买入

@vkHuMQ1RUwkQoIE 那肯定得死啊

@TonyXu31 你说话真的蛮好笑的，国债过去30年那可是大牛市。。。 从1985年一路跌停下来的 😅😅😅 https://t.co/vMhojVXWZj

so still, we actually need engineers to make AI work....

就等着看能不能破10%吧

等这轮崩盘腰斩之后，关掉游戏再杀进去！ 😅😅😅

A market of 猴子

@Zvodka47 多少钱？一个月能赚到的5000吗😅😅

@Edward____Zhang 好

其实就是这样，伊朗也是如此 要和谈了就涨 要打仗了就跌 请问这群机构有脑子吗？ 很显然没有 现在美股已经没脑子了 只剩下一群玩期权的猴子 我们只需紧盯着国债 看他们还能上蹿下跳几天 😅😅😅

博通是真的不行😅😅

美国的国债是一定要完蛋的 这是毫无疑问，也是毫无质疑的

MSFT退出所谓Agent OS之后，Meta股价下跌 美股的投机者是不是脑子都有病？ 我说的是所有机构们 😅😅😅

@DssShen 请你做多，坚持到年底！

@XuwenC9238 QQQI可以

每一家公司轮流推出一个Muse，估计轮流大涨20% 请问最终是谁为此功能买单？ 产生的多余的收入，究竟来自哪里？ 你也提高了效率，我也提高了效率，但效率又不等于钱，也不等于收入 美股上涨，真的就只剩下通胀一条路了吗？ 那美联储真的得死啊！！

那是竞争导致的，大概100bps？这里说的是通胀 美联储加息从来不会考虑国债。

@kuchazi731 那是竞争导致的，大概100bps？这里说的是通胀 美联储加息从来不会考虑国债。

如果这个图是真的，你就是我的亲爹

25年之后，AI之所以发展这么顺，其实和共和党有很大关系 也和特朗普炒股有很大关系 表面上是pro-america 实际上是pro-business，军工商政复合体，走的是帝国主义复兴的路线 那么，就让被AI践踏，彻底失去工作，堕入贫穷深渊的美国选民们 投下自己神圣的一票吧 难怪特朗普天天喊“共产主义是人类毒瘤” 原来是想要和后AI时代的穷人，彻底划清界限？

发表一个爆论，虽然现在，Nasdaq肉眼可见沦为了石油价格的函数，对加息彻底脱敏了 但随着中期选举后，共和党“政商复合体”被AI放弃、对K型经济深恶痛绝的美国选民彻底抛弃，民主党上台 美股这一轮牛市，也就正式走向了尽头 其实也就今年年底了的事了，不超过3个月 大家等着看吧 一定要有耐心，耐心！！

“全球货币贬值500倍”，通胀100%一年，你炒股炒币不也是亏钱？？ 我真的蛮好笑的。

@fly2_1000000u “全球货币贬值500倍”，通胀100%一年，你炒股不也是亏钱？？ 我真的蛮好笑的。

超大规模云服务商预计将在2027年投入1.2万亿美元用于AI基础设施 高盛预计，美国五大超大规模云服务商（Hyperscalers）在2027年的AI基础设施总支出将同比增长 54%，达到 1.2万亿美元，高于华尔街目前 1.1万亿美元 的一致预期；相比之下，今年的相关支出约为 8000亿美元。 高盛预计，到2028年，AI基础设施支出的增速将放缓至 12%，总支出将达到约 1.4万亿美元。 该行还估算，为了使这些AI基础设施投资实现盈亏平衡，每年需要产生约 3000亿美元的AI相关收入。与此同时，目前相关业务的订单积压规模已经超过 1.5万亿美元。 从2025年至2032年，这一轮AI基础设施建设投资累计规模相当于美国GDP的 3.63%，超过历史上的基础设施投资高峰。 不过，电力、土地以及芯片供应方面的限制，可能成为这一轮大规模AI基础设施扩张面临的主要挑战。

这是日本政府的问题，政府开销太大。

数据证明，美国的非AI部门经济其实已经彻底崩盘 但加息又影响不了AI过热发展 在共和党激进、粗放、争霸的产业政策下，美联储会疯吗？？ 我看大概率真的会 因为本质上说，共和党政府是“政-商”超级联合体，以马斯克为首 天天喊pro-america，实际上是pro-business 根本不会管普通美国人的死活 你吃不吃的饭、找不找得到工作 关他们屁事 死亡螺旋，已经正式开始

嗯，祝你好运！

据说现在美国已经完全不招入门程序员了 请问大家 美国人现在应该如何就业？

美国银行将耐克评级下调至“跑输大盘”，因销售持续低迷 美国银行证券（BofA Securities）将耐克（Nike）的股票评级下调至 “跑输大盘”（Underperform），并将目标价从 47 美元大幅下调至 30 美元。该机构指出，耐克核心经典鞋款正面临较大压力，这种疲软抵消了新品创新带来的积极影响；同时预计，耐克销售额可能会一路下滑至 2027 财年。 耐克股价今年以来已经大幅下跌，与三年前相比更是累计下跌约 60%。在 9 月 24 日以 35.99 美元收盘后，盘前交易价格一度在 35.40 美元附近。 此次降级也延续了近期华尔街对耐克的谨慎态度。此前，Baird、Morgan Stanley 等机构也下调了相关预期，部分原因是耐克合作伙伴的销售表现疲软，例如其在中国的重要合作伙伴 宝胜国际（Pou Sheng） 销售不佳。 不过，也有市场人士认为，耐克股价近期的大幅下跌可能带来潜在的逢低买入机会，尤其是在公司 10 月 1 日公布财报之前。

Railroad construction in the early 1880s peak near 6% In addition, the railroad investment excludes rolling stock but the AI number includes 68% computing and networking

@AntonLaVay 影响通胀体感的不是原油价格，影响的是汽油、柴油价 7月开放了路条，接着可能会加大放开，油价很可能下不去。但并不是什么大事 这不是烧了山，更像是烧了草垛

@ashinagen32 @AntonLaVay 我无法与杠精聊 新加坡裂解= 70 山东裂解差=10-20 你告诉我新加坡用北美油？

@Ryanhingshing 增肉1斤 😝

@AntonLaVay 但，可出口量= 发改委路条 我有明确信息，发改委会加快放开路条。你如果觉得无足轻重就无足轻重呗 得理性，不要把对Trump个人厌恶带到对事实判断上。我也很讨厌trump，他瞎作了，但是坏不到哪里去。事实是这么个事实

@photonjaeger @SuJinyan6 @RYANHINGSHING Im changing my name to Herman Kim

@photonjaeger @SuJinyan6 @RYANHINGSHING You r overestimating us 😂

@cassian_w76008 Nand > dram

@Jespabe Because mkt is expecting AMD to take shares

@kik8964 @swh16888 不～～当你需要用到自己企业的数据的时候就需要去做了。不能简单挂知识库，这样AI跟从性很差

很简单，想想Gpu比例开始提高时候的Nv 不要觉得夸张，只会更夸张

@kik8964 另外上下文^2=kv 你准备多少内存？

@kik8964 肯定不够，而且context里面模型效果很差

@llm_kv 举例而已，烧个post train立刻可以写很好

@llm_kv 不是这样的，再好的模型真干活，特别是特定场景下干活都是鹦鹉学舌 都必须要post train了才能用 你试着让astra写一篇网络小说试试看就知道了

Trump loves to play this trick after market close to pressure the opponents. Let’s see if any good news before future opens.

@Z04_A1 But I think oil price stays higher for longer will be a big support to the long end yield, right?

https://t.co/JhhKhe35xV 😓😓😓

Bought CBRS calls at the close

@Hauge319 Yes look at RSP/IWM, the breadth is very bad now

@bubbleboi I’m not very optimistic about making a good Iran deal in the short term. And I think the market might already priced in a lot of it. Also I think the current bottleneck is the refinery not crude oil.

@Z04_A1 Dollar bulls are crowded, bond bears are crowded, Russel bears are crowded. We’re in a favorable environment for bulls and need a catalyst to ignite the rally. I don’t know what it’d be and when it’d be. So I chose leap calls And when the tide turns, I can chase quickly.

@bubbleboi I’m bullish on AI but I think the headwind is mostly from the macro side.

I’ve further reduced the exposure by converting into leap calls today and entered the standby mode until Oct FOMC. Also holding few short dated calls to bet OAI dev day next week. I think bond bears might cover shorts before PCE to give the equity market some relief. Be Patient!

@btc__gabriel @kayliatyyy I think if they manufacture and sell in US, they have to follow the US rules and privacy standards , and the US company must be a co-owned structure with US shareholders taking the main control of the board. it would be similar to what TikTok is doing and its structure now

@Staveley22 Brilliant! 🫡

@timjiunanchen Thanks, Tim. I’ll try connecting Gmail and WhatsApp first 😄

@PhotonCap So… still Nvidia running the show, huh?

I just started using Muse from Meta. So… what exactly can I do with it? 😅

@damnang2 @PhotonCap Always a great time with you guys!

@PhotonCap 앗 국민 뭐더라.. 유모차는 아니고.. 저도 저거 썼었는데 ㅎ

@RYANHINGSHING @SupplyRadarX @SupplyRadarX 加油！人才不該被埋沒！

@CompaCompu @jukan05 Shame on MTK🤣🤣

Bigger AI Chip Packages Create Bigger Materials Challenges Economic Daily News reports that larger packages and more complex chip stacks are increasing warpage, heat-management and reliability challenges. That raises demand for underfills, encapsulants, redistribution-layer materials and cleaning chemicals as CoWoS and WMCM production expands. Taiwanese chemical suppliers now have an opening to localize more of the advanced-packaging supply chain. Liwei’s take: Taiwan’s materials moat is more than chemistry. Dense supplier clusters, fast on-site troubleshooting and years of customer trust help turn process problems into qualified solutions faster. Subsidies can build a fab; rebuilding that working network takes time. Sources https://t.co/SxFrCkruHo

@jukan05 @MediaTek Haha I’m calling Mr. Tsai right now, this is so inappropriate!

Brilliant Analysis from @harry03994688 : also $AVGO is a natural rival to $NVDA. Its ASIC business directly competes for the same AI compute dollars—and that conflict may also be costing Broadcom opportunities elsewhere in the NVIDIA ecosystem.

@whang_97 👀沒研究，感謝分享

@Leoskie_L 這張有用！ https://t.co/b7c2Gb18Ct

@JonkooTrades @glocalinvestor @outliercapx @PreIPOMedia @JoeyOnMarkets @photonjaeger @SupplyRadarX

@photonjaeger You're in our inner circle of course, but I wasn't sure if you also publicly said you hold $AEHR 🤣, so I didn't tag you.

@photonjaeger Do you also have $AEHR😲

Totally agree. While some creators naturally lean toward a more text-heavy style where the main point can sometimes get lost, those who follow our small circle know we like to keep things simple. Just one word: testing, testing, testing. and just in case anyone missed it, we repeated it three times: Testing, testing, testing. How much alpha is packed into that single word? We'll let you figure that out. 🤫 Oh, by the way, looks like @RYANHINGSHING , @ShanghaoJin Jim, and Liwei’s public holding, $AEHR, is already up 40%+ in September alone.

@Dabaojian_A @ShanghaoJin 可能他們兩個多少還是可以講一兩句中文避免尷尬吧🤣

For a deeper analysis into the Engram layer, check out: (6/6) https://t.co/DuIgPg9mOa

What survives training-data cleanup may shape the value of adding more Engram memory. It was also designed with serving in mind, using token IDs mean retrieval can be async and overlapped, allowing DRAM offload without performance loss. (5/6) https://t.co/KPb8aVuMiP

Copyright notices, licensing text and sharing prompts also show up. Engram learns what helps predict text, not what humans think deserves remembering. Even boring web boilerplate can offer useful shortcuts. (4/6) https://t.co/kHGrZsEoPb

Layer 1: “pints of frozen yogurt” Layer 14: “three times as many” The first describes the objects. The second expresses a reusable relationship. Different layers can use memory differently. (3/6)

Engram retrieves learned vectors for short token sequences, helping the model reuse familiar patterns instead of reconstructing them. Our scan found names like “Ian Goodfellow”, code fragments, task instructions and website boilerplate. (2/6)

DeepSeek’s memory lights up for "Wright : Ace Attorney" We probed V4.1 Flash’s Engram gates to see which text patterns it uses. The results go well beyond names and facts. (1/6)🧵 https://t.co/EplmijW3d9

The SemiAnalysis STEEL teardown lab is hiring! Are you driven to explore the technical depths and nuances of advanced semiconductor manufacturing and design? Love a good floorplan and understand how it all goes together? Join a killer team. Apply today at (3/3) https://t.co/uydSzy75Cz

SemiAnalysis STEEL teardown lab has created something big. We're tearing down advanced datacenter and AI hardware and for fun we're sharing consumer teardowns for free!  To learn more about our pipeline or to commission a teardown, contact sales@semianalysis.com (2/3)

Tearing down Apple M6 and TSMC N2. We're sharing it all here, free. TSMC makes three (GAAFET foundries), scaling, optimization, and a few surprises. Stay tuned! (1/3) 🧵 https://t.co/nvTpaICoWR

It is worth noting that our hands-on testing is only a part of the overall ratings. There are many things that we cannot test hands on, and need to use other research methods to understand in detail. Namely, performance at scale, reliability over time, support experience, pricing, GPU availability and delivery timelines. Read more in ClusterMAX 3.0 (7/7) https://t.co/hX3qLA6fR8

This is our most critical test. Providers where we hear a bunch of customer complaints about reliability generally do not have health checks, monitoring dashboards, and autoremediation in place. Reliability is the #1 most important criteria to many of the biggest customers in the world, as we discussed in great detail in our article. (6/7) https://t.co/QsZgMrKRl1

For injecting failures, we start by using the DCGM injection method. If it doesn’t work properly, we follow up by writing a synthetic NVIDIA XID or SXID message into the kernel log, and watch how the provider’s existing health checks and scheduler respond. We leave their health agents and drain automation alone, so it’s up to their tooling to detect the error and act on it. Most health checks are set up to read from the kernel ring buffer, but some are not, so we communicate with the provider to make sure that our trigger will work and that we have access to pull it. As a final, trusty method, we reset the GPU’s upstream PCIe bridge, which produces a genuine XID 79. (5/7)

First, we reboot all the nodes in the cluster. You would hope that this is not a destructive test, but it is. On Kubernetes, we cordon and drain the node, issue the reboot, and require a changed boot ID, then wait for it to return to the cluster. The timer stops when we can run nvidia-smi in a fresh pod on the node. With Slurm, we just get an allocation or SSH and run sudo reboot. Slurm-on-Kubernetes is a little different, so we attempt to do things from the Kubernetes layer to test it correctly. Our scripts time everything throughout. (4/7)

Next, we test the fabric, storage, and orchestration software individually with specific tests designed to measure the sustained performance under load. Finally, we break things on purpose (or simulate them breaking). (3/7)

We start with an 8-hour burn-in on the GPUs and network, by running large GEMMs on the tensor cores and all-to-all communications at the same time, with a monitoring script tracking temperature, power, clock frequency, FLOPs, network connectivity, latency, bandwidth, and of course tailing the kernel ring buffer for any errors. You would be shocked how many hardware issues we induce with just this simple test. We cannot emphasize strongly enough how important it is to stress the GPUs and the network at the same time. Burn-in requires simultaneous thermal expansion and contraction of both of these components to approximate the real behaviour of these systems under real stress from real workloads. Lots of providers still use scripts that burn the GPUs and network separately. (2/7)

In our testing for ClusterMAX 3.0, one of the biggest things that we tested was reliability. we built on previous research to determine which providers can drive solid goodput by 1. identifying failures occurred, and 2. recovering from those failures. (1/7)🧵

ClusterMAX 3.0: The Industry Standard GPU Cloud Rating System Returns: https://t.co/lLAtzulRsy

The Chinese AI Infrastructure Boom: Introducing the SemiAnalysis China Datacenter Model 1,000+ facilities across 60+ operators mapped, built retail-first and flipped by AI, largest hyperscaler leases 1/5 national capacity, 100MW in 12 months, Eastern Data Western Compute https://t.co/twnBOg773d

MONEY PRINTER ALERT🚨 NVIDIA vLLM B200 CAN GENERATE UP TO💰️$15 BILLION💰️OF ANNUAL PROFITS PER GIGAWATT serving the open DeepSeekv4.1 Flash model at the official interactivity & official selling prices. Using Engram DRAM offloading on NVIDIA results in a 50% increase in revenue per GigaWatt.