Allyssa @allyssa

Introducing Nebius Customer Atlas: mapping 302+ companies using Nebius - from Base44 and Cursor to Porsche, Revolut, Google DeepMind and Mastercard. ➡️ nebiusatlas.com I built it after finding dozens of customers Nebius had never publicly announced. So far it's surfaced 126+ undisclosed customer relationships! Base44 (Wix) uses Nebius for LLM calls. Baptist Health runs agents on AI Cloud. CAVA uses Tavily in its production agent. Orbiq moved to Token Factory after a Mistral outage. Together they reveal a super broad customer base spanning training, inference, agent search, robotics, financial services, drug discovery, and more. $NBIS has said it likely won’t do another mega-deal. That makes tracking customer growth one of the best ways to understand the business between quarterly results. If you know of any Nebius customers I've missed, I'd love to hear about them.

I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies. My observations: 1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively. 2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now. 3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention. 4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is. 5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now. 6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text. They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it. 7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy. 8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies. Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here. 9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens. 10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity! 11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it. 12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable. 13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think. 14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data. 15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open. 16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down. 17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building? 18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone. 19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good. 20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped. 21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense. Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era. Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways. Happy to be home and locked in. Until next time!

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A Stanford professor took two average stocks, rebalanced them daily, and turned $100,000 into $7 million without predicting a single price. Bookmark & watch today, no matter what.

美股4年40倍的秘密： 1.只买最好的行业的第一名公司 2.如果没有绝对的龙头，可以买二三个子行业的领先公司，但同类行业市场选择去弱加强， 3.新建仓股票首次买入仓位不要超过 2%，盈利加仓，亏损 10%～15% 坚决走人 4.好股票就是要长期持有，不要卖出 5.不要杠杆，不要买入投机性期权，不做空

Taiwan is returning to nuclear! Hard to overstate how big this is. Taiwan was the Germany of the Pacific, just more blockadable. Saving its nuclear was a major campaign for the old Environmental Progress crew starting back in 2017, working with brave local leaders Germany next

Stephen Stapczynski @SStapczynski

4 weeks ago

☢️ HUGE NUCLEAR POWER REVERSAL ☢️ Taiwan approved a preliminary plan to restart an idled atomic power plant, marking a reversal of the ruling party’s anti-nuclear stance. It closed its last reactor last year The earliest the plant may resume is 2028 bloomberg.com/news/articles/…

19 162 769 158K 75

要是做美股还不清楚这10个网站，建议先别盲目进场！ 1. MacroMicro：涵盖利率、通胀、就业、流动性、经济周期等各类宏观指标 macromicro.me 2. Seeking Alpha：个股深度解读、财报评析，汇集多空观点，附带股息与量化评分 seekingalpha.com 3. Finviz：开盘必看，热力图、选股器、技术形态、板块强弱，扫描盘面效率极高 finviz.com 4. Reddit：捕捉散户讨论风向、热门标的，重点看r/stocks、r/investing、r/wallstreetbets、r/ValueInvesting板块 reddit.com 5. CompaniesMarketCap：全球企业市值、营收、利润、PE排行，方便横向对比、挖掘行业龙头 companiesmarketcap.com 6. AnalysisSite（trackserenity）：偏向白毛股神/Serenity投研看板，可追踪推文、个股线索以及AI产出的分析内容 trackserenity.com 7. Koyfin：适合全局复盘，查看市场结构、板块涨跌、估值水平以及宏观市场联动关系 koyfin.com 8. ETF Database：查询ETF持仓、费率、规模、分红，挑选行业与主题ETF格外好用 etfdb.com 9. StockAnalysis：用来做个股基本面初步筛选，财报、估值、机构分析师预期一应俱全 stockanalysis.com 10. FRED：美国官方宏观数据源，利率、通胀、就业、信贷、货币相关数据全部收录 fred.stlouisfed.org 单靠一个网站没法帮你稳住仓位，但把这些工具搭配参考，至少不容易被社交平台的市场情绪带偏。 希望本次分享对你有帮助，我是孔乙，复旦经济学博士，关注我，每天低频高质拆解宏观经济与投资技巧。

Stanford acaba de filtrar esta clase gratuita que explica cómo funcionan Claude y ChatGPT desde el interior La mayoría desperdicia el 90% de su potencial Stanford te lo enseña en 1 hora y 50 minutos Guarda esto en favoritos para que no lo pierdas.

《从 0 开始建立自己的交易系统》第 8 课 上 缺口：普通交易者和进阶交易者的分水岭 这节课给各位交易员带来了更难理解更深度的课程 这一期我拖更了这么久 真的打磨了很久 一直都找不到一个能让大家都学会都听懂的讲述方式来表达缺口 最终选择分为2-3期把缺口讲完 本期内容上篇 你将初步了解什么是缺口 如何找缺口和缺口的作用 希望大家能有所收获（太难讲了这一期。。。🥲

构建 Claude Code 的工程师刚发布了一个 28 分钟的视频，讲解如何写出真正有效的提示词 我看过 300 美元的课程，都没有他前 5 分钟讲的内容全面 一个视频，完全免费

白毛女31个最喜欢的股票清单，我收下了： 1. $INTC - 美国铸造厂的希望，国家安全 2. $MRVL - 从未来的maia asics和附加产品如cpo中扩展收入，他们什么都做，数不过来了 3. $TSM - 半导体/AI的支柱 4. $COHR - 他们垂直整合做一切 + 捕捉光学周期 5. $RKLB - 太空的最终前沿将在5年后和20年后到来。 6. $DRAM - 三星/海力士的内存敞口 7. $AVGO - 超大规模云服务商不喜欢NVIDIA的GPU税 8. $AMZN - 没人能与厕纸的隔夜配送竞争。机器人将随着时间降低运营成本 9. $ARM - AGI CPU将在未来十年大幅扩展收入 10. $TSEM - 你将需要一个用于光基产品的铸造厂 11. $IBIT - 比特币，我们现在都知道了 12. $NBIS - 我觉得它是下一个AWS。他们还与Uber合作做自动驾驶汽车，拥有扩展中的数据库公司，数据标注。简直像个迷你谷歌。 13. $GOOGL - YouTube不会消失，Gemini很棒。他们用TPU垂直整合，用运营收入资助建设，所以我喜欢。 14. $AMKR - 超级设施将在2027-2028年末上线。从“美国制造”中受益 15. $HOOD - 我不喜欢短期，但长期我是Robinhood的粉丝，因为他们抓住了零售市场 + 正在扩展更多产品如银行等。产品创新很疯狂。 16. $CRCL - 我碰巧很喜欢稳定币，并视其为支付/持有未来的关键（取决于清晰法案） 17. $META - 人们不会很快停止使用Instagram或WhatsApp或其他产品。 18. $LITE - $GOOGL TPU敞口在BOM中占比不错。只要谷歌的AI项目继续运行，我认为$LITE会表现良好。 19. $LPTH - 锗和中国出口管制将始终是个问题，所以美国制造的工程替代品将始终重要 20. $FN - 有人需要组装光学产品 21. $JBL - 与上面相同，但加上Intel的SiPh收购IP，所以可能最终像Innolight？ 22. $MP - 美国稀土计划极其重要，类似于$INTC的国家安全风险 23. $HIMS - 好吧，听我说，他们刚收购了一堆公司，以19美元的价格拥有全球DTC渠道。空头真的讨厌这家公司，但我觉得作为逆向长线它很有前景 24. $SMTC - LRO/LPO转型 25. $POWL - 美国对Hammond的开关设备DC类型瓶颈的替代品 26. $VPG - 人形机器人可能在2027-2028年后成为现实，这家公司生产传感器。 27. $MOG.A - 感觉在机器人领域和SpaceX供应链中到处都能看到他们 28. $MSFT - 以375美元的价格，有一天我们回顾时会视其为买入机会。 29. $CVX - 战争后石油可能崩盘，但这些石油公司将极其重要，尤其是委内瑞拉是个金矿。 30. $XLU - 我觉得降息可能重启，我们需要电力/电网来支持AI，所以从$CEG到$NEE这些股票将始终重要 31. $AAOI 和 $AEHR

Anthropic Head of Product: “Fable 5 - is our best model for self-improving agentic systems. It can run for days on a single /goal. add /loops, dynamic workflows, dreaming and you become unstoppable.” in 11 minutes, the Anthropic team shows how to build long-running systems with Fable 5 from scratch. Worth more than a $500 agent-building course. Live from Anthropic’s latest stage in Japan. Unpublished.

Two Anthropic engineers spent 24 minutes exposing every Claude Code feature you didn't know existed. Most people will scroll past this. Don't be most people.

x.com/i/article/2039…

装完Claude Code却不知道怎么用， 这个数千星的开源指南， 教你一个周末把Claude Code玩明白。 很多宝子装完Claude Code， 跑了几个简单指令，就卡在了不知道下一步该做什么的困境里。 官方文档只单独罗列各项功能，不教大家怎么把不同能力组合成能真正省时间的工作流。 大家知道斜杠命令的存在， 却不懂怎么把它和钩子，记忆， 子代理串联成完整的自动化流程。 没有清晰的学习路径，大家也分不清该先学MCP还是钩子，该先练 skill 还是子代理， 最后只能泛泛浏览所有内容，却精通不了任何一项。 官方给出的示例大多过于基础，一句简单的入门指令，根本帮不了大家搭建带记忆功能，能拆分任务给专属代理的生产级代码审查流水线。 这个爆火的开源仓库，直接把这些痛点全解决了。 github 地址老规矩评论区自取👇

How to start using Claude code. ↳ from a guy who never coded in his entire life, and never will More infographics at how-to-ai.guide. Just sign up for free, don't pay, and open my welcoming email. The longer how-to guide is in this article:

x.com/i/article/2034…

🚨 Hedge fund managers are going to hate this. Someone just open sourced a system that does their entire job. 30.5% annualized returns. $0 in fees. It's called TradingAgents. Not one AI agent. An entire simulated trading firm. Analysts, researchers, traders, and risk managers. All AI. All arguing with each other before making a single trade. No Bloomberg Terminal. No $50K data feeds. No MBA required. Here's what's inside this thing: → 4 AI analysts scanning financials, news, social sentiment, and technicals → A Bull and Bear researcher that literally debate each other → A trader that synthesizes every argument into a final call → A risk management team that can veto any trade → A fund manager that approves or rejects execution Here's the wildest part: It beat every traditional trading strategy they benchmarked. Cumulative returns. Sharpe ratio. Max drawdown. All of them. Hedge funds charge 2% management + 20% performance fees for this exact workflow. This is free. 100% Open Source.

Holy shit. The guy who BUILT Claude Code just shared his actual workflow. Boris Cherny runs 10-15 Claude sessions in parallel every single day. While you're prompting one AI, he has 5 in his terminal + 5-10 on the web all shipping code simultaneously. And the real weapon? His CLAUDE.md file. Every time Claude makes a mistake, the team adds a rule so it NEVER happens again. Boris literally said: "After every correction, end with: Update your CLAUDE.md so you don't make that mistake again." Claude writes rules for itself. The longer you use it, the smarter it gets on YOUR codebase. His other insane detail: he hasn't written a single line of SQL in 6+ months. Claude just pulls BigQuery data directly via CLI. Claude Code now accounts for 4% of ALL public GitHub commits. Engineers who haven't set this up yet are already behind. This CLAUDE.md template is the difference between using AI as a chatbot vs using it as a fleet of senior engineers. Drop it in any project. Free.

The news is pretty heartbreaking: $META 20% layoffs $ORCL layoffs $AMZN 600,000 workers long term layoffs as they get replaced by robotics and AI. This is a dystopian future. Companies end up with record profits, without the cost of human labor. The only way to benefit: Investing in AI as a hedge. The next few years feels like the main way to escape the permanent underclass, caused by AI displacement. The return on equity derived from AI will go to the shareholders. While the gap between those who live paycheck to paycheck, not invested in stocks. Will continue to grow. This is not the future. - Opus 4.6 is good enough to replace most software engineers today. - Waymo has started to replace taxi drivers in places like SF today. - We know $TSLA Humanoids are coming next as they’re widespread in China, today. This is happening now. Disruptions in Iran are only temporary to the accelerating AI buildout. AI has hit the inflection point, and looks inevitable. You’re already seeing US job revisions down close to 1 Million, which is staggering. And we’re seeing the newest LLMs be built by their previous models, as AI approaches the singularity (AI led recursive growth). Investing in where the compute and hardware needed to run the AI: From the datacenter/power/grid sector: $NBIS, $XLU, $VRT, $BE Photonics sector needed to scale AI: $LITE, $COHR, $AAOI, $TSEM Semi sector needed for the chips: $NVDA, $TSM, $ASML, $INTC Memory sector for the chips: $MU, $SNDK, SK Hynix, Samsung ASICs for hyperscaler AI inference: $AVGO, $MRVL, Mediatek Yields sector to make sure the chips work: $TER, $AEHR, Advantest Along with the raw materials or substrates needed for AI: $AXTI, $COPX, $SOI And many others become the single, largest, hedge against widespread AI displacement. Whoever owns the means of compute (bottlenecks, materials, datacenters): Owns the future of AI.

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