Allyssa @allyssa

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 Unpublished.

Codez @0xCodez

a week ago

x.com/i/article/2065…

33 139 1K 1.2M 3K

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👇

Tom Dörr @tom_doerr

3 months ago

Visual guide to master Claude Code github.com/luongnv89/clau…

20 535 4K 734K 7K

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:

Ruben Hassid @rubenhassid

3 months ago

x.com/i/article/2034…

13 90 655 1.6M 2K

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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Most people think using Claude Code is about writing better prompts. It’s not. The real unlock is structuring your repository so Claude can think like an engineer. If your repo is messy, Claude behaves like a chatbot. If your repo is structured, Claude behaves like a developer living inside your codebase. Your project only needs 4 things: • the why → what the system does • the map → where things live • the rules → what’s allowed / forbidden • the workflows → how work gets done I call this: The Anatomy of a Claude Code Project 👇 ━━━━━━━━━━━━━━━ 1️⃣ CLAUDE.md = Repo Memory (Keep it Short) This file is the north star for Claude. Not a massive document. Just three things: • Purpose → why the system exists • Repo map → how the project is structured • Rules + commands → how Claude should operate If CLAUDE.md becomes too long, the model starts missing critical signals. Clarity beats size. ━━━━━━━━━━━━━━━ 2️⃣ .claude/skills/ = Reusable Expert Modes Stop repeating instructions in prompts. Turn common workflows into reusable skills. Examples: • code review checklist • refactoring playbook • debugging workflow • release procedures Now Claude can switch into specialized modes instantly. Result: More consistent outputs across sessions and teammates. ━━━━━━━━━━━━━━━ 3️⃣ .claude/hooks/ = Guardrails Models forget. Hooks don’t. Use hooks for things that must always happen automatically. Examples: • run formatters after edits • trigger tests after core changes • block sensitive directories (auth, billing, migrations) Hooks turn AI workflows into reliable engineering systems. ━━━━━━━━━━━━━━━ 4️⃣ docs/ = Progressive Context Don’t overload prompts with information. Instead, let Claude navigate your documentation. Examples: • architecture overview • ADRs (engineering decisions) • operational runbooks Claude doesn’t need everything in memory. It just needs to know where truth lives. ━━━━━━━━━━━━━━━ 5️⃣ Local CLAUDE.md for Critical Modules Some areas of your system have hidden complexity. Add local context files there. Example: src/auth/CLAUDE.md src/persistence/CLAUDE.md infra/CLAUDE.md Now Claude understands the danger zones exactly when it works in them. This dramatically reduces mistakes. ━━━━━━━━━━━━━━━ Here’s the shift most people miss: Prompting is temporary. Structure is permanent. Once your repository is designed for AI: Claude stops acting like a chatbot... …and starts behaving like a project-native engineer. 🚀

我不明白为什么人们不用Gemini来炒股。 大多数交易员看的是6个月前的图表。Gemini分析X上的实时情绪来预测未来。 这是找到下一个10倍股的20个提示：

Anthropic 一不小心又干掉了一批创业公司。 现在可以在 Claude 上，通过 MCP 直接接入并使用 Financial Datasets 金融数据库。 包含 1.7 万只股票 30 年来的损益表、资产负债表、现金流量表等数据。 接入指南：docs.financialdatasets.ai/mcp-server

这篇文章含金量最高， 学Claude code才是每个人最值得投入的事情。

Tw93 @HiTw93

3 months ago

x.com/i/article/2032…

193 2K 9K 3.1M 18K

BREAKING: AI can now automate daily options income with 78% win rate like professional theta traders (for free). Here are 12 insane Claude prompts that generate consistent 0.5-2% daily returns (Save for later)

I don't understand why people don't use CLAUDE for stock trading. It analyzes charts, digests earnings reports, and spots trends in seconds. Here are 16 prompts to turn it into your personal hedge fund analyst:

Morgan Stanley最近发布的这段28分钟的《惨痛的教训》非常的深刻！这是传奇投资者斯坦德鲁肯米勒的自我总结： 如果今天重新开始，他会怎么开始投资？ 这位大神把自己几十年赚大钱、踩大坑的真东西全抖出来了，比如，如何构建赚钱的投资组合？为什么投资中不要跟趋势对着干？还有他后悔过早卖出的股票？ 没有空话套话，全是能直接抄作业的干货。 1. 如何从零建投资组合 核心就是抓两件事：宏观大方向以及 挑好个股。 1.1 长期看好铜，因为现在铜供应紧张，加上AI和数据中心需求越来越大，这是大家都认可的机会； 1.2 会拿点黄金，但不是为了投机赚钱，主要是怕地缘政治出问题，用来避险的； 1.3 做空美元和美国国债，会往生物科技和那些被低估的股票上靠，专找未来3-4年能有大变革、大突破的领域。 他认为，美国经济现在已经很强了，再加上各种刺激政策，只会更强，美联储大概率不会再大幅加息。 2. 别迷信逆向投资 2.1 他认为，市场80%的时间都是对的，跟着大众走，大部分时候都不会出错。 不是说逆向投资没用，而是只有你自己极度有把握、别人却完全不相信的时候，逆向操作才能赚大钱。 他不会硬逼自己跟市场反着来，只会等那种风险小、收益大的不对称机会，才出手。 2.2 说白了，跟别人不一样不算本事，选对方向、控制好风险，才是真本事。 3. 索罗斯教他最核心的一课 3.1 不是你判断对不对，而是对的时候能赚多少，错的时候能亏多少。 3.2 尤其是仓位管理，比你判断行情方向重要多了，这是索罗斯实打实教他的。 简单说就是，错了就赶紧止损，别硬扛；对了就大胆加码，多赚点。 4. 他能成功，靠的不是智商，是心态和果断 4.1 他认为自己的优势不是比别人聪明，而是敢果断出手，也敢快速认错、改变立场。 4.2 只要事实变了，之前的判断不对，就立刻调整，不纠结、不自我内耗。 4.3 投资里，亏损和回撤是难免的，关键是别被自我怀疑打垮，快速调整过来，相信自己长期的战绩就好。 5. 他最大的遗憾，很多人都犯过 5.1 太早卖掉了一些大牛股，比如像英伟达这种高成长的科技股。 5.2 也正因为这个遗憾，他现在更严格要求自己：一旦买对了好股票，就别轻易卖，让赢家一直涨，别过早下车。 6.最后一句话总结： 投资想成功，一定要对的时候敢下重注，错的时候赶紧止损，长期相信自己的投资体系和过往战绩，别被一时的对错和自我怀疑影响。

Year to Date post $NVDA earnings: 477.27%. Majority of the gains are the result of the research I've done the past few months: From the $AXTI's InP chokepoint that went up few hundred percent recently. or profiting off Jane Street from $EWY IV vega expansion for Sk Hynix/Samsung. Many others were tens of % or hundreds of percent returns each in a short timeframe. Like $XLU going up 3% in a week to the epic directional rally of $MU and $SNDK. I think people just like to see the end results like this, which is understandably the most eye-catching. But most of the groundwork for the current returns was laid out months ago from $LITE Google BOM analysis to semi supply chain bottlenecks from Unimicron, Nittobo, and even $TSM last year. Even now I’m planting the seeds for the future with analysis on $XLU for the power/grid sector, or understandably higher risk companies like $IQE as a $LITE supplier for the photonics supply chains. I typically shift from: > Research Posts (Initial thesis post) > Map that into actual ideas + trades > Follow-Up DDs on Alpha (eg. SMM InP pricing) > celebrate when things go up. cross-industry, and typically on sectors with momentum. Rather than sticking single stocks, or just analysis only (instead of trading). And I think people might have found this style refreshing. I think recently, I’m is just capitalizing on two different trends: 1. Focusing on active bottlenecks in AI supply chains - Memory like $SNDK, $MU, Sk Hynix, Samsung, $SIMO - Photonics like $LITE, $COHR, $AAOI, $IQE, $AXTI, and Yamamura - Power Grid like $XLU - Advanced Packaging/Yields - $AMKR, $ONTO, $CAMT, $KLIC, $FORM, and $AEHR 2. Then focusing on Capital Rotation into Taiwan, Japan, Korea. Basically past week capital rotation was rotating from US/China -> Korea, Taiwan, Japan. ETFs like $EWJ or individual stocks from Nanya Plastics have been taking off. - Taiwan Equity Funds recently took in over $1 billion in a single week for the first time in months - For Japan: GS chart's +0.37 long buying - For Korea, foreigners were net buyers of roughly 1.37 trillion won (~$1 billion USD) in the first half of February While GS chart shows a staggering -1.52 SD in short activity for North America. So that's probably my assumption on why $HOOD investors haven't been doing too well from a lack of Asian equity exposure. The reason being Hyperscaler capex trade flows into Asian countries in the supply chains (eg. Some analysts projected Sk Hynix to have 2.2 2027 fwd p/e, which is absurd) -> institutions following the flow with capital rotation. As for some reflection, I'm genuinely surprised by how many people read my posts nowadays and it’s really humbling! I don’t really celebrate this much (last year I only did one time with a 600%+ 1Y return) but I’m amazed by how lucky I am this year with timing and getting a lot my thesis right. I’m not perfect, I do get a few things wrong, but what’s more important is I get more green than red every day. But thanks to everyone, I grew from a little account to 83K in like two or three months!

Ex-OpenAI Peter Deng says AI may be rewiring how kids think, and education could shift with it. The skill won't be memorizing answers. It'll be learning how to ask better questions to unlock deeper thinking. “When the calculator was invented, people didn't stop doing math. They just did higher-level math.”