与「Arc」相关的搜索结果

最近 Mole CLI 有非常多的更新，Mac、Windows 和 Linux 都有很多优化和增强，打算了一起同步给大伙。 首先非常建议使用 CLI 小伙伴看到后，辛苦直接 mo update 更新到最新，因为有可能你本地还是半年前的版本，现在版本相比之前要完善更多，特别是对于一些AI 工具清理的加强和安全的保护上。 首先 Mac 这一块的卸载能力差不多完成了之前 GUI 版本差不多 900 款软件的卸载详细同步，可以享受一样的残留清理效果，然后对于开发工具的清理、以及代码编译过期内容的清理也有很多完善，甚至其实iPhone和Android模拟器工具里面也有非常多的缓存，也可以一起清理了。 然后 Windows 版本补了很多大伙常用的软件，360 极速浏览器、夸克、QQ、飞书、剪映、Trae 这些缓存都能清了，卸载的时候游戏存档、Epic 游戏和 WSL 磁盘会留着，还新加了 mo installer 命令，可以把下载文件夹里放了很久的安装包挑出来让你删除。 最后 Linux CLI 在上线后继续完善，Arch、Omarchy、Debian、Ubuntu 都可以用了，clean、uninstall、analyze、status、purge 这些命令和 Mac 上一样，像 Omarchy 这种会打 btrfs 快照的系统，删掉的旧包要等快照轮换后空间才回来，Mole 也会如实告诉你详细的清理现状。 最后假如你对各个系统的清理、维护、电脑优化、卸载有任何的建议，都可以找我，我来帮你把需要的地方完善好，把 Mole 做成好用易用效果好的电脑优化工具，哈哈，发完这个，我就去看李志去，期待今晚的演出！

Arcus × Lighter 第四周复盘 A 组｜本金 $1,000 · 交易量：$19.4 万 · 平均持仓：$2,830 · 已实现盈利：+$16.9 · 积分：6.94 B 组｜本金 $4,000 · 交易量：$21.0 万 · 平均持仓：$17,900 · 已实现盈利：+$59.3 · 积分：14.33 B 组的持仓是 A 组的 6 倍多，交易量差不多，积分却只有 2 倍。 用之前回测的积分公式已经拟合不上这周的数据了，猜测是官方下调了持仓权重。 零磨损的策略，分数还是低。你们是咋刷 1M 的？ 顺便分享一下逆向选择的研究 实测：币安 30ms 内跳 ≥3bps 时，Arcus 中位要盘中 49ms、盘前 63ms 才跟上。 也就是说，能抢的时间窗口只有几十毫秒。 护栏规则：币安在 W 毫秒内不利移动 ≥3bps，就撤掉 Arcus 的挂单。 不同 W 的结果 （拦掉笔数｜拦掉的单均值｜留下的单均值｜净效果）： 30ms　｜28 笔｜−0.26bps｜+2.11bps｜+$0.28 50ms　｜30 笔｜ 0.00bps｜+2.09bps｜+$0.26 100ms ｜34 笔｜+0.29bps｜+2.08bps｜+$0.08 300ms ｜44 笔｜+0.83bps｜+2.04bps｜−$0.16 1000ms｜62 笔｜+1.34bps｜+1.97bps｜−$1.05 窗口越长，拦下来的越是赚钱的单。 只有 30–50ms 才勉强为正；把阈值降到 2bps，拦截也是负效果。

AI 写代码最容易埋的坑之一，就是数据库表设计错了，等上线后才发现改不动。 发现一个 Claude Code 专用 Agent：Database Architect。 它不只是帮你生成 SQL，而是提前参与数据库架构设计。 主要能做这些事： 数据建模：表结构、主外键、索引、约束 技术选型：PostgreSQL、MySQL、MongoDB、向量数据库 多租户设计：RLS、Schema 隔离、独立数据库 性能规划：读写模式、分库分片、扩展方案 数据迁移：生成迁移脚本和回滚方案 安装命令： npx claude-code-templates@latest --agent database/database-architect 我觉得最实用的是让 Agent 先分析业务访问模式，再设计表结构，而不是代码写完才补数据库。 不过生成的 DDL 和迁移脚本仍需要人工审核，尤其是生产环境的数据变更。 你现在用 AI 开发项目，会让它直接设计数据库，还是自己先把表结构定好？ 项目： #ClaudeCode# #AI# #Agent# #Database#

50 Sites That Show What Billionaires Buy and Read for Free🤯 1.) → Current portfolios of famous investors 2.) → Quarterly fund trades 3.) → Stock transactions of US politicians 4.) → Executives' own company stocks 5.) → Making fund filings readable 6.) → Companies' official reports 7.) → Buffett's letters since 1977 8.) → Video archive of Buffett meetings 9.) → Howard Marks' investor memos 10.) → Research from Ray Dalio's firm 11.) → Dalio's principles for free 12.) → Jeremy Grantham's market notes 13.) → Quantitative fund research 14.) → Damodaran's valuation data 15.) → Valuation professor's blog 16.) → Morgan Housel's money writings 17.) → Mental models for decision-making 18.) → JPMorgan's free market guide 19.) → Innovation-focused fund research 20.) → Venture capital essays 21.) → Value investing lecture notes 22.) → Columbia's value investing archive 23.) → Fed's economic data archive 24.) → Fed decisions and statements 25.) → European Central Bank data 26.) → Reports from the central bank of central banks 27.) → Countries' economic indicators 28.) → Companies' long-term charts 29.) → Company financials for free 30.) → Companies' market cap rankings 31.) → Stock screener and heat map 32.) → Portfolio backtesting 33.) → Charts and market analysis 34.) → Professional market screener 35.) → Company activity reports archive 36.) → SEC's investor education site 37.) → Passive investing encyclopedia 38.) → Financial terms dictionary 39.) → Free finance guides 40.) → CFA research publications 41.) → Fund and stock evaluations 42.) → Companies' visual analysis 43.) → Archive of selected investment theses 44.) → Seeing what's inside funds 45.) → European fund backtests 46.) → Investment writings with data 47.) → Market history notes 48.) → Investor psychology writings 49.) → Explaining the economy with charts 50.) ourfiniteworld? → (see note) The rich don't hoard information. They just don't tell you where it is. Save it, follow @Liyana_Coder you'll need it. Not investment advice. @Liyana_Coder

I LAUNCHED A GROK BOT TERMINAL OVERNIGHT, GAVE IT $44.82 AND STOPPED ASKING AI WHAT IT WOULD DO. BY MORNING IT HAD $11,643.21 AND SIX AGENTS LOGGING EVERY TRADE I spent a year asking agents what they'd trade and watching the chat scroll. Last weekend I built the thing that actually trades, in Grok Bot, with no code I touched by hand. The six agents share one screen, each one job: > scanner reads new token pairs and volume spikes > rug check filters suspicious tokens before anything executes > sizer sets position from account equity, not a fixed ticket > executor opens and closes with slippage tracking > logger writes the reason behind every trade > equity curve updates on every close I went to bed on $44.82 and left the laptop open on the dashboard. By morning the balance read $11,643.21. Every trade on the activity feed had a reason next to it and a line in the log. That's the shift I didn't see coming. Not that an agent made money overnight. That I stopped being the one asking "would you long this?" and started being the one reading the log of what already happened. Starting balance: $44.82 Current balance: $11,643.21 Grok Bot built the terminal, the agent architecture and the dashboard. I described what I wanted and the thing went live. The full build is in the article below ↓ The whole build is in the post below ↓ Comment " Grok_Bot " 2️⃣ Like and Repost 3️⃣ Follow me @lihazadn_Ai I'll DM you the complete setup.

Everyone believes CUDA is NVIDIA's moat, the reason we've maintained our dominance in the AI era. I've never believed that. NVIDIA has only one true moat: its people. And the most important of them is Jensen Huang. I've worked on CUDA compilers, libraries, and DSLs for eight years, from Volta to Rubin. I think I've earned the right to share my perspective on what NVIDIA's moat really is. I hope you'll bear with me. 1. What is CUDA? Many people think CUDA is just a programming language and its compiler, and that NVIDIA's moat comes from the massive amount of software written in CUDA, making it impossible for developers to leave NVIDIA GPUs. This is completely wrong. It's an incredibly naive understanding of CUDA. To me, CUDA is everything NVIDIA does. CUDA isn't just a language, software, or hardware architecture. It's a product defined by a unique customer experience. When you find GPU programming too difficult, CUDA gives you an easier way. When your GPU code isn't fast enough, NVIDIA engineers respond and help you solve it. When a handful of GPUs can no longer push your technology forward, NVIDIA has already scaled its computing platform from desktops to massive data centers. When you need more data centers than anyone else can supply, NVIDIA has already secured wafer and HBM capacity years in advance to meet your demand. That experience is CUDA. It's made possible by the relentless, day-to-day work of everyone at NVIDIA. And the person who brings all these people together, who gives them a shared vision and purpose, is Jensen Huang. 2. What made CUDA possible? Patience. Perseverance. An unwavering commitment to a vision. Around 2006, NVIDIA bet that the future would demand far more computing power, and began transforming GPUs from graphics processors into a platform for general-purpose computing. Initially, it was a commercial disaster. The added costs hurt margins, the market wasn't ready, and the strategy faced enormous skepticism and pressure, including questions from the board. But Jensen persisted. For roughly a decade, NVIDIA kept investing in CUDA, building its hardware, software, and developer ecosystem long before the commercial payoff became clear. Eventually, deep learning arrived. And NVIDIA was ready. Today, this sounds like an inspiring story. But for those who lived through it, it meant a decade of perseverance, doubt, and being misunderstood. Looking back at the history of technology, how many people could have done the same? This is what I've always loved most about NVIDIA. And now, Jensen has been pushed out. This won't kill NVIDIA. The company can still create tremendous commercial value. But it has killed NVIDIA's soul. And how far can a body go without its soul? If this is how Jensen Huang is treated today, how will those of us who built CUDA be treated tomorrow? I love NVIDIA deeply, but I have to say it: NVIDIA is dead.

可能因为今天老黄的新闻，今天翻到了@zhang_benita 采访 @liu_mingyu 的视频。 中间有提到老黄习惯性的会torture everybody to greatness，而且这是一件很耗费精力的事情。创始人的文化并不一定和成功与否强挂钩，但是这也是一家公司价值观的最好表现。特别是当我们不断焦虑和不断考虑单体作战的现在，如何让身边的兄弟同事能够意识到正确的焦虑，正确的懂得苦涩背后的原因，获得一些改变和突破的动力。可能对leader来说更加重要。我们始终不是孤立的个体，群体合作的成功远比个体创新的成功长久与持续。 当我们开始组建@Arcshipsai 的时候，我给组织个体的定位是玩家（player），每个人得有自己的追求，并且追逐卓越。除了追逐这个元素，我其实很希望突出的是组织的人味。就是组织得保证每个人都是正常的，没有社会性毛病，只有认真的在追逐的目标。正常的上班，正常的学习，正常的交流，正常的研发，正常的告别。因为没有什么比每天发现和改造新东西要更重要，纯粹点。

Jensen Huang resigned effective immediately as President, CEO, and board member of Nvidia. 🇺🇸 Set aside the scandal that cost him his job for a moment and consider what Jensen Huang actually built. He leaves behind the first company in human history to reach a $5 trillion valuation, the undisputed engine room of the artificial intelligence revolution, and a chip architecture that powers virtually every major AI breakthrough. Thank you Jensen. 🙏

Elon Musk, I decided to bring Jensen Huang onto the team because you simply do not pass up a once-in-a-generation opportunity to recruit the greatest compute architect on Earth. When it comes to building massive AI infrastructure, designing next-generation silicon, and scaling hardcore engineering at extreme velocity, nobody operates on Jensen’s level, and his vision for accelerated computing aligns 100% with what we are building across xAI, Tesla, and SpaceX. Bringing his unmatched expertise in-house to merge with our Dojo supercomputers, Grok clusters, and real-world robotics will compress years of innovation into months—and when legacy boards hesitate on true greatness, I am always going to bet big on the best talent in the world.