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Markus Veith, Partner at @GrantThorntonUS, joins @AnastasiaKinsky at the @AlpacaHQ Partner Summit to discuss how banks are racing to adopt stablecoins after the GENIUS Act, from mega banks to community banks and credit unions.

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！

老铁，别再掏大几千块去买交易软件和行情终端了！ 这一周 GitHub 上「金融 + AI」涨星最快的 10 个开源项目，我已经替你梳理完毕，全部免费！ 💡 预测与研判 · Kronos（+2653★）：专注 K 线分析的开源模型，以 45+ 交易所的数据训练而成，输入历史价格便能预测后续几根 K 线 · daily_stock_analysis（+487★）：由大模型每日分析美股、A 股、港股，决策看板推送到 Telegram / Discord / 邮箱 🤖 交易 Agent · TradingAgents（+1944★）：多 Agent 交易框架，分析师、研究员、交易员、风控团队先吵上一架再下单，总星数 11.3 万 · Vibe-Trading（+821★）：港大 HKUDS 出品，用大白话描述思路，就能起草策略、跑回测，支持 MCP · day-trader-skill（+972★）：11 个 Claude 技能，覆盖扫盘、新闻、进场和止损，还能把一个交易时段重放 1 万次，默认模拟盘 🪙 币圈重点 · freqtrade（+360★）：老牌开源加密交易机器人，自带 FreqAI 机器学习、回测和 Telegram 控制，已支持 Hyperliquid 🔍 投研工具 · last30days-skill（+730★）：跨 Reddit、X、YouTube、HN、Polymarket 做调研，按点赞和真金白银的下注热度排序 · OpenBB（+294★）：给分析师和 AI Agent 用的开放数据平台，股票、期权、加密、宏观都有，带 MCP · FinceptTerminal（+412★）：开源版「彭博终端」，行情、研究、AI Agent 集成在一个桌面端 · tick-stock-panel（+346★）：A 股自托管量化工作台，用大模型写策略、回测、盯盘，65 个接口加 MCP AI 正把机构级投研工具一路拉到散户的桌面上。工具在走向平权，但真正拉开差距的，依旧是你的判断和风控。 建议先收藏，下次做研究直接拿出来用。

Agent 最缺的不是更大的上下文窗口，而是理解整个代码库的能力：当一个项目有数万文件、跨模块调用和海量文档时，为什么还要让 AI 一遍遍 Grep、Read、重新理解整个工程？ 📦 项目档案 项目：Graphify-Labs/graphify GitHub Stars：⭐ 147299 技术栈：Python + Tree-sitter + NetworkX + Leiden 核心定位：将代码、文档、SQL Schema、PDF、图片及音视频转换成可查询、可追溯的知识图谱，让 AI Agent 从「搜索文件」升级为「理解工程关系」。 GitHub： 🛠️ 一、底层架构：不是传统 RAG，而是图结构知识引擎 Graphify 最值得研究的地方，是它没有走传统向量检索路线。 传统 RAG 通常需要经历： 文件切分 → Embedding → 向量入库 → 相似度检索 → 上下文拼接。 Graphify 直接构建 Graph。 底层采用 NetworkX 图数据结构，以 Node 和 Edge 表达工程实体及其关系。 整个处理链路可以概括为： detect() → extract() → build() → cluster() → analyze() → report() → export() 每个阶段职责独立，通过 Python Dict 和 NetworkX Graph 传递数据。 这意味着它不只是索引代码，而是在构建整个项目的结构化关系网络。 🛠️ 二、Tree-sitter AST：让代码解析摆脱 LLM 依赖 这是最有工程价值的设计。 Graphify 使用 Tree-sitter AST 解析约40种编程语言，提取： • Classes：类定义及结构。 • Functions：函数与方法。 • Imports：模块依赖关系。 • Call Graph：跨文件函数调用。 • SQL：表、视图、外键及 JOIN 关系。 其中33种语言使用 Tree-sitter Grammar，另外7种使用内置解析器。 关键在于：代码结构提取完全在本地完成，不需要调用 LLM，也不需要把代码上传到云端。 对于大型代码库，这种确定性解析比单纯依赖模型阅读文件更容易复现和追踪。 🛠️ 三、EXTRACTED / INFERRED：让每条知识关系都有证据 很多知识图谱最大的隐患是：模型推断出来的关系，很容易被误认为真实事实。 Graphify 给关系增加了置信度标签： • EXTRACTED：源码中明确存在的关系，例如 import、函数调用。 • INFERRED：通过上下文、命名和调用关系推断出来的连接。 • AMBIGUOUS：存在不确定性的关系，需要人工复核。 INFERRED 关系还会附带 0.0—1.0 的 confidence_score。 这套机制非常重要。 它实际上把知识图谱从一个简单的关联网络，变成了一个能够区分事实、推断与不确定性的工程知识系统。 🛠️ 四、Leiden + SHA256 Cache：解决规模与重复计算 Graphify 使用 Leiden 社区发现算法，根据节点之间的连接密度，将复杂工程拆分成不同功能子系统。 例如，一个大型后端项目，可以自动识别认证、数据库、路由、权限等不同社区。 同时，SHA256 内容哈希缓存会记录文件指纹。 文件没有变化，就跳过重复提取；只有新增或修改的文件才重新处理。 音视频还可以通过本地 faster-whisper 转录，再进入统一知识图谱。 这让增量更新不必每次重新扫描整个工程。 五、真正解决了什么工程问题？ 想象一个场景： 你接手一个陌生的微服务项目，需要搞清楚： 用户认证最终连接了哪些数据库模块？ 修改某个核心函数会影响哪些下游组件？ 某个架构决策为什么会存在？ 两个看似无关的模块究竟有什么隐藏依赖？ 传统方式：Grep、Read、跳转、反复询问 Agent，消耗大量上下文。 Graphify：直接执行 query、path、explain。 它还能生成三个核心产物： • graph.json：完整知识图谱，可供后续查询。 • GRAPH_REPORT.md：核心节点、社区及关键关系分析。 • graph.html：可交互的可视化图谱。 甚至支持 Mermaid 调用链导出、Markdown Wiki 和 Obsidian 知识库。 最重要的设计思想是： 不要让 AI 每次都重新阅读整个世界，而是提前把世界组织成它能够理解和遍历的结构。 这才是 AI Coding 从上下文堆砌走向工程知识管理的重要一步。 对于大型 Monorepo、遗留系统维护、跨模块依赖分析，以及需要长期理解代码库的 Agent，这套架构尤其值得研究。 🔗 GitHub 开源地址：

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

GCSA Insight: Critical Security Vulnerability Discovered in a Leading Robotics Manufacturer’s Product, with System-Level Privilege Compromise Verified on Physical Hardware As artificial intelligence and embodied AI technologies continue to advance, robots are evolving from conventional automated machines into intelligent systems capable of perception, decision-making, and autonomous operation. At the same time, cybersecurity risks are extending beyond software and data into the physical world. The security of robotic systems is no longer solely a matter of protecting data and devices; it can also directly affect operational safety, physical environments, and user safety. Recently, the security research team of the Global Cybersecurity Alliance (GCSA) discovered and verified a critical security vulnerability in a product developed by a leading robotics manufacturer. Through in-depth analysis and multiple rounds of testing on physical hardware, the team successfully breached the device’s system security boundaries and obtained the highest level of operating system privileges in a real-world device environment, demonstrating the vulnerability’s actual impact under specific testing conditions. This research encompassed the entire process, from identifying the security flaw and assessing its risks to validating its impact on physical hardware. The findings also highlight significant challenges facing the underlying security mechanisms of embodied AI systems. The research findings and supporting evidence have been formally submitted to the manufacturer. On November 13, 2026, the manufacturer responded by email, preliminarily confirming the existence of the vulnerability and stating that it had initiated internal remediation efforts and an assessment of the scope of affected products. It is important to emphasize that confirmation of a vulnerability does not mean the associated security risks have been eliminated. GCSA will continue to monitor the manufacturer’s assessment of the affected scope, remediation plans, and subsequent security validation, and will update its research findings as new information becomes available. For intelligent systems capable of physical interaction and movement, timely, effective, and verifiable security remediation is particularly important. In accordance with responsible vulnerability disclosure principles, GCSA is currently withholding sensitive information, including the manufacturer’s identity, specific product details, the vulnerability’s underlying mechanisms, and exploitation methods. GCSA is currently seeking guidance from relevant authorities and regulatory bodies regarding the associated security risks. Taking into account the manufacturer’s remediation progress and a comprehensive assessment of potential security implications, GCSA will carefully determine the appropriate arrangements for further disclosure. Subject to appropriate conditions and upon obtaining the consent of the relevant authorities and regulatory bodies, GCSA will publicly release a detailed security insight report, providing the industry with ongoing updates on the research findings and associated risk assessments. The findings of this research are based on specific testing environments and the evidence obtained to date. They do not imply that other product models or similar robotic systems are affected by the same security issue. This security research and validation on physical hardware represent one of GCSA’s initiatives in the field of embodied AI security. GCSA will continue to advance security research, coordinated vulnerability disclosure, and industry collaboration to promote stronger security response mechanisms across the artificial intelligence sector, enhance product security, and strengthen industry trust. 🔗 Read the full report: · Stay Connected with GCSA Follow GCSA's official social media channels for the latest news, product launches, research developments, and ecosystem updates. GCSA Official Telegram Channel: GCSA Official WeChat Account: #GCSAInsight# #GCSACybersecurity# #GCSASecVibe#

🚨 A 19-YEAR-OLD STUDENT TURNED $68 INTO $750K USING AI. No trading experience. No emotional decisions. No endless chart-watching. Just Claude Code, GPT-6 Astra, and 6 AI agents working together. I built a self-learning AI trading system that generated +$1,840.82 in just 48 hours. [ 𝐍𝐨𝐭𝐞: 𝐅𝐨𝐥𝐥𝐨𝐰 𝐌𝐞: @EaStEnRaY1 (𝐅𝐨𝐫 𝐢𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲 𝐚𝐮𝐭𝐨 𝐃𝐌)] Here's what makes it different: 🧠 6 AI agents monitor newly launched Solana coins. ⚡ The system analyzes buyer activity, connected wallets, selling pressure, and holder concentration. 🎯 Every launch is evaluated across 3 probability scenarios: - +60% reached first - -30% reached first - Neither happens within 60 minutes QUANT evaluates profitability after execution costs. GUARD filters out trades with unacceptable risk. And here's the crazy part: Even the trades it SKIPS become learning data. Every new signal helps the system improve its next decision. No panic. No FOMO. No emotional trading. Just AI-powered market analysis and execution. 🔥 NOW I'M GIVING AWAY MY EXACT CLAUDE CODE SETUP FOR FREE! Want to build your own AI trading system? I'm sharing the setup for the next 24 HOURS ONLY. 👇 HOW TO GET IT: 1️⃣ Like this post 2️⃣ Repost 3️⃣ Comment "CLAUDE" 4️⃣ Follow @EaStEnRaY1 so I can DM you the setup. Don't just watch AI change trading. Learn how to build with it. 🚀

I analyzed eleven New York Times reports on the Jensen Huang story. Most notably, NYT makes the "spy" claim 30 times in these 11 reports. The original report cites unnamed officials who had been "briefed" on the intelligence. Not who had seen it themselves -- but who had been "briefed". This is a game of telephone with plausible deniability. Nobody named. No specific details of the intelligence used to draw the conclusion that the woman was a spy. Anonymous "officials" "briefed". This was used to try to take Jensen Huang down. When that didn't work, journalists resorted to viciously digging up and scandal mongering about Jensen Huang's other alleged affairs. Then he was removed as CEO. Without an investigation. Without due process. And now officials and intelligence agencies are contesting the New York Times characterization. This is unethical. It is unjust. It is an absolute disgrace. If anyone should resign now, it is Nvidia's board who aggressively removed Jensen Huang without an investigation, after he gave decades of his life to make the company what it is. And any investigation that proceeds from here should involved not just the "spy" claim, but the New York Times -- who has engaged in potentially unethical behavior. Jensen Huang deserves due process -- not mob justice. He deserves respect for everything he has done for technology and America -- not a disgraceful removal after anonymous, unsubstantiated allegations, several parties removed from the source.

🚀 近期 GitHub 金融与 AI 领域增长最快的 10 个开源项目，AI 正在加速进入量化交易市场 从 K 线预测、AI 多智能体交易，到自动制定策略、历史回测和风险管理，越来越多的交易工具开始由 AI 驱动。 以下是本周关注度增长最快的 10 个项目： 1. Kronos｜AI 预测 K 线走势（⭐ +2,653） 基于全球 45 家以上交易所的数据训练，只需输入历史行情，就能预测后续 K 线走势。虽然项目自 4 月以来没有更新代码，但近期关注度明显上升 2. TradingAgents｜AI 交易决策团队（⭐ +1,944） 模拟真实投资机构的运作方式，让多个 AI 智能体分别担任分析师、研究员、交易员和风控人员，通过讨论与评估形成交易决策。项目累计获得约 11.3 万颗 Star 3. day-trader-skill｜AI 日内交易助手（⭐ +972） 整合 11 项 Claude 技能，覆盖行情扫描、新闻分析、进出场策略及止损管理。内置时间回放功能，可对交易过程进行上万次模拟，默认采用模拟交易模式 4. Vibe-Trading｜自然语言量化交易（⭐ +821） 香港大学 HKUDS 团队开发的 AI 交易代理。无需复杂编程，只需用自然语言描述交易思路，就能辅助生成策略并执行历史回测，同时支持 MCP 5. last30days-skill｜全网金融信息研究（⭐ +730） 自动整合 Reddit、X、YouTube、Hacker News 和 Polymarket 等平台的信息，并结合点赞、互动和真实资金参与情况评估市场关注度 6. daily_stock_analysis｜AI 每日股票分析（⭐ +487） 利用大语言模型分析美股、A 股、港股等市场，自动生成每日投资决策看板，并通过 Telegram、Discord 或邮件推送 7. FinceptTerminal｜开源金融终端（⭐ +412） 类似 Bloomberg 的桌面金融分析平台，将市场行情、金融研究和 AI 智能体整合到同一个终端 8. freqtrade｜加密货币量化交易机器人（⭐ +360） 成熟的开源自动交易框架，支持策略回测、Telegram 远程控制，并通过 FreqAI 引入机器学习能力，同时支持 Hyperliquid 9. tick-stock-panel｜A 股 AI 量化工作台（⭐ +346） 面向中国 A 股市场的自托管量化系统，支持使用大语言模型辅助编写交易策略、历史回测及实时监控，提供 65 个 API 接口和 MCP 服务 10. OpenBB｜AI 金融数据基础设施（⭐ +294） 面向分析师、量化研究员和 AI 智能体的开放金融数据平台，覆盖股票、期权、加密货币及宏观经济数据，并提供 MCP 接口。 值得关注的趋势是，AI 交易工具正在从单纯的行情分析，逐步发展为集数据获取、策略生成、回测验证、风险管理和自动执行于一体的完整系统 对于量化交易开发者来说，这 10 个开源项目值得收藏研究

I have a major result to share. I’ve found conclusive forensic evidence that the top 4 Chinese AI labs—DeepSeek, Moonshot, MiniMax and Ant Group—have built their newest models on NVIDIA’s most advanced Blackwell chips, which are barred from export to China. This sounds insane, but I discovered that almost every AI model carries a hidden signature of the GPU that trained it. When a model is first set up, the starting values of its weights are laid out in a pattern that depends on the exact GPU. Each combination of GPU and seed number produces a unique pattern—a sort of fingerprint of the hardware used to create the model. Traces of that pattern survive training into the published model. Again, this evidence isn’t based on any insider information or leaking. It’s based on a forensic analysis of the model weights released by the companies themselves. I searched more than six trillion combinations of chip layout and seed number and matched them to open-weights models. In doing so, I found conclusive evidence that six Chinese AI models—including DeepSeek-V4-Pro, Kimi K3, and MiniMax-M3—were initialized on NVIDIA Blackwell systems. Four of the models match Grace Blackwell, the GB200 and GB300 parts in NVIDIA’s flagship liquid-cooled rack-scale systems. I then verified these results on real hardware. Running unmodified PyTorch with the seeds I’d recovered, a Blackwell B300 reproduced DeepSeek-V4-Flash’s untouched starting weights bit for bit, and a rented GB200 reproduced Kimi K3’s to a correlation of 0.999995. A Hopper H100 reproduced none of DeepSeek-V4-Flash’s, and changing the seed by one broke the match. (DeepSeek’s seed is 953049583 by the way.) The method also was verified to match 24 known open-weights models to the hardware that developers said they used. It is exceedingly unlikely that these results are a coincidence. Across six trillion combinations, the best accidental match should reach only about 8 sigma (i.e., eight standard deviations above what random chance would produce). The real matches range from 49 to 1,385 sigma, and even the weakest single test hits 20. These were data-center-scale training runs. With some analysis of release schedules and model details, I estimate that the largest, Kimi K3 and DeepSeek-V4-Pro, used 2,000 to 3,000 Blackwell GPUs each. Clusters of this size cost on the order of $150 to $200 million each to buy, and across these labs alone would add up to more than half a billion dollars of export-controlled GPUs, not counting experimental compute or otherwise. It’s quite likely that we see multi-billion dollar expenditure on export-controlled GPUs. I am highly confident in this result, and it has been independently verified by senior researchers at OpenAI and Anthropic. You can read the full report at There is much more I’ve identified about these models through other forms of checkpoint forensics like this, and will have more to share soon.