标签：#Tech

Indian algo traders and quant developers hit the same wall: they reach for pandas_market_calendars , set up XNSE , and get back answers that are silently wrong for three segments that matter most in India. Here is what breaks and what to use instead. The three failure cases 1. MCX evening sessions MCX commodity markets (crude oil, natural gas, gold, silver) run until 23:30 IST. pandas_market_calendars has no MCX calendar. Any check after 15:30 returns a wrong answer. # pandas_market_calendars — no MCX at all # mcal.get_calendar("MCX") → KeyError # aion-indian-market-calendar — works correctly from aion_indian_market_calendar import IndiaMarketCalendar from datetime import datetime from zoneinfo import ZoneInfo cal = IndiaMarketCalendar . bundled ( 2026 ) tz = ZoneInfo ( " Asia/Kolkata " ) cal . is_market_open ( " MCX " , datetime ( 2026 , 6 , 18 , 20 , 0 , tzinfo = tz )) # True 2. NSE Currency Derivatives (CDS) — wrong hours, wrong holidays USDINR, EURINR, GBPINR, JPYINR futures and options trade on NSE CDS from 09:00 to 17:00 IST — 90 minutes longer than NSE equity. CDS also has a separate holiday calendar. pandas_market_calendars has no CDS calendar. Using XNSE gives you wrong close times and potentially wrong holiday answers for any currency derivative workflow. from aion_indian_market_calendar import IndiaMarketCalendar cal = IndiaMarketCalendar . bundled ( 2026 ) # These resolve correctly to their respective segments cal . is_market_open ( " USDINR " , at ) # NSE_CURRENCY_DERIVATIVES: closes 17:00 cal . is_market_open ( " NSE " , at ) # NSE_EQUITY: closes 15:30 cal . is_market_open ( " MCX " , at ) # MCX: closes 23:30 3. Muhurat trading (Diwali special session) On Diwali, NSE runs a one-hour equity session in the evening. pandas_market_calendars marks this day as a holiday. Schedulers that rely on it will skip execution entirely. cal = IndiaMarketCalendar . bundled ( 2026 ) events = cal . events_on ( " 2026-11-08 " , exchange = " NSE " ) # Returns the Muhurat t

It's a big year for America. It's the semiquincentennial, otherwise known as America250, and the United States is cohosting the World Cup. But spectators at these events - and the millions of people who live in the cities hosting them - may not realize that they, too, are being watched. From Kansas City to New […]

Despite the fact that smartphones have become impressively capable shooters, standalone point-and-shoot cameras are enjoying a renaissance. The tiny Kodak Charmera is still wildly popular, while influencers are scrambling to find aging Canon cameras on eBay. Godox, a company best known for its photography lighting products, is the latest to join the simple camera craze, […]

YouTuber Jon Prosser has finally filed a formal response to Apple's lawsuit made against him and another defendant over allegedly stealing iOS secrets. In his response, Prosser denied that he "planned or participated in any conspiracy or coordinated scheme" for the "purpose of injuring Apple." However, Prosser admitted to recording a FaceTime call showing unreleased […]

企业尽调智能体实战：60+真实企业的AI尽调报告 从5天到10分钟：AI如何重构企业尽调 企业贷前尽调，银行和金融机构最头疼的环节。一位信贷经理曾这样描述他的工作：打开天眼查查工商信息，切到Wind拉行情，再打开百度搜新闻，最后把散落在七八个系统里的数据拼进Word模板。一家企业，至少5天。如果碰上集团客户、关联方众多的，两周起步。 一家支行行长曾无奈地说："25个客户经理，每个人做的尽调报告格式都不一样。同样的企业，A经理评'低风险'，B经理评'中等风险'，谁对谁错无从判断。"问题的根源不是人的能力差异，而是工具链的碎片化——数据散落在不同系统里，没有统一入口，也没有标准化的采集流程。 我们调研了12家金融机构的尽调流程，发现三个共性痛点： 信息散落 （数据分布在6-10个系统中）、 耗时漫长 （单家企业5-10个工作日）、 质量参差 （依赖个人经验，无标准化流程）。 本文记录的，是一个用AI Agent解决这个问题的实战项目——企业尽调引擎v5.0。它不是概念验证，不是Demo，而是在60+家真实企业上跑通的生产级系统。 技术架构：多源数据整合的数据流 尽调的核心难题不是"分析"，而是"采集"。一家上市公司的完整画像，需要从至少6个异构数据源拉取信息。传统方式是人肉Copy-Paste，我们的方案是用Agent自动编排数据流： 用户输入 "美的集团" │ ▼ ┌─────────────────────────────────┐ │ Step 1: 股票代码查询 │ │ 联网搜索 → 000333.SZ │ └──────────────┬──────────────────┘ │ ┌──────────┴──────────┐ ▼ ▼ ┌─────────┐ ┌──────────┐ │ Step 2a │ │ Step 2b │ │ 实时行情 │ │ 新闻舆情 │ │ ifind │ │ 联网搜索 │ └────┬────┘ └─────┬────┘ │ │ └─────────┬──────────┘ │ ┌──────────┼──────────┐ ▼ ▼ ▼ ┌────────┐ ┌────────┐ ┌────────┐ │Step 3a │ │Step 3b │ │Step 3c │ │工商信息 │ │风险扫描 │ │估值指标 │ │ MCP │ │ MCP │ │ MCP │ └───┬────┘ └───┬────┘ └───┬────┘ │ │ │ └──────────┼──────────┘ │ ▼ ┌─────────────────────────────────┐ │ Step 4: 舆情分析 + 综合评分 │ │ 多源交叉验证 → 生成尽调报告 │ │ 输出: JSON(5KB) + Markdown(4KB) │ └─────────────────────────────────┘ 这个数据流的核心设计原则是 并行采集、串行推理 。Step 2的行情和舆情可以并行获取，Step 3的三个MCP调用也可以并行，但Step 4的综合评分必须等所有数据到齐后才能做交叉验证。这种设计把端到端耗时压到了10分钟以内。 另一个关键设计是 渐进式降级 ：如果MCP工具不可用（比如企业是非上市公司），引擎会跳过行情和估值模块，仅返回工商+风险+新闻的"基础版"报告，而不是直接报错退出。这一设计在实际使用中至关重要——我们的60+企业样本中，有11家是非上市企业，如果要求所有数据源齐备才能出报告，这11家就会被拒之门外。 五大能力详解 1. 股票代码查询 输入企业名称，自动搜索匹配股票代码。比如输入"美的集团"，引擎通过联网搜索拿到 000333.SZ 。这个步骤看似简单，却是后续所有数据获取的前提——行情、估值、历史走势全部依赖股票代码。对于非上市企业，引擎会标记 stock_code: null 并跳过相关模块。在实际测试中，股票代码查询的成功率超过98%，少数失败案例主要是名称变更（如"格力地产"更名为"珠免集团"）尚未被搜索引擎索引。 2. 实时行情数据 通过ifind接口获取实时股价、涨跌幅、成交量、换手率等指标。这些数据直接写入报告的"行情数据"章节，避免分析师手动从交易软件抄录。更重要的是，行情数据与后续的估值指标做交叉验证——如果PE_TTM显示14倍但股价异常波动，报告会标注"数据一致性待确认"。 3. 企业新闻舆情 联网搜索获取企业最新新闻，引擎对新闻做情感分析后输出舆情等级（正面/中性/负面）和舆情得分（0-100）。这不是简单的关键词匹配，而是基于上下文的语义判断。当正面信号和风险信号同时出现时，报告会分别列出，而非简单抵消。一条"美的集团海外营收创新高"和一条"美的集团遭反倾销

New York City’s Summer of Ludd festival is teaching people how to live offline.

AI and Robotics in the New Age of Industrialization Since 2025, a new era of...

As the parent of two little girls, I often think about how their childhood is different from mine. The seven-year-old is learning about AI at school. The five-year-old is given internet-based homework every week. And they are both absolutely repulsed by the idea of smoking. That was not the prevailing sentiment when I was young.…

In September, some PlayStation customers will no longer be able to access some purchased movies and shows. It underscores the fact that digital purchases are really more like long-term rentals.

Todo projeto que envolve autenticação via telefone acaba esbarrando no mesmo problema chato: como testar isso de verdade? Você não pode ficar digitando seu próprio número toda vez que roda um fluxo de cadastro. Definitivamente não deveria pedir para os colegas de equipe cederem o deles. E a maioria dos pipelines de CI não tem uma pessoa sentada ali, pronta para ler uma mensagem de texto e digitar o código num formulário. É uma daquelas coisas que parecem pequenas até você estar três sprints dentro de um projeto com 2FA via SMS e perceber que a cobertura de teste desse fluxo inteiro é "testei uma vez, manualmente, antes do almoço". Por Que a Verificação por Telefone É Complicada de Testar A maioria dos fluxos de autenticação de um stack típico é fácil de automatizar. Verificação por e-mail, dá para interceptar com uma caixa de entrada de teste ou um serviço de captura de e-mails. Tokens de sessão, dá para mockar. Redefinição de senha, você controla o loop inteiro. O SMS quebra esse padrão porque o código precisa sair completamente do seu sistema, ser entregue por uma rede de telecomunicação real e voltar antes que o teste possa continuar. Essa ida e volta introduz vários pontos de falha que não têm nada a ver com o seu código: atrasos de operadora, filtros de spam, peculiaridades de entrega por região, limites de taxa. Se você já viu um pipeline de CI falhar numa etapa de verificação por telefone e depois passar numa nova tentativa sem nenhuma mudança de código, é quase sempre por causa disso. O instinto de muitas equipes é pegar um número público gratuito de um dos vários sites de "receber SMS online" para checagens manuais rápidas. Isso funciona bem para uma verificação pontual. Mas desmorona rápido quando você tenta automatizar, porque esses números são compartilhados potencialmente por milhares de outras pessoas usando o mesmo pool. Códigos podem se perder numa caixa de entrada lotada, o próprio número pode já estar bloqueado pela plataforma que você está testand

If you're going to apply for Startup Battlefield Australia, now is the time. Applications close July 6, and once the deadline passes, the opportunity is gone.

Fable 5 got jailbroken again Researcher Vitto Rivabella tested Fable 5’s defenses and managed to find a bypass. According to him, most attempts failed. The protection is multi-layered: the model checks the prompt, conversation history, system context, and its own response. Some filters run during generation and can stop the answer halfway through. The checks are not based on keywords. The system looks at meaning, intent, language, wording, and suspicious chains of requests. The bypass took around 20 hours. It required rare languages, academic framing, long build-ups, Unicode, breaking the task into parts, and working with the chain of thought. The author did not get a stable bypass for long tasks. According to him, regular search is faster and cheaper.

Mozilla shut down the well-loved read-it-later Pocket app last year, and now Meta is launching an app called Pocket with an entirely different, AI-focused pitch: this new app lets you make and share little interactive "gizmos" built from an AI prompt, as reported by Business Insider. Meta CEO Mark Zuckerberg is all in on AI […]

The Federal Bureau of Investigation (FBI) said today it worked with industry partners to seize hundreds of domains associated with NetNut, a sprawling residential proxy service operated by the publicly-traded Israeli company Alarum Technologies [NASDAQ: ALAR]. The action comes roughly two weeks after KrebsOnSecurity published findings from multiple security firms connecting NetNut to the Popa botnet, a collection of at least two million devices that have been compromised by malicious software with little or no consent from victims.

New US rules would legalize quiet supersonic flights without the sonic boom.

Plex is pushing customers to newer features and more frequent payments.

For decades, to be a gamer was to accumulate a lot of stuff. Consoles, controllers, accessories, weird VR gloves that never worked properly, but mostly the games themselves. Over the years, games have come in every shape and size you can imagine. And now that era appears to be ending. On this episode of The […]

The EU went after Google for the practice of bundling its search engine and browser with Android.

We’ve got a special Decoder today — I had the chance to talk with Amy Lanzi, the CEO of Digitas North America, in front of a live audience at the Uber Villa at the Cannes Lions advertising festival in the South of France. I know, it’s a hard gig, but I do it for you. […]

Financial disclosures show how much Trump loves tech stocks.