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Codex、WorkBuddy 等 Agent 已经能读取 PDF。面对扫描件、多栏排版、跨页表格和公式时，解析质量仍会直接影响后续的检索、问答和数据提取。 Mac 上可以本地运行的 PDF parser 很多，PaddleOCR、GLM-OCR、MinerU 等工具的安装方式和运行环境各不相同。文档类型变化后，想换一个 parser 重新处理，通常还要重新配置。 DocDot @docdotai 把多个本地 parser 集中到了同一个 CLI 中管理。 官方安装命令： curl -fsSL | bash 安装 DocDot 后，可以在 NanoDoc、PaddleOCR、GLM-OCR、MinerU、LiteParse 之间安装和切换，并将 PDF 解析为 Markdown 或 JSON。后续还会继续增加新的 parser。 DocDot 也提供了 Web Mode。运行 `docdot web` 后，可以在浏览器中并排比较 3 个 parser 的结果。遇到复杂表格、公式或特殊排版时，可以根据实际输出选择更合适的解析器。 根据官方说明，安装程序还能扫描本机的 Codex、Claude Code、OpenClaw、Hermes 等 Agent，并配置相应的 Skill；需要接入其他应用时，也可以通过 MCP 调用。解析在本机运行，PDF 无需上传，目前主要面向 Apple Silicon Mac。 我在 Mac 上安装了 DocDot 和 NanoDoc，并准备了一页包含中英文、表格和公式的 PDF。 首次启动完成模型编译后，再次解析同一份 PDF 用时约 2.3 秒。正文和公式基本准确，表格结构得到保留，小字号表头仍有少量误识别。 这只是一份单页样本，不能替代完整的性能测试。官方技术报告使用了 4,231 页、64 类文档进行比较，NanoDoc 的综合质量和 Table TEDS 在参测解析器中均排名第一。 DocDot 今天在 Product Hunt 上线：

The Long-Horizon Terminal-Bench paper landed around May and concluded that the results showed headroom for improvement. The best of the 15 models they tested finished seven of the 46 tasks, and the mean across all models was about two. That ceiling is what fifth place looks like on the current board. Grok 4.5 is now at 13, and Fable 5 is at 12. A single task costs around 9.9M tokens, 231 episodes, and 85 minutes of wall clock time. That means agents are holding a plan across all of it and finishing, and that capability nearly doubled in two months. SpaceXAI is on top, and they marketed the 4.2x output token efficiency, which undersells it. Two dollars in, six out, per million. On a benchmark where one task burns ten million tokens, the bill is dominated by input replay, and they say Grok 4.5 solves tasks in under half the number of steps, so there is less accumulated context to resend on every call. The efficiency compounds on the input side, which is the side that costs money. Fable 5 is one task behind. Their own launch chart has them losing DeepSWE 1.1 to Fable by 17 points, and Grok 4.20 sits on this same board at 0.080 with zero completions, so whatever happened in 4.5 is not a family trait. My read is that the 4.5 jump came out of training alongside Cursor, which is a stream of real agentic edit trajectories nobody else has at that volume, and nothing in the counterevidence argues against it compounding into the next checkpoint.

Cam Newton v Philip Rivers (Week 15 vs #Chargers# - 2012) Cam Newton 🏈 231 Passing | 2 TDs | 99.4 Passer Rating DeAngelo Williams 🏈 24 Total Touches | 144 Total Yards | 6 YPT | TD

纳指也会跌82%！别盲目信仰纳指了 纳斯达克100指数(NASDAQ 100)几乎被奉为永远滴神。打开长线走势图，看上去就是一路向上的黄金大牛市 但是，长期向上，真的就等于一路向上吗？ 如果你只看它涨了多少，却不知道它曾经跌得有多惨，那说明你还没真正做好面对市场的准备 指数也是会腰斩再腰斩的 我盘点了纳指历史上几次著名的深坑，每一次对当时的投资者来说，都是一场心理和财富的巨大考验： ▪️ 1987年：黑色星期一 短时间跌了39.9%，花了599天(将近一年半)才勉强回本。 ▪️ 2000年：互联网泡沫 这是最惨烈的一次，直接跌了82.9%！更绝望的是修复期，重回巅峰让投资者整整等了15年多。试想一下，如果当年在高点一把全仓进去，人生能有几个15年用来干等解套？ ▪️ 2018年：第四季度 回撤23.0%，熬了231天走出来。 ▪️ 2020年：疫情闪崩 跌了28.0%，不过这次运气好，赶上了全球大放水，只用了107天就完成了深V反弹。 ▪️ 2022年：加息周期 指数跌了35.6%，大家在熊市里苦熬了756天(两年多)才迎来曙光。 ▪️2025年：阶段性调整 回撤22.9%，历时125天收复。 总结 长期向上，不等于一路向上

星图数据公布数据显示，2026年“618”购物节，综合电商、即时零售、社区团购的全网电商累积销售额为9340亿元人民币，同比增长4%，但增速低于去年的15.2%。 电商销售额为8636亿元，同比增长0.9%；电商日均销售额从去年的231亿元增至252亿元。

This AI just exposed the BIGGEST legal insider trading operation in America. A platform called GovGreed built a seven-layer machine learning system that cross-references every stock trade disclosed by every sitting politician against the bills their committees control, the campaign donations they receive, and the companies their votes directly impact. It scored all 540 politicians currently in Congress. And the numbers are crazy: 56% of every stock purchase made by Congress in the last 16 months was on a stock directly affected by a bill the buyer later voted on. That is 6,170 out of 11,016 total purchases. More than HALF of all congressional stock buys are on companies whose fate that same politician is about to decide. 343 of 540 Congress members actively trade stocks while holding access to nonpublic legislative information. That is 63.8% of the entire legislature making market bets with an informational edge that would put any hedge fund manager in prison. The AI identified 752 active "Triple Signals" in the current Congress. A Triple Signal fires when three conditions line up at once: The politician sits on the committee controlling a bill, they traded stock in a company affected by that bill, AND they received campaign contributions from that same industry. Bills carrying these insider indicators pass at 5.4 TIMES the normal rate. Now look at the individual leaderboard: - Nancy Pelosi's estimated portfolio sits at $194 million with a Greediness score of 98.1 out of 100 - Ro Khanna made 13,231 trades across 800+ different tickers - Michael McCaul made 32,302 trades and filed 6,670 of them late - Thomas Suozzi filed 86.4% of his trades late with an average delay of 396 days, meaning his disclosures landed over a YEAR after he made the trade And then there is Lisa McClain, the fourth-ranking Republican in the House. She has made 1,443 trades in three years, more than 98% of all politicians tracked. She violated the STOCK Act twice in a single year, disclosing up to $900,000 in trades months after the legal deadline. Her husband bought up to $250,000 in Elon Musk's xAI, which quietly converted into SpaceX equity before last Friday's $2 trillion IPO. The penalty for all of this? A $200 fine. The number of Congress members ever prosecuted under the STOCK Act since it passed in 2012? Zero. And the cruelest part is this: A bill to ban congressional stock trading was introduced in January 2026. It has bipartisan support. Over 80% of American voters want it passed. But Congress is sitting on it, because the people who would have to vote yes are the same people making millions from the system staying exactly the way it is. They write the insider trading laws, they exempt themselves from enforcement, they trade on the information those laws generate, and when they get caught, they pay a fine that is basically nothing. The AI didn't discover anything Congress was hiding. It just organized what was already public into a pattern so obvious that nobody can pretend it isn't there anymore.

DeFi金库TVL腰斩背后的真相，原来这几个赛道正在悄悄重塑格局！ DeFi 的下一站会是什么样？钱到底去了哪里，机构又在押注什么？一份深度研报全面拆解了 1204 亿美元的链上金库（Vaults）生态，提炼了最核心的资金流向： 首先定个基调：金库现在的净 TVL 大约是 1204 亿美元，比去年峰值直接腰斩。因为近期的连环清算和黑客事件，借贷、质押赛道疯狂失血，而无加密资产风险敞口的 RWA 金库却逆势增长了近 40%。 细分赛道的冰火两重天： 1、借贷金库：巨头们开始卷“模块化” 这是最大的金库类别。Morpho 靠着允许创建多市场敞口的“策展人模式”，在以太坊和 Base 上狂揽超 75 亿美元 TVL。逼得老大哥 Aave 赶紧推出 V4 版本向模块化转型，同时推出 Horizon 争夺机构资金。不过 KelpDAO 事件让 Aave 大幅失血，两者差距正在迅速缩小。 2、流动质押与再质押：大而不稳 流动质押赛道 Lido 依然一家独大（218亿美元），但这也带来了极高的集中度风险，且整体收益率已经稀释到 2.5% 左右。再质押赛道则是 EigenCloud 和 EtherFi 主导，虽然体量庞大，但近期的安全事件彻底暴露了再质押资产在整个 DeFi 中的可组合性系统风险。 3、风险策展金库：向头部集中的暴利生意 这类金库类似传统对冲基金，但费率极低。目前 65 亿美元 TVL 中，约 75% 被 Sentora、SteakhouseFi、Gauntlet 这三巨头瓜分。虽然高度垄断，但这恰恰反映了资本在动荡期极度向有业绩和信任优势的头部管理者靠拢的趋势。 4、收益优化器：从聚合器进化为底层基建 Veda、Upshift 和 Gearbox 等协议正在成熟。它们已经不再是简单的收益聚合器，而是变成了创建隔离收益产品的基础设施。比如 Gearbox 引入了策略级防火墙，防止单一策略崩盘波及整个协议的资金安全。 5、RWA金库：避险天堂，但苦于流动性 RWA 金库过去 5 年复合增长率高达 231.3%，在近期市场暴雷中展现了极强的抗跌韧性。但它在 DeFi 扩展的最大痛点是结算时间差导致的赎回机制和流动性问题。目前行业正在疯狂探索异步赎回、即时赎回以及桥接流动性方案来强行破局。 6、永续合约LP与期权金库：转型求生 永续合约 LP 金库（如 Jupiter Perps 和 Hyperliquid HLP）因为市场动荡，TVL 经历了显著回撤，目前正通过架构调整降低风险敞口。而期权金库（DOV）在经历衰退后，正通过引入询价系统、支持多抵押品，向精明资金友好的方向演进。 总结来看，DeFi 金库早就不是同质化的资金池了。借贷走向策展模块化，收益优化器变成基建，RWA 正在拼命打通与传统金融的结算壁垒。机构资金的涌入和底层架构的创新，正在彻底重塑链上金融的下半场。

背番号シリーズ ⚾️ 2025年6月17日 💙 85番 （はまっこ） 2026年4月8日 💙 231番 （横浜スタジアム郵便番号 上3桁） 2026年5月27日 💙 045番 （横浜市 市外局番） ユニフォームのデザイン 全部違くて可愛い🫶🏻

NVIDIA’s RTX 5090 cracks MD5 password hashes at 220 GH/s and was tested on 231 million unique leaked passwords collected by Kaspersky from the dark web. >One card cracked 48% in under one minute >60% in less than one hour >77% in one year The speed gain over the RTX 4090 is small because most passwords follow weak patterns like common words with numbers or birth years. Cloud GPU rentals make this power cheap and easy to access, and the results show poor password habits and reuse across accounts are the biggest security risk. Strong unique passwords from a password manager, plus two-factor authentication and passkeys where possible, are the best protection.

一个鲸鱼在过去半小时里，在 Hyperliquid 上用 500 万 USDC 5x 开多价值 $2500 万的 BTC。 他以 $81,231 的价格进行开多，并计划如果赌错方向那么亏损 25% (BTC 跌 5%) 就认输：他在 $77,000 挂上了市价止损。 ---------------------------------------------------- #Bitget# VIP 费率更低，福利更狠！买美股秒级入场