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2026-08-19

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harry0703/MoneyPrinterTurbo

Python · ★ 108,798 · 🍴 16,520 · 📈 2,304 stars today

利用 AI 大模型和自动化工作流,根据主题或关键词一键生成高清短视频。Generate HD short videos from a topic or keyword with an automated AI workflow.

中文介绍 利用 AI 大模型和自动化工作流,根据主题或关键词一键生成高清短视频。整合文案、素材、配音、剪辑等环节,适合自媒体创作者快速产出视频内容。

chaitanyagiri/munder-difflin

TypeScript · ★ 2,144 · 🍴 251 · 📈 306 stars today

local multi-agent harness

中文介绍 本地多智能体编排框架(local multi-agent harness),用于在本地环境协调多个 AI agent 协同完成任务。适合需要离线或私有化部署 agent 系统的开发者。

akitaonrails/ai-memory

Rust · ★ 2,819 · 🍴 242 · 📈 648 stars today

Solution for long term memory for agent coding CLIs and to facilitate handoff between different agent vendors

中文介绍 为 AI 编程命令行工具提供长期记忆,并支持在不同 agent 供应商之间交接上下文。解决 agent 会话丢失状态的问题,适合频繁切换 AI 编程助手的开发者。

volcengine/OpenViking

Python · ★ 29,529 · 🍴 2,311 · 📈 213 stars today

Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.

中文介绍 面向 AI agent 的自进化上下文数据库,统一管理 agent 记忆、知识 RAG 与技能。通过动态组织和更新上下文,提升 agent 在复杂任务中的长期表现。

mukul975/Anthropic-Cybersecurity-Skills

Python · ★ 29,285 · 🍴 3,498 · 📈 730 stars today

817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·

中文介绍 为 AI agent 准备的标准结构化网络安全技能库,含 817 项技能,映射到 MITRE ATT&CK、NIST CSF 2.0 等六大框架。适合构建安全自动化 agent 的开发者。

public-apis/public-apis

Python · ★ 464,749 · 🍴 51,335 · 📈 1,005 stars today

A collective list of free APIs

中文介绍 持续维护的免费公开 API 集合列表,涵盖动物、金融、游戏、新闻等众多类别。开发者可快速查找可用接口,适合原型开发与学习测试。

basecamp/omarchy

Shell · ★ 26,484 · 🍴 2,693 · 📈 356 stars today

Beautiful, Modern & Opinionated Linux

中文介绍 一款注重美观、现代且有主见的 Linux 发行版。由 Basecamp 团队打造,强调开箱即用的精致体验与一致设计,适合追求高效和审美的 Linux 用户。

agalwood/Motrix

TypeScript · ★ 53,763 · 🍴 4,951 · 📈 609 stars today

A full-featured download manager.

中文介绍 一款功能全面的开源下载管理器,支持 HTTP、FTP、BT、磁力链等协议。界面简洁,支持多任务并行和限速,适合日常文件下载与批量任务管理。

NawfalMotii79/PLFM_RADAR

PLSQL · ★ 24,357 · 🍴 5,598 · 📈 192 stars today

Open-source, low-cost 10.5 GHz PLFM phased array RADAR system

中文介绍 开源低成本的 10.5 GHz 相控阵雷达系统,采用平面阵列设计。可用于教学实验、业余雷达研究或低成本探测场景,提供完整的软硬件方案。

jundot/omlx

Python · ★ 19,463 · 🍴 1,672 · 📈 370 stars today

LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar

中文介绍 为 Apple Silicon 设计的 LLM 推理服务器,支持连续批处理和 SSD 缓存,并通过 macOS 菜单栏进行管理。适合在 Mac 上本地部署和运行大模型。

genlayerlabs/genlayer-project-boilerplate

TypeScript · ★ 16,006 · 🍴 802 · 📈 535 stars today

中文介绍 GenLayer 项目的样板脚手架,帮助开发者快速创建基于 GenLayer 协议的应用。具体功能未在描述中说明,推测用于初始化项目结构和基础配置。

OpenCut-app/OpenCut

TypeScript · ★ 84,872 · 🍴 8,358 · 📈 192 stars today

The open-source CapCut alternative

中文介绍 开源的 CapCut(剪映国际版)替代品,提供视频剪辑功能。目标用户是寻找免费且可自主掌控的剪辑工具的内容创作者,支持本地处理和自定义扩展。

StateM: Reaching 95.3% Raw Accuracy, or a \$15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling

👍 171

Long-horizon agents can fail even when their underlying models can solve the constituent steps. They may lose track of mutable state, fail to reactivate lessons from earlier executions, skip known procedures, or stop prematurely. We bet on harness scaling to improve the execution system around an ag

中文介绍 提出 StateM,通过 harness 扩展强化长程智能体执行系统,缓解状态追踪丢失、流程跳过等问题,在 Terminal-Bench 2.1 上以约 15 美元成本达到 95.3% 原始准确率,实现前沿级性能。

HarnessEval-W: Agentifying the Evaluation of Visual Worlds

👍 114

A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially critical for world models, where judging a rollout requires understanding whether physics, causality, and world state evolve correctly. Humans spot

中文介绍 提出 HarnessEval-W,将世界模型评测智能体化,不只输出分数,还给出解释分数合理性的推理,帮助判断物理、因果与世界状态演化是否正确,提升评测可信度。

Learn What's Left, Not What's Mastered: Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization

👍 55

Reinforcement learning (RL) with group-relative advantages has become the de facto standard for post-training language model reasoners. However, when optimizing multiple reward objectives, existing methods typically scalarize the reward vector with a fixed weighted sum before group-wise standardizat

中文介绍 针对多奖励强化学习中固定加权标量化导致已掌握目标过度优化的局限,提出饱和感知优势重加权方法,依据各奖励饱和程度动态调整权重,避免浪费训练信号,提升多目标策略优化表现。

Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search

👍 52

Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs. Generative models such as large language models (LLMs) provide expressive priors over such spaces, but

中文介绍 提出大规模发现模型,将经验数据与模型驱动的开放式搜索结合,利用生成模型对分子、蛋白质序列等结构化假设空间的先验,高效优化昂贵目标,推进科学发现。

VibeWorlding: Can Multimodal Agents Construct 3D Open Worlds End-to-End?

👍 51

Constructing an interactive 3D open world from a user query is important. However, existing methods are primarily evaluated on idealized, simple queries, making it difficult to systematically analyze and compare how multimodal agents understand user intent, use 3D tools, and reason over textual and

中文介绍 提出 VibeWorlding,构建多模态智能体从用户查询端到端搭建交互式 3D 开放世界的评估体系,分析意图理解、3D 工具使用及文本/视觉推理能力,弥补现有简单理想化查询评测的不足。

MOSS-VL Technical Report

👍 39

We present MOSS-VL, an open vision-language model family that treats real-time interaction -- perceiving while it speaks -- as a first-class capability. It is co-designed across the stack: the language decoder attends to vision only through gated cross-attention, so the model can naturally see incom

中文介绍 提出开源视觉语言模型族 MOSS-VL,将实时交互(边感知边说话)作为一等能力;语言解码器仅通过门控交叉注意力获取视觉信息,支持边看边生成的交互范式。

UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations

👍 38

Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution. Routine workflows rely on user-specific tools and tacit conventions, so unstated instructions can produce arbitrary variations across

中文介绍 提出 UI-Mate,用上下文示例增强开源权重 GUI 智能体,缓解训练数据稀缺与偏差、指令歧义和执行不可靠问题,提升复杂数字任务的自动化能力。

ClawGym II: Exploring Black-Box RL on Agent Harness

👍 35

Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment. However, reinforcement learning through complex harnesses remains largely unexplored, as scaling such training to long-horizon agent tasks introduces fundamental cha

中文介绍 提出 ClawGym II,探索通过黑盒强化学习训练 agent harness,克服长程任务中奖励稀疏与训练扩展的挑战,为智能体工具链的端到端学习提供新路径。

How Do Agents Fail on AutoResearch: End-to-End Diagnostic Evaluation on 100 Real-World Frontier Research Tasks

👍 23

AI has long assisted scientific research, but the rapid advance of LLMs and agentic scaffolds is reshaping the landscape; a single system can now carry whole-stage research from an initial hypothesis all the way to final published paper, which is a paradigm now referred to as AutoResearch. Existing

中文介绍 对 100 项真实前沿科研任务进行端到端诊断评测,系统分析 AutoResearch 智能体从假设提出到论文发表的完整流程中的失败模式,为改进科研智能体提供依据。

GenRouter: Unified Workflow Routing for Agentic Image Generation

👍 20

The rapid evolution of text-to-image (T2I) generation models has effectively solved the foundational challenge of raw pixel synthesis, shifting the community's focus toward fulfilling increasingly intricate user requests. While recent agentic image generation workflows enhance static inference with

中文介绍 提出 GenRouter,为智能体图像生成提供统一工作流路由,根据用户复杂意图动态调度生成流程,避免固定管线,在提升生成质量的同时降低计算开销。

AVA-Encoder: Towards Agent-Native Video Representation Learning

👍 17

Creative agents still lack an effective way to learn from high-quality human films, limiting their ability to produce cinematic-grade videos. A key challenge is the absence of a structured video representation that is both faithful to film content and directly usable for agentic reasoning and manipu

中文介绍 提出 AVA-Encoder,面向智能体视频理解学习结构化的视频表示,既忠实于电影内容又可直接用于智能体推理与操作,弥补创意智能体缺乏高质量影像学习能力的短板。

GRNEdit: Efficient General Video Editing from a New Binary-Evidence Perspective in Generative Refinement Networks

👍 13

Instruction-based general video editing seeks to unify diverse editing operations within a single, intuitive interface. Existing approaches often rely on resource-intensive conditioning, using either heavyweight branches or costly source concatenation. Is there any efficient way to model editing int

中文介绍 提出 GRNEdit,从二元证据视角在生成精炼网络中实现高效通用视频编辑,避免重型分支或源拼接等高成本条件建模,降低资源需求的同时统一多种编辑指令。

Ventor-QTest: Threat-Model-Driven Verification of Vendor-Hosted LLM APIs

👍 12

As large language models become increasingly widespread, third-party providers that deploy open-weight models have become an important part of the ecosystem. Auditing the quality of their inference APIs is therefore an open problem. We formalize hosted model routing as a stochastic process and propo

中文介绍 提出 Ventor-QTest,基于威胁模型对第三方托管的 LLM API 进行验证:将模型路由建模为随机过程,审计 open-weight 模型推理质量,解决开放权重模型部署生态中的审计难题。

HarmProfile: Characterizing Harmful Distributions in Frontier LLMs

👍 11

Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is known about the harmful outputs produced during model misbehavior, partly because large-scale, high-quality collections of

中文介绍 提出 HarmProfile,系统刻画前沿 LLM 的有害输出分布,超越将有害生成仅视为攻击结果的做法,构建大规模高质量有害样本集,为安全评估与风险分析提供分析对象。

R^3-Bench: LLMs Struggle with Resource-Rational Reasoning under Shared Budgets

👍 11

In cognitive science, resource rationality asks how an agent should allocate limited computation to maximize expected value. Most reasoning and agent benchmarks use independent per-task budgets; existing shared-budget studies do not calibrate suite performance against the same model's demonstrated s

中文介绍 提出 R^3-Bench,评测 LLM 在共享预算约束下的资源理性推理,校准同模型在独立任务预算上的表现,揭示模型在预算分配与价值最大化方面的不足。

Improving the matrix multiplication exponent with modern optimization and AlphaEvolve

👍 10

The current best bounds on the matrix multiplication exponent ω are obtained through a refinement of the laser method called combination loss analysis (Duan et al., 2022; Williams et al., 2024; Alman et al., 2025). In this note, we address the optimization problem at the core of this approach and pr

中文介绍 使用现代优化方法与 AlphaEvolve 求解组合损失分析的核心优化问题,改进矩阵乘法指数 ω 的上界,推进激光法变体的最优界分析。

PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments

👍 10

Self-evolving agents improve future behavior from interaction experience, yet existing evaluations typically optimize under fixed execution conditions and do not test recovery after those conditions change. To address this gap, we introduce PACE-Bench (Physics Adaptation via Code Evolution), a simul

中文介绍 提出 PACE-Bench,通过代码演化模拟动态环境下的物理适配,测试自进化智能体在执行条件变化后的恢复能力,弥补现有评测只在固定条件下优化的不足。

Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements

👍 9

Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning. However, long-horizon agentic reasoning introduces increasingly branching interactions and sparse rewards, exposing several limitations of RL: its heavyweight backpropagation-based training stack makes it impractical to f

中文介绍 提出 Agentic ESOpt,面向长程 LLM 智能体的轻量微调方法,规避 RL 中高开销反向传播训练,在极小 GPU 需求下处理分支交互与稀疏奖励,提升智能体策略优化效率。

TRACE-Bench: Decomposing and Diagnosing Multi-Reference Image Generation

👍 9

Despite recent advances in unified multimodal models for multi-reference image generation, existing benchmarks remain organized around predefined task types (e.g., "subject composition"), which are ill-suited to this combinatorial setting and lead to fragmented coverage, uncontrolled complexity, and

中文介绍 提出 TRACE-Bench,对多参考图像生成进行分解与诊断,摆脱预定义任务类型的局限,系统性控制组合复杂度,为统一多模态模型的多参考生成提供细粒度评测。

VideoGAIA: A Benchmark for General AI Assistants on Agentic Video Understanding

👍 9

Video understanding is a fundamental task for evaluating the capabilities of multimodal large language models (MLLMs). However, existing leading models have already achieved approximately 90% accuracy on the Video-MME leaderboard, suggesting that conventional single-turn video understanding tasks ar

中文介绍 提出 VideoGAIA,面向通用 AI 助手的智能体视频理解基准,突破传统单轮视频理解任务已饱和(如 Video-MME 约 90% 准确率)的现状,评测智能体在真实交互中的视频理解能力。

How to build a $2M video production pipeline

@EXM7777 · 131.5K 粉丝 · 172.8K 阅 · 516 赞 · 38 转

i'm giving you 7 free skills and the exact pipeline directors are using to produce AI films with millions of dollars in budget in higgsfield... the whole system revealed step-by-step, with the skill

Grok Bot: The Ultimate Guide

@milesdeutscher · 676.5K 粉丝 · 142.9K 阅 · 515 赞 · 65 转

Elon has officially done it. He's created the most powerful agentic software in the world. In this guide, I'm going to lay out everything you need to know about the new Grok Bot. I've been ruthlessly

Securing the Infrastructure of Intelligence

@JensenHuang · 1.0M 粉丝 · 110.9K 阅 · 1.9K 赞 · 234 转

Land, power and shell: The next critical resource for AI factories. AI factories are the defining infrastructure of the AI era—where compute transforms energy and data into intelligence that powers

Update to Pi App Studio Creation Pricing

@PiCoreTeam · 4.3M 粉丝 · 74.1K 阅 · 850 赞 · 222 转

Starting August 24, 2026, Pi App Studio will update how app creation and editing are priced. Until now, Pi has heavily subsidized the cost of creating and editing apps in App Studio for all creators.

Grok Bot Masterclass

@_avichawla · 73.4K 粉丝 · 54.7K 阅 · 525 赞 · 48 转

Everything you need to understand, set up, and get real work out of Grok Bot. It covers what a Bot actually is, how the shared computer works underneath, how it compares to Hermes Agent, and how to

Bring your spreadsheet data to life

@GoogleAI · 2.4M 粉丝 · 49.3K 阅 · 516 赞 · 53 转

Turn your spreadsheets into live, interactive mini-apps with Sheets canvas and Gemini 3.7 Flash — no coding required. How does it work? Let’s dive into it: Most AI UI tools produce prototypes that

Grok Bot for GTM

@kristaletz · 4.2K 粉丝 · 41.6K 阅 · 513 赞 · 24 转

This is how I use Grok Bot for Enterprise GTM at SpaceXAI. When we were initially dogfooding this product internally, Grok Bot rapidly gained adoption throughout the company, and I created an internal

Frontier Model Cost and Open-Weights Popularity is Driving Demand for Model Routing

Glean CEO Arvind Jain explains why model routing helps control AI costs for organizations, and how human feedback loops at scale improve its routing systems.

中文介绍 Glean CEO Arvind Jain 表示,前沿模型成本上升与开源权重模型流行,正推动组织对模型路由的需求。模型路由有助于控制 AI 成本,而大规模人类反馈循环能持续改进路由系统。

Strengthening democratic oversight in national security

OpenAI launches an initiative to strengthen democratic oversight of AI in national security, supporting government institutions with tools, training, and expertise.

中文介绍 OpenAI 启动新倡议,旨在加强国家安全领域 AI 应用的民主监督。该计划为政府机构提供工具、培训与专业知识,以强化对 AI 的民主问责。

How Much Memory Does Your Agent Actually Need?

中文介绍 Hugging Face 博客发文,探讨 AI 智能体实际需要多少内存资源,分析其内存需求与任务复杂度之间的关系,为优化智能体部署提供参考。

Partnering with CodeAI to prepare the first AI generation

OpenAI and CodeAI are partnering to help students build AI literacy, think critically about AI, and develop the skills to use and shape it responsibly.

中文介绍 OpenAI 与 CodeAI 合作,旨在帮助学生建立 AI 素养、培养批判性思维,并掌握负责任地使用和塑造 AI 所需的技能。

Pacing model development in an era of cyber-critical capabilities

OpenAI is strengthening monitoring, alignment, and security for frontier AI models. See how new safeguards are guiding the pace of model development.

中文介绍 OpenAI 正在加强前沿 AI 模型的监控、对齐与安全措施,并以此指导模型开发节奏,应对网络安全关键能力时代的挑战。

Introducing ChatGPT for Teens: Built for learning, backed by protections

ChatGPT for Teens helps teens learn, think critically, and use AI with confidence, with stronger built-in protections, healthy-use features, and additional controls for parents.

中文介绍 OpenAI 推出面向青少年的 ChatGPT for Teens,帮助其学习、批判性思考和自信使用 AI。该版本内置更强保护、健康使用功能及家长控制。

We still don’t know how people are really using AI

AI companies like Anthropic and OpenAI regularly publish reports on how people are using products like Claude and ChatGPT, but they only release the data they want us to see, AI researchers say. “There is no independent source to corroborate it,” says Anka Reuel, a computer science PhD candidate at

中文介绍 MIT Tech Review 报道称,Anthropic、OpenAI 等公司定期发布用户如何使用 Claude、ChatGPT 的报告,但研究人员指出这些数据由公司选择性披露。计算机科学家 Anka Reuel 表示没有独立来源可证实。

AI’s recursive self-improvement might not come so quickly after all

The AI industry’s boldest promise right now is that AI will soon improve itself, with almost no need for human oversight. LLMs can already write code, generate synthetic data for training, and optimize the computer chips they run on. Forecasts of explosive AI progress predict that what researchers c

中文介绍 MIT Tech Review 刊文质疑 AI 递归自我改进的进度。行业预测 AI 将很快在几乎无需人类监督下自我改进,如编写代码、生成训练数据、优化芯片,但文章认为爆炸式进展可能不会迅速到来。

Asana cleared 5 years of engineering work in 2 weeks with Codex

Asana used OpenAI Codex to replace an outdated testing system in two weeks, completing work expected to take five years for about $12K.

中文介绍 OpenAI 称,协作工具公司 Asana 使用 Codex 在两周内替换了过时的测试系统,完成了预计耗时五年的工程工作量,成本约 1.2 万美元。

How NVIDIA scales expertise with ChatGPT Work

NVIDIA teams use ChatGPT Work to reduce manual tasks, connect fast-moving signals, and scale successful workflows globally.

中文介绍 OpenAI 报道称,NVIDIA 团队使用 ChatGPT Work 减少人工任务、快速连接动态信号,并在全球范围规模化复制成功的工作流程。

[AINews] Stripe buys OpenRouter for $7B

No GPUs, no Agents, just really, really, really good infra and distribution.

中文介绍 据 Latent Space 报道,Stripe 以 70 亿美元收购 AI 路由平台 OpenRouter。评论称其不靠 GPU 或代理,而凭借极强的基础设施与分发能力取胜。

What Flock’s defenders are missing

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Flock, the police-tech giant known for its network of some 120,000 automatic license plate readers around the US, announced some changes to its platform last Thur

中文介绍 MIT Tech Review 评论警用科技巨头 Flock 的最新变动。Flock 在美国运营约 12 万个自动车牌读取器,文章指出其支持者忽视了一些关键问题。

市场总览

综合 21 个标的技术面:美股指数保持高位但内部分化,SPY 距 52 周高 1.53%、RSI 56.6,多头排列仍成立;QQQ 单日 -1.69%,Meta 单日 -4.45%、5 日 -9.26%,大型科技股短期回落。加密市场在恐慌贪婪指数 46(恐慌)下窄幅震荡,BTC 徘徊于 64302 美元,站上 20/50 日均线但低于 200 日线,MACD 柱转正,整体中性;ETH、SOL 同样在 200 日线下方,反弹力度有限。中概股分化明显:阿里巴巴单日 +2.76%、RSI 60.7,京东 5 日 -9.71%、RSI 37.8,腾讯维持空头排列。商品与外汇方面,黄金 RSI 63.7、MACD 正柱偏强,WTI 呈多头排列;美元/人民币 RSI 26.8 超卖且接近 52 周低位,技术面偏弱。整体看,上行与下行标的同时存在,但弱势/超卖资产多于强势突破资产。

今日关注

META Meta (META)
偏下行

META 现报 543.67 美元,单日 -4.45%、5 日 -9.26%,RSI 14 为 36.2,已接近偏弱区;股价同时低于 SMA20(585.82)、SMA50(594.98)与 SMA200(625.68),呈空头排列,MACD 为 -9.82 且低于信号线 -6.64,柱体为负,技术 setup 偏下行。

USDCNY=X 美元/人民币 (USDCNY=X)
偏下行

美元/人民币报 6.74,RSI 14 为 26.8,处于超卖区;汇价距 52 周低位仅 0.17%,并低于 SMA20(6.75)、SMA50(6.77)与 SMA200(6.88),空头排列延续,MACD 柱 -0.0006 持续为负,技术面偏下行。

GC=F 黄金期货 (GC=F)
偏上行

黄金期货收于 4406.7,单日 +0.93%,RSI 14 为 63.7,动量偏强;MACD 为 76.28,高于信号线 53.08,柱体 +23.2 为正,价格站上 SMA20(4222.73)与 SMA50(4158.85),但仍低于 SMA200(4492.47)。短期指标偏上行,中期趋势尚未完全翻多。

BTC-USD 比特币 (BTC-USD)
中性

BTC 报 64302.48 美元,5 日 +1.42%,RSI 14 为 53,读数中性;价格站上 SMA20(63834.25)与 SMA50(63751.27),但仍低于 SMA200(69039.95);MACD -94.96 高于信号线 -97.62,柱体 +2.66 转正并出现金叉信号,多空信号并存,技术状态中性。

全部资产

^VIX

VIX 恐慌指数

$15.84 +4.28%
5 日
+3.66%
距 52w 高
-55.1%
RSI(14)
48.3
趋势
空头
SMA 20 / 50 / 200
16.40 / 17.06 / 18.52
MACD / 信号
-0.642 / -0.555
空头排列

^TNX

10Y 美债收益率 (%)

$4.71 -0.38%
5 日
+0.47%
距 52w 高
-0.9%
RSI(14)
57.5
趋势
多头
SMA 20 / 50 / 200
4.67 / 4.57 / 4.32
MACD / 信号
0.035 / 0.037
接近 52 周高多头排列

DX-Y.NYB

美元指数 DXY

$99.57 -0.08%
5 日
-0.44%
距 52w 高
-2.2%
RSI(14)
37.6
趋势
中性
SMA 20 / 50 / 200
100.18 / 100.54 / 99.20
MACD / 信号
-0.281 / -0.233
接近 52 周高

SPY

S&P 500 ETF

$767.45 -0.68%
5 日
-0.40%
距 52w 高
-1.5%
RSI(14)
56.6
趋势
多头
SMA 20 / 50 / 200
758.68 / 749.84 / 706.28
MACD / 信号
7.669 / 6.871
接近 52 周高多头排列

QQQ

Nasdaq 100 ETF

$717.51 -1.69%
5 日
-0.13%
距 52w 高
-4.2%
RSI(14)
52.2
趋势
多头
SMA 20 / 50 / 200
706.24 / 712.98 / 651.51
MACD / 信号
5.550 / 2.823
多头排列

AAPL

Apple

$310.03 +1.45%
5 日
+1.68%
距 52w 高
-10.0%
RSI(14)
47.9
趋势
多头
SMA 20 / 50 / 200
316.49 / 309.13 / 280.87
MACD / 信号
-2.459 / -0.800
多头排列

MSFT

Microsoft

$481.63 +0.27%
5 日
-4.40%
距 52w 高
-13.0%
RSI(14)
62.6
趋势
中性
SMA 20 / 50 / 200
458.34 / 415.12 / 431.87
MACD / 信号
24.584 / 25.187
MACD 死叉 (今天)

NVDA

Nvidia

$219.74 -2.34%
5 日
+1.03%
距 52w 高
-7.1%
RSI(14)
56.6
趋势
多头
SMA 20 / 50 / 200
212.10 / 206.94 / 195.10
MACD / 信号
5.200 / 3.985
多头排列

GOOGL

Alphabet

$344.20 +0.06%
5 日
+0.12%
距 52w 高
-15.8%
RSI(14)
46.2
趋势
中性
SMA 20 / 50 / 200
345.86 / 352.84 / 332.11
MACD / 信号
-1.774 / -1.218
MACD 死叉 (2 天前)

TSLA

Tesla

$336.87 -0.72%
5 日
+1.22%
距 52w 高
-32.5%
RSI(14)
46.3
趋势
空头
SMA 20 / 50 / 200
325.52 / 368.42 / 404.85
MACD / 信号
-8.966 / -13.838
空头排列

META

Meta

$543.67 -4.45%
5 日
-9.26%
距 52w 高
-31.3%
RSI(14)
36.2
趋势
空头
SMA 20 / 50 / 200
585.82 / 594.98 / 625.68
MACD / 信号
-9.820 / -6.637
MACD 死叉 (1 天前)空头排列
加密恐慌贪婪
46
恐慌
加密总市值
$2.28 T
+0.37% / 24h
BTC 主导率
56.5%
ETH 10.1%
24h 成交量
$46.2 B
活跃币 18,476

BTC-USD

Bitcoin

$64,302.48 -0.32%
5 日
+1.42%
距 52w 高
-49.0%
RSI(14)
53.0
趋势
中性
SMA 20 / 50 / 200
63,834.25 / 63,751.27 / 69,039.95
MACD / 信号
-94.957 / -97.621
MACD 金叉 (今天)

ETH-USD

Ethereum

$1,911.18 -0.05%
5 日
+1.44%
距 52w 高
-61.4%
RSI(14)
56.5
趋势
中性
SMA 20 / 50 / 200
1,887.50 / 1,849.08 / 2,004.21
MACD / 信号
10.322 / 12.052

SOL-USD

Solana

$76.96 +1.34%
5 日
+1.03%
距 52w 高
-69.6%
RSI(14)
57.3
趋势
中性
SMA 20 / 50 / 200
74.70 / 76.18 / 81.37
MACD / 信号
0.241 / 0.017

BABA

阿里巴巴 (BABA)

$128.15 +2.76%
5 日
+0.23%
距 52w 高
-33.5%
RSI(14)
60.7
趋势
中性
SMA 20 / 50 / 200
122.54 / 113.88 / 138.43
MACD / 信号
3.352 / 3.553
MACD 死叉 (2 天前)

PDD

拼多多 (PDD)

$87.27 +0.38%
5 日
-3.57%
距 52w 高
-37.4%
RSI(14)
50.4
趋势
中性
SMA 20 / 50 / 200
87.74 / 84.09 / 101.25
MACD / 信号
0.640 / 1.165
MACD 死叉 (3 天前)

JD

京东 (JD)

$28.82 +1.12%
5 日
-9.71%
距 52w 高
-21.8%
RSI(14)
37.8
趋势
空头
SMA 20 / 50 / 200
31.38 / 29.19 / 29.32
MACD / 信号
0.015 / 0.616
MACD 死叉 (4 天前)空头排列

0700.HK

腾讯控股 (0700.HK)

HK$445.40 +0.68%
5 日
-3.51%
距 52w 高
-34.8%
RSI(14)
43.5
趋势
空头
SMA 20 / 50 / 200
462.03 / 454.83 / 528.56
MACD / 信号
-3.205 / 1.210
空头排列

GC=F

黄金期货

$4,406.70 +0.93%
5 日
-0.05%
距 52w 高
-21.1%
RSI(14)
63.7
趋势
中性
SMA 20 / 50 / 200
4,222.73 / 4,158.85 / 4,492.47
MACD / 信号
76.283 / 53.077

CL=F

WTI 原油期货

$84.73 -0.25%
5 日
+1.75%
距 52w 高
-29.1%
RSI(14)
56.0
趋势
多头
SMA 20 / 50 / 200
82.47 / 79.14 / 77.20
MACD / 信号
0.902 / 0.475
多头排列

USDCNY=X

美元 / 人民币

¥6.74 -0.00%
5 日
-0.08%
距 52w 高
-6.3%
RSI(14)
26.8
趋势
空头
SMA 20 / 50 / 200
6.75 / 6.77 / 6.88
MACD / 信号
-0.009 / -0.009
RSI 超卖接近 52 周低空头排列
风险提示

本报告仅基于公开行情数据的技术指标进行中性描述,不构成投资建议;过去走势不代表未来表现,行情可能随时变化。以上读数仅供技术指标解读参考。

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