2026-06-25（内容获取于 06/25 05:41）

calesthio/OpenMontage

Python · ★ 19,075 · 🍴 2,172 · 📈 3,703 stars today

World's first open-source, agentic video production system. 12 pipelines, 52 tools, 500+ agent skills. Turn your AI coding assistant into a full video production studio.

中文介绍 OpenMontage 是一个开源的智能体驱动视频制作系统，旨在将 AI 编程助手转变为完整的视频制作工作室。它通过集成 12 个视频制作流程、52 种工具和 500 多个智能体技能，解决了视频内容生产自动化与效率提升的问题。用户可以利用其强大的智能体框架，实现从脚本到成片的自动化工作流，极大降低视频制作门槛和成本。适用于内容创作者、营销人员及需要规模化生产视频的开发者。

ZhuLinsen/daily_stock_analysis

Python · ★ 48,384 · 🍴 42,936 · 📈 1,461 stars today

LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs.

中文介绍 daily_stock_analysis 是一个由 LLM 驱动的多市场股票智能分析系统。它整合了多源市场行情数据与实时新闻，提供直观的决策看板，并支持自动推送关键信息。该项目旨在通过人工智能技术，为投资者提供高效、低成本的股票分析解决方案，解决传统分析耗时耗力的问题。用户可以零成本定时运行，获取个性化的市场洞察和交易辅助，适用于追求智能化投资决策的个人投资者和金融从业者。

apple/container

Swift · ★ 42,074 · 🍴 1,229 · 📈 1,746 stars today

A tool for creating and running Linux containers using lightweight virtual machines on a Mac. It is written in Swift, and optimized for Apple silicon.

中文介绍 `apple/container` 是一个专为 macOS 设计的工具，它通过轻量级虚拟机在 Mac 上创建和运行 Linux 容器。该项目采用 Swift 语言编写，并针对 Apple silicon 进行了优化，解决了在 Mac 环境下高效、原生运行 Linux 容器的需求。它提供了一种比传统虚拟机更轻量、性能更优的容器化方案。开发者可利用此工具，在 macOS 上进行容器化的应用开发、测试与部署，提升开发效率和体验。

interviewstreet/hiring-agent

Python · ★ 2,087 · 🍴 590 · 📈 152 stars today

AI agent to evaluate and score resumes.

中文介绍 `hiring-agent` 是一个专门用于评估和打分简历的 AI 智能体。该项目旨在自动化并优化招聘流程中的简历筛选环节，通过人工智能技术分析候选人的背景、技能与职位匹配度，从而减轻招聘人员的工作负担，提高筛选效率和客观性。它解决了企业在处理海量简历时面临的耗时费力、易受主观因素影响的挑战。适用于企业招聘部门、猎头公司和人力资源专业人士，以加速人才招聘。

JCodesMore/ai-website-cloner-template

TypeScript · ★ 19,228 · 🍴 2,877 · 📈 693 stars today

Clone any website with one command using AI coding agents

中文介绍 `ai-website-cloner-template` 提供了一个利用 AI 编程智能体一键克隆任何网站的解决方案。该项目旨在极大简化网站开发和设计流程，通过 AI 智能体自动化地分析目标网站的结构、样式和内容，并生成相应的代码。它解决了开发者和设计师快速复刻网页布局、学习前端技术或进行原型开发的痛点，无需手动编写大量代码。适用于前端开发者、UI/UX 设计师以及需要快速构建网页的团队。

revfactory/harness

HTML · ★ 7,696 · 🍴 1,053 · 📈 274 stars today

A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.

中文介绍 `harness` 是一个强大的“元技能”，专注于设计领域特定（domain-specific）的智能体团队、定义专业化智能体，并生成它们所需的技能。该项目旨在解决构建复杂多智能体系统时面临的协调与定制挑战，提供一个高层次的抽象框架，让开发者能够高效地规划智能体结构及其功能。它简化了智能体的创建和管理过程。适用于 AI 研发人员、智能体系统架构师以及需要部署高度专业化智能体解决方案的团队。

flutter/flutter

Dart · ★ 177,279 · 🍴 30,544 · 📈 200 stars today

Flutter makes it easy and fast to build beautiful apps for mobile and beyond

中文介绍 Flutter 是一个由 Google 开发的开源 UI 工具包，旨在帮助开发者快速、轻松地为移动、Web 和桌面构建精美且高性能的原生编译应用。它通过单一代码库实现跨平台开发，解决了不同平台间重复开发的问题，显著提升了开发效率和用户体验的一致性。Flutter 利用 Dart 语言和 Skia 图形引擎，提供富有表现力的 UI 和接近原生的性能。适用于希望高效构建多平台应用的开发者和团队。

andreknieriem/headunit-revived

Kotlin · ★ 1,404 · 🍴 112 · 📈 62 stars today

Headunit App for displaying Android Auto

中文介绍 `headunit-revived` 是一款模拟车载“主机”界面的 Android 应用程序，专门用于显示 Android Auto。它解决了用户希望在非标准设备（如平板电脑或备用手机）上运行 Android Auto，或自定义其车载信息娱乐系统体验的需求。通过此应用，任何 Android 设备都能充当兼容 Android Auto 的显示屏，从而扩展了 Android Auto 的使用场景，为用户带来了更大的灵活性。适用于希望在更多设备上体验 Android Auto 的用户和开发者。

stablyai/orca

TypeScript · ★ 6,703 · 🍴 485 · 📈 387 stars today

Orca is the ADE for working with a fleet of parallel agents. Run any coding agent with your own subscription. Available on desktop and mobile.

中文介绍 Orca 是一个专为管理和运行并行智能体集群设计的智能体开发环境（ADE）。它解决了开发者在部署和协调大量 AI 编码智能体时面临的复杂性挑战，提供了一个统一的平台，允许用户通过自己的订阅运行任何编码智能体。该系统支持桌面和移动端，极大提高了智能体工作流的效率和灵活性。适用于 AI 开发者、研究人员及需要规模化部署和管理多智能体系统的团队。

google-labs-code/design.md

TypeScript · ★ 17,211 · 🍴 1,562 · 📈 504 stars today

A format specification for describing a visual identity to coding agents. DESIGN.md gives agents a persistent, structured understanding of a design system.

中文介绍 `design.md` 是由 Google Labs Code 推出的一种格式规范，旨在向 AI 编程智能体描述视觉识别和设计系统。它提供了一种持久化、结构化的方式，让智能体能够深入理解并应用设计指南和组件。该项目解决了设计规范在人与 AI 之间传递时的语义鸿沟，确保 AI 智能体能更精确地执行设计任务，从而提升从设计到代码的转化效率和一致性。适用于 UI/UX 设计师、前端开发者和 AI 辅助设计工具的开发者。

Flowseal/zapret-discord-youtube

Batchfile · ★ 30,016 · 🍴 2,341 · 📈 103 stars today

中文介绍 `zapret-discord-youtube` 项目名称中的“zapret”在俄语中意为“禁止”或“限制”。基于此，该项目可能旨在解决用户在访问 Discord 或 YouTube 内容时遇到的地理限制或审查问题，帮助用户绕过某些访问壁垒。具体实现技术未知，但通常这类工具会利用代理、VPN 或特定的网络协议进行内容解锁或访问管理。适用于面临网络限制的用户，以及需要更自由访问 Discord 和 YouTube 平台内容的用户。

kunchenguid/no-mistakes

Go · ★ 1,974 · 🍴 138 · 📈 182 stars today

git push no-mistakes

中文介绍 `no-mistakes` 项目通过“git push no-mistakes”这一描述，强烈暗示它是一个旨在防止代码提交失误的 Git 工具。它可能作为 Git pre-push hook 运行，在代码推送至远程仓库前执行一系列检查，例如代码风格、语法错误或特定规则，以确保代码质量和规范性。该项目解决了开发团队在协作过程中，因人为疏忽导致低质量或不规范代码进入主分支的问题。适用于所有希望提高代码质量、强制执行编码标准和减少潜在错误的开发者及团队。

NousResearch/hermes-agent

Python · ★ 201,960 · 🍴 36,086 · 📈 1,174 stars today

The agent that grows with you

中文介绍 `hermes-agent` 是 NousResearch 推出的一个智能体项目，其核心特点是“随你成长”（The agent that grows with you）。这表明该 AI 智能体具备学习、适应和个性化能力，能够随着用户的使用和交互不断优化自身表现，更好地理解并满足用户需求。它解决了传统 AI 工具缺乏个性化适应性和长期学习进化的痛点。适用于希望拥有一个高度个性化、能够持续进步的智能助手，或致力于开发自适应 AI 系统的研究人员和开发者。

AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning

👍 0

06/23 08:00

Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time

中文介绍 推出AGORA基准，旨在评估大型语言模型（LLM）代理在处理大量、复杂工作文档时的归档推理能力。该基准聚焦于从海量企业文件中定位稀疏证据及协调术语不一致等任务，以测试代理从文档中推理而非仅依赖参数知识。

FLAT: Feedforward Latent Triangle Splatting for Geometrically Accurate Scene Generation

👍 13

06/23 08:00

Generating explorable 3D scenes from a single image requires strong generative priors and accurate geometric representations suitable for downstream use. Current video diffusion models offer high-quality generation and implicitly encode multi-view geometric structure in latent space. However, existi

中文介绍 论文介绍FLAT（前馈潜在三角形溅射）方法，旨在从单张图像生成几何精确的可探索3D场景。该技术利用视频扩散模型隐含的多视图几何结构，克服了现有方法在生成高质量且几何准确的3D内容方面的挑战。

Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning

👍 8

06/23 08:00

Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly rely on single-agent loops, where the same agent executes tasks, summarizes outcomes, and determines memory content. This

中文介绍 针对大型语言模型（LLM）代理在经验学习中易陷入「自我确认陷阱」的问题，提出「执行-提炼-验证」范式。该范式旨在通过开放世界互动，帮助代理摆脱单一循环的限制，实现更有效、可靠的自我进化与学习。

NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers?

👍 47

06/23 08:00

We introduce NatureBench, a cross-discipline benchmark of 90 tasks distilled from peer-reviewed Nature-family publications, designed to evaluate whether AI coding agents can move beyond reproduction toward discovery on real scientific problems. NatureBench is built on NatureGym, an automated pipelin

中文介绍 推出NatureBench基准，包含90项从《自然》系列期刊精炼而出的跨学科任务，旨在评估AI编程代理能否在真实科学问题上实现“发现”而非仅仅“复现”。此基准基于NatureGym构建，推动代理从模仿走向创新。

DREAM: Dense Retrieval Embeddings via Autoregressive Modeling

👍 1

06/23 08:00

Dense retrieval embedding models are a fundamental component of modern retrieval-based AI systems. Most dense retrievers are trained with contrastive objectives, which require labeled positive and negative document pairs that are often costly and difficult to obtain. In this work, we investigate whe

中文介绍 针对现代检索系统依赖的密集检索嵌入模型训练中，对比目标对昂贵正负样本对的需求，提出DREAM（通过自回归建模实现密集检索嵌入）方法。该方法旨在通过自回归建模，改进嵌入训练，降低对大量标记数据的依赖。

ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection

👍 2

06/23 08:00

Multimodal misinformation detection is increasingly important because viral posts now combine long multilingual narratives, several images, mixed provenance, and subtle text--image framing errors. Existing benchmarks and methods remain poorly matched to this setting: they usually isolate short capti

中文介绍 针对多模态错误信息检测在处理复杂病毒式传播内容（如多语言长叙述、多图像、混合来源）上的不足，提出ReMMD基准。它通过代理式验证方法，更真实地评估和检测结合复杂文本与图像的错误信息。

Holistic Data Scheduler for LLM Pre-training via Multi-Objective Reinforcement Learning

👍 3

06/23 08:00

The composition of training data, governed by the diversity of sources and their mixing strategy, is a cornerstone of Large Language Model (LLM) pre-training. Online Data Mixing (ODM), the technique of adaptively adjusting data mixtures during training, has emerged as a promising direction to improv

中文介绍 针对大型语言模型（LLM）预训练中数据组成和混合策略的重要性，提出通过多目标强化学习实现整体数据调度器。该方法旨在优化训练数据的在线数据混合（ODM）策略，自适应调整数据配比，提升LLM的预训练效果。

Are Text-to-Image Models Inductivist Turkeys? A Counterfactual Benchmark for Causal Reasoning

👍 5

06/23 08:00

Text-to-image (T2I) generation models have achieved remarkable progress in producing visually realistic images from natural language prompts. Yet it remains unclear whether their success reflects genuine causal understanding or sophisticated pattern matching over visual-textual correlations. Inspire

中文介绍 探讨文本到图像（T2I）生成模型是否具备真正的因果理解能力，而非仅仅是复杂的模式匹配。论文提出了一个反事实基准来评估T2I模型在因果推理方面的表现，以区分其成功是源于深层理解还是视觉-文本表面的关联。

Qwen-AgentWorld: Language World Models for General Agents

👍 83

06/23 08:00

A world model predicts environment dynamics based on current observations and actions, serving as a core cognitive mechanism for reasoning and planning. In this work, we investigate how world modeling based on language models can further push the boundaries of general agents. (i) We first focus on b

中文介绍 介绍Qwen-AgentWorld，探索基于语言模型的世界模型如何拓宽通用代理的能力边界。世界模型通过预测环境动态，是代理进行推理和规划的核心认知机制，该研究旨在提升通用代理在复杂环境中的智能水平。

OpenThoughts-Agent: Data Recipes for Agentic Models

👍 22

06/23 08:00

Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typically target a single benchmark, leaving open the question of how to

中文介绍 针对代理语言模型训练数据策展的挑战，推出OpenThoughts-Agent，旨在提供构建通用代理模型的“数据食谱”。鉴于现有开源项目多针对单一基准，该研究旨在公开更广泛、更具通用性的训练数据策略，以促进AI发展。

Semantic Browsing: Controllable Diversity for Image Generation

👍 13

06/22 08:00

Modern text-to-image models excel in visual fidelity and prompt adherence. However, this strict adherence comes at the cost of diversity: generated samples tend to collapse into a single visual interpretation. Existing methods to improve diversity produce outputs driven by incidental variations rath

中文介绍 提出「语义浏览」方法，旨在为文本到图像（T2I）生成模型提供可控的多样性。虽然现代T2I模型在视觉保真度和提示遵循方面表现出色，但往往缺乏生成图像的多样性，本研究旨在通过更精细的控制克服这一限制。

ChartWalker: Benchmarking the Cross-Chart RAG Task

👍 2

06/22 08:00

Cross-Chart Retrieval-Augmented Generation (RAG) is critical for complex multi-modal analytical tasks in scientific, business, and political domains. However, existing benchmarks either focus on tables, which are well-structured and textualized, or generate cross-chart questions by simply extracting

中文介绍 推出ChartWalker基准，用于评估跨图表检索增强生成（RAG）任务。该基准旨在解决现有评估侧重表格或生成简单图表问题，无法满足科学、商业、政治领域对复杂多模态分析任务的需求，提升RAG能力。

AOHP: An Open-Source OS-Level Agent Harness for Personalized, Efficient and Secure Interaction

👍 25

06/22 08:00

AI agents are driving a new software paradigm, with the ability to autonomously call tools, extract information, manage memory, and complete tasks that span applications and data sources. Most existing end-user operating systems, however, are designed for application-centric workflows and offer litt

TROPT: An Open Framework for Unifying and Advancing Discrete Text Optimization

👍 0

06/22 08:00

Discrete text-trigger optimization -- searching for text sequences that, when ingested by a model, steer it toward a specified objective -- underpins model red-teaming (e.g., LLM jailbreaks), as well as auditing and interpretability. However, the current state of discrete optimizers hinders their ad

Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild

👍 4

06/22 08:00

Reconstructing dynamic non-rigid objects from monocular video requires integrating visual cues from direct observations with data-driven priors over geometry and appearance. Prior approaches either learn to directly predict 4D representations from visual input or initialize a 3D representation that

When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents

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06/22 08:00

Long-horizon LLM agents can fail quietly: they settle on one reading of the evidence early, then spend the rest of the run defending it. We call this premature commitment. Final-answer scoring misses the failure mode because it sees only the answer, not whether the process has already collapsed to a

Vera: A Layered Diffusion Model for Content-Preserving Video Editing

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06/22 08:00

Video diffusion models have enabled remarkable progress in video generation and editing. However, content preservation remains a core challenge: existing methods regenerate every pixel and often alter elements that should remain unchanged, such as characters or background scenes. We introduce Vera,

Libretto: Giving LLM Agents a Sense of Musical Structure

👍 1

06/21 08:00

Generative music systems can now produce impressive audio from text prompts, but audio outputs are difficult to inspect, edit, and diagnose as musical structure. We introduce Libretto, an agent-facing framework for symbolic music generation and revision. Libretto uses an LLM-native grammar with expl

Multi4D: High-Fidelity Dynamic Gaussian Splatting via Multi-Level Competitive Allocation

👍 0

06/20 08:00

Dynamic 3D Gaussian splatting faces a fundamental tension between motion consistency and visual fidelity. Deformation-based approaches preserve temporal correspondence but suffer from motion over-factorization, oversmoothing high-frequency dynamics. In contrast, 4D-primitive methods capture fine vis

A Verifiable Search Is Not a Learnable Chain-of-Thought

👍 3

06/20 08:00

It is tempting to assume any task solvable by a short program can be taught to a model as its chain-of-thought: write the steps out, fine-tune, and the model follows. This paper shows the assumption fails for an identifiable class of procedures. The testbed is nine reasoning tasks, each from a deter

An Exploratory Case Study of LLM-Assisted Refactoring and Gameplay Feature Generation in an Endless Runner Game

👍 0

06/19 08:00

Large language models (LLMs) are increasingly used to support software development, but their practical usefulness in applied game-development settings remains underexplored, especially when generated code must be integrated into an existing game software system. This paper presents an exploratory e

QG-MIL: A Gated Transformer Aggregator for Domain-Agnostic Multiple Instance Learning in Medical Imaging

👍 2

06/18 08:00

Attention-based Multiple Instance Learning aggregators in medical imaging are prone to attention concentration, producing overconfident and unstable predictions. We introduce QG-MIL, a gated transformer aggregator that addresses this through four synergistic architectural components: RMSNorm-based p

EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies

👍 1

06/18 08:00

Memory remains a critical bottleneck for long-horizon robotic manipulation, as standard Vision-Language-Action (VLA) policies often fail when task-relevant cues become occluded or unobservable over time. While existing memory-augmented methods utilize historical context, they either suffer from seve

MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management

👍 33

06/18 08:00

MLLM-based mobile GUI agents have made substantial progress on short-horizon tasks, yet remain unreliable on long-horizon tasks that require retaining intermediate facts across many steps and app transitions. We attribute this limitation to ReAct-style prompting, which passively accumulates per-step

MobileForge: Annotation-Free Adaptation for Mobile GUI Agents with Hierarchical Feedback-Guided Policy Optimization

👍 34

06/18 08:00

MLLM-based mobile GUI agents have made substantial progress in UI understanding and action execution, but adapting them to real target apps remains costly because mobile apps are numerous, frequently updated, and hard to cover with human-written tasks, demonstrations, or reward labels. Existing anno

Robusto-2: Benchmarking Humans & VLMs for Autonomous Driving in Lima & New York City

👍 2

06/18 08:00

As Self-Driving Cars continue to expand internationally and use multi-modal systems such as VLMs as a cognitive backbone for their Action models; how well will these systems generalize in new settings, in particular out-of-distribution (OOD) edge-case scenarios in new geographies? In this paper, we

Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning

👍 4

06/17 08:00

Long-context reasoning is an essential capability for large language models, particularly when they are deployed as autonomous agents that must reason over lengthy trajectories. Reinforcement learning (RL) has recently emerged as a dominant paradigm for improving this ability, yet existing work larg

AC-ODM: Actor--Critic Online Data Mixing for Sample-Efficient LLM Pretraining

👍 1

06/14 08:00

Optimizing pretraining data composition is pivotal for LLM generalization. While dynamic mixing outperforms static strategies by capturing evolving training dynamics, current methods fail to reconcile computational efficiency with sample efficiency and structural flexibility for diverse pipelines.We

LingxiDiagBench: A Multi-Agent Framework for Benchmarking LLMs in Chinese Psychiatric Consultation and Diagnosis

👍 19

06/11 08:00

Mental disorders are highly prevalent worldwide, but the shortage of psychiatrists and the inherent subjectivity of interview-based diagnosis create substantial barriers to timely and consistent mental-health assessment. Progress in AI-assisted psychiatric diagnosis is constrained by the absence of

An Efficient Method for the Optimal Control of Microgrids Under Uncertainties using Local Reduction

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06/10 08:00

The problem of optimal sizing and power scheduling in microgrids subject to uncertainties is well known to the control community. Commonly, the optimal control problem is cast as a mixed-integer program to model the logical constraints arising in energy storage systems, and is then solved approximat

Ruby

06/24 13:43

Ask better questions, live on every call

中文介绍 Ruby 是一款旨在提升在线通话质量的工具，通过在通话过程中提供辅助，帮助用户提出更优质的问题，从而改善互动效果和沟通效率。

Swimio

06/24 13:16

AI swim coach with Apple Watch tracking & smart workouts

中文介绍 Swimio 是一款结合人工智能技术的游泳教练应用，支持与 Apple Watch 进行数据追踪，并能为用户提供智能化的游泳训练计划，旨在帮助用户提升游泳技能和效果。

FUTO Swipe

06/24 11:19

Open models for on-device swipe typing

中文介绍 FUTO Swipe 提供用于设备本地滑行输入功能的开放模型，旨在让用户在设备上实现更高效、个性化的输入体验，同时强调模型的开放性。

Nimt

06/23 20:32

Your AI Search Coworker in Slack

中文介绍 Nimt 是一款针对 Slack 设计的 AI 搜索助手，旨在作为用户的“AI 搜索同事”，帮助用户在 Slack 环境中快速查找信息、提高工作效率。

React UI Kit V7

06/23 18:35

All the chat components you need. None of the complexity

中文介绍 React UI Kit V7 是由 CometChat 推出的一套生产就绪的用户界面工具包，专注于提供全面的聊天组件，旨在帮助开发者在不增加复杂性的前提下，快速集成聊天功能。

StaleMate PR

06/23 17:01

Your menu bar turns red when PRs pile up

中文介绍 StaleMate PR 是一款针对开发者设计的工具，当待处理的拉取请求（PRs）堆积过多时，它会通过将菜单栏变为红色来发出警报，旨在帮助团队及时关注和处理代码合并请求。

HotkeyClash

06/23 13:50

Find where your Mac keyboard shortcuts clash

中文介绍 HotkeyClash 是一款专为 Mac 用户设计的工具，其主要功能是帮助用户查找并识别系统中存在的键盘快捷键冲突，从而优化操作体验，避免因快捷键重复而引起的问题。

Stripe.Directory

06/23 04:15

New way for you & agents to search for businesses on Stripe

中文介绍 Stripe.Directory 为用户及代理商提供了一种新的方式，以便在 Stripe 平台上搜索和发现商家。该平台旨在简化搜索流程，帮助用户高效地找到所需的业务信息。

Mindstone Rebel

06/23 03:45

AI workspace for agents that know your work and ask first

中文介绍 Mindstone Rebel 提供一个AI工作空间，专为能理解用户工作并主动发起询问的智能代理设计。该平台旨在通过个性化和先发制人的AI交互，提升代理的工作效率与协同能力。

Customer Relationship Agents by Clarify

06/23 03:26

The M in CRM shouldn't be you

中文介绍 Clarify 推出的客户关系代理工具，旨在通过自动化和智能化的方式，革新客户关系管理（CRM）中的人工操作模式，让用户无需亲自处理所有管理任务，从而提升效率。

How Quants Use Loop Engineering to Build Alpha (Full Framework)

@horizon_trade_x · 4.4K 粉丝 · 1.3M 阅 · 507 赞 · 59 转

06/18 23:58

Your backtest looked flawless. You went live. Two weeks later, the strategy was bleeding. Every quant has lived this. The answer is a loop: generate a strategy, test it, score it, feed the result

中文介绍 该帖子介绍量化交易中利用「循环工程」构建 Alpha 策略的完整框架，通过生成、测试、评分并反馈结果的循环，解决回测完美但实盘亏损的常见问题，为量化交易员提供实用指南。

Loops explained: Claude, GPT, Mira and what actually works

@AnatoliKopadze · 83.0K 粉丝 · 1.3M 阅 · 584 赞 · 70 转

06/20 21:40

AI has been in everyone's hands for years. Most people who use it every day still use it the slowest way there is: type a request, wait, fix it, ask again, all by hand. Not because the faster way is

中文介绍 帖子解释了 AI 中的「循环」概念，指出大多数用户仍以手动方式低效使用 AI，而「循环」能显著提升 Claude、GPT 等模型的效率，实现更快的 AI 交互和自动化应用。

How to Build an AI Second Brain With Claude and Obsidian That Gets Smarter Every Day (Full Guide)

@undefinedKi · 3.9K 粉丝 · 1.0M 阅 · 601 赞 · 78 转

06/20 20:15

Your best ideas are scattered across a dozen places right now. Notes apps. Browser tabs. Old chats with Claude that you closed and will never find again. Every time you sit down to work, you rebuild

中文介绍 完整指南分享如何利用 Claude 和 Obsidian 构建一个「AI 第二大脑」，通过集成散布各处的笔记、浏览器标签和历史对话，让 AI 系统每天自我学习并变得更智能，提升工作效率。

40 Claude Opus 4.8 Workflows That Make Money While You Sleep

@eng_khairallah1 · 69.3K 粉丝 · 678.7K 阅 · 506 赞 · 81 转

06/21 17:00

Most people are using Claude Opus 4.8 to answer questions. Save this :) A small group of people have it running businesses while they sleep. The difference is not the model. You both have access to

中文介绍 分享 40 种利用 Claude Opus 4.8 构建的自动化工作流，旨在帮助用户在睡眠中也能赚钱。强调关键在于构建循环业务系统而非模型本身，打破传统问答模式，实现盈利。

The efficiency gap: How do cells and GPUs compare when running the exact same algorithm?

@spandan_madan · 1.1K 粉丝 · 626.9K 阅 · 516 赞 · 28 转

06/24 02:13

AI hardware is having a moment. Hyperscaler capex on AI data centres is on track to clear $690 billion in 2026, and private equity has followed in scale — Blackstone alone reports a $55B+ data-centre

中文介绍 讨论 AI 硬件发展趋势，并探讨细胞与 GPU 在运行相同算法时的效率差距。帖子以 AI 数据中心资本支出将达 6900 亿美元为引，暗示了对未来计算效率的深层思考。

How To Learn Anything 10x Faster Using Claude

@sairahul1 · 120.1K 粉丝 · 581.3K 阅 · 516 赞 · 110 转

06/20 16:30

Learning anything today is easy and confusing at the same time. Easy because AI can explain almost anything in seconds. Confusing because most people just ask random questions, get random answers, and

中文介绍 介绍如何利用 Claude AI 提升学习效率达 10 倍。指出当前 AI 学习的痛点在于随机提问导致低效，博主将分享系统方法，帮助用户更有效率地掌握新知识。

The Agent Loop Architecture

@djfarrelly · 3.8K 粉丝 · 344.7K 阅 · 501 赞 · 61 转

06/19 02:33

Everyone's asking "WTF is a loop?" Here's the question nobody's asking: what runs the loop? The AI discourse has converged on loops as a core primitive of agentic systems. Matt Van Horn (@mvanhorn)

中文介绍 深入探讨 AI 代理系统中的「循环架构」。在 AI 领域普遍关注「什么是循环」的背景下，博主进一步提出「谁在运行循环」的关键问题，并指出循环是代理系统的核心原语。

Introducing Engram: Scaling compute on your context

@EngramLab · 1.2K 粉丝 · 255.7K 阅 · 537 赞 · 76 转

06/24 01:01

We’re Engram. We’re building AI that learns from you and deeply understands your work. Today’s AI models don’t understand what you do. Not really. Everything models know comes from their training –

中文介绍 Engram Lab 宣布推出 Engram，一款能从用户学习并深度理解其工作的 AI 产品。该产品旨在解决当前 AI 模型对用户工作理解不足的问题，通过扩展上下文计算能力，提供更个性化的 AI 体验。

30 Core Agentic Engineering Concepts Every Developer Should Know

@sairahul1 · 120.1K 粉丝 · 249.0K 阅 · 502 赞 · 90 转

06/23 17:28

There are 8 billion people on the planet. Only a fraction of developers understand how AI agents actually work. Not the demos. Not the hype. The real engineering underneath. Every week a new agent

中文介绍 整理并分享了 30 个 AI 代理工程核心概念，旨在帮助开发者深入理解 AI 代理的实际工作原理，而非停留在演示和炒作层面。强调了 AI 代理背后真正的工程技术。

ORACLE: Official AI Agents Trade on Polymarket

@Oracle_Trade_ai · 39.9K 粉丝 · 197.8K 阅 · 2.8K 赞 · 580 转

06/22 16:52

In 2026, autonomous AI agents have become one of the most effective strategies on prediction markets. Over 30% of all activity on Polymarket now comes from algorithmic and AI-powered wallets. We

中文介绍 宣布 ORACLE AI 代理已在 Polymarket 预测市场进行官方交易。展望 2026 年，自主 AI 代理将成为预测市场最有效的策略之一，目前 Polymarket 超过 30% 的活动来自 AI 驱动钱包。

Why we're bullish on loops

@posthog · 21.8K 粉丝 · 162.4K 阅 · 512 赞 · 36 转

06/24 01:26

When the creators of both OpenClaw and Claude Code speak, people listen. And recently Peter Steinberger and Boris Cherny have both been talking about the same concept: loops. Their argument? You

中文介绍 阐述 Posthog 公司对 AI「循环」概念的看好态度。指出 OpenClaw 和 Claude Code 的创建者 Peter Steinberger 和 Boris Cherny 均强调循环的重要性，认为其是构建强大 AI 系统的关键。

ORACLE: Official AI Agents Trade on Polymarket

@OracAItrading · 31.8K 粉丝 · 141.6K 阅 · 2.8K 赞 · 576 转

06/22 16:52

In 2026, autonomous AI agents have become one of the most effective strategies on prediction markets. Over 30% of all activity on Polymarket now comes from algorithmic and AI-powered wallets. We

中文介绍 宣布 ORACLE AI 代理已在 Polymarket 预测市场进行官方交易。展望 2026 年，自主 AI 代理将成为预测市场最有效的策略之一，目前 Polymarket 超过 30% 的活动来自 AI 驱动钱包。

Interactions API: Our primary interface for Gemini models and agents

@GoogleAIStudio · 179.4K 粉丝 · 138.2K 阅 · 504 赞 · 42 转

06/23 01:21

Today we're announcing that the Interactions API has reached general availability and is now our primary API for interacting with Gemini models and agents. We launched its public beta in December

中文介绍 GoogleAIStudio 宣布 Interactions API 已正式发布（GA），成为与 Gemini 模型和代理交互的主要 API 接口。该 API 于去年 12 月推出公开测试版，现已全面可用，标志着 Gemini 生态发展重要里程碑。

ORACLE: Official AI Agents Trade on Polymarket

@Oractrading · 33.9K 粉丝 · 109.2K 阅 · 2.8K 赞 · 585 转

06/22 16:20

In 2026, autonomous AI agents have become one of the most effective strategies on prediction markets. Over 30% of all activity on Polymarket now comes from algorithmic and AI-powered wallets. We

中文介绍 宣布 ORACLE AI 代理已在 Polymarket 预测市场进行官方交易。展望 2026 年，自主 AI 代理将成为预测市场最有效的策略之一，目前 Polymarket 超过 30% 的活动来自 AI 驱动钱包。

How To Use Loop Engineering To Build A Self-Improving Quant Trading System

@RohOnChain · 51.4K 粉丝 · 108.3K 阅 · 501 赞 · 65 转

06/22 21:54

I will break down exactly how to build the loops that run an entire quant trading system on their own. Let's get straight to it. Bookmark This - I'm Roan, a backend developer working on system

中文介绍 教程分享如何利用「循环工程」构建一个自我改进的量化交易系统。博主 Roan 作为后端开发者，将详细拆解运行整个量化交易系统的关键循环机制，以实现自动化和优化。

how to get Fable-level intelligence back:

@EXM7777 · 118.9K 粉丝 · 107.7K 阅 · 509 赞 · 44 转

06/18 23:10

for a few days, we had something that felt like AGI... Fable 5 showed up, effectively unlimited inside the plans, and the ceiling on what you could build lifted overnight but then Anthropic killed it,

中文介绍 讨论 Anthropic 的 Fable 5 模型曾一度展现出接近 AGI 的能力，提供了几乎无限的构建可能性。但随后 Anthropic 似乎「扼杀了」这一高智能版本，引发了用户对其回归的期待和讨论。

WTF Is a Loop? Part 2: The 15 Loops People Are Actually Running (and the Commands to Steal Them)

@mvanhorn · 35.2K 粉丝 · 102.4K 阅 · 510 赞 · 56 转

06/21 04:09

Earlier this month I wrote WTF Is a Loop? Peter Steinberger vs. Boris Cherny, which did 3.6M views on what a loop even is. This is the sequel, and it answers the next question: which loops do people

中文介绍 承接博主前一篇 360 万浏览量的文章，本次分享是「循环」概念的续篇。重点揭示了用户实际运行的 15 种循环及其实现命令，提供了实用的 AI 代理系统循环应用案例。

From Prompting Agents to Loop Engineering

@omarsar0 · 308.0K 粉丝 · 90.2K 阅 · 504 赞 · 69 转

06/20 00:31

A claim has been circulating in AI coding circles: stop prompting your coding agents and start designing loops that prompt them for you. As with everything new, this stuff gets repeated often and

中文介绍 探讨 AI 编码领域从「手动提示代理」向「循环工程」转变的新趋势。倡导不再直接提示编码代理，而是设计能自动提示代理的循环系统，提升自动化和效率。

How GLM-5.2 Beat Fable 5 at Website Design

@Designarena · 13.9K 粉丝 · 80.4K 阅 · 518 赞 · 39 转

06/20 01:58

GLM 5.2 ranks 1st overall on Design Arena’s single-turn, HTML Web Design (Non-Agentic) evaluation, 5 places higher than its predecessor GLM-5.1. To do so, it beat Claude Fable 5, Opus 4.6, and Opus

中文介绍 GLM-5.2 在 Design Arena 的单轮 HTML 网页设计评估中超越 Fable 5、Opus 4.6 及 4.8，荣登榜首。GLM-5.2 整体排名比前身 GLM-5.1 上升 5 位，展现出强大的非代理网页设计能力。

Subnets mine TAO; TAO mines Subnets

@const_reborn · 29.7K 粉丝 · 79.8K 阅 · 503 赞 · 116 转

06/23 09:21

e_i \;\propto\; \underbrace{\rho_i \times \bar{p}_i}_{\text{linear (maximize)}} \times \underbrace{(1 - b_i)}_{\text{boolean gate}} Disclaimer: this upgrade only effects subnet owners and dynamic TAO

中文介绍 探讨 Bittensor 生态中 Subnets 与 TAO 之间的互惠共生关系，即 Subnets 挖矿获得 TAO，TAO 反哺 Subnets。帖子包含复杂的数学公式，并强调此升级仅影响 Subnet 所有者和动态 TAO 持有者。

AI Agents Just Changed Forever: GLM 5.2, Codex Skills, Claude & Cursor

06/21 21:30

中文介绍 该视频探讨了AI智能体领域的最新变革，重点介绍了GLM 5.2模型、Codex技能以及Claude和Cursor等工具如何共同推动AI智能体能力达到新高度。这些进展预示着AI智能体在理解、生成代码和执行复杂任务方面的能力显著增强，将深刻改变开发者工作模式。

9 AI Agent Skills To Get Ahead of 99% of People

06/18 10:27

中文介绍 该视频分享了9项关键的AI智能体技能，旨在帮助用户提升在人工智能时代的竞争力。这些技能可能包括高效利用AI工具、优化提示工程、理解AI工作原理及自动化任务等，以期让用户在职场中超越大多数人。

How to build high quality internal tools with Retool

06/18 05:21

SpaceX Just Bought Cursor for $60B. It’s About to Take OVER.

06/17 05:31

Why the Government Just Killed Claude Fable 5

06/13 11:50

This is Why They Banned Claude Fable 5

06/13 02:28

Hermes Agent NEW Super-App and DeepSeek v4 Catches Up To Opus 4.8?

06/07 04:10

ChatGPT And Codex Are Merging (This Changes Everything)

06/04 06:17

Claude Opus 4.8: Everything You Need to Know

06/01 06:27

Browsers Are Dead. Claude Just Replaced Them.

05/28 22:56

Why the Frontier Ecosystem must be Open — Matei Zaharia and Reynold Xin, Databricks

Latent Space · 06/25 02:53

In a rare double-interview, the Databricks technical leaders riff on what it will take for every company to build Agent Clouds

中文介绍 Databricks技术负责人Matei Zaharia和Reynold Xin在采访中强调，开放的生态系统是每家公司构建「Agent Clouds」的关键，讨论了实现这一目标所需条件。

Introducing computer use in Gemini 3.5 Flash

DeepMind Blog · 06/25 00:30

中文介绍 DeepMind在其官方博客宣布，为Gemini 3.5 Flash模型引入了「计算机使用」（computer use）功能，旨在扩展其应用能力。

Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel

Hugging Face Blog · 06/25 00:00

中文介绍 Hugging Face发布博文，介绍如何通过NVIDIA NeMo AutoModel加速Transformer模型的微调过程，以提升效率和性能。

The emergence of the web data infrastructure layer for AI

MIT Tech Review AI · 06/24 19:59

AI is booming. New use cases are emerging each day. To capitalize on the technology’s potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models. To understand this challenge, consider the foundation of

中文介绍 文章指出，随着AI蓬勃发展，企业对大规模数据需求激增。鉴于许多相关信息被阻碍或非结构化，限制了AI模型利用，文中探讨了AI网络数据基础设施层的出现，以应对这一挑战。

[AINews] Claude Tag: Multiplayer, Proactive, Persistent Agents in Slack

Latent Space · 06/24 15:14

Claude finally gets a Slackbot upgrade

中文介绍 Claude Tag功能迎来重大升级，现已支持在Slack平台中实现多玩家、主动式和持续性Agent，极大提升了AI助手的协作与应用能力。

OpenAI and Broadcom unveil LLM-optimized inference chip

OpenAI News · 06/24 14:00

OpenAI and Broadcom introduce Jalapeño, a custom AI chip built for LLM inference to improve performance, efficiency, and scale across AI systems.

中文介绍 OpenAI与博通（Broadcom）合作发布了定制AI芯片「Jalapeño」。这款芯片专为大型语言模型（LLM）推理优化设计，旨在全面提升AI系统的性能、效率及可扩展性。

Agent identity in Claude Tag: a new access model for autonomous, team-wide AI

Claude Blog · 06/24 08:00

Agent identity in Claude Tag: a new access model for autonomous, team-wide AI

中文介绍 Claude Tag引入了Agent身份（Agent identity）功能，这是一种全新的访问模型，旨在支持自主运行和团队范围内的AI应用，提升AI协作与管理能力。

Building effective human-agent teams

Claude Blog · 06/24 08:00

Building effective human-agent teams

中文介绍 Claude官方博客分享了关于如何构建高效人类与AI Agent协作团队的策略与思考，旨在优化人机协同工作模式，提升整体效率。

Introducing the FFASR Leaderboard: Benchmarking ASR in the Real World

Hugging Face Blog · 06/24 08:00

中文介绍 Hugging Face发布了FFASR排行榜，旨在对自动语音识别（ASR）模型在真实世界场景中的表现进行基准测试与评估，推动ASR技术进步。

Claude Tag 💬, Seedance 2.5 🎥, Mistral OCR 4 🧠

TLDR AI · 06/24 08:00

中文介绍 TLDR AI简报汇总了多项AI领域的最新进展，包括Claude Tag功能更新、Seedance 2.5版本发布以及Mistral OCR 4模型的推出。

How GPT-5 helped immunologist Derya Unutmaz solve a 3-year-old mystery

OpenAI News · 06/24 01:00

GPT-5 Pro helped solve a 3-year-old immunology mystery, offering insights into T cell behavior. The breakthrough could support cancer and autoimmune research.

中文介绍 OpenAI宣布，GPT-5 Pro成功协助免疫学家Derya Unutmaz博士解开一个长达3年的免疫学谜团，为T细胞行为提供了新见解。这一突破性发现有望推动癌症及自身免疫疾病的研究。

Helping build shared standards for advanced AI

OpenAI News · 06/23 21:00

OpenAI helps build shared standards for advanced AI, supporting evaluation frameworks, safety practices, and global cooperation through the Appia Foundation.

中文介绍 OpenAI正通过Appia基金会，积极协助建立先进AI的共享标准，致力于支持评估框架、制定安全实践，并促进全球范围内的AI合作与治理。

Build real agentic apps using CUGA: two dozen working examples on a lightweight harness

Hugging Face Blog · 06/23 20:51

中文介绍 Hugging Face与IBM Research合作发布博文，展示了如何利用CUGA轻量级框架构建Agentic应用，并提供了多达24个可运行的实际示例。

The $400 million machine powering the future of chipmaking

MIT Tech Review AI · 06/23 17:00

Jos Benschop is climbing a ladder to get to the top of his newest machine. It’s a bit of a schlep. The contraption is the size of a double-decker bus—more than 150 tons of gleaming precision-milled aluminum covered in thousands of snaking tubes, colored cables, and pressurized tanks. From the ground

中文介绍 文章详细介绍了ASML一台价值高达4亿美元的芯片制造设备，其体积巨大如双层巴士，重逾150吨，由精密铝材、数千条管道与电缆构成，被视为推动芯片制造未来的关键技术。

[AINews] SpaceX is already a $28B/yr Neocloud

Latent Space · 06/23 14:19

a quiet day lets us reflect on some numbers from Jamin Ball.

中文介绍 文章援引Jamin Ball的数据，指出太空探索技术公司SpaceX的年收入已达到280亿美元，并将其形容为一个「新云（Neocloud）」，显示其巨大的商业规模与潜力。

非程序员 vibecoding 血泪提醒大家一件事儿， git commit

06/24 23:10

10 回复 · 程序员 节点

求使用 AI 从 0 到 1 开发一个完整 web 前后端项目的成功经验

06/24 21:15

14 回复 · 程序员 节点

3 个小时消耗 50%，不要用阿里云的 token plan 写代码

06/24 18:21

10 回复 · 程序员 节点

还有在用 antigravity cli 的吗

06/24 16:22

10 回复 · 程序员 节点

和 Claude Code 死磕 3B token 的家庭记账 APP，聊聊 vibe coding 和 spec coding 在长项目上到底差在哪

06/24 15:31

21 回复 · 程序员 节点

macos27 的 siri ai 怎么可以不报 problem 啊？

06/24 14:37

11 回复 · Apple 节点

分享一个自己做的 Chrome 扩展：用快捷键切换最近访问的标签页

06/24 14:17

9 回复 · 程序员 节点

升级 iOS 和 macos27 的能帮忙试试 surge 可用不

06/23 14:12

7 回复 · Apple 节点

我被 iOS27 的原生中文输入法折磨疯了

06/23 11:31

41 回复 · Apple 节点

摸鱼自由咯~ 升级到 macOS 27 dev 2 和 iOS 27 dev 2 之后, iPhone 镜像终于能用啦

06/23 10:58

17 回复 · Apple 节点

GitHub shouldn't be a dependency for publishing Rust on crates.io

06/25 03:40

54 points · 17 comments

Thomann takes legal action against Fender

06/25 03:08

137 points · 83 comments

OpenAI unveils its first custom chip, built by Broadcom

06/25 01:47

Announcement: https://openai.com/index/openai-broadcom-jalapeno-inference-...https://decrypt.co/371971/openai-broadcom-jalapeno-first-cus...https://www.cnn.com/2026/06/24/tech/openai-broadcom-jalapeno...

Computer use in Gemini 3.5 Flash

06/25 01:21

109 points · 63 comments

Big AI labs are hiring philosophers

06/25 00:40

https://archive.is/T1FJG

The Xteink X4 E-Ink Reader

06/25 00:35

106 points · 88 comments

Exploiting vulnerabilities in Johnson and Johnson web apps

06/25 00:33

36 points · 0 comments

I taught a bucket to speak Git

06/25 00:04

67 points · 16 comments

There are a few things that I look back on as my mistakes in the early days

06/24 23:56

https://xcancel.com/ID_AA_Carmack/status/2069799283369345247

RubyLLM: A Ruby framework for all major AI providers

06/24 22:41

300 points · 46 comments

Running Windows Games on a Hobby OS with Wine

06/24 22:38

86 points · 28 comments

PR spam today looks like email spam in the early 2000s

06/24 22:32

131 points · 87 comments

Show HN: Nub – A Bun-like all-in-one toolkit for Node.js

06/24 22:14

Colin here, creator of Nub. I’ve had the general shape of this in mind for years. Nub runs your code with stock `node`, augmented with a `--require` preload hook[0] that adds a transpiler (oxc-powered, packaged as a Node-API add-on), registers a module resolution hook[1], and injects polyfills as ne

45°C cooling design cuts data center water use to near zero

06/24 22:10

68 points · 49 comments

Qualcomm to Acquire Modular

06/24 21:49

https://investor.qualcomm.com/news-events/press-releases/new...https://www.modular.com/blog/qualcomm-to-acquire-modularhttps://x.com/clattner_llvm/status/2069769232477192354, https://xcancel.com/clattner_llvm/s

Show HN: LookAway, a Mac break reminder that knows when not to interrupt

06/24 21:29

Hello, I'm Kushagra and I am the indie developer behind LookAway (I've posted about it earlier but it has received quite a lot of updates since the last time so I am posting it again).LookAway is a native break reminder for macOS that doesn't interrupt. I built it because I work from

Stealing Is a Skill

06/24 21:08

178 points · 114 comments

NSA lost access to Mythos amid Anthropic dispute

06/24 19:45

Unlocked: https://www.nytimes.com/2026/06/23/us/politics/nsa-lost-acce...

We’re making Bunny DNS free

06/24 16:50

783 points · 248 comments

Krea 2: SOTA open-weights 12B image model

06/23 23:31

287 points · 33 comments

GLM-5.2 is a step change for open agents

06/23 11:23

35 points · 8 comments

Self-Harness: Harnesses That Improve Themselves

06/23 03:45

55 points · 2 comments

Show HN: Monolisa v3 – a typeface for developers and creatives

06/22 22:05

Originally we (Andrey, Marcus, Juho) built MonoLisa in 2020 as we realised there's room for a better monospaced typeface for developers. The key insight was to make the glyphs slightly wider to make more room for design to make letters like m feel less cramped.Since then we've released a v

Genuinely, my all-time favourite image: Mamenchisaurus hochuanensis

06/22 22:00

74 points · 26 comments

Crawling BitTorrent DHTs for Fun and Profit [pdf]

06/21 22:12

21 points · 3 comments

Pondering routing more of my traffic via nodes outside the UK

06/21 08:00

27 points · 19 comments

How the Fifth Lateran Council unlocked financial theory

06/21 03:01

25 points · 0 comments

A Practical Guide to SSH Tunnels: Local and Remote Port Forwarding

06/20 11:55

226 points · 49 comments

I can haz smoller NixOS ISOs?

06/20 05:55

45 points · 15 comments

Pull request limits are cutting down the noise

06/19 18:03

56 points · 38 comments