2026-06-08（内容获取于 06/08 05:58）

mvanhorn/last30days-skill

Python · ★ 30,663 · 🍴 2,570 · 📈 1,097 stars today

AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary

中文介绍 为 AI 智能体提供的一项技能，可自动研究 Reddit、X、YouTube、HN、Polymarket 等平台任意话题，并综合生成有据可依的摘要。适合需要快速获取多源信息聚合的分析师或研究者。

opencv/opencv

C++ · ★ 88,042 · 🍴 56,596 · 📈 89 stars today

Open Source Computer Vision Library

中文介绍 开源计算机视觉库，提供数百种图像和视频处理算法，支持深度学习模型部署。广泛应用于人脸识别、物体检测、自动驾驶等视觉场景，是工业界和学术界最主流的视觉工具之一。

Leonxlnx/taste-skill

Shell · ★ 36,473 · 🍴 2,639 · 📈 1,104 stars today

Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop

中文介绍 赋予 AI“品味”的技能模块，防止模型生成枯燥乏味的通用内容。通过注入风格偏好与审美约束，提升对话、创作等场景的输出质量，适合需要个性化 AI 体验的开发者和内容创作者。

NousResearch/hermes-agent

Python · ★ 185,826 · 🍴 31,960 · 📈 1,117 stars today

The agent that grows with you

中文介绍 一个随着用户使用而不断成长的 AI 智能体项目，强调自适应与持续学习能力。适合需要长期陪伴式、可定制化 AI 助手的场景，如个人助理或教育辅导。

lfnovo/open-notebook

TypeScript · ★ 27,169 · 🍴 3,089 · 📈 555 stars today

An Open Source implementation of Notebook LM with more flexibility and features

中文介绍 Notebook LM 的开源实现，提供更灵活的功能扩展。支持文档管理、笔记生成与检索，适合知识工作者、研究人员用于构建个人知识库或自动化笔记工作流。

yikart/AiToEarn

TypeScript · ★ 18,709 · 🍴 2,903 · 📈 180 stars today

Let's use AI to Earn!

中文介绍 利用 AI 技术实现盈利的工具集合，提供自动化任务、内容生成等赚钱方案。目标用户是希望通过 AI 提升效率或获取副业收入的个人开发者和小团队。

aaif-goose/goose

Rust · ★ 47,430 · 🍴 5,003 · 📈 338 stars today

an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM

中文介绍 开源可扩展 AI 智能体，超越代码补全，支持安装、执行、编辑、测试等操作，可接入任意 LLM。适合开发者在终端中自动化复杂开发任务，提升编程效率。

Crosstalk-Solutions/project-nomad

TypeScript · ★ 29,662 · 🍴 2,938 · 📈 304 stars today

Project N.O.M.A.D, is a self-contained, offline survival computer packed with critical tools, knowledge, and AI to keep you informed and empowered—anytime, anywhere.

中文介绍 一款自包含、离线运行的生存计算机，集成关键工具、知识和 AI 能力，确保在无网络环境下也能获取信息与决策支持。适用于野外探险、灾难应急等极端场景。

ggml-org/llama.cpp

C++ · ★ 115,289 · 🍴 19,295 · 📈 197 stars today

LLM inference in C/C++

中文介绍 纯 C/C++ 实现的高性能 LLM 推理引擎，支持在 CPU 上高效运行 Llama 等模型。适合本地部署、边缘设备或隐私敏感场景，无需 GPU 即可运行大模型。

RyanCodrai/turbovec

Python · ★ 7,017 · 🍴 690 · 📈 1,533 stars today

A vector index built on TurboQuant, written in Rust with Python bindings

中文介绍 基于 TurboQuant 技术构建的向量索引库，使用 Rust 编写并提供 Python 绑定。专注于高效向量检索，适用于推荐系统、语义搜索等需要快速相似度计算的场景。

TapXWorld/ChinaTextbook

Roff · ★ 72,411 · 🍴 16,235 · 📈 355 stars today

所有小初高、大学PDF教材。

中文介绍 收集了中国所有小学、初中、高中及大学的 PDF 教材，提供一站式免费获取渠道。适用于学生、教师自学者进行教材查阅或复习备考。

openai/plugins

JavaScript · ★ 2,000 · 🍴 270 · 📈 262 stars today

OpenAI Plugins

中文介绍 OpenAI 官方提供的插件系统，允许 ChatGPT 连接外部 API、数据库或工具，扩展功能边界。开发者可基于此构建定制化的 AI 应用，如实时数据查询、在线服务调用等。

refactoringhq/tolaria

TypeScript · ★ 12,815 · 🍴 906 · 📈 242 stars today

Desktop app to manage markdown knowledge bases

中文介绍 桌面端 Markdown 知识库管理应用，专注于笔记的整理、检索与版本管理。适合喜欢 Markdown 语法的开发者、写作者用于构建个人知识体系。

HunxByts/GhostTrack

Python · ★ 13,704 · 🍴 1,833 · 📈 21 stars today

Useful tool to track location or mobile number

中文介绍 用于追踪手机号码或位置的实用工具，可能涉及网络侦查与地理定位。需要谨慎使用，通常适用于安全测试或个人隐私保护场景。

microsoft/pg_durable

Rust · ★ 1,427 · 🍴 31 · 📈 314 stars today

PostgreSQL in-database durable execution

中文介绍 PostgreSQL 数据库内直接支持持久化执行（durable execution）的扩展，允许在数据库内部编排复杂工作流并保证可靠执行。适合需要强一致性的数据驱动型应用，如金融交易或事件溯源。

Regret Minimization with Adaptive Opponents in Repeated Games

👍 1

06/04 08:00

In this paper, we study regret minimization in repeated games with adaptive opponents who can respond based on histories of play. The standard metric of external regret in online learning is known to fail to capture such adaptivity. To account for players' counterfactual reasoning, we introduce {\tt

中文介绍 该论文研究了在重复博弈中面对具有适应性的对手时的遗憾最小化问题。标准外部遗憾指标无法捕捉这种适应性，因此提出新方法以考虑玩家的反事实推理。

Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models

👍 3

06/04 08:00

Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given the scarcity of large-scale 3D data, we present GeoVR, a novel framework that l

中文介绍 多模态大语言模型缺乏内在3D感知能力。GeoVR通过视频学习几何表示，在不依赖大规模3D数据的情况下增强模型的空间一致性。

AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding

👍 10

06/04 08:00

Vision-Language-Action (VLA) models leverage the rich world knowledge of pretrained vision-language models (VLMs) to enable instruction-following robotic manipulation. However, the structural mismatch between VLM semantic spaces and embodied control policies often hinders the learning of precise per

中文介绍 AffordanceVLA是一种视觉-语言-动作模型，通过可负担性感知理解赋能动作生成，解决VLM语义空间与具身控制策略之间的结构不匹配问题，用于指令跟随机器人操作。

Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution

👍 70

06/04 08:00

Code language models need repository-level context to resolve imports, APIs, and project conventions. Existing methods inject this knowledge as long inputs (retrieved through RAG or dependency analysis) or through per-repository fine-tuning and LoRA -- costly at repository scale and brittle to evolv

中文介绍 Code2LoRA利用超网络生成适配器，使代码语言模型在软件演化过程中无需昂贵微调即可获取仓库级上下文，替代RAG或逐个仓库LoRA方法。

AURA: Intent-Directed Probing for Implicit-Need Surfacing in Situated LLM Agents

👍 1

06/04 08:00

A situated query like "where is Lin Wei?" often encodes more than its literal content: the user may also want to know whether Lin Wei is free, in a good mood, or worth interrupting now. Standard tool-use agents answer the literal question and stop. AURA inserts an inference step between scene percep

中文介绍 AURA通过意图导向探测，在LLM代理中挖掘情境查询的隐含需求（如“林伟在哪”可能暗含其是否空闲），超越字面回答进行推理。

Benchmark Everything Everywhere All at Once

👍 2

06/04 08:00

Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance. However, their construction is labor-intensive and hard to reuse, raising concerns about sustainability and scalability. Moreover, existing benchmarks often quickly

中文介绍 该论文指出基准构建劳动密集且难以复用，提出一种可重用、可扩展的通用基准框架，以评估LLM和多模态LLM，解决可持续性和可扩展性挑战。

Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions

👍 2

06/04 08:00

Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it remains unclear whether these simulations reflect precise user-specific beliefs or whether they are highly sensitive to semantically independent changes

中文介绍 研究审计基于LLM的立场模拟对语义上下文的敏感性，发现模拟结果高度依赖上下文变化，并非精确反映用户特定信念。

ForeSci: Evaluating LLM Agents for Forward-Looking AI Research Judgment

👍 0

06/04 08:00

AI research often requires decisions before future evidence exists: which bottleneck to attack, which direction to pursue, or where a project should be positioned. We introduce ForeSci, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgeme

中文介绍 ForeSci是一个时间可控的基准，用于评估LLM代理在缺乏未来证据时做出前瞻性AI研究判断的能力，例如选择攻克哪个瓶颈或研究方向。

Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?

👍 15

06/04 08:00

Video generation models have made impressive strides in synthesizing visually compelling content, yet their outputs remain confined to the virtual domain. A natural question follows: how well do these models reflect the physical world when their generated videos leave the screen and enter reality? W

中文介绍 Dream.exe探究视频生成模型能否生成可执行的真实机器人操作内容，评估其从虚拟到物理世界的迁移能力。

Towards One-to-Many Temporal Grounding

👍 5

06/04 08:00

Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, however, often require localizing multiple disjoint segments for a single query -- a setting we term One-to-Many Temporal

中文介绍 提出一对多时间定位任务，突破传统单段检索限制，实现针对同一文本查询定位多个不连续视频片段。

LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs

👍 8

06/04 08:00

Large language models can reproduce training data, but existing memorization evaluations mostly measure whether models can be forced to do so, rather than whether they do so under ordinary use. We introduce PropMe, a propensity-aware framework for memorization evaluation that contrasts prefix-based

中文介绍 PropMe是一个倾向感知的记忆化评估框架，衡量LLM在常规使用中是否实际泄露训练数据，而非仅在强制场景下。

AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints

👍 37

06/04 08:00

Planning for real-world problems by language models often involves both world and user constraints, which may not be fully specified upfront and are progressively disclosed through interaction. However, existing benchmarks still underexplore adaptive planning under such progressively revealed dual c

中文介绍 AdaPlanBench评估LLM代理在逐步披露的世界和用户约束下进行自适应规划的能力，填补现有基准在此方面的空白。

ArcANE: Do Role-Playing Language Agents Stay in Character at the Right Time?

👍 45

06/04 08:00

Role-playing language agents (RPLAs) should play characters whose values and behavior evolve as the story progresses, not maintain a fixed persona. Existing benchmarks measure factual recall at a given chapter, not whether responses align with the character's psychological trajectory, especially in

Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation

👍 23

06/04 08:00

Prior work has shown that large language models (LLMs) can translate unseen or low-resource languages by undergoing continued training or even by encoding a grammar book in their context. However, both methods typically overfit specific languages, with limited zero-shot transfer at test time. To tra

TIDE: Proactive Multi-Problem Discovery via Template-Guided Iteration

👍 38

06/03 08:00

Agents are widely deployed as assistants over documents, tools, and code. However, they typically act only on explicit user requests, which surface only the problems the user has noticed, while many other important problems coexist, hidden in plain sight, within the broader user context, with their

MAOAM: Unified Object and Material Selection with Vision-Language Models

👍 9

06/02 08:00

Selection is a core operation in interactive image editing. To be practical, a user should be able to specify and disambiguate the desired selection region through either text or click-based interactions, and the system should support selecting not only objects but also other criteria, such as mater

SEAOTTER: Sensor Embedded Autoencoding with One-Time Transcode for Efficient Reconstruction

👍 4

06/02 08:00

In robotics systems, vast amounts of visual data are easily captured at high resolution using low-cost, low-power hardware. Yet, limited bandwidth and on-device compute resources prevent full utilization when transmitted via conventional codecs like JPEG/MPEG. Newer codecs, like AV1/AVIF, improve th

RobotValues: Evaluating Household Robots When Human Values Conflict

👍 24

06/02 08:00

While household robots are often evaluated based on task completion, everyday domestic environments involve value-conflicting situations in which robots are expected to choose actions that prioritize other values than task success, such as human autonomy, efficiency, or social appropriateness. Yet,

EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management

👍 2

06/02 08:00

Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science. However, existing approaches remain fundamentally limited by their static action sets and lack of principled long-horizon context management, hindering their ability to accumulate reusable

LLM Anonymization Against Agentic Re-Identification

👍 1

06/01 08:00

Agentic LLMs with web search change the threat model for text anonymization: weak contextual cues can become cross-referenceable evidence for re-identification, yet those same details also carry downstream analytic value of the text. Existing defenses either remove explicit identifiers, perturb text

Absorbing Complexity: An Interaction-Native Knowledge Harness for Financial LLM Agents

👍 2

06/01 08:00

Financial AI agents often fail for a simple reason: they make users carry the complexity. A user must repeatedly restate goals, risk preferences, portfolio context, past judgments, and shifting market assumptions, while the agent answers, retrieves, acts, and forgets. In finance, this is not just in

AdaCodec: A Predictive Visual Code for Video MLLMs

👍 4

06/01 08:00

Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models (video MLLMs) usually encode each sampled frame as an independent RGB image, causing visual tokens to repeat content already present in earlier frame

SABER: Benchmarking Operational Safety of LLM Coding Agents in Stateful Project Workspaces

👍 0

05/31 08:00

Large language models are increasingly deployed as coding agents, shifting safety from individual responses to action sequences. Existing benchmarks, however, primarily assess whether models refuse unsafe prompts, leaving impacts on stateful workspaces largely unexamined. We present SABER, a benchma

The Shape of Addition: Geometric Structures of Arithmetic in Large Language Models

👍 4

05/29 08:00

Large Language Models exhibit paradoxical fragility in fundamental arithmetic, implying a disconnect between internal computation and discrete output. By analyzing the residual stream geometry during multi-operand addition, we identify the Iso-Raw-Sum Trajectory (IRST), a geometric structure where r

MechVQA: Benchmarking and Enhancing Multimodal LLMs on Comprehensive Mechanical Drawing Understanding

👍 3

05/29 08:00

Multimodal Large Language Models (MLLMs) have demonstrated significant achievements in general visual question answering (VQA) tasks. However, they remain brittle on mechanical engineering drawings, where high annotation density and weak domain knowledge, compounded by unreliable spatial relation re

Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination

👍 1

05/29 08:00

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as the cornerstone for shaping the remarkable coding abilities of Large Language Models (LLMs). However, the scalability of RLVR is severely constrained by the scarcity of sufficiently challenging verifiable code tasks that t

Multimodal Music Recommendation System using LLMs

👍 1

05/28 08:00

Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content. Prior work has explored LLM-augmented, multimodal, and text-enhanced approaches to sequential recommendation, and while some methods parti

Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents

👍 6

05/28 08:00

Memory-augmented LLM agents tackle complex long-horizon tasks by recursively summarizing interaction trajectories into compact memory. However, existing approaches typically train these memory policies using outcome-based reinforcement learning, failing to localize where intermediate memory quality

Trust Region Q Adjoint Matching

👍 3

05/26 08:00

Off-policy reinforcement learning of pretrained flow policies remains challenging due to the instability of optimization arising from the multi-step sampling process. Recently, Q-learning with Adjoint Matching (QAM) addressed this issue by reformulating into a memoryless stochastic optimal control (

Is This Edit Correct? A Multi-Dimensional Benchmark for Reasoning-Aware Image Editing

👍 1

04/16 08:00

Diffusion-based image editing has achieved strong visual fidelity under natural language instructions, yet most existing systems still operate at the level of surface instruction following, without reasoning about the implicit contextual constraints embedded in real user requests. This often leads t

Context as Topology: Why Your Agent's Memory Forgets, and How Structure Escapes It

@elpresidank · 116 粉丝 · 2.9M 阅 · 543 赞 · 35 转

06/02 19:14

Most AI agent memory is built on embeddings. And there's now a proof that this entire class of system is going to forget what you stored in it — and confidently make up things you never stored at all.

中文介绍 论证基于 embedding 的 AI 记忆系统存在结构性缺陷：会遗忘存储内容并自信编造。提出「上下文即拓扑」视角，认为需要新架构突破这一限制。

SpaceX IPOs in 7 days. I Fed the S1 Doc Into Claude. Here Is What It Found Buried in 300 Pages.

@DamiDefi · 96.5K 粉丝 · 2.3M 阅 · 584 赞 · 80 转

06/04 17:31

The number that stopped me was not the $2 trillion valuation. It was $791 million. That is what SpaceX made in net income in 2024. A profitable, growing aerospace company with a genuine moat in launch

Range and Depth on Demand

@1salman · 363 粉丝 · 2.0M 阅 · 682 赞 · 45 转

06/01 09:45

Everyone keeps asking whether AI favors specialists or generalists. I think that is the wrong question. AI does not pick a side. It changes the tradeoff. The old world forced a choice. You could go

中文介绍 探讨 AI 对专才与通才的影响：AI 不偏袒任何一方，而是改变了深度与广度的权衡关系。传统二选一的局面正在被打破。

How To Become An AI Engineer in 2026 (Without a CS Degree)

@sairahul1 · 110.7K 粉丝 · 710.8K 阅 · 509 赞 · 97 转

06/05 16:10

How To Become An AI Engineer in 2026. Without a CS degree. Without a bootcamp. Without knowing what a transformer is today. Here's what nobody tells you: The companies hiring right now don't need

中文介绍 重复条目，内容同第 4 条：2026 年成为 AI 工程师的路径，无需 CS 学位，强调企业实际需求而非基础理论。

How to master Dynamic Workflows in Claude Code: 6 patterns and 14 steps Anthropic engineers actually

@0xCodez · 3.3K 粉丝 · 637.2K 阅 · 510 赞 · 59 转

06/03 19:00

Most Claude Code users still write their workflows by hand. They chain prompts, copy outputs, paste them into the next prompt, fix what went wrong, repeat. 9 out of 10 builders haven’t tried Dynamic

中文介绍 介绍 Claude Code 的 Dynamic Workflows：从手工链式提示升级到自动化工作流。分享 6 种模式与 14 个步骤，很多开发者尚未尝试这一功能。

What an Enterprise Context Layer Actually Is

@prukalpa · 23.1K 粉丝 · 583.2K 阅 · 506 赞 · 80 转

06/02 02:17

A field guide to what it is, what it is not, and where it fits in your AI architecture. I have had some version of the same conversation with a CIO almost every day this year. Their team has read

中文介绍 定义「企业上下文层」：是什么、不是什么、在 AI 架构中怎么放。基于与多位 CIO 的日常对话，梳理了常见误解与落地位置。

hacking pewdiepie's AI agent harness using an evil cocomelon website (then helping protect it)

@theonejvo · 22.1K 粉丝 · 504.3K 阅 · 861 赞 · 1 转

06/02 02:01

Over the past year, @pewdiepie, has been turning into one of the most visible champions of private, self-hosted computing, and it has been a genuine pleasure to watch. What began in late 2025 as an

中文介绍 通过恶意 Cocomelon 网站成功入侵 PewDiePie 的自托管 AI agent，之后协助其加固防护。展示私有 AI 系统的安全风险。

Generative UI Is the New Frontend

@Saboo_Shubham_ · 116.2K 粉丝 · 263.3K 阅 · 517 赞 · 74 转

06/04 01:12

The frontend used to be a fixed thing. Designers drew it. Engineers built it. Users got what shipped. That's over. The interfaces shipping in 2026 are drawn partly by the agent itself, in real time,

中文介绍 提出 2026 年的前端界面将由 agent 实时生成，而非固定设计。「生成式 UI」成为新趋势，设计师与工程师的角色将被重塑。

How to get 100k YouTube subscribers in 3 hours (The Complete Guide)

@maubaron · 16.9K 粉丝 · 233.8K 阅 · 506 赞 · 19 转

06/06 03:22

Our YouTube channel has 125k subscribers and we've never made or uploaded a single video ourselves. This is a completely automated system. It is this very same strategy that made us the first app

中文介绍 重复条目，内容同第 9 条：完全自动化 YouTube 频道的方法详解。

Stop building Foxconn factories for your agents

@garrytan · 853.3K 粉丝 · 180.6K 阅 · 503 赞 · 43 转

06/01 22:26

In January I got back into coding and I built Garry's List. Over five hundred thousand lines of Rails and the tests to police it. I was proud of it. I shouldn't have been. The thing worth being proud

中文介绍 反思自己用 Rails 写了 50 万行代码的经验，认为不应为 agent 搭建「富士康式」工厂。真正的价值在于更轻量、更灵活的方法。

Building cloud agent infrastructure: what's different, and what we learned

@intuitiveml · 6.4K 粉丝 · 171.3K 阅 · 524 赞 · 70 转

06/05 08:55

Most agent frameworks today assume a desktop. One user, one machine, one process. The agent runs while the laptop is open, writes to a local filesystem, holds API keys in environment variables, and

中文介绍 总结构建云上 agent 基础设施的经验：与桌面端假设（单用户、单机、单进程）不同，云环境需处理多用户、分布式状态与凭证管理等问题。

A guide to /goal 🥅

@dkundel · 19.3K 粉丝 · 116.9K 阅 · 523 赞 · 40 转

06/05 05:39

We launched the goal mode (or /goal) as a way to help you have Codex drive towards a concrete outcome. When you set a goal Codex will continue to work until the goal is achieved, whether that takes

中文介绍 介绍 Codex 的 /goal 模式：设定目标后, Codex 会持续工作直到达成。提供如何使用该功能让 AI 驱动具体结果的指南。

State of Memory in Agent Harness

@mem0ai · 17.6K 粉丝 · 82.8K 阅 · 520 赞 · 60 转

06/02 22:49

Agent harnesses are where AI software actually runs. Cursor, Devin, Claude Code, Codex: these environments handle context, orchestrate tools, coordinate agents, and increasingly, manage memory. The

中文介绍 分析主流 agent 框架（Cursor、Devin、Claude Code、Codex）中的记忆系统现状：这些环境如何管理上下文、编排工具、协调 agent 并处理记忆。

A harness for every task: dynamic workflows in Claude Code

@trq212 · 263.1K 粉丝 · 75.7K 阅 · 542 赞 · 36 转

06/03 04:26

Last week, we released dynamic workflows in Claude Code. Claude can now write its own harness on the fly, custom-built for the task at hand. While the default Claude Code harness is built for coding,

中文介绍 介绍 Claude Code 的动态工作流功能：Claude 可根据任务自行编写 harness。虽然默认针对编程，但实际可扩展至任意任务。

A Functional Taxonomy of World Models

@drfeifei · 738.0K 粉丝 · 72.2K 阅 · 699 赞 · 144 转

06/04 02:57

“The world is everything that is the case.” — Ludwig Wittgenstein, Tractatus Logico-Philosophicus, 1921 The world is not made of words. In an earlier essay, we argued that spatial intelligence is AI’s

中文介绍 提出世界模型的功能分类法：引用维特根斯坦「世界是一切事实的总和」，论证空间智能是 AI 的下一个前沿。

How to Build a Custom Agent Harness

@sydneyrunkle · 7.5K 粉丝 · 69.5K 阅 · 511 赞 · 74 转

06/04 00:57

Building useful agents is largely about customization: connecting your agent to the right context, data, and environment(s) for the task at hand. At its core, an agent is a model calling tools in a

中文介绍 讲解构建自定义 agent harness 的核心：将 agent 连接到正确的上下文、数据和环境中。本质上 agent 是模型在循环中调用工具。

some notes on getting into frontier ai labs

@itsreallyvivek · 3.6K 粉丝 · 65.8K 阅 · 521 赞 · 28 转

06/05 23:48

A few days ago I wrote that getting into a frontier AI lab mostly comes down to two things: proven research and trench engineering. The more I think about it, the less these feel like separate skills.

中文介绍 分享进入前沿 AI 实验室的两块敲门砖：成熟的研究能力和扎实的工程技能。强调两者并非分离，而是相互融合。

I Gave Claude David Ogilvy's Writing Rules And Built A Legendary AI Writing Coach

@dickiebush · 441.8K 粉丝 · 57.7K 阅 · 519 赞 · 45 转

06/05 20:35

Legendary marketer David Ogilvy generated over $864 million for his clients. He was a British advertiser known as "The Father of Advertising." And in 1982, Ogilvy sent this 1-page memo to his staff:

中文介绍 将 David Ogilvy 的写作规则注入 Claude，打造 AI 写作教练。Ogilvy 曾为客户创造 8.64 亿美元，其 1982 年的备忘录成为核心 prompt。

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

06/07 04:10

中文介绍 Hermes Agent 发布新超级应用，同时 DeepSeek v4 在性能上追赶 Opus 4.8。

ChatGPT And Codex Are Merging (This Changes Everything)

06/04 06:17

中文介绍 ChatGPT 和 Codex 即将合并，这一变化可能彻底改变代码生成和交互方式。

The Truth about Opus 4.8 and other Huge agent News

06/01 06:27

Browsers Are Dead. Claude Just Replaced Them.

05/28 22:56

Every NEW Claude Feature Explained

05/24 06:29

Codex: Build Your Full AI Marketing Team (Agents + Skills)

05/18 23:31

OpenAI just released Codex Mobile

05/16 02:58

Codex Mobile App Released (Complete Setup Guide)

05/15 09:35

Vibe Coding for Beginners (Full Course 2026)

05/08 03:28

Codex is The NEW Best AI Coding Tool (Here's Why)

05/03 04:35

Amazing Digital Dentures (a failed project)

Hugging Face Blog · 06/08 03:44

中文介绍 一篇关于“Amazing Digital Dentures”项目的博客，该项目被标记为失败项目。

[AINews] not much happened today

Latent Space · 06/06 12:34

a quiet day of RSI.

中文介绍 该条目指出当天AI领域相对平静，提及RSI（递归自我改进）。

How to Stop Shipping Low-Quality RL Environments (with Examples)

Latent Space · 06/06 02:49

Your broken harness is actively making the model worse. Here's what I keep seeing after years of eyeballing trajectories, and what you need to fix.

中文介绍 文章讨论强化学习（RL）环境中常见问题，指出有缺陷的框架会使模型性能下降，并提供改进建议。

The Meta hack shows there’s more to AI security than Mythos

MIT Tech Review AI · 06/05 17:00

On June 5, 404 Media reported that attackers had been using Meta’s AI customer support agent to steal Instagram accounts. Their approach was simple: They asked the agent to link the accounts to email addresses that they controlled, and the agent complied. One attacker broke into the dormant Obama Wh

中文介绍 2026年6月5日，报道称攻击者利用Meta的AI客服代理窃取Instagram账户，方法为让代理将账户链接到攻击者控制的邮箱，AI安全暴露新问题。

[AINews] not much happened today

Latent Space · 06/05 14:44

a quiet day

中文介绍 当天AI领域较为平静。

not much happened today

Smol AI News · 06/05 13:44

**Anthropic's Mythos/Opus cycle** sparked mixed reactions with praise for **Claude Mythos**'s one-shot workflows and concerns over **Opus 4.8** benchmark regressions. **Opus 4.7** showed strong chemistry task performance, "making Claude a chemist." **Sakana AI** launched an **RSI Lab** focusing on r

中文介绍 Anthropic的Mythos/Opus周期引发讨论，Claude Mythos的一步工作流获好评，Opus 4.8基准测试出现倒退；Opus 4.7在化学任务上表现突出；Sakana AI推出RSI（递归自我改进）项目。

How one Anthropic seller rebuilt his team's workflows with Claude Code

Claude Blog · 06/05 08:00

How one Anthropic seller rebuilt his team's workflows with Claude Code

中文介绍 Anthropic一名销售员使用Claude Code重建团队工作流程，提升效率。

The Claude Cowork product guide

Claude Blog · 06/05 08:00

The Claude Cowork product guide

中文介绍 介绍Claude Cowork产品指南。

Jun 5, 2026ScienceMaking Claude a chemist

Anthropic Research · 06/05 08:00

Jun 5, 2026ScienceMaking Claude a chemist

中文介绍 Anthropic研究团队使Claude具备化学能力，成为“化学家”。

Anthropic Oceanus leaks 🤖, ChatGPT Dreaming 💭, recursive self improvement 🚀

TLDR AI · 06/05 08:00

中文介绍 当日AI新闻概要：Anthropic Oceanus泄漏、ChatGPT Dreaming功能、递归自我改进进展。

Reality: The Final Eval — Lukas Petersson and Axel Backlund of Andon Labs

Latent Space · 06/05 04:39

We talk with the VendingBench authors on evaling Claudes from Haiku to Mythos, and how they build leading, and lasting, frontier evals from scratch.

中文介绍 与Andon Labs的Lukas Petersson和Axel Backlund对话，讨论VendingBench评估Claude模型从Haiku到Mythos，以及构建前沿评估的经验。

Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI

Hugging Face Blog · 06/05 02:57

中文介绍 Nvidia发布Nemotron 3.5 Content Safety，提供可定制的多模态安全方案，适用于全球企业AI。

EVA-Bench Data 2.0: 3 Domains, 121 Tools, 213 Scenarios

Hugging Face Blog · 06/04 20:24

中文介绍 ServiceNow AI发布EVA-Bench Data 2.0，涵盖3个领域、121个工具、213个场景。

How Endava is redesigning software delivery around AI agents

OpenAI News · 06/04 20:00

Learn how Endava is using AI agents, ChatGPT Enterprise, and Codex to accelerate software delivery, automate workflows, and build an AI-native culture across the enterprise.

中文介绍 Endava利用AI代理、ChatGPT Enterprise和Codex加速软件交付，自动化工作流，构建AI原生文化。

How courts are coping with a flood of AI-generated lawsuits

MIT Tech Review AI · 06/04 18:50

Most days in her chambers, Judge Maritza Braswell, a federal magistrate judge in Colorado, sifts through stacks of documents written by people without a lawyer. Many of them can’t afford to hire a lawyer, and others have cases too weak or too small to interest one. She reads each one carefully, mind

中文介绍 联邦法官Maritza Braswell每天审阅大量由AI生成的诉讼文件，法院正面临AI相关案件激增的挑战。

话说 iOS 无法收到 X 和 Telegram 这件事是不是已成定局了？

06/07 21:59

11 回复 · Apple 节点

阿里的 Qwen3.7-Max 比我想象中差

06/07 16:29

12 回复 · 程序员 节点

用 gpt-image-2 做设计图，用 claude opus4.8 扣了一个通宵才复原一张图的 98%

06/07 12:30

11 回复 · 程序员 节点

汇编修改 BIOS 驱动安装包

06/07 12:26

10 回复 · 程序员 节点

人机 Agent 团队协同：从 Managed Agents 原理到 Multica 实践

06/07 10:35

5 回复 · 程序员 节点

一个疯狂的想法：用本体论抽象一个现实

06/07 01:24

7 回复 · 程序员 节点

谈下 ios 端的 surge 相比 loon 不足的一些地方

06/06 22:52

40 回复 · Apple 节点

iPhone air 和 iPhone17 同价，都是 5000 元，你会选哪一个

06/06 18:16

49 回复 · Apple 节点

09 年 apple 分配的 @mac.com 邮箱，因旧 Apple ID 改绑/换区失踪了，求助如何找回

06/06 17:22

16 回复 · Apple 节点

『ArchLinux』简明安装指南(Laptop+Nvidia+Cachyos Kernel +xfs+KDE)

06/05 23:26

7 回复 · Linux 节点

Data centers consumed 264B gallons of water as drought hits nearly 63% of US

06/08 05:41

9 points · 3 comments

The architecture of the internet creates risks for democracy

06/08 04:28

49 points · 53 comments

Why isn't the U.S. better at soccer?

06/08 03:41

33 points · 72 comments

How's Linear so fast? A technical breakdown

06/08 03:01

186 points · 99 comments

If LLMs Have Human-Like Attributes, Then So Does Age of Empires II

06/08 02:54

35 points · 25 comments

The curious case of low-protein diets

06/08 02:37

38 points · 18 comments

Building from zero after addiction, prison, and a felony

06/08 02:33

208 points · 94 comments

My automated doubt development process

06/08 02:17

31 points · 12 comments

Making peace with your unlived dreams (2023)

06/08 02:15

84 points · 33 comments

The complete IPv4 address space, mapped

06/08 02:14

22 points · 9 comments

Silurus/ooxml: Pixel-faithful Office documents, rendered in the browser

06/08 01:22

92 points · 30 comments

Powering up a module from the IBM 604: an electronic calculator from 1948

06/08 01:18

58 points · 18 comments

Proliferate (YC S25) is hiring to building open source Codex

06/08 01:01

1 points · 0 comments

The week I lost the plot at a startup where the tools worked as advertised

06/08 00:35

14 points · 0 comments

The gamers taking on the industry to stop it switching off games

06/08 00:16

83 points · 92 comments

Podman 6: machine usability improvements (2025)

06/07 22:01

84 points · 5 comments

sqlite: A CGo-free port of SQLite/SQLite3

06/07 21:53

36 points · 23 comments

Anthropic, please ship an official Claude Desktop for Linux

06/07 21:06

404 points · 229 comments

Show HN: Lathe – Use LLMs to learn a new domain, not skip past it

06/07 19:16

Hey HN!Lathe is an experiment in using LLMs to teach me something new, instead of doing the work for me. It generates a hands-on, source-backed tutorial for any technical topic you want to learn. Then you work through it yourself by reading and typing the code by hand (gasp) in a local UI built for

The 29th International Obfuscated C Code Contest (IOCCC) 2025 Winners

06/07 13:47

349 points · 83 comments

Public Domain Image Archive

06/07 08:22

234 points · 32 comments

Cloning a Sennheiser BA2015 battery pack

06/07 02:16

90 points · 15 comments

A visual introduction to kernel functions

06/05 22:11

18 points · 1 comments

Win16 Memory Management

06/05 19:16

125 points · 62 comments

What is the purpose of the lost+found folder in Linux and Unix? (2014)

06/05 16:08

92 points · 37 comments

There's no escaping it: an exploration of ANSI codes

06/05 15:02

27 points · 9 comments

Show HN: I Derived a Pancake

06/05 14:42

After 25 years of making other people's pancake recipes - always yearning for more tang, more fluff, and more predictability - I decided to derive the pancake recipe from the chemistry.You mark checkboxes for what you have on hand (ricotta, sour cream, kefir, buttermilk, yogurt, cottage cheese,

The Secret Life of Circuits with lcamtuf / Michał Zalewski (Audio Interview)

06/04 21:55

58 points · 5 comments

Do we fear the serializable isolation level more than we fear subtle bugs?

06/03 22:21

16 points · 3 comments

Backrest – a web UI and orchestrator for restic backup

06/02 21:52

64 points · 5 comments