2026-06-12（内容获取于 06/12 06:48）

apple/container

Swift · ★ 32,241 · 🍴 905 · 📈 2,419 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 苹果开源的 Linux 容器运行工具，利用轻量级虚拟化技术在 Mac（特别是 Apple Silicon）上创建和运行 Linux 容器。以 Swift 编写，为开发者提供原生 macOS 环境下的容器化开发与测试能力。

addyosmani/agent-skills

Shell · ★ 54,591 · 🍴 5,933 · 📈 3,275 stars today

Production-grade engineering skills for AI coding agents.

中文介绍 为 AI 编码代理提供生产级工程技能的集合。帮助开发者将高质量编程实践注入 AI 代理，提升代码生成、调试与维护能力，适用于自动化开发流程。

maziyarpanahi/openmed

Python · ★ 2,725 · 🍴 268 · 📈 427 stars today

open-source healthcare ai

中文介绍 开源医疗 AI 项目，聚焦于医疗领域的智能应用。为研究人员和开发者提供构建医疗诊断、数据分析等 AI 工具的基础框架。

phuryn/pm-skills

★ 16,161 · 🍴 1,682 · 📈 1,944 stars today

PM Skills Marketplace: 100+ agentic skills, commands, and plugins — from discovery to strategy, execution, launch, and growth.

中文介绍 产品经理技能市场，包含 100+ 代理技能、命令和插件，覆盖从发现、策略到执行、发布和增长的全流程。帮助 PM 通过 AI 代理自动化项目管理任务。

NVIDIA/SkillSpector

Python · ★ 2,594 · 🍴 208 · 📈 308 stars today

Security scanner for AI agent skills. Detect vulnerabilities, malicious patterns, and security risks.

中文介绍 NVIDIA 出品的 AI 代理技能安全扫描器，用于检测漏洞、恶意模式和风险。帮助开发者在集成第三方 AI 技能前评估安全状况，保护系统安全。

soxoj/maigret

Python · ★ 32,573 · 🍴 2,386 · 📈 665 stars today

🕵️‍♂️ Collect a dossier on a person by username from 3000+ sites

中文介绍 通过用户名从 3000+ 个网站收集人员档案的开源工具，用于开源情报（OSINT）调查。适用于安全研究、背景调查和社交工程评估。

x1xhlol/system-prompts-and-models-of-ai-tools

★ 139,844 · 🍴 34,615 · 📈 369 stars today

FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Trae, Traycer AI, VSCode Agent, Warp.dev, Windsurf, Xcode, Z.ai Code, Dia & v0. (And other Open Sourced) System Prompts

中文介绍 收集了 Augment Code、Claude Code、Cursor、Devin AI、Perplexity、Replit 等众多 AI 工具的系统和模型提示词。方便开发者了解不同工具的内部配置与行为。

refactoringhq/tolaria

TypeScript · ★ 15,365 · 🍴 1,059 · 📈 604 stars today

Desktop app to manage markdown knowledge bases

中文介绍 用于管理 Markdown 知识库的桌面应用。提供本地化的笔记组织、检索与协作能力，适合个人知识管理和技术文档维护。

obra/superpowers

Shell · ★ 224,756 · 🍴 19,980 · 📈 1,323 stars today

An agentic skills framework & software development methodology that works.

中文介绍 一个代理技能框架与软件开发方法论。提供可复用的 AI 代理技能和开发流程，帮助团队高效构建和部署 AI 驱动的软件项目。

restic/restic

Go · ★ 34,133 · 🍴 1,785 · 📈 33 stars today

Fast, secure, efficient backup program

中文介绍 快速、安全、高效的备份程序，支持加密、去重和增量备份。适用于个人和服务器数据保护，支持多种后端存储（本地、云服务等）。

msitarzewski/agency-agents

Shell · ★ 111,483 · 🍴 18,246 · 📈 1,235 stars today

A complete AI agency at your fingertips - From frontend wizards to Reddit community ninjas, from whimsy injectors to reality checkers. Each agent is a specialized expert with personality, processes, and proven deliverables.

中文介绍 一个完整的 AI 代理机构，包含前端专家、社交媒体运营、创意注入等多种专业代理。每个代理拥有独特个性与流程，可协同完成复杂任务。

masterking32/MasterDnsVPN

Go · ★ 5,661 · 🍴 518 · 📈 510 stars today

Advanced DNS tunneling VPN for censorship bypass, optimized beyond DNSTT and SlipStream with low-overhead ARQ, resolver load balancing, high packet-loss stability and speed.

中文介绍 先进的 DNS 隧道 VPN 工具，用于绕过网络审查。优化了原有 DNSTT 和 SlipStream 技术，具备低开销 ARQ、解析器负载均衡和高丢包稳定性，提升速度与可靠性。

chatwoot/chatwoot

Ruby · ★ 30,332 · 🍴 7,503 · 📈 191 stars today

Open-source live-chat, email support, omni-channel desk. An alternative to Intercom, Zendesk, Salesforce Service Cloud etc. 🔥💬

中文介绍 开源即时通讯、邮件支持和全渠道客服平台，可作为 Intercom、Zendesk 等商业工具的替代方案。支持多渠道消息聚合、自动化回复和客户管理。

kenn-io/agentsview

Go · ★ 1,602 · 🍴 172 · 📈 98 stars today

Local-first session intelligence and analytics for coding agents, supporting Claude Code, Codex, and more than 20 other agents. Also: 100x faster replacement for ccusage!

中文介绍 面向编码代理的本地化会话智能与分析工具，支持 Claude Code、Codex 等 20+ 代理。提供 100 倍于 cusage 的性能提升，帮助开发者监控和优化代理行为。

alchaincyf/zhangxuefeng-skill

★ 7,926 · 🍴 2,411 · 📈 131 stars today

张雪峰.skill — 张雪峰的认知操作系统。高考志愿/考研/职业规划的实战思维框架。由女娲.skill生成。

中文介绍 张雪峰认知操作系统，聚焦高考志愿、考研和职业规划的实战思维框架。由女娲.skill 生成，适用于教育咨询和人生规划场景。

TapXWorld/ChinaTextbook

Roff · ★ 73,914 · 🍴 16,537 · 📈 345 stars today

所有小初高、大学PDF教材。

中文介绍 收录中国小学、初中、高中和大学各阶段的 PDF 教材。方便学生、教师和研究人员获取教学资源。

hexo-ai/sia

Python · ★ 1,243 · 🍴 156 · 📈 177 stars today

SIA is a Self Improving AI framework to autonomously improve the performance of any AI system (Model / Agent) on a benchmark task.

中文介绍 自我改进 AI 框架（SIA），能够自主提升任何 AI 系统（模型或代理）在基准任务上的表现。通过自动化优化流程，适用于模型调优和代理效果提升。

mattermost/mattermost

TypeScript · ★ 37,284 · 🍴 8,696 · 📈 26 stars today

Mattermost is an open source platform for secure collaboration across the entire software development lifecycle..

中文介绍 开源协作平台，专为软件开发生命周期中的安全沟通设计。提供即时消息、文件共享、集成工具链等功能，是 Slack 的自托管替代方案。

bannedbook/fanqiang

Kotlin · ★ 46,728 · 🍴 8,002 · 📈 243 stars today

翻墙-科学上网

中文介绍 提供翻墙（科学上网）相关资源和指南的集合。包含工具、教程和配置方法，帮助用户绕过网络限制访问互联网。

Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training

👍 3

06/10 17:30

There are two main Parameter-Efficient Fine-Tuning (PEFT) techniques for Large Language Models (LLMs). While Low-Rank Adaptation (LoRA) introduces additional weights between the LLM layers, Soft Prompting introduces additional fine-tuning-specific raw tokens to an LLM input. However, both require mo

中文介绍 提出ART（基于艺术强化训练）方法，用于多模态大语言模型的参数高效微调，融合LoRA和软提示两种技术。

Which Models Are Our Models Built On? Auditing Invisible Dependencies in Modern LLMs

👍 1

06/10 08:00

Modern LLM training pipelines increasingly rely on other models to generate data, filter corpora, judge outputs, and guide development decisions. These dependencies are recursive: a model may depend on an upstream artifact whose own dependencies are documented only in separate releases and artifacts

中文介绍 研究现代大语言模型训练中依赖其他模型生成数据、过滤语料等递归性依赖关系，并提出审计方法。

Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models

👍 14

06/10 08:00

Vision-language models (VLMs) project images into hundreds to thousands of visual tokens, making decoder inference expensive in both attention computation and KV-cache memory. Existing visual-token reduction methods largely follow a rank-and-remove paradigm: they score visual tokens, keep a compact

中文介绍 提出可恢复视觉令牌路由方法，用于视觉语言模型解码时通过重路由而非删除减少视觉令牌，降低计算和KV缓存开销。

Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models

👍 2

06/10 08:00

Large language models (LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently, reusable natural language skills have emerged as a popular paradigm to inject procedural knowledge into LLM applications. Since popular skills are often invoked repeatedly, placing their full te

中文介绍 提出自适应多分辨率程序知识压缩方法，将可复用的自然语言技能注入大语言模型，提升复杂任务中程序知识注入效率。

Grammar-Constrained Decoding Can Jailbreak LLMs into Generating Malicious Code

👍 17

06/10 08:00

Large Language Models (LLMs) are increasingly used for code generation, raising concerns that they may be misused to produce malicious code. Meanwhile, Grammar-Constrained Decoding (GCD) has been widely adopted to improve the reliability of LLM-generated code by enforcing syntactic validity. In this

中文介绍 研究发现语法约束解码（GCD）可被利用使大语言模型生成恶意代码，揭示该技术在安全防护中的漏洞。

Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation

👍 3

06/10 08:00

Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled datasets to train task-specific sequence models. In this work, we introduce a lightweight learning approach, in which w

中文介绍 提出基于时间序列基础模型嵌入的轻量学习方法，用于工业剩余使用寿命预测，减少特征工程和标注数据需求。

Reason, Then Re-reason: Cross-view Revisiting Improves Spatial Reasoning

👍 28

06/10 08:00

Spatial reasoning from egocentric videos is inherently challenging because the observable evidence is constrained by the camera trajectory. Existing methods rely on single-turn inference, forcing models to resolve geometric ambiguity through semantic priors rather than verifiable evidence. We argue

中文介绍 提出跨视角回顾推理方法，改善从第一视角视频进行空间推理的能力，通过多轮推断解决几何歧义。

Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks

👍 55

06/10 08:00

General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring. We introduce Claw-SWE-

中文介绍 发布Claw-SWE-Bench基准，用于评估OpenClaw等通用智能体在编程任务中的能力，解决SWE-bench难以直接测试通用智能体的难题。

Toward Generalist Autonomous Research via Hypothesis-Tree Refinement

👍 68

06/10 08:00

Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the resulting lessons into later attempts. We study how an AI agent can run this loop autonomously over long horizons. We introduc

中文介绍 研究如何让AI智能体通过假设树细化自主运行探索、实验和抽象的科学循环，实现长期自主研究。

Verifiable Environments Are LEGO Bricks: Recursive Composition for Reasoning Generalization

👍 6

06/10 08:00

Reinforcement Learning (RL) with verifiable environments has emerged as a powerful approach for enhancing the reasoning capabilities of Large Language Models (LLMs). While prior research demonstrates that scaling environment quantity improves RL performance, existing manual or individual constructio

中文介绍 提出可验证环境作为乐高积木递归组合的方法，用于增强大语言模型推理能力，提升强化学习在不同环境中的泛化。

Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application

👍 56

06/10 08:00

Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model capabilities. Despite this importance, existing work lacks a systematic categorization and deep analysis. This paper syst

中文介绍 综述基于大语言模型的智能体环境工程，涵盖环境建模、合成、评估与应用，为智能体提供系统化分类与分析。

InternVideo3: Agentify Foundation Models with Multimodal Contextual Reasoning

👍 17

06/10 08:00

Recent progress in foundation models has shifted toward agentic behavior involving multi-step reasoning and tool use. However, open-source efforts largely focus on text-dominant settings, leaving long-horizon multimodal tasks underexplored. This gap is evident in video tasks requiring sustained temp

中文介绍 提出InternVideo3，通过多模态上下文推理智能体化基础模型，在视频任务中实现长时多步推理和工具使用。

World Model Self-Distillation: Training World Models to Solve General Tasks

👍 6

06/10 08:00

Pretrained video generators are promising visual world models that exhibit emergent task-solving abilities; however, their reliance on detailed textual descriptions limits their direct use for planning and decision-making. Existing approaches either outsource this reasoning to language or vision-lan

Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling

👍 16

06/10 08:00

Reinforcement learning (RL) has become a key component in modern large language models, yet the rollout stage remains the key bottleneck in RL training pipelines. Although Multi-Token Prediction (MTP) offers a natural solution to accelerate rollouts through speculative decoding, many studies have ob

Building Social World Models with Large Language Models

👍 1

06/10 06:15

Understanding and predicting how social beliefs evolve in response to events -- from policy changes to scientific breakthroughs -- remains a fundamental challenge in social science. Given LLMs' commonsense knowledge and social intelligence, we ask: Can LLMs model the dynamics of social beliefs follo

TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning

👍 15

06/10 01:16

Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models. However, rollout-intensive policy optimization is often limited by insufficient reward contrast, arising when overly simple or complex prompts generate

Towards Diverse Scientific Hypothesis Search with Large Language Models

👍 1

06/09 16:52

Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many discovery settings, the goal is not to identify a single best hypothesis since validation can be noisy and expensive, and sc

ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics

👍 18

06/09 08:00

Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structural insight. Recent evidence suggests that even today's strongest frontier models remain uneven on Olympiad combinatorics, revealing a gap in creati

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

👍 8

06/09 08:00

We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied cognition, task planning, correction, and pointing, within a single architecture toward general physical intelligence. Leveraging three automated dat

i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models

👍 5

06/09 08:00

Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific modeling and data choices: state-of-the-art open-weight models provide limited ablations, and do not disclose their training data and full training deta

τ-Rec: A Verifiable Benchmark for Agentic Recommender Systems

👍 1

06/08 08:00

As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation paradigms have struggled to keep pace. Current benchmarks often rely on "LLM-as-a-judge" evaluations, which introduce subjectivity, high costs and inconsistency. We present τ-Rec, a benchmark for agent

FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching

👍 1

06/08 08:00

Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain Age Prediction (BAP), which estimates an individual's biological brain age from MRI data. Effective BAP models require large, diverse, and age-balanced

Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions

👍 51

06/08 08:00

Reward models are central to text-to-image post-training, but visual preference is subjective and better represented as a distribution over rubric scores than as a deterministic scalar. Existing scalar, score-token, and pairwise reward models over-compress uncertainty and fine-grained score differen

POISE: Position-Aware Undetectable Skill Injection on LLM Agents

👍 4

06/06 08:00

Agent skills provide a lightweight mechanism for extending general-purpose agents, but their open format exposes them to skill-poisoning attacks. A practically dangerous injection must stay invisible: if executing the payload derails the user's legitimate task, the resulting failure signal invites i

ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction

👍 4

06/05 08:00

Computer-use agents (CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual tokens. As interaction trajectories grow, the token cost increases rapidly, limiting the amount of history that can be incorporated under fixed context

DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models

👍 3

06/04 08:00

Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens. While text-based output interfaces enable scalable pretraining and strong zero-shot generalization across diverse tasks, they are poorly suited for problems that require precise continuous outputs, such as

SparDA: Sparse Decoupled Attention for Efficient Long-Context LLM Inference

👍 1

06/03 08:00

Sparse attention reduces compute and memory bandwidth for long-context LLM inference. However, two key challenges remain: (1) KV cache capacity still grows with sequence length, and offloading to CPU memory introduces a PCIe transfer bottleneck; (2) the sparse selection step itself retains O(T^2) co

Large Language Models Are Overconfident in Their Own Responses

👍 3

06/02 08:00

Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However, little is known about the frequently used chat template's effect on the calibration of conversational LLMs. In this work, we investigate the mechanisms

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning

👍 9

06/02 08:00

Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static. This limitation sharpens in agentic RL, where shifting bottlenecks and scalar rewards mask diverse failure modes. We introduce EvoTrainer, an autonomous training framework that co-evolves LLM

Distilling LLM Feedback for Lean Theorem Proving

👍 1

05/29 08:00

Post-training for reasoning models typically combines supervised fine-tuning with reinforcement learning from verifiable rewards, most commonly with GRPO. However, this algorithm suffers from sparse rewards, limited exploration, and mode collapse. Building upon recent works on self-distillation, we

Loops: What Every AI Engineer Needs to Know in 2026

@sairahul1 · 113.0K 粉丝 · 852.6K 阅 · 600 赞 · 79 转

06/09 17:26

Peter Steinberger, creator of OpenClaw, who now works with OpenAI. Yesterday he posted this: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents."

中文介绍 核心观点：从手动 prompt 转向设计自动循环来驱动 AI 智能体，这是 2026 年 AI 工程师的关键技能。

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

@sairahul1 · 113.0K 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 非 CS 背景如何成为 AI 工程师：不需要学位、不需要 bootcamp、不需要了解 Transformer。当前真正招人的公司看重的是实践能力而非传统学历背景。

Harness Engineering: What Every AI Engineer Needs to Know in 2026

@sairahul1 · 113.0K 粉丝 · 546.4K 阅 · 536 赞 · 94 转

06/07 16:53

In February 2026, a small OpenAI team shipped 1 million lines of production code. They didn't write a single line by hand. The AI agents wrote it. The humans designed the system that made the agents

中文介绍 2026 年 2 月，一个小型 OpenAI 团队用 AI 智能体完成了 100 万行生产代码，人类一行未写。关键在于设计让智能体高效工作的系统，而非手动编码本身。

Everything Is Recorded Now

@dhaber · 50.0K 粉丝 · 497.3K 阅 · 500 赞 · 57 转

06/10 22:09

One of the biggest ways that AI is transforming work (and also one of the most taboo subjects inside companies at the moment) is that most work discussions are being recorded now by default. This

中文介绍 AI 推动工作讨论默认被录音记录，成为公司内部敏感但普遍的趋势，影响沟通方式和隐私。

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 一个完全自动化的 YouTube 频道系统：频道拥有 12.5 万订阅者，从未自行制作或上传过任何视频。分享这套全自动化增长策略的完整指南。

The Untrainable

@saranormous · 143.5K 粉丝 · 194.8K 阅 · 614 赞 · 40 转

06/10 08:49

The mid-2026 investor's version of AI psychosis is a despair that nothing is investable, that we should put all our money into Anthropic and Nvidia and go home. I have never felt it. I have been sure

中文介绍 投资者中出现的「AI 精神病」：认为无物可投，只想把钱全投 Anthropic 和 Nvidia。作者认为真正机会在别处。

How to Build a Self-Improving Loop in Claude Code (Exact Setup Inside)

@0x_rody · 1.7K 粉丝 · 193.2K 阅 · 513 赞 · 72 转

06/10 23:15

Claude writes your code, hands it over, and 3 tests are failing. You paste the errors back, it fixes one thing, breaks another, and you spend the evening as a messenger between Claude and your

中文介绍 在 Claude Code 中搭建自改进循环：不再手动粘贴错误信息，而是设计自动反馈回路让智能体自我纠错。

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 构建云端智能体基础设施的实战经验：传统框架假设桌面环境——单个用户、单台机器、单进程。迁移到云端需要重新设计文件系统、环境变量和 API 密钥管理等核心能力。

My Week with Fable

@MatthewBerman · 121.3K 粉丝 · 108.0K 阅 · 661 赞 · 26 转

06/10 01:05

tl;dr I've been testing Fable (Mythos) for the past week and it feels unlike any other model I've used. It feels, and is priced, like a next-generation model. It also has some real quirks. The Good

中文介绍 亲测 Fable（Mythos 系列）一周的感受：感觉像新一代模型，定价也符合新一代标准，但存在一些真实缺陷。优缺点均有详细评测。

Kimi to Predict All 104 World Cup Matches: Germany May Be Underestimated

@Kimi_Moonshot · 172.7K 粉丝 · 106.6K 阅 · 500 赞 · 61 转

06/09 19:38

Our predictions will probably be wrong. But the World Cup offers a rare, public, verifiable, and constantly evolving real-world setting. Through this initiative, we hope to place analysis,

中文介绍 Kimi 宣布预测 2026 年世界杯全部 104 场比赛，承认预测可能不准确。德国队被低估，世界杯作为公开可验证的实时场景是检验 AI 分析能力的理想舞台。

Loop engineering: the 14-step roadmap from prompter to loop designer.

@0xCodez · 5.3K 粉丝 · 97.8K 阅 · 510 赞 · 80 转

06/09 23:50

Most developers still prompt their coding agents by hand. They type, they wait, they read the diff, they type again. 9out of 10 builders have never written a single loop that prompts the agent for

中文介绍 从手工 prompt 到循环设计工程师的 14 步路线图：90% 的开发者从未写过一条让智能体自动 prompt 智能体的循环。分享系统性成长路径。

Designing loops with Fable 5

@RLanceMartin · 30.4K 粉丝 · 84.7K 阅 · 660 赞 · 50 转

06/10 01:21

Mythos-class models like Claude Fable 5 have changed the way many of us work at Anthropic. I want to share two tips for getting the most out of this class of models. Self-correction loops There’s been

中文介绍 在 Anthropic 内部使用 Mythos 类模型（如 Claude Fable 5）的两个技巧：一是自我修正循环——让模型自动检查并修复自己的输出；二是针对这类新模型的特定设计模式。

Principled Thinking and AI Need to Go Together

@RayDalio · 2.2M 粉丝 · 72.6K 阅 · 515 赞 · 93 转

06/11 01:15

What is the best approach to being effectively intelligent now that human intelligence and artificial intelligence are merging? Because I have been building computerized investment decision-making

中文介绍 当人类智慧和人工智能融合时，如何变得「有效智能」？原则性思考与 AI 必须结合，作者基于其构建自动化投资决策系统的经验分享最佳应对方法。

some notes on getting into frontier ai labs

@itsreallyvivek · 4.3K 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 进入前沿 AI 实验室的关键：经过验证的研究能力和扎实的工程能力。这两者并非独立技能，而是同一核心理念的不同侧面——解决棘手问题。

How to Build an AI GTM Brain using Claude Code

@nifinet · 10.2K 粉丝 · 60.9K 阅 · 522 赞 · 54 转

06/10 01:21

When a team says they want AI for growth, they usually mean a faster send. An agent that fires the same template at a longer list, day and night. That is the cheap half of the job, and it stopped

中文介绍 用 Claude Code 构建 AI GTM 大脑：团队通常只想更快发送模板，但那只是表面工作。真正有效的增长系统应该做更深层的分析与策略。

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 将传奇广告人大卫·奥格威的写作规则融入 Claude，打造出一位 AI 写作教练。奥格威 1982 年的一页备忘录曾为客户创造超 8.64 亿美元价值，这些规则现可被 AI 复用。

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

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

Google DeepMind is worried about what happens when millions of agents start to interact

MIT Tech Review AI · 06/11 19:00

Google DeepMind is funding research into the potential dangers of situations where millions of different AI agents interact with each other online. According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without

中文介绍 谷歌DeepMind资助研究数百万AI代理在线交互的潜在危险。该公司AGI安全与对齐研究负责人Rohin Shah表示，大规模面向市场的代理到来可能带来风险。

[AINews] Open Models, Model Labs vs Agent Labs, and What's Untrainable — Sarah Guo

Latent Space · 06/11 11:14

a quiet day lets us reflect on a great essay

中文介绍 该新闻摘要提及开放模型、模型实验室与代理实验室的对比，以及不可训练的内容，但缺乏具体细节。

OpenAI to acquire Ona

OpenAI News · 06/11 08:00

OpenAI plans to acquire Ona to expand Codex with secure, persistent cloud environments, enabling long-running AI agents across enterprise workflows.

中文介绍 OpenAI计划收购Ona，以扩展Codex平台，提供安全、持久的云端环境，支持企业工作流中的长期运行的AI代理。

Supporting Europe’s work in ensuring a trustworthy AI ecosystem

OpenAI News · 06/11 08:00

OpenAI supports the EU Code of Practice on AI content transparency, advancing provenance standards and tools to help people understand AI-generated content.

中文介绍 OpenAI支持欧盟关于AI内容透明度的行为准则，推动溯源标准和工具，帮助用户理解AI生成内容。

How an astrophysicist uses Codex to help simulate black holes

OpenAI News · 06/11 08:00

Discover how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein’s theory of general relativity.

中文介绍 天体物理学家Chi-kwan Chan使用Codex构建黑洞模拟，帮助科学家研究极端物理并检验爱因斯坦广义相对论。

BBVA puts AI at the core of banking with OpenAI

OpenAI News · 06/11 08:00

Learn how BBVA scaled ChatGPT Enterprise to 100,000 employees and partnered with OpenAI to accelerate AI-powered banking transformation worldwide.

中文介绍 BBVA将ChatGPT Enterprise推广至10万名员工，并与OpenAI合作加速全球银行业AI转型。

Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

Hugging Face Blog · 06/11 08:00

中文介绍 本文介绍PyTorch中的性能分析，从nn.Linear到融合MLP的实现，涵盖优化技术。

Dario Amodei policy 🏛️, DiffusionGemma ⚡, WhatsApp to unblock bots 🤖

TLDR AI · 06/11 08:00

中文介绍 该摘要涵盖Dario Amodei政策、DiffusionGemma快速度文本生成、以及WhatsApp解封机器人等AI新闻。

Access OpenAI models and Codex through your Oracle cloud commitment

OpenAI News · 06/11 04:00

Access OpenAI models and Codex through Oracle Cloud, using existing commitments to build and deploy AI with enterprise security and governance.

中文介绍 用户可通过Oracle Cloud的现有承诺访问OpenAI模型和Codex，在具备企业安全与治理的环境下构建和部署AI。

DiffusionGemma: 4x faster text generation

DeepMind Blog · 06/11 00:24

中文介绍 DeepMind发布DiffusionGemma，实现比传统模型快4倍的文本生成速度。

PRC-linked influence operations are targeting AI debates in the US

OpenAI News · 06/10 20:00

A new report from OpenAI details PRC-linked influence operations using AI to target U.S. tech debates, data center narratives, tariffs, and false claims about ChatGPT.

中文介绍 OpenAI报告详述与PRC关联的影响力行动，利用AI针对美国科技辩论、数据中心叙事、关税以及关于ChatGPT的虚假声明。

Investing in multi-agent AI safety research

DeepMind Blog · 06/10 18:21

Google DeepMind and partners announce a $10M funding call for multi-agent safety research.

中文介绍 谷歌DeepMind与合作伙伴宣布1000万美元资金征集，用于多代理AI安全研究。

[AINews] Anthropic Claude Fable 5 — Mythos but Safe, with Controversial Terms

Latent Space · 06/10 11:50

The much anticipated launch of the Mythos-class model was marred by some controversial usage policies

中文介绍 Anthropic发布Claude Fable 5模型，属于Mythos级别，但伴随有争议的使用政策。

From data to decisions: how LSEG is scaling trusted AI

OpenAI News · 06/10 08:00

See how LSEG uses OpenAI to scale trusted AI across its global business, accelerating insights, shrinking release cycles, and empowering 4,000 employees.

中文介绍 LSEG利用OpenAI在全球业务中扩展可信AI，加速洞察、缩短发布周期，并赋能4000名员工。

The evolution of agentic surfaces: building with Claude Managed Agents

Claude Blog · 06/10 08:00

The evolution of agentic surfaces: building with Claude Managed Agents

中文介绍 文章探讨使用Claude托管代理构建代理表面（agentic surfaces）的演进过程。

mimo cli 好难用

06/11 23:02

4 回复 · 程序员 节点

有没有 claude 拼车的朋友

06/11 22:12

5 回复 · 程序员 节点

做为 IT 工程师十几年，你做的最不后悔的决定是什么？

06/11 14:24

10 回复 · 程序员 节点

港版 17， iOS27，开启新版 AI 流程

06/11 14:00

9 回复 · Apple 节点

mbp m1 pro 挺耐用啊，战力大于 m4?

06/11 10:40

44 回复 · Apple 节点

tg 已更新. 修复 macOS27 聊天界面布局显示异常问题.

06/11 08:45

12 回复 · Apple 节点

Siri AI 用了 Gemini，国内会用谁的

06/10 22:26

9 回复 · Apple 节点

在国行 Mac(macOS 27)上开启完整 Apple 智能

06/10 22:06

28 回复 · Apple 节点

HomePod 在发 Thread/HomeKit IPv6 RA，怎么规避？

06/10 20:29

8 回复 · Apple 节点

求问， iOS27 的新 siri 一直在排队……

06/10 18:35

12 回复 · Apple 节点

The unreasonable effectiveness of simple HTML

06/12 06:17

11 points · 0 comments

Show HN: FablePool – pool money behind a prompt, and Fable builds it in public

06/12 05:17

84 points · 30 comments

Why I'm Forced to Say Farewell: Google Management Has Lost Its Moral Compass

06/12 05:04

96 points · 34 comments

Show HN: Boo – screen-style terminal multiplexer built on libghostty

06/12 04:52

23 points · 6 comments

Show HN: TunnelMind – reputation API for IPs, ASNs, and ad-tech supply chains

06/12 04:27

I'm a network engineer that likes to think about the future of the internet and this is what I've built over many nights and weekends. One reputation graph over IPs, ASNs, domains, and entities, exposed as a JSON API. Try it: curl https://api.tunnelmind.ai/v1/check&#x2F

Travel Locally, Where You Are

06/12 04:08

67 points · 32 comments

Shall we play a game? – LLMs use tactical nukes in 95% of simulations

06/12 03:54

130 points · 117 comments

Who Runs the Ransomware Group 'The Gentlemen?'

06/12 03:23

44 points · 3 comments

Apple didn't revolutionize power supplies; new transistors did (2012)

06/12 01:35

56 points · 6 comments

Software Is Made Between Commits

06/12 00:28

176 points · 111 comments

Solar generates more energy in US than coal for first time

06/12 00:10

378 points · 184 comments

Waymo Premier

06/12 00:10

129 points · 342 comments

The RCE that AMD wouldn't fix

06/12 00:03

See also https://www.youtube.com/watch?v=4HjWHNLRMB0Related: The RCE that AMD won't fix - https://news.ycombinator.com/item?id=46906947 - Feb 2026 (173 comments)

Claude Fable 5: mid-tier results on coding tasks

06/12 00:03

165 points · 65 comments

Petition to Withdraw Canada's Bill C-22

06/11 23:37

295 points · 105 comments

FPS.cob: A first person shooter in COBOL

06/11 23:13

89 points · 53 comments

MiMo Code is now released and open-source

06/11 22:27

389 points · 217 comments

Show HN: Homebrew 6.0.0

06/11 21:24

Today, I’m proud to announce Homebrew 6.0.0. The most significant changes since 5.1.0 are a new tap trust security mechanism, the new faster, smaller, default internal Homebrew JSON API, sandboxing on Linux, better defaults informed by our user survey, many brew bundle improvements, improved perform

Open Reproduction of DeepSeek-R1

06/11 21:14

182 points · 16 comments

Lines of code got a better publicist

06/11 20:26

336 points · 236 comments

Emacs appearances in pop culture

06/10 18:37

211 points · 45 comments

Programming a GBA Game on an iPhone

06/10 06:12

39 points · 5 comments

Show HN: Claw Patrol, a security firewall for agents

06/10 00:06

At Deno we've been using OpenClaw and other agents increasingly for addressing production problems in Deno Deploy - when a PagerDuty alert fires, the agent starts researching the cause and making fixes.In order to do this, the agent needs access to real production systems - postgres, kubernetes

macOS 27 Beta breaks the ability to boot Asahi Linux

06/09 23:01

https://social.treehouse.systems/@AsahiLinux/116719749555082...

Gram Newton-Schulz: A Fast, Hardware-Aware Newton-Schulz Algorithm for Muon

06/09 12:58

10 points · 0 comments

Developer gets Half-Life running at 30 FPS on a Nokia N95

06/09 05:02

https://twitter.com/dante_leoncini/status/206303501506830790...

Ear Training Practice Exercises

06/09 00:38

108 points · 64 comments

Doing nothing at work

06/08 16:57

324 points · 116 comments

A new era for software testing

06/07 17:57

104 points · 36 comments

Discovery of Cold War-era rare Eastern Bloc computers in a German hangar

06/07 05:00

88 points · 19 comments