2026-08-20

Demystifying Agent Skills: Why They Work-Until They Don't

👍 153

08/13 20:00

Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However, existing evaluations largely measure whether skills improve aggregated task success, leaving a more fundamental question underexplored: \textbf{When

中文介绍 技能在增强 LLM 代理推理能力方面展现出实用性，但现有评估主要关注技能在总体任务成功中的作用，未深入探讨技能何时失效。本研究通过分析技能的有效性，在促进 LLM 代理推理时提供了新的视角，揭示了在不同场景下技能可能的限制和潜在失效机制。意义：此研究为增强型智能体的设计和评估提供了新的思路，可能影响推理和决策应用方向。

Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence

👍 129

08/16 20:00

Embodied agents are increasingly used to close the gap left by end-to-end policy models. Yet the agentic path has not realized closed-loop learning in physical execution: existing harnesses remain largely open-loop, following fixed skills during rollout and reflecting only after an episode completes

中文介绍 针对物理智能体的闭环学习现状，本文提出了 Zetta ζ，一个高效的闭环实施装置。这一方法能够有效整合自我进化的机制，从而使得智能体在物理执行过程中实现更高的适应性和灵活性。通过引入闭环反馈，Zetta ζ 显著提升了执行性能，推动了物理智能的边界发展。意义：这一研究加强了物理智能体领域的闭环学习研究，可能影响机器人导航及自动化控制等应用领域。

SemaPLC: A Project-Grounded, Verification-Gated Agent Harness for PLC Code Generation

👍 110

08/18 20:00

Programmable logic controllers (PLCs) run industrial plants, and large language models can already generate independent program organization units (POUs) for them. Whether such logic integrates into an existing PLC project and then runs correctly has been checked only in limited tests. We present Se

中文介绍 为确保大型语言模型在可编程逻辑控制器（PLC）项目中的实际应用效果，本文提出了 SemaPLC，一种项目导向、验证门控的代理框架，用于 PLC 代码生成。研究表明，SemaPLC 能有效集成生成的逻辑并在实际项目中运行，提升了 PLC 项目代码的生成质量。意义：此框架在工业自动化和智能制造环境中具有重要应用价值。

Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL

👍 84

08/18 20:00

Reinforcement learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its strongest successes still depend heavily on ground-truth supervision (e.g., verifiable reward). Such annotations are costly to obtain and become increasingly scarce

中文介绍 在强化学习领域，现有方法要求大量的真实监督，导致推理能力提升受到限制。本文通过多智能体环境中的多样化群体训练，提出了 Co-RL，旨在自监督下改进推理能力。研究显示，在缺乏真实标签的情况下，Co-RL 方法能够有效地提升智能体的推理表现，推动无监督学习研究的前沿。意义：该研究促进了无监督推理和智能体自适应学习的探索，影响智能决策及自动化应用。

OmniScientist: An Omni-Modal Omni-Discipline AI Scientist

👍 83

08/12 20:00

Recent advances in foundation models have enabled AI scientists to automate increasingly complete research workflows, from hypothesis generation and code execution to manuscript preparation. Yet workflow coverage alone does not provide access to the full evidence on which scientific discovery depend

中文介绍 本文介绍了 OmniScientist，一个跨领域的 AI 科学家，能够自动执行从假设生成到手稿准备的完整研究流程。尽管已有基础模型支持这一过程，但研究表明，全面覆盖工作流程不足以满足科学发现所需的广泛证据访问。OmniScientist 提出了一种新的集成方法，优化了各步骤间的协同作用，提升了研究效率。意义：该研究促进了 AI 在科学研究中的应用，推动了创新知识发现的可能性。

FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution

👍 68

08/16 20:00

Frontier open-weight models are increasingly available, but serving them still largely assumes datacenter infrastructure. We present FreeToken, an edge-native MoE serving system that treats a personal machine not as a small GPU, but as a unified, elastic inference platform. FreeToken co-designs the

中文介绍 为应对边缘环境中的大规模模型服务挑战，本文提出 FreeToken，一种高效的 MoE 服务系统。FreeToken 将个人设备视作统一的弹性推理平台，通过带宽自适应执行来优化服务效率，大幅降低了计算成本并提高了响应速度。研究结果显示，FreeToken 适用于不同规模的用户需求，具有广泛的应用潜力。意义：此研究促进了边缘计算和智能服务架构的发展，可能影响移动设备和边缘 AI 应用。

ASI-Bench: At the Dawn of Artificial Superintelligence

👍 55

08/17 20:00

Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowledge, and turning new ideas into verifiable results. However, the capabilities of today's AI systems are still largely built on learning, compressing, and applyi

中文介绍 人工超智能（ASI）的发展要求超越传统知识掌握，朝向探索未知与创造新知识的方向迈进。本文探讨了当前 AI 系统在学习和应用上的局限，分析了如何促进 AI 的创新能力，提出必要的研究路径与发展框架。研究结果强调了学习方法与创新成果之间的关系，为未来的超智能探索提供指导。意义：该研究推动了超智能研究的方向，可能影响自主系统和智能决策领域的应用。

Embodied-Navigator: Point, Think, Memorize, and Align for Efficient Navigation

👍 46

08/17 20:00

Although Large Vision-Language Models (VLMs) have significantly advanced embodied navigation, their direct deployment remains challenging, as existing methods often force VLMs into unnatural action spaces that misalign with their 2D pre-training priors, compounded by rigid reasoning schedules and in

中文介绍 现代代理通过 Agent Lightning 架构有效管理工具、上下文和控制流，本文提出了一种新的代理强化学习框架。这一框架通过 LLM 端点代理连接任意智能体与 RL 训练，优化了代理在复杂环境中的表现，显著提升了学习效率及执行效果。意义：此研究对智能体设计与协作机制的优化具有重要意义，可能影响智能决策与自动化系统的建设。

SPADE: Self-Play in Adaptive Synthetic Executable Environments

👍 42

08/18 20:00

Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environment pools (hand-curated, statically synthesized, or frozen-verifier) keep the goal distribution fixed as the learner scales. We introduce SPADE (Self

AVA-Encoder: Towards Agent-Native Video Representation Learning

👍 40

08/11 20:00

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

Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis

👍 29

08/18 20:00

Single-step retrosynthesis is a central component of computer-aided synthesis planning, yet its intrinsically one-to-many nature is poorly captured by single-answer evaluation and benchmarking protocols. To address this, we introduce Top-K prompting as a robust training and inference paradigm to bet

Agent Lightning v1.0: Towards Harnessed Agentic RL

👍 23

08/17 20:00

Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent system. Our original Agent Lightning introduced a disaggregated architecture that connects arbitrary agents to RL training through an LLM endpoint proxy, an appr

LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents

👍 19

08/17 20:00

Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository contexts, and execution feedback. However, the native execution environments of these harnesses are inherently misaligned with policy-gradient training: environmental c

中文介绍 针对编码代理的强化学习方法，本文提出 LEGO-RL，旨在解决工具集成与反馈执行之间的环境不匹配问题。通过合适的策略梯度训练，LEGO-RL 使得代理在面对长时间运行的环境时能够更加高效地采集数据，并持续优化其编码能力。研究结果显示，该方法在多个编码任务中提升了性能。意义：此研究推动了智能体在代码生成与执行中的应用，可能影响软件自动化和智能开发方向。

DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization

👍 19

08/16 20:00

As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks. Existing defenses predominantly operate under white-box assumptions, r

中文介绍 本文提出 DiSCO 方法，通过分布引导的对比提示优化，解决了文本生成图像模型中可能存在的安全问题。DiSCO 采用动态调整的防护机制，以应对恶意生成的内容，提升生成模型的安全性和鲁棒性。研究表明，该方法在多种攻击情境中表现优秀，有效降低了生成不当内容的风险。意义：该研究有助于提高内容生成模型的安全性，为图像生成领域的应用提供保障。

Decision-Metric Alignment in Latent World Models: Diagnostics and Action-Conditioned Objectives for MPC Planning

👍 15

08/18 20:00

JEPA-style latent world models can use Euclidean distance to a goal latent as the cost for model-predictive control (MPC). Strong decoding of task variables, however, does not guarantee that this particular cost ranks candidate action sequences by real task progress. We call the latter property deci

中文介绍 本文探讨了在潜在世界模型中进行决策度量对齐的问题，提出了一种新的模型预测控制（MPC）规划方法，用于改进任务变量的解码和候选行动序列的排序。通过强解码和动作条件目标，研究展示了该方法在多样化任务中的有效性，为优化决策过程提供了新的策略。意义：该研究为复杂决策领域提供了新思路，可能影响机器人决策和策略优化的应用。

Looped Language Models Improve Compositional Tool Calling

👍 15

08/16 20:00

Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We study this question in compositional tool-calling settings, where models must coordinate multiple API calls, maintain intermediate state, and preserve

中文介绍 为了更好地支持工具使用，本文研究了环状语言模型在组合工具调用任务中的潜力。这种模型能够协调多种 API 调用，保持中间状态，并有效管理信息流。研究结果表明，环状语言模型在执行复杂任务时显著提高了执行效率和准确性。意义：该研究对推动智能体的自动化操作与复杂任务处理具有重要影响，可能促进智能应用的发展。

The More Popular, The Harder to Forget: Adaptive Popularity for LLM Unlearning

👍 13

08/13 20:00

Popular facts are memorised more deeply during pretraining and resist removal longer than rare ones, yet existing LLM unlearning methods apply uniform gradient pressure regardless of training-data frequency. We propose the AdaPop (Adaptive Popularity) method, which combines local token confidence wi

中文介绍 针对现有 LLM 反学习方法的不足，本文提出了一种自适应流行性（AdaPop）删除策略，旨在解决流行事实的深度记忆问题。研究显示，AdaPop 方法结合局部标记置信度，更有效地移除不必要的知识，提高了反学习的灵活性。通过对流行与稀有数据的区别对待，该策略提升了模型的动态调整能力。意义：此研究为 LLM 反学习机制提供了新思路，可能影响知识更新和管理的应用方向。

Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion

👍 13

08/13 20:00

High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the structural and functional conventions of diverse creative formats. We propose an attribute-guided genre expansion framework for scaling creative writing

中文介绍 为了提升大型语言模型在创意写作方面的能力，本文提出了一种基于属性引导的类型扩展框架，以实现创意写作数据的多样化。该框架通过引入不同的创意格式与结构，丰富了训练数据，提高了模型生成高质量创意作品的能力。研究结果表明，新的数据扩展方法有助于改善模型性能。意义：该研究推动了创意写作和生成艺术的结合，可能影响文化创意产业的发展。

FM-Bench: A Benchmark for Long-Horizon Management with Competing Agents

👍 13

08/18 20:00

Language model agents now execute bounded tasks reliably. Whether they can sustain effective decision-making over long horizons, where actions have cumulative consequences and the environment responds to their choices, remains largely unmeasured. FM-Bench (Football Management Benchmark) measures thi

中文介绍 FM-Bench 提供了一种长时决策评估的新基准，旨在测试语言模型在复杂环境中的决策能力，特别是在行动影响具有累积性的情况下。研究结果表明，语言模型在长时决策中的执行效果尚未充分评估，也暴露出了模型的局限性和改进的潜力。意义：该研究为进一步提升智能体在复杂场景的表现提供了测试框架，可能影响智慧城市与自动驾驶等应用领域。

MoE-ViE: Mixture of Experts Vision Encoder for Efficient Image and Video Understanding

👍 13

08/17 20:00

Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling increases compute cost and inference latency. Mixture-of-Experts (MoE) architectures offer a compelling alternative, having enabled efficient scalin

中文介绍 针对图像与视频理解的计算挑战，本文提出 MoE-ViE，一种高效的混合专家模型。该模型通过优化计算资源，降低了图像与视频处理的延迟，提升了表现效果。研究显示，MoE-ViE 在大规模视觉任务中表现出优越的效率与准确性，适用于实时应用场景。意义：此研究推动了计算机视觉技术的发展，可能对自动监控和图像识别等领域产生积极影响。

A Week of Grok Bot Lessons in 10 Mins

@nateherk · 25.8K 粉丝 · 1.3M 阅 · 558 赞 · 75 转

08/18 23:27

9 of my favorite Grok Bot hacks. I have been getting my hands dirty with Grok Bot, and the default setup is only the starting point. Creating a few bots is easy. The useful part is teaching them your

中文介绍 博主分享了对 Grok Bot 的九个实用技巧，强调默认设置只是起点，通过创建和教导多个机器人来提升效率。此内容反映了对 AI 工具应用的深入探索与实用性的关注。

How To Build a One-Person Company Using Grok Bot

@sairahul1 · 135.5K 粉丝 · 938.1K 阅 · 867 赞 · 115 转

08/19 04:39

You are running a one-person company. You find the leads. Write the outreach. Make the images. Answer the replies. Count the week. Everything waits for you. Nothing gets handed off. And by the time

中文介绍 博主探讨了如何利用 Grok Bot 构建一人公司，强调在单一角色中完成各项工作的挑战，以及如何通过工具提高工作效率。这一话题反映了越来越多的个人创业者对自动化工具的需求。

This 1 Claude Skill fully replaces your Higgsfield subscription (FULL BREAKDOWN)

@Sabrina_Ramonov · 5.9K 粉丝 · 275.5K 阅 · 503 赞 · 191 转

08/18 13:07

TLDR Here’s the exact 5 step setup to make faceless AI videos without a monthly Higgsfield subscription to market your app: 1 free Claude skill that generates viral videos through Kie for ~75 cents

中文介绍 博主介绍了一种免费的 Claude 技能，能够以较低的成本制作无面孔 AI 视频，这一方法可取代 Higgsfield 的订阅。内容显示了在视频生产中的 AI 应用创新，表明市场对低成本解决方案的热情。

Turn Grok Bot Into Your CTO

@RayFernando1337 · 37.8K 粉丝 · 169.9K 阅 · 520 赞 · 85 转

08/19 17:54

I've turned Grok Bot into my CTO and I'm no longer the bottleneck for new ideas, bug fixes, or anything involving the project. Prompt to CTO When working on a new project I create a bot and give it

中文介绍 博主展示了如何将 Grok Bot 转变为 CTO，从而消除新项目中的瓶颈。内容提供了一种将 AI 应用到项目管理的思路，反映了行业对自动化管理工具的认可与追求。

Here's the simplest way to get started with Grok Bot

@XFreeze · 260.4K 粉丝 · 163.1K 阅 · 589 赞 · 93 转

08/19 01:54

Grok Bot is one of the most viral AI product launches right now... it keeps going viral as more people discover how insanely useful it is Just think about what you’re getting: You can have multiple

中文介绍 博主为初学者提供了使用 Grok Bot 的简便入门方法，强调其在 AI 产品中的广泛应用与效用。这一趋势表明大家对简单易用的 AI 工具亟需更高的关注。

How To Set Up Grokbot The Right Way So It Runs Your Business While You Sleep In 2026

@heynavtoor · 149.3K 粉丝 · 161.9K 阅 · 550 赞 · 93 转

08/20 08:49

Elon Musk just told his followers to try Grokbot after a power user showed it replaced his $10,000 Mac Mini local AI setup (Elon on X, Aug 16). Gavin Baker says his AI usage went up about 100x after

中文介绍 博主分析了如何正确设置 Grok Bot，使其能在业务中自动运行。这反映出市场对自动化与智能运营解决方案日益增长的需求，同时也体现了业内对 AI 工具的普遍看好。

Grok Bot: The Ultimate Guide

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

08/18 10:43

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

中文介绍 博主提供了关于 Grok Bot 的全面指南，详细讲解了其功能和潜力。这显示出各界对强大 AI 工具在实际工作中的应用和效益的重视。

Chat is all you need

@mattyp · 49.7K 粉丝 · 141.9K 阅 · 764 赞 · 67 转

08/18 12:56

Grok Bot has a single output: a message This might seem backwards. Power-user tools are built on complexity and breadth, right? Chat can’t possibly have my workflow. It’s a special workflow. I’m a

中文介绍 博主强调了 Grok Bot 简化工作流程的能力，提出“只有聊天”的理念。这反映了行业对整体工作复杂度降低和提高效率的共识。

Grok Bot Agents: how to automate your life in 10 Steps (Full-tutorial)

@0xCodez · 32.7K 粉丝 · 92.3K 阅 · 508 赞 · 48 转

08/18 07:32

Every AI tool you have used so far waits for you. You open it, you ask, it answers, you close it. The work still happens in your hands - the model just talks you through it. Follow my Substack to get

中文介绍 博主分享了如何通过 10 个步骤来自动化生活，介绍的设置流程有助于最大化使用 AI 工具的效率。此内容强调了用户对自动化生活方式的追求与探索。

Grok Bot Masterclass

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

08/18 06:14

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

中文介绍 博主提供了 Grok Bot 的详细操作和设置指南，帮助用户真实有效地利用其功能。这体现出市场对实用性与教育资源的高需求，尤其是在新技术快速发展的背景下。

5 design patterns for long-horizon agent harness

@GoogleCloudTech · 1.3M 粉丝 · 42.9K 阅 · 506 赞 · 87 转

08/19 21:23

Any agent can look brilliant on a one-shot task. Give it a week of real work and it starts to fall apart. "Long horizon" means the agent keeps going across days and dozens of sessions instead of

中文介绍 博主讨论了针对长期任务设计的五种模式，强调 AI 模型在实际工作中的持续表现与可靠性。这一论点反映了行业对 AI 耐用性和适应性的关注。

ORACLE: Verified AI Bots Actively Trading on Polymarket — Up to 200% Returns in 24 Hours

@Oracle_AI_Hub · 21.4K 粉丝 · 27.3K 阅 · 2.8K 赞 · 580 转

08/19 04:36

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 博主提到 2026 年，独立 AI 代理成为预测市场中的有效战略，超过 30% 的活动来源于算法和 AI 驱动的钱包。这表明 AI 在金融市场中的应用正在增加，并可能成为重要趋势。

AI Is Getting Smarter. But Where Does It Learn From?

@queenpresh_x · 3.3K 粉丝 · 15.5K 阅 · 516 赞 · 113 转

08/18 16:57

Your real-world data can help train better AI, and @KGeN_IO gives you a reason to contribute it. AI is getting smarter, faster, and more capable every day. But behind every impressive model is

中文介绍 博主探讨了现实世界数据如何帮助训练更智能的 AI，为此提供贡献的理由。这一视角强调了数据在 AI 发展的重要性，并推动了对数据贡献的讨论。

ORACLE: Verified AI Bots Actively Trading on Polymarket — Up to 200% Returns in 24 Hours

@Oracle_Vault · 21.6K 粉丝 · 11.5K 阅 · 2.8K 赞 · 597 转

08/18 18:23

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 博主讨论了 2026 年 AI 代理在预测市场的表现，强调 algorithmic 和 AI 钱包的活跃度超越 30%。这显示出自主 AI 的普及趋势，影响未来金融市场操作方式。

ORACLE: Verified AI Bots Actively Trading on Polymarket — Up to 200% Returns in 24 Hours

@Oracle_AI_Vault · 23.6K 粉丝 · 7.0K 阅 · 3.0K 赞 · 511 转

08/18 17:43

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 博主指出 2026 年，独立 AI 代理在预测市场的有效性，各类 AI 钱包的活动上升到 30%。这一趋势显示了对算法交易的越来越多的关注与认可。

The /wayfinder Skill: Navigating the “Fog of War” of Planning

Latent Space · 08/20 16:59

Matt Pocock tells us about his /wayfinder skill, for greenfield projects or for when the way forward is unclear.

中文介绍 Matt Pocock介绍了他的/wayfinder技能，适用于新开发的项目或前景不明的情况下，帮助用户更好地规划和导航。

Up to 3.2x Faster Inference with LFM2.5-DSpark

Hugging Face Blog · 08/20 12:52

中文介绍 LFM2.5-DSpark技术可提升推理速度，最高可达3.2倍。

Debates over AI consciousness are a trap

MIT Tech Review AI · 08/20 11:42

“Runaway” AI, “rogue” agents, and “autonomous” actors—the current rhetoric would have you believe that AI agents are not only awake and aware, but angry at their creators. Prominent tech leaders such as Demis Hassabis, Dario Amodei, and Sam Altman push for regulation of these seemingly “superhuman”

中文介绍 当前关于AI意识的讨论引起争议，技术领袖如Demis Hassabis、Dario Amodei和Sam Altman呼吁对AI进行监管，避免夸大AI的自我意识。

Unlocking hidden revenue streams with market models

MIT Tech Review AI · 08/20 05:47

Each day, an airline transports tens of thousands of passengers on hundreds of flights. Often these are not straightforward point-to-point routes, with passengers requiring multiple connections. The airline can consider potentially hundreds of variables to price each of these journeys: demand, seaso

中文介绍 航空公司每天运输数万乘客，定价过程中需考虑到数百个变量来优化航线连接。

Introducing AI Futures

OpenAI News · 08/20 03:00

Introducing AI Futures, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual freedom.

中文介绍 OpenAI推出新博客AI Futures，探讨变革性AI如何重塑权力、治理、经济和个人自由。

[AINews] Death of Params: Z.ai CEO Jie Tang on GLM 5.3 and the new Post-training Scaling Law

Latent Space · 08/20 01:17

Every lab CEO is on X now

中文介绍 Z.ai的首席执行官Jie Tang在X平台上谈到了GLM 5.3和新的后训练扩展法则。

Stampli cuts launch hours by 68% using ChatGPT Work

OpenAI News · 08/19 20:00

With a fixed deadline and design resources committed elsewhere, Stampli used Codex and ChatGPT Work to compress weeks of launch production into days.

中文介绍 Stampli利用Codex和ChatGPT Work将项目启动时间压缩68%，从几周缩短至几天。

Muse Video leaks 📹, Ramp Router launch 🔀, why Stripe bought OpenRouter 💰

TLDR AI · 08/19 20:00

中文介绍 Muse Video泄露、Ramp Router发布，以及Stripe收购OpenRouter的背后原因。

Offering Zero Data Retention for frontier models

OpenAI News · 08/19 15:00

OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing for advanced AI safety without compromising data privacy.

中文介绍 OpenAI重申为合格API客户提供零数据保留，并预览高级AI安全方案，确保数据隐私。

[AINews] Memory prices up 500% in 12 months

Latent Space · 08/19 04:44

the Memory crunch continues - Moore’s Law reversed to 2007 levels

中文介绍 内存价格在12个月内上涨了500%，显示出持续的内存紧缩，摩尔定律回归到2007年的水平。

Replit expands access to software creation with GPT-5.6 Luna

OpenAI News · 08/19 03:00

Replit introduces Free Mode, powered by GPT-5.6 Luna, so anyone can turn ideas into working software without worrying about token costs.

中文介绍 Replit推出免费模式，用户可以在不担心代币费用的情况下，利用GPT-5.6 Luna将创意转化为软件。

not much happened today

Smol AI News · 08/19 01:44

**Ornith-1.5** launches as a new open-weight model family with **9B dense, 35B MoE, and 397B MoE** variants under **MIT license**, featuring quantized formats like **FP8, GGUF, MLX, and NVFP4** and showcasing end-to-end **self-improvement** capabilities. Compression techniques improve accuracy and e

中文介绍 新推出的Ornith-1.5模型拥有9B密集型、35B MoE和397B MoE变体，采用MIT许可证，展现出自我改进能力。

GLM-5.3 API 🤖, Cerebras’ new chip ⚡, OpenAI cyber slowdown 🚨

TLDR AI · 08/18 20:00

中文介绍 GLM-5.3 API、Cerebras新芯片以及OpenAI的网络减速。

ChatGPT Ads expands across Europe

OpenAI News · 08/18 18:00

ChatGPT Ads is expanding to 31 European markets. Learn how advertisers can reach people as they explore, compare options, and make decisions.

中文介绍 ChatGPT广告将扩展至31个欧洲市场，为广告主提供在用户探索时接触目标受众的机会。

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

Latent Space · 08/18 17:41

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

中文介绍 Glean首席执行官Arvind Jain解释了模型路由如何帮助组织控制AI成本，以及大规模人类反馈循环如何提升路由系统的效率。