Общее1 мин
人工智能的隐藏存储费用：推理留下了什么
如果让大多数高管想象一下人工智能的成本，他们很可能会看到 GPU（具有千兆瓦功率的加速器机架）和硅的候补名单。但这张图遗漏了一些东西：数据存储成本。模型响应后数据会发生什么？大部分后端数据...
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5331
Stories
Исследования1 мин
Anthropic 以其永远在线的代理“Claude Duet”挑战 Grok Bot、Meta 的 Muse 和 OpenAI 的 Dots
Anthropic 推出了 Claude Duet，这是一款主动式 AI 代理，即使在用户离线时也能自行处理任务。该应用程序基于 Claude Opus 5.5 构建，通过 6,000 多个连接器连接到 Gmail、Slack 和 Stripe 等服务。在此之前需要先获得批准...
The Decoder
Общее1 мин
数百美元起的服务：11 项收费服务
数百美元起的服务：11 种服务集合要成为自由职业者并获得客户，您不必立即了解代码、安装和设计。 ㅤ 有些服务的成品几乎是从头开始组装的。选择一个，学会接受它......
Telegram — Paragon | Нейросети и ИИ
Hugging Face Blog
Общее1 мин
AI学会从照片中确定拍摄地点，精度达到2米
📍 AI 学会了从照片中确定拍摄位置，精度高达 2 米。人工智能代理出现了一项新技能，可以让你使用普通照片搜索地理坐标。该工具使用卫星地图、街景和地形数据来比较......
Telegram — GPT Time | Нейросети | ИИ
Политика и регулирование1 мин
Открытые модели NVIDIA поддерживают проекты в европейском госсекторе
Новые открытые модели NVIDIA помогают европейским правительствам эффективно управлять данными.
SMNTCN (Semanticon) — глобальные новости ИИ
Исследования1 мин
Европейские пионеры используют модели NVIDIA Earth-2 для прогноза погоды
Модели NVIDIA Earth-2 помогают прогнозировать погоду и климат в Европе.
SMNTCN (Semanticon) — глобальные новости ИИ
Исследования1 мин
NVIDIA трансформирует европейскую розницу с помощью ИИ и роботов
NVIDIA внедряет открытые модели и физический интеллект в европейскую розницу.
SMNTCN (Semanticon) — глобальные новости ИИ
Инфраструктура1 мин
Companies are using the NVIDIA DSX platform to solve their energy challenges
Companies are adopting the NVIDIA DSX platform to solve energy challenges and optimize infrastructure.
SMNTCN (Semanticon) — глобальные новости ИИ
Инфраструктура1 мин
NVIDIA расширяет платформу Cosmos для создания мировых моделей
NVIDIA расширила платформу Cosmos новыми инструментами для создания мировых моделей.
SMNTCN (Semanticon) — глобальные новости ИИ
Инфраструктура1 мин
Европейские финансовые организации принимают открытые модели для ИИ
Европейские финансовые организации внедряют открытые модели для создания интеллекта.
SMNTCN (Semanticon) — глобальные новости ИИ
Общее1 мин
玩家要求 Valve 在 Steam 上实施全局 AI 过滤器
✔️ 游戏玩家要求 Valve 在 Steam 上实施全局 AI 过滤器 最新的 Steam Next Fest 演示节遇到了大量使用生成神经网络创建的低质量项目，引起了社区的尖锐批评。尽管…
Telegram — AI Machine Learning Big Data
Общее1 мин
每个 Claude Code 用户都应该安装 /stack。
每个 Claude Code 用户都应该安装 /stack。 /stack mod 显示了 Claude Code 中已安装和可用的所有内容：技能、子代理、命令、挂钩、MCP 服务器、插件、设置和输出样式。一切都按配置级别分组：企业，...
Telegram — Вайб-кодинг
Исследования1 мин
Anthropic 推出了 Claude Duet，一款无需永久命令即可工作的个人 AI 代理
⚡️ Anthropic 推出了 Claude Duet，这是一款无需永久命令即可工作的个人人工智能代理。新服务建立在Claude Opus 5.5的基础上，将成为OpenAI和Grok Bot的Dots的竞争对手。 Duet 在云中运行，使用浏览器和连接的应用程序，并执行...
Telegram — GPT Time | Нейросети | ИИ
Исследования1 мин
Anthropic 决定发布基于极其强大的 Opus 5.5 的 Claude Duet，来挑战 OpenAI Dots 和 Grok Bot。
Anthropic 决定发布基于极其强大的 Opus 5.5 的 Claude Duet，来挑战 OpenAI Dots 和 Grok Bot。 • 适用于Claude Pro 计划（Dots 需要每月支付100 美元订阅ChatGPT Pro）。 • 运行Opus 5.5，被称为世界上最好的型号。 • 有…
Telegram — Вайб-кодинг
Исследования1 мин
Forging More Tasks into Better Harnesses: Agentic Harness Evolution via Successive Scale Racing
arXiv:2610.28815v1 Announce Type: new Abstract: Agentic harness evolution optimizes an agent's instructions, tools, and control flow by using its task execution experiences as evolving evidence. While more evolution tasks should intuitively improve performance, our preliminary…
arXiv — cs.AI
Исследования1 мин
Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
arXiv:2610.28698v1 Announce Type: new Abstract: Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws. Whether these gains extend to virtual worlds with different…
arXiv — cs.AI
Исследования1 мин
SpecEvo: Speculative Evolution with Large Language Models for Cost-Efficient Scientific Discovery
arXiv:2610.28706v1 Announce Type: new Abstract: LLM-driven evolutionary search is a promising paradigm for scientific discovery, but existing methods remain costly and slow, often applying expensive reasoning uniformly while underusing evidence from failed, redundant, and…
arXiv — cs.AI
Исследования1 мин
Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
arXiv:2610.28763v1 Announce Type: new Abstract: Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires domain insight beyond routine ML…
arXiv — cs.AI
Исследования1 мин
A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
arXiv:2610.28686v1 Announce Type: new Abstract: Query clarification is essential for retrieval-augmented generation (RAG) systems that face ambiguous user queries. We consider multi-turn grounded conversational scenarios where a user query is either ill-formed or ambiguous in…
arXiv — cs.AI
Исследования1 мин
DiffResearch: Accelerating Deep Research with Diffusion Language Models
arXiv:2610.28909v1 Announce Type: new Abstract: Deep research systems iteratively plan, gather evidence, synthesize, and revise, making end-to-end latency a key bottleneck for interactive use. Almost all production systems are built on autoregressive (AR) language models and…
arXiv — cs.AI
Исследования1 мин
Decision language models as affordable and rapid alternatives to LLM judges of clinical generative AI
arXiv:2610.28826v1 Announce Type: new Abstract: Automated judges of clinical artificial intelligence (AI) outputs are necessary, but large language model (LLM) judges are costly and slow at scale. We compared Jev, a decision language model that returns structured judgments, with…
arXiv — cs.AI
Исследования1 мин
Hidden Messages Between Diffusion Language Model Agents: Causal Evidence for Latent Communication
arXiv:2610.28905v1 Announce Type: new Abstract: Diffusion language models form answers through a sequence of revisable continuous states, but multi-agent systems usually discard those states and make agents communicate only after decoding text. We ask whether one independent…
arXiv — cs.AI
Исследования1 мин
Simply Logical -- The First Three Decades
arXiv:2610.28912v1 Announce Type: new Abstract: This paper charts the evolution of the Prolog textbook Simply Logical -- Intelligent Reasoning by Example from print with runnable programmes on a 3.5-inch diskette published in 1994, via various intermediate online versions, to…
arXiv — cs.AI
Исследования1 мин
Back Up Before You Regret: Measuring Backup Awareness and Automating Backup in Coding Agents
arXiv:2610.28934v1 Announce Type: new Abstract: Coding agents now complete tasks autonomously, modifying real-world state such as artifacts, software environments, and configurations along the way. Yet they show limited backup awareness: recognizing that an action will modify…
arXiv — cs.AI
Исследования1 мин
Self-Improving Agents with World Model Predictive Paths
arXiv:2610.28945v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly expected to interact with environments and improve their decisions during deployment. However, how anticipating future outcomes and drawing on accumulated interaction experience…
arXiv — cs.AI
Исследования1 мин
Speedrunning Relational Foundation Model Pretraining
arXiv:2610.28855v1 Announce Type: new Abstract: Progress on foundation model pretraining is gated by who can afford a trial, and relational pretraining is no exception. Recipes are developed inside individual labs, reported under private protocols, and do not accumulate.…
arXiv — cs.AI
Исследования1 мин
To Manipulate or Not: Opinion Dynamics in LLM Interactions
arXiv:2610.28875v1 Announce Type: new Abstract: Large language models increasingly take part in extended conversations where they can both shape and be shaped by another party's views, yet most persuasion research measures only one side of that exchange. We introduce a…
arXiv — cs.AI
Исследования1 мин
GRAIL: From Saliency to Semantics in Time-Series Explanation
arXiv:2610.29036v1 Announce Type: new Abstract: Explaining a time-series model requires both locating the evidence and extracting meaningful knowledge from it. Yet a single timestep is not a semantic unit: the meaningful patterns live across many samples, not in any one of them.…
arXiv — cs.AI
Исследования1 мин
Does Every Instance Count Equally? Order Effects in LLM Multi-Instance Processing
arXiv:2610.29057v1 Announce Type: new Abstract: Users increasingly rely on large language models (LLMs) to analyse collections of independent inputs, such as product reviews or social media posts, and return a single aggregated answer. This setting requires multi-instance…
arXiv — cs.AI