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5543
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Общее1 мин

为 Blender 创建了一个 AI 代理,可以通过聊天控制 3D 场景

🧊 为 Blender 创建了一个 AI 代理,可以通过聊天控制 3D 场景。开发人员推出了 Loopcut——带有内置人工智能助手的 Blender 的修改版本。现在,您无需手动设置场景,只需用文字描述任务,代理就会执行必要的操作……

Telegram — GPT Time | Нейросети | ИИ
Образование1 мин

如何处理数据? 💬

如何处理数据? 💬 构建报告。而且,例如: ✅在大型数组中查找模式; ⬜️预测用户行为; ⬜️构建推荐系统; ⬜️训练和改进机器学习模型; ⬜️创造产品并帮助企业成长。在分析师学院...

Telegram — AI Machine Learning Big Data
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...

MIT Technology Review
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
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
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

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

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

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

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