BBC:为什么机器人披萨店在失败
BBC 回顾机器人披萨制作的失败案例,说明接触物理、卫生与成本让餐饮自动化反复翻车。
Pai 的 AI 雷达
含近几日仍热话题,按时间衰减排序。今天先看 kajisho5/ffmpeg-skill,其次 ltdrdata/ComfyUI-Impact-Pack。并行还有 Anthropic takes steps to block Claude from reverse-engineering video games、ATLAS-Bench: An Agentic Tool-Use Benchmark for Local Area Search。
BBC 回顾机器人披萨制作的失败案例,说明接触物理、卫生与成本让餐饮自动化反复翻车。
Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through textual reflection. Despite strong empirical results, these
Text-to-image (T2I) models have achieved remarkable success at faithfully rendering specified objects and attributes, yet their ability to produce visual metaphors, images that con
You can now block or unblock users directly from discussion comments in repositories owned by personal GitHub accounts, extending the in-context blocking controls already available
Breaking Claude Code Opus 5 Auto Mode Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attack
按结构质量做输入自适应稀疏 prefilling。
ChatGPT Ads 年化收入运行率突破 10 亿美元,并扩展至全球市场。该广告业务通过免费和低价选项,支持更多人使用 AI 服务。 🔗 阅读原文 via AIHOT · https://aihot.virxact.com/items/cmthb75t6092frodmg7ic1fc0
RoboBusiness 预告/报道认为食品加工的可变性让物理 AI 比仓储更难,Chef Robotics 分享现场经验。
Claude Fable 5.1 现已上线 Claude Code 和 Claude Platform,定价与 Fable 5 相同,API 缓存读取便宜 75%。模型在长任务中能更久自主推进、更善于提示用户它已卡住,写作风格也更自然。Anthropic 同时发布了 Claude Fable 5.1 和 Claude Mythos 5.1,称其为编码与知识工
Artificial Analysis 评测 Claude Fable 5.1,其在 max effort 下得 66 分登顶 Artificial Analysis Intelligence Index。 🔗 阅读原文 via AIHOT · https://aihot.virxact.com/items/cmtj484a00592roh9jjdf5xkd
Claude 官方宣布 Claude Cowork 和 Claude Code 新增后台使用电脑的能力。用户把任务交给 Claude 后,它会像人一样点击、输入和打开应用,用户可同时去做其他事。 🔗 阅读原文 via AIHOT · https://aihot.virxact.com/items/cmtkh71ky017vrolly7trswyx
Anthropic publish the system prompts for their Claude consumer applications ( Claude.ai and the Claude mobile apps - sadly not for Claude Cowork or Claude Code). I love that they d
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language model (LLM) post-training, but its reliance on coarse outcome rewards le
用首次失误学过程奖励,不必等结局。