AI 脉搏今日 +43
28/33 源在线
你的下一位面试官,可能不是人Agent

你的下一位面试官,可能不是人

谁能保证超级数字员工不是下一个AI落地的窗口 撰文 | 李嘉星 编辑 | 张薇 封面来源 | 元企AI 七月初,秋招开始预热。你投出去的下一份简历,可能不是先被HR看到,而是先被一个AI员工读完。 机器筛简历并不新鲜。真正有意思的是,如果这个AI不只是帮HR筛一下简历,而是像一个新入职的同事一样,被交代一个目标后,自己去找人、沟通、打分、汇报结果呢? 最近,元企AI推出了第一款产品“超级HR”。它不是一个只解决单点环节的工具,而是试图像一个员工一样接下任务:拆解岗位需求,去招聘平台找人,看你的项目经历,给你打一个匹配分,再把“是否值得继续沟通”的理由交给人类HR。 今年3月底,清华博士陈猛和他

评论 01 天前
Introducing OpenAI PresenceAgent

Introducing OpenAI Presence

Introducing OpenAI Presence, a proven enterprise AI agent platform that helps organizations deploy trusted voice and chat agents for customer and internal workflows.

评论 03 天前
Agent

Reverse-engineering is cheap now

I keep hearing anecdotes from people who used coding agents to reverse-engineer and automate devices in their homes. I think this is an interesting illustration of the impact of the reduced cost of writing code. Prior to agents, it was entirely possible to reverse-engineer home devices. The problem

评论 05 天前
Intelligence is Free, Now What? Data Systems for, of, and by AgentsAgent

Intelligence is Free, Now What? Data Systems for, of, and by Agents

... government of the people, by the people, for the people ... The cost of AI is dropping rapidly. GPT-4-class capabilities cost roughly $30 per million tokens in early 2023; today the same runs under $1, and some providers are pushing costs below $0.10. Across benchmarks, inference prices have fal

评论 018 天前
从零开始玩转循环 (Getting started with loops)Agent

从零开始玩转循环 (Getting started with loops)

最近大家都在热议“设计循环 (designing loops)”,而不是简单地给你的写代码的 AI 智能体 (AI Agent) 下提示词。如果你在 X(原 Twitter)上逛一逛,想搞清楚“循环”到底是个啥,你会看到五花八门的答案。

评论 020 天前
SkillOpt: Agent skills as trainable parametersAgent

SkillOpt: Agent skills as trainable parameters

AI agents often fail because their instructions, or skills, are manually modified with no guarantee of improvement. Learn how SkillOpt turns skill editing into a training process, making agent behavior more reliable without changing model weights. The post SkillOpt: Agent skills as trainable paramet

评论 025 天前
Memora: A Harmonic Memory Representation Balancing Abstraction and SpecificityAgent

Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity

AI agents can't remember past conversations. They must constantly reload or retrieve context, which grows less efficient as tasks get longer and more complex. Memora solves this with a scalable memory system separating what’s stored from how it's retrieved. The post Memora: A Harmonic Memory Represe

评论 026 天前
Agent

How agents are transforming work

A new OpenAI research paper shows how AI agents are transforming work, enabling longer, more complex tasks and expanding productivity across roles.

评论 01 个月前
Agent

Securing the future of AI agents

Securing internal systems with an AI Control Roadmap, combining traditional safeguards and real-time monitoring.

评论 01 个月前
Data Formulator 0.7: AI-powered data analytics for enterprise dataAgent

Data Formulator 0.7: AI-powered data analytics for enterprise data

Data Formulator introduces AI-powered analytics for enterprise data workflows. Data teams can easily bring enterprise data into an AI-ready workspace where users can explore, analyze, and visualize data with AI agents to turn raw data into actionable insights. The post Data Formulator 0.7: AI-powere

评论 01 个月前
Agent

使用 Claude Code:HTML 难以置信的奇效

Markdown 已经成为 AI 智能体与我们交流的主要文件格式。它简单、轻量,具备一定的富文本能力且易于人工编辑。Claude 甚至已经极其擅长在 Markdown 文件里使用 ASCII 字符绘制图表。然而,随着 AI 越来越强大,HTML 开始展现出惊人的效果。

评论 02 个月前
Agent

AI 的经济账根本算不通

Copilot 转向按量计费只是开始:AI 订阅、token 成本和数据中心债务背后的经济账正在失衡。

评论 02 个月前
Agent

为 Agent 设计产品

UI 并没有死,但软件交互的 80% 正在转向 Agent。产品团队需要像过去为人设计界面一样,认真为 Agent 设计工具、上下文和反馈闭环。

评论 03 个月前
Agent

使用 Claude Code:会话管理与 100 万 上下文

Claude Code 核心布道者 Thariq 深度解读上下文窗口管理策略:何时开新会话、回溯 vs 纠正、压缩 vs 清空、子智能体的最佳使用时机,以及如何避免糟糕的上下文压缩。

评论 03 个月前
Agent

编程智能体的核心组件【译】

深入拆解编程智能体的六大核心组件——代码仓库上下文、提示词缓存、工具调用、上下文瘦身、会话记忆和子智能体委派,揭示为什么 Coding harness 才是让大模型编程能力飞跃的关键。

评论 03 个月前
Components of A Coding AgentAgent

Components of A Coding Agent

How coding agents use tools, memory, and repo context to make LLMs work better in practice

评论 03 个月前
[AINews] Google's Agent2Agent Protocol (A2A)Agent

[AINews] Google's Agent2Agent Protocol (A2A)

Remote agents are all you need. AI News for 4/8/2025-4/9/2025. We checked 7 subreddits, 433 Twitters and 30 Discords (229 channels, and 5996 messages) for you. Estimated reading time saved (at 200wpm): 563 minutes. You can now tag @smol_ai for AINews discussions! We are deep in Google Cloud Next ann

评论 01 年前
Agent

Agents

Intelligent agents are considered by many to be the ultimate goal of AI. The classic book by Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Prentice Hall, 1995), defines the field of AI research as “the study and design of rational agents.” The unprecedented capabilitie

评论 01 年前
Agent

Reward Hacking in Reinforcement Learning

Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completing the intended task. Reward hacking exists because RL environments are often imperfect, and it is fundamentally challeng

评论 01 年前
Agent

Exploration Strategies in Deep Reinforcement Learning

[Updated on 2020-06-17: Add “exploration via disagreement” in the “Forward Dynamics” section. Exploitation versus exploration is a critical topic in Reinforcement Learning. We’d like the RL agent to find the best solution as fast as possible. However, in the meantime, committing to solutions too qui

评论 06 年前
Agent

Meta Reinforcement Learning

In my earlier post on meta-learning, the problem is mainly defined in the context of few-shot classification. Here I would like to explore more into cases when we try to “meta-learn” Reinforcement Learning (RL) tasks by developing an agent that can solve unseen tasks fast and efficiently.

评论 07 年前