AI 智能体1 month ago
Pullboard — Your agents already know what's next
为 AI 代理分配任务和管理依赖关系的优先级工作队列平台。
Olscore · HN
Pullboard – a work queue for agents, built to run a quant desk
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为 AI 代理分配任务和管理依赖关系的优先级工作队列平台。
Olscore · HN
Pullboard – a work queue for agents, built to run a quant desk
MCP协议任务队列，让AI编码代理请求和完成工作。
JulianQuinn · HN
Show HN: TaskPeace – a task queue my AI coding agents pull work from over MCP
向AI团队负责人分配任务,由其调度AI专家完成工作。
@StevenCen75554 · X
我们最新推出的 Product Hunt 上线了！ 专为电商团队而生，让你只需一句话，就能拥有一整支 AI 团队——写SEO Blog、做用户调研、优化Listing、生成AI短视频，全都不在话下。不用招人，不用在一堆工具间来回切换，只需要说出你想要什么。 专为想要「一个人打出一支团队的仗」的电商运营团队打造。 如果这个理念打动了你，今天的一个 upvote 对我们来说意义重大 🙏 #ProductHunt #BuildInPublic #AIAgents #IndieHackers
检查AI代理输出和操作是否符合策略，获得批准、拒绝、重写或升级决定。
u/danielbaker06072001 · Reddit
Most AI agent SaaS is just bad security with a nice dashboard Give a new employee access to Stripe, GitHub, Slack, and your CRM on day one, and you’d call it reckless. Give the same access to an AI agent, and we call it “autonomous.” That isn’t innovation. It’s skipping basic security because the demo looks cool. Full disclosure: I’m building TrustLoopGuard around this problem, so I’m obviously biased. While testing one MCP connection, I realized I had decided what the agent could a
Todos — 为 AI 代理团队分配工作，让它们自主规划、构建和审查任务。
@supezen · X
😂我做了一个截然相反的产品，把「过程」留给 AI，把「结果」留给自己
供多个 AI 编码代理协作的可视化工作台。
austinvhuang · HN
Collaborate.dev – Multiplayer Visual Desktop for Coding Agents
在Google日历中调度AI代理任务
matt413 · HN
Schedule tasks for your AI agents from Google Calendar
为AI编码代理提供共享记忆，存储团队发现和产品知识。
snakaya · HN
Vibsync – One Shared Memory for Claude Code, Cursor and Codex (MCP)
All your coding agents in one chat on your phone. beafk runs the real CLIs — Claude Code, Codex, Grok Build and Kimi Code — together on a machine you own, on the plans you already
@talk2bogdan · X
向 Discord 服务器添加 AI 代理进行协作聊天和团队工作。
david_shi · HN
mob.so – vibecode with friends in Discord
在统一平台上结合人工和AI智能体来管理项目。
@1T31062175 · X
与团队和AI代理在一个工作空间中构建、验证和发布软件。
u/OGMYT · Reddit
Validating a micro-SaaS for checking AI-built software before it ships I am building **Flows**, a system that turns an AI software goal into ordered implementation steps, checks, repair prompts, and a build report. The business thesis is not “better prompts.” It is reducing the cost of getting from an AI-generated first draft to a checked, shippable result. Metrics I want to measure: - prompts per completed project - tokens or credits per checked step - first-pass check rate - repair atte