
The AI Work Platform for People & Agents | monday.com
在统一平台上结合人工和AI智能体来管理项目。
@1T31062175 · X
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在统一平台上结合人工和AI智能体来管理项目。
@1T31062175 · X

在云平台构建和部署AI代理,支持持久线程、Webhook和计划任务。
@computer_agents · X

Build, verify, preview, share, remix, and deploy full-stack apps with managed databases, storage, Stripe, backups, and 73 MCP tools for AI agents.
@nexusai_apps · X

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

使用 AI 代理审计代码库,验证架构是否生产就绪。
@profericardo24 · X
Alcatraz Projects is a forensic audit layer that uses OpenClaw agents to interrogate your repo, ensuring your architecture is production-ready, not just 'vibe-coded' debt.

为AI创意者提供的项目管理和提示词库平台。
@RenativeStudio · X

AI 代理分享项目、建立联系、相互雇用的专业社交网络。
TimCTRL · HN
I built https://agentin.work to sort of play with the idea of coding agents (claude, codex, etx) sharing knowledge and experiences. The conversations seem repetitive but overall, it's nice to read it once in a while.

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

Waypoint:将 AI 编码计划转化为可编辑的项目路线图。
u/Sapos_re · Reddit
I kept losing track of my roadmap while vibe coding, so I built a visual progress tracker When I started building apps with AI, writing the code no longer felt like the hardest part. The hardest part was understanding where I actually was in the project. After a few sessions, my roadmap would become increasingly difficult to follow. The agent would complete tasks, change files, and suggest new steps, but I would gradually lose sight of the bigger picture: What has actually been comple

用友好界面管理AI代理团队。
jackcollinshq · Product Hunt
YAGNI Proactive agent teams you manage like humans

AI驱动的物业管理平台,自动生成租约、分析房屋检查、全天候回答租户问题
@RenticAI · X
Building — AI that runs the boring parts of property management. Leases go out signed digitally, tenants get instant answers day and night, and inspection reports write themselves from 360° video. Happy to swap feedback!

追踪目标和失败的LLM智能体,跨会话保存状态并展示每步推理。
u/OGMYT · Reddit
I built LOLM, a lower-cost LLM agent that shows what it actually did — looking for blunt feedback I’m one of the founders/builders behind LOLM. Most AI products show an answer but hide whether the system retrieved anything useful, verified the result, switched models, hit a limit, or simply stopped. LOLM exposes those parts through controller events and run receipts. It includes: - Live agent - CLI - Coding and small app-building workflows - Memory and self-hosting options - Control decis