其他1 week ago
AI Adoption Platform For SMBs | WorkLLM
WorkLLM is the simplest way to adopt AI across your company. Turn your team's everyday work into AI-powered workflows. No expertise required.
@dhimant_x · X
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WorkLLM is the simplest way to adopt AI across your company. Turn your team's everyday work into AI-powered workflows. No expertise required.
@dhimant_x · X
DeepVista: 从笔记生成AI工作流自动化销售和营销流程。
@jingconan · X
Hi! Let's Connect! I am building which helps founders to turn ideas to reusable workflows
浏览器中的AI视频编辑器,支持云工作流和快速编辑。
@AtifIqrar · X
Building TurboCut 🚀 An AI video editor that runs directly in your browser for private, lightning-fast editing, with optional cloud AI for heavier workflows. Built for YouTubers, content creators, educators and agencies who want to save hours editing.
Enterprise-grade identity and access management. Manage users, roles, permissions, MFA, and API credentials from a single portal. Free tier available.
@FPIAM2026 · X
Zippy 让你向 AI 描述业务需求来生成可工作的应用和工作流。
@Nirukt · X
Wurk.Flow unifies schema design, a live SQL workspace, column-level lineage, and source-to-target mapping in one local-first modeling IDE — in your browser or on your desktop. Free
@WurkHaus · X
What happens when you got told no to a 3k quote for a data tool and spend a year building one instead.
Soontra：通过AI对话自动化内容生产的工作空间。
@wesselsHQ · X
粘贴 n8n 或 Make 工作流 JSON，获得可靠性、成本和安全分析。
@dash12_dev · X
analyzes n8n workflows and Make scenarios, finding breakpoints, cost risks, and hidden reliability issues.
Workser: 构建、部署AI应用和自动化业务工作流的平台。
@khemmapich · X
itself in the early days focus on help small business to run their business on ai agents with vibe coding, workflow automation, and ai employees.
Rafter 让团队将工作流程转化为可复用的 AI 代理和技能，在现有工具中运行。
kristohb · HN
Rafter – an MCP server that shares one team's memory, skills and agents
一个云端AI编码助手，让团队在共享房间里协作编程。
u/Wide-Echo-548 · Reddit
Slack for AI Coding Workspaces I’m the founder of Worklone ( www.worklone.com ) . Disclosing that up front. Worklone is a multiplayer AI cloud coding agent: the agent runs in a cloud Linux sandbox, the team shares one live room, everyone watches and steers the same run. Not a local CLI on one laptop. What we kept seeing building this: the agent isn’t the bottleneck. Handoff is. One person starts a run, closes the lid, nobody else can see the session, and review lives in a different tool t
