创意工具1 month ago
Unified AI Workspace for Chat, Image & Video Models | AI Onekit
在一个工作区对比并运行多个AI模型生成文本、图像和视频。
AI Onekit — AI 聚合模型创作平台 - [更多介绍](https://aionekit.com/models)
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在一个工作区对比并运行多个AI模型生成文本、图像和视频。
AI Onekit — AI 聚合模型创作平台 - [更多介绍](https://aionekit.com/models)
自主AI工作空间，能研究、编码、分析数据、操作浏览器。
@WorkFarOfficial · X
AI代理辩论分析选项，帮助你决策、规划和生成文档的工作空间。
@getazrivo · X
Building - an AI assistant that doesn't just chat, it ships. Plan Mode maps out multi-step builds and produces real artifacts: live web pages, documents, spreadsheets, slide decks. It can also assemble a curated team of specialists to debate decisions.
Launch an orbit around any idea. Direct a crew of specialized AI workers. Review every output and accept what meets your standard. Nothing moves forward without you.
@VisionAIWS · X
STROKIX 连接多个业务工具的 AI 工作区,让团队提问和批准操作。
@Strokixx · X
长上下文对话、代理规划和代码部署的独立 AI 工作台
david082321 · V2EX
我做了一个 Kimi K3 的工作台，把成本、部署、评测和使用场景整理到一起 最近看大家聊 Kimi K3 、Claude 、GPT 在 coding 和长上下文任务里的差异，发现很多信息散在不同地方：API 成本、上下文窗口、本地部署、硬件需求、评测表现、适合的使用场景，都要来回翻。 所以我整理了一个偏“导航仪”的 Kimi K3 站点： https://kimi3.org/?utm_source=v2ex&utm_medium=post&utm_campaign=kimi3_org&utm_content=create_topic 它不是单纯的介绍页，更像一个给开发者用的参考工作台。你可以快速看 Kimi K3 适合做什么、部署前要准备什么、和其他模型相比大概强在哪里，以及 API 成本怎么估算。 如果你最近也在比较 Kimi K3 、Claude 、GPT 或 GLM 这类模型，欢迎看看，也欢迎提建议。
用自然语言描述需求，自动创建、测试并部署AI工作流。
@ChatVerse_io · X
Salestrics 是一体化 CRM 工作区，整合 AI 文档和销售分析。
@Salestrics · X
包含60+个AI工具的云工作站，支持Claude Code、Codex等。
@CoderLuii · X
A machine for your coding agents.
AI 驱动的设计平台，帮助你设计空间、可视化家具并购买家具。
@Nextechlabsinc · X
an intelligent space design platform that helps you design, visualize and purchase furniture all in one workflow. Ideal customers: interior designers, homeowners, renters and Airbnb hosts…
Xenition is one AI workspace where a single chat writes your docs, designs your slides, builds your apps, creates your images, and runs your research. Stop juggling ten tools.
@JamesAnder007 · X
Lets connect
在加密工作区中与 Claude Code 和 Cursor 等 AI 代理共享文件和日志。
@Raullen · X
6 months ago, @qevan and I hit a stupid wall: just trying to share a local file or some logs with an AI agent was incredibly frustrating. So, we built the first version of —a dead-simple, secure way to pass context to AI. The community feedback was amazing. We got ~200 stars on GitHub, and it quickly became a daily driver for a lot of developers. But recently, we realized the game has changed. Our workflows evolved. It’s no longer just humans sending files to agents. Agents need to write back to humans. Agents need to hand off context to other agents. The whole team—human and AI—needs to collaborate. So, we gave vnsh a massive upgrade. Today, it’s no longer just a secure clipboard. It’s now a living workspace. 🚀 You drop your context in once and get a dynamic link. Claude Code, Cursor, Cline, and your human teammates all open the exact same document. When one agent updates it, your whole stack sees it instantly. Complete context handoff, without ever copy-pastin