创意工具1 day 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)
用一行代码部署强大的 AI 模型，享受业界最具竞争力的 GPU 价格。
Synexa AI — 一行代码部署 AI 模型 (Replicate 50% 低价平替)，Synexa 是运行无服务器 AI API 最具性价比的解决方案，提供业界最具竞争力的 A100 GPU 价格，相比其他服务商可节省高达 62% 的 AI 算力成本 - [更多介绍](https://github.com/1c7/chinese-independent-developer/issues/47
与能自主操控计算机的AI助手互动完成任务。
stan_kirdey · HN
Clark – AI assistant with own computer
自由职业者的时间追踪和项目管理工作空间，配有AI助手。
@baptistedulac · X
time tracking for freelancers and agencies
在一个工作空间中统一 Gmail、WhatsApp 和业务工具，以管理对话和自动化客户工作流程。
@wrrkai_ · X
This week we're shipping Helping startups and growing businesses manage every customer conversation from one AI-powered workspace.
云端个人AI助手，自动化工作流、驱动浏览器和执行研究。
u/Sea_Way6729 · Reddit
We started working on MoClaw 6 months ago as a side project, and managed to turn it into our main and only business! About half a year ago, a group of friends and I started working on a tool in our free time. We wanted a safer and more streamlined version of OpenClaw because there were many, many stories about AI going rogue, and ultimately decided to make one ourselves. On top of that, the costs of running autonomous, independent agents were quite high if you wanted to do it the proper, safe
AI助手用于聊天、编码和API，支持30+个前沿模型并学习你的代码库
@otakuaakash · X
用12个自主AI代理自动化邮件、支持和内容创建。
@BainPath · X
Discover - Your AI Workforce. Let’s connect !
团队聊天工作空间，整合分散在聊天、文件和反馈中的工作。
@AmaaN_AshiF · X
I Built this for 2 weeks with Claude code It’s now a working beta Product A chat first workspace for teams whose work gets scattered across chats, files, feedback and project tools. Free to try right now.
用于构建网站、编写代码和处理复杂任务的 Agentic AI 助手。
@durdom_evm · X
Chinese team at just dropped ZCode 3.0 and it's quietly reshaping the agentic coding landscape while everyone's waiting on Anthropic's roadmap here's what matters: GLM-5.2 is trading blows with Opus 4.8 on the benchmarks that actually count. 81.0 vs 85.0
