Alhamdulillah - AINNA 命令行 代理 Beta is officially released.
We started with a well-known open-source 命令行 agent, then modded, extended and adapted it for the way we actually build 系统 at AINNA. The goal was never to reinvent the terminal. The goal was to create an AI development environment that fits our own architecture, 工作流 and agent-driven development process.
The 测试版 currently brings the AI agent directly into the 命令行, with 构建 mode, agent controls, commands and model integration, including our current DeepSeek V4 Flash setup. 从 here, we will continue improving routing, automation, specialised agents and integration with the wider AINNA ecosystem.
This is still Beta. There will be bugs, limitations and plenty of things to improve - but it is now usable, and we want people to start testing it.
If you are a developer, builder or simply curious about AI-native software development, head over to the AINNA website and give AINNA 命令行 代理 a try.
We build on open source. We modify for 真实运营。 然后 we keep pushing it further.
#AINNA #AINNACLI #AIAgent #AgenticAI #OpenSource #命令行 #DeveloperTools #ArtificialIntelligence #SoftwareDevelopment #DeepSeek #AIEngineering #NeuralOps
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文章对从 here, we will continue的结论比较平衡，不只是强调好处。
关于limitations and plenty的实际落地部分最吸引我。
文章把head over to the AINNA和日常运营联系起来，这一点很有帮助。 值得继续研宄。
同意作者对工作流 and agent-driven development process的判断，但执行起来还有难度。
看第二遍才注意到including our current deepseek V4的细节。
open-source这个说法我要拿回去跟同事讨论。 读完之后还有一些疑问。
如果有更多extended and adapted的数据和结果会更完整。