Hello! This is my CLI framework that I made because I thought about the limitations of scalability other big CLIs encounter. When I was building apps with Click, the startup time gets really slow. Additionally, I have not seen any other Python CLI library do this: file-based routing.
Having contributed to some of Poetry's codebase, I realize that this is actually a very scalable pattern. The only issue is that there isn't a library that embraces it.
Finally, I envision a fully-fledged, batteries-included framework that handles everything from verbosity flags to configuration files.
While I had concieved of this idea a long time ago, I had only been able to finish and launch this recently with the help of AI. As you can tell, the documentation is AI-generated. However, that shouldn't detract from the core idea.
> ChatIOCCC is the world’s smallest LLM (large language model) inference engine - a “generative AI chatbot” in plain-speak. ChatIOCCC runs a modern open-source model (Meta’s LLaMA 2 with 7 billion parameters) and has a good knowledge of the world, can understand and speak multiple languages, write code, and many other things. Aside from the model weights, it has no external dependencies and will run on any 64-bit platform with enough RAM.
(Model weights need to be downloaded using an enclosed shell script.)
Interestingly the UK Supreme Court ruled on this in the Emotional Perception AI case - though I'd need to check if that was obiter (not part of the legal ruling itself).
This is essentially just setting up an MCP connection to your kanban provider and instructing the agent to plan out an epic. I did this this morning for some data modeling our team needed to do. For the most part it generated a good set of tickets, but there were some hallucinations due to ambiguity. Reviewing the already written out tickets was much better than writing them out myself.
But the standard that will hopefully take over in most mature shops is spec driven development where instead of a team reviewing code, they review a spec which is used to generate tasks and subsequently code to satisfy the spec. Then 2 kanban boards exist. One for writing and submitting specs and another for the agents themselves to implement the approved specs.
It's not clear that you will need an account to see the problems. Logged in with my account and it's exactly the same page. It's not Dec 1st everywhere yet, so they might open up for everyone when they do open them up.
Having contributed to some of Poetry's codebase, I realize that this is actually a very scalable pattern. The only issue is that there isn't a library that embraces it.
Finally, I envision a fully-fledged, batteries-included framework that handles everything from verbosity flags to configuration files.
While I had concieved of this idea a long time ago, I had only been able to finish and launch this recently with the help of AI. As you can tell, the documentation is AI-generated. However, that shouldn't detract from the core idea.
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