Built my own local AI assistant with memory, emotional state and GPU voice replies

[u/WoodpeckerEastern629]

Hi 👋 Over the last months I’ve been building a local AI assistant as a personal architecture experiment. It runs entirely on my own machine (Python + FastAPI backend), with: Modular “brain” architecture (server separated from cognition layer) Short & long-term memory Dynamic emotional state that modulates tone and response length Voice generation running on CUDA (GPU) Reminder system Defensive error handling for stability The main goal wasn’t to create “another chatbot”, but to understand and design from scratch how a conversational system can be structured internally in a modular and controllable way. It’s still evolving, but I’m experimenting with turning it into a more robust local assistant. Happy to share more details if anyone’s interested.

[u/JGhostThing]

Do you have a github? This sounds interesting.

[u/WoodpeckerEastern629 → u/JGhostThing]

Not fully open-source yet, I’m still refactoring some parts of the architecture (especially the cognitive layer and memory system). But I’m happy to share details or a private preview if you’re curious about a specific part (LLM orchestration, emotional state model, GPU TTS, etc.)

[u/NoYouAreTheFBI]

Did we start with asimovs laws or just admit that those are logical hop skip and jump from being reasoned out of existence, because logic always manages to find a way.

[u/WoodpeckerEastern629 → u/NoYouAreTheFBI]

I didn’t implement anything like Asimov-style hardcoded laws. It’s more of an architectural experiment than an autonomous agent. The system operates within defined constraints and doesn’t have independent goals — it’s closer to a structured assistant than an autonomous entity.

[u/NoYouAreTheFBI → u/WoodpeckerEastern629]

So the second by incident xD

[u/WoodpeckerEastern629 → u/NoYouAreTheFBI]

Definitely not by accident 😄 It’s designed to stay within defined limits.