hello and welcome

Hey everyone,

I’ve been heavily focused on local AI development recently, and one of the biggest bottlenecks I keep running into is session amnesia. Most local LLMs reset their context the moment you close the terminal, which severely limits autonomous workflows.

To solve this, I engineered a tiered memory protocol (ACORP) that saves states locally to disk. To prevent Python bottlenecks as the memory logs scale into the gigabytes, I built a custom C++ performance bridge to handle the raw disk I/O and SHA-256 cryptographic hashing.

I just deployed a web app to showcase the architecture called Kaida Titan.

I’m curious to hear from other makers running local setups:

• How are you currently managing long-term memory for your agents?

• Are you relying on standard vector databases, or rolling your own custom storage solutions?

Would love to hear about your tech stacks and get your thoughts on the C++/Python hybrid approach!