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.
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