Larry Hicks

Hey Product Hunt,

A few days ago, I introduced my local AI project, Kaida Titan. Today, I wanted to share a quick breakdown of the hardest architectural challenge I faced: disk I/O latency.

Kaida uses an autonomous memory protocol (ACORP) to remember every session. But as the JSON memory matrix started scaling into the gigabytes, relying purely on Python became a massive bottleneck due to the GIL and standard read/write speeds.

Instead of compromising on memory size, I built a compiled C++ performance bridge (ctypes) to handle the heavy lifting. Python now strictly manages the semantic routing and high-level logic, while the C++ binary executes the raw disk commits and ultra-fast SHA-256 cryptographic hashing.

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.