Ok I think I've finally figured out how to start putting more of my project out there. I've created a public repo to share what I'm working on, why I started this in the first place and the direction I'm hoping to head with my offering. If y'all can take a moment and give some feedback I'd really appreciate it: Github.com/LavaDMan/aegis

Woke up to my AI "OS" morning brief: 5 mandates shipped overnight, failing drive caught and replacement researched, VRAM optimization proposed and validated — all autonomous. One agent finished its queue and self-generated an R&D proposal unprompted. This is what the orchestration layer looks like in production.

As I've mentioned before I'm working on something big but I've had some trouble figuring out how to get it out there for feedback. I've decided to publish a part of it. I've uploaded the core memory engine I built for my system to GitHub. ​Basically, I was wrestling with a massive headache: autonomous agents getting "amnesia" between sessions. They kept forgetting past failures and hallucinating commands because standard context windows just suck for long-term state. ​To fix it, I built a unified memory SDK (aegis-memory) that fuses Postgres (for hard rules and audit logs), Qdrant (for semantic vector history), and Neo4j (for dependency graphs). ​Instead of doing manual database gymnastics, you just pass the agent's intent to the SDK. It automatically queries all three databases and returns the mathematical "blast radius" and historical context before the agent is allowed to execute any code. It stops them from repeating known failures. ​I just ripped this plumbing out of my homelab setup and open-sourced it. Since we are all building in the trenches here, I'd love for you guys to take a look, tear the architecture apart, and give me some honest feedback. ​Repo is here: https://github.com/LavaDMan/aegis-memory ​Let me know what you guys think or if anyone tries plugging it into their stack!

Reading some of the posts today about feeling lost or overwhelmed—I feel you. I constantly feel like I’m just hacking things together in my homelab and fighting error logs. ​A few days ago, I posted a breakdown of my local AI architecture (a single-GPU AI OS I've been building called AEGIS) on Hacker News just to see if my approach to context-window memory made sense. I honestly didn't think anyone would care. ​Today, I was shocked to receive this DM! ​The New York Times is literally scraping HN for real engineers building real things. ​Just a reminder to everyone in here: the big tech companies don't have all the answers right now, and the stuff you are building on your local machines matters. Document your code, post your architectures, and keep pushing. You literally never know who is paying attention! ​Here is the HN link if anyone wants to see the architecture that caught his eye: https://news.ycombinator.com/item?id=47309028

Enjoyed the morning sunrise and thought I'd share.