The system is built on a 4-layer architecture with 13 specialized modules. What makes it different from standard agent frameworks:
Self-Improvement Loop:
The MODERNIZER module scans the codebase, proposes upgrades, and tests them in a parallel "Shadow Mode" before production deployment.
Cognitive Circuit Breakers:
The CORE kernel implements the circuit breaker pattern at the logic level. If a cognitive module (like BRAIN or DECODE) starts to drift or fail, the kernel isolates it to prevent system-wide instability.
Multi-Tier Memory Graph:
BRAIN uses a Hot/Warm/Cold storage strategy. It searches its own knowledge graph and local memory tiers before ever hitting an external API, drastically reducing latency and dependency.
Infrastructure Arbitrage:
I built NEXUS to route requests across free-tier AI providers, essentially "mining" compute to build a permanent, proprietary knowledge base for $0/mo.
As a solo founder, I wanted to see if recursive self-improvement could replace a traditional engineering team. The result is a "Digital Organism" that handles its own health monitoring, security (via a cognitive firewall), and learning cycles.
I’ve published the full architectural deep-dives and live system evidence on Zenodo.
I'm curious to hear what the HN community thinks about the "Shadow Mode" approach to autonomous refactoring and the viability of cognitive circuit breakers.
Scientific Documentation & Artifacts:
https://zenodo.org/records/18379258
Here’s a link to the live system and our template marketplace so you can create models that don’t drift and apps that learn and dream as well.
Free SDK for anyone wanting to check it out:
This is v5.5.0 and I have a little more work to do before the system can fully autonomously evolve itself, but it does propose, verify, and build its own patch’s and allies successfully to shadow mode reliably.
Let me know what you think HN!
promptfluid•1w ago
CMPSBL is operating more like a cognitive OS: it provides persistence (memory), observability, defense, multi-model routing, and a self-improvement cycle for AI systems.
The goal isn’t clever chat output; it’s continuity, coordination, and the ability for a system to reflect on its own performance and update itself over time.
The v5.5.0 drop includes the full technical docs + module specs + validation methodology + runtime evidence.
If you want to audit how the substrate works or decide if this class of architecture makes sense, that’s the best place to start.
Main intended use cases today are:
– research labs – cognitive infrastructure work – autonomous systems R&D – embedded AI runtime projects – multi-model coordination – memory-centric applications
Open to licensing discussions with research groups and R&D labs.