One Major Challenge I faced was memory quality at scale.
Two hard problems:
- Duplication → similar memories crowd the system
- Conflict resolution → when memories disagree, what’s truth?
To handle this, Cortex uses Google DeepMind's Gemini 3.1 Pro in the Dreamer pipeline to:
- merge duplicate memories
- resolve conflicts
It works... but it’s expensive and not scalable. Right now, I’m intentionally trading cost for simplicity to prove the system.
The good news: I’ve already designed a new memory architecture (Cortex v2) that:
- eliminates duplication properly
- introduces a significantly more efficient and scalable memory architecture that improves memory quality while reducing compute overhead
- supports years of memory without degradation in retrieval quality
- costs < $1/month per user at moderate usage
I’ll also benchmark it against LongMemEval and share results. Stay tuned.
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