The Fundamental Problem
Large Language Models are remarkable. They can write code, explain complex concepts, and engage in nuanced conversations. But they have a critical limitation that becomes more apparent the more you use them: every conversation starts from zero.
Think about how you interact with colleagues. Over time, they learn your preferences, understand your communication style, remember past conversations, and build context about your work. This accumulated understanding makes collaboration increasingly efficient.
Now compare that to your AI assistant. Every session, you re-explain your tech stack. You re-describe your coding style. You provide the same context about your project. The AI treats you the same as it treats everyone else in the world.
The Personal Language Model Hypothesis
What if AI could build a persistent understanding of you? Not just storing chat history, but actually learning from your digital life to develop genuine context about who you are, how you think, and what you're trying to accomplish.
This is the core hypothesis behind Memory: that combining powerful foundation models with rich personal context will produce dramatically better AI interactions than either component alone.
The Equation
Foundation Model + Personal Context = Personal Language Model
What Personal Context Enables
When AI has genuine context about you, several things become possible:
- Communication adapts to your style - Technical depth, formality, and explanation level match your preferences
- Responses reference your actual work - Your codebase, your projects, your specific challenges
- Historical context persists - Past decisions, learned preferences, and previous conversations inform current responses
- Proactive assistance becomes meaningful - The AI can anticipate needs based on patterns in your work
The Three Tiers of Memory
Human memory isn't monolithic. We have working memory for immediate tasks, episodic memory for experiences, and semantic memory for knowledge. Memory's architecture mirrors this with three distinct tiers:
Short-Term Memory
Recent conversation context. What we've been discussing in the current session and immediate past. Optimized for fast retrieval and recency.
Long-Term Memory
Semantic knowledge extracted from your digital life. Vector embeddings of your emails, documents, code, and conversations. Searchable by meaning, not just keywords.
Persistent Memory
Core facts and preferences that define who you are. Your name, your expertise, your communication preferences. Stable facts that rarely change.
Privacy as a Foundation
For personal context to work, it must be trustworthy. Memory is designed with privacy as a fundamental principle, not an afterthought:
- Local processing by default - Your data stays on your machine
- You control what's ingested - Explicit consent for each data source
- Local SLM option - Run entirely offline with models like Mistral 7B or Phi-3
- Cloud LLMs are optional - Use Claude or GPT-4 only when you choose to
- Open source - Inspect exactly what the system does with your data
A Research Project
Memory is not a commercial product. It's an exploration of what becomes possible when AI has genuine personal context. Some aspects work well today. Others are experimental. The goal is to learn, share findings, and push the boundaries of personalized AI.
If this vision resonates with you, I'd love to hear your thoughts. Whether you want to contribute code, share ideas, or just follow along, the project is open and evolving.