The Problem: Vector DB Costs and Complexity
When building LLM applications, “memory” is essential. It’s used for conversation history, context maintenance, and knowledge retrieval.
Currently, most developers rely on Vector Databases:
- Pinecone (cloud)
- Chroma (local)
- pgvector (PostgreSQL extension)
- Milvus, Qdrant, etc.
But this approach has issues:
- Cost: Cloud Vector DB expenses scale quickly
- Complexity: Requires embedding generation, indexing, and search pipeline
- Latency: Additional network RTT
- Infrastructure: Server management overhead
The Solution: Client-Side O(1) Memory
Remember Me is an LLM memory library that runs client-side without Vector DBs.
Core ideas:
- O(1) time complexity: Hash-based memory access
- Client-side: Runs in browser/app without servers
- 40x cost reduction: Zero infrastructure costs vs Vector DBs
Technical Approach
Traditional (Vector DB):
User Query → Embedding → Vector Search → Top-K Results → LLM
Remember Me:
User Query → Semantic Hash → O(1) Memory Lookup → LLM
Inspired by MIT’s Recursive LM paper, combining semantic hashing with hierarchical memory structures.
Market Analysis
Target Users
- LLM application developers
- Side project developers (cost-sensitive)
- Privacy-conscious users (local processing)
Competitive Landscape
| Solution | Type | Cost | Complexity |
|---|---|---|---|
| Pinecone | Cloud | $$$$ | Low |
| Chroma | Local | Free | Medium |
| LanceDB | Local | Free | Medium |
| Remember Me | Client | Free | Low |
Business Model
Revenue Strategy
- Open Source Core: MIT licensed base features
- Pro Features: Advanced optimization, analytics dashboard
- Enterprise: Team collaboration, security features
Pricing
- Free: Core features
- Pro: $5-15/month
- Enterprise: Contact us
MVP Scope
6-8 weeks estimated
- Core memory library (TypeScript/Rust)
- LangChain integration
- React Hook (useMemory)
- Basic documentation
Score: 85 points
| Criterion | Score | Notes |
|---|---|---|
| Pain | 8/10 | Vector DB cost/complexity is real |
| Market | 7/10 | LLM developer market growing fast |
| Competition | 5/10 | Free alternatives exist (Chroma, LanceDB) |
| Tech | 8/10 | Technical differentiation possible |
| Revenue | 6/10 | Dev tools monetization is challenging |
| Domain Fit | 8/10 | dev_tools domain aligned |
Recommendation
Conditional Recommendation
- Technical differentiation must be clear
- Performance comparison with free alternatives essential
- Developer community building crucial
The LLM memory problem is real, and there’s demand for client-side solutions. However, strong open-source alternatives like Chroma and LanceDB exist, so clear differentiation is needed.
This post analyzes a side project idea from HackerNews Show HN feed using AI.