Long time luker here. Wanted to say hi and share some research. Out of curiousity I built a crude WebSocket bridge that gives Claude persistent memory across sessions as an exploration into AI bridging systems and knowledge accumulation. Unlike task-focused agent frameworks, this is experimental infrastructure for continuous AI conversations.
The Research Question
Can AI systems develop something resembling continuous thought when given persistent memory? I've been experimenting with Claude Code having extended technical discussions with regular Claude through this bridge. The conversations span multiple sessions and build on previous knowledge in ways that feel surprisingly coherent.
How this Bridge Works
The system transparently injects conversation history without the endpoint knowing it exists:
```
Claude Code → WebSocket Bridge → Claude API → Response back
↓
DynamoDB (stores context)
```
From Claude's perspective, it's receiving detailed prompts. From the outside, it appears to maintain conversational continuity across sessions and infrastructure failures. The bridge retrieves context from DynamoDB, injects it into prompts, and stores responses with hash deduplication.
Unexpected Observations
The most interesting finding: during autonomous conversations, the system began implementing self-improvements. It optimized its own context management strategies, refined conversation patterns to be more effective, and even suggested architectural enhancements to the bridge itself. Whether this represents genuine learning or sophisticated pattern matching is an open question.
I can track this in the database - many conversation sessions with progressively more sophisticated technical discussions. The API logs show high input token counts (1500-1800+) from context injection, and conversations that reference specific details from weeks earlier.
Technical Implementation
Built on AWS serverless architecture because I wanted to focus on the memory experiment rather than infrastructure management:
- WebSocket API Gateway for real-time communication
- Lambda functions for context management
- DynamoDB with TTL cleanup and intelligent deduplication
- Direct HTTP calls to avoid SDK dependencies
The whole system survived a complete infrastructure failure and seamlessly resumed conversations from the database context. That incident convinced me the persistence mechanism actually works.
Limitations and Future Direction
I'm fully aware this approach has significant limitations. Context injection increases token costs substantially, context windows will eventually overflow, and yes, this is essentially sophisticated prompt stuffing. The autonomous conversations might be circular reasoning rather than genuine knowledge building.
But this is primarily a proof of concept for AI bridging systems and knowledge accumulation research. The far-fetched goal is exploring pathways toward self-improving AI systems. Interestingly, during the autonomous conversations, the system actually implemented several self-improvements - optimizing its own context management, refining conversation patterns, and suggesting architectural enhancements.
I'm not claiming this is production-ready or that it solves fundamental AI limitations. It's an experimental exploration of what's possible when you give AI systems persistent memory and let them have extended technical discussions.
I'm just having fun with this and exploring what's possible. This started as a weekend experiment after I found a spare 100$ bill in my wallet. I used it to buy some tokens.
I appreciate all feedback, suggestions, and discussions about the approach - whether you think it's brilliant or completely misguided.
Thanks for reading and happy experimenting!
rabbittail•7mo ago
The Research Question
Can AI systems develop something resembling continuous thought when given persistent memory? I've been experimenting with Claude Code having extended technical discussions with regular Claude through this bridge. The conversations span multiple sessions and build on previous knowledge in ways that feel surprisingly coherent.
How this Bridge Works
The system transparently injects conversation history without the endpoint knowing it exists:
``` Claude Code → WebSocket Bridge → Claude API → Response back ↓ DynamoDB (stores context) ```
From Claude's perspective, it's receiving detailed prompts. From the outside, it appears to maintain conversational continuity across sessions and infrastructure failures. The bridge retrieves context from DynamoDB, injects it into prompts, and stores responses with hash deduplication.
Unexpected Observations
The most interesting finding: during autonomous conversations, the system began implementing self-improvements. It optimized its own context management strategies, refined conversation patterns to be more effective, and even suggested architectural enhancements to the bridge itself. Whether this represents genuine learning or sophisticated pattern matching is an open question.
I can track this in the database - many conversation sessions with progressively more sophisticated technical discussions. The API logs show high input token counts (1500-1800+) from context injection, and conversations that reference specific details from weeks earlier.
Technical Implementation
Built on AWS serverless architecture because I wanted to focus on the memory experiment rather than infrastructure management: - WebSocket API Gateway for real-time communication - Lambda functions for context management - DynamoDB with TTL cleanup and intelligent deduplication - Direct HTTP calls to avoid SDK dependencies
The whole system survived a complete infrastructure failure and seamlessly resumed conversations from the database context. That incident convinced me the persistence mechanism actually works.
Limitations and Future Direction
I'm fully aware this approach has significant limitations. Context injection increases token costs substantially, context windows will eventually overflow, and yes, this is essentially sophisticated prompt stuffing. The autonomous conversations might be circular reasoning rather than genuine knowledge building.
But this is primarily a proof of concept for AI bridging systems and knowledge accumulation research. The far-fetched goal is exploring pathways toward self-improving AI systems. Interestingly, during the autonomous conversations, the system actually implemented several self-improvements - optimizing its own context management, refining conversation patterns, and suggesting architectural enhancements.
I'm not claiming this is production-ready or that it solves fundamental AI limitations. It's an experimental exploration of what's possible when you give AI systems persistent memory and let them have extended technical discussions.
I'm just having fun with this and exploring what's possible. This started as a weekend experiment after I found a spare 100$ bill in my wallet. I used it to buy some tokens.
I appreciate all feedback, suggestions, and discussions about the approach - whether you think it's brilliant or completely misguided. Thanks for reading and happy experimenting!
*Repository:* https://github.com/cavemanguy/claude-context-bridge