Hello @chilarai
That’s a fantastic question—thank you! We designed memorr.ai specifically with developers and power users in mind.
The primary interaction for developers is taking total control of the context architecture:
Architecture Control (The RAG Loop): Developers can ensure their LLM (via their own API key) is fed the exact context required. If the automated summary misses a crucial piece of technical debt or a specific variable name, they can jump into the Canvas and edit the Memory Card manually. This guarantees the AI’s coherence where simple auto-summaries often fail on complex code.
Privacy for Code & Specs: Because all memory data is stored locally on their Mac/Windows machine (BYO-API model), developers can confidently discuss sensitive code snippets, internal specs, or proprietary information without sending context summaries to any third-party database (including ours).
Efficiency and Cost: Developers running extensive coding sessions or documentation projects save significantly on tokens. Instead of sending 50 messages of history, memorr.ai only injects the relevant, 1KB memory summary into the RAG prompt, optimizing costs and latency.
In short: It’s a visual RAG system that puts the human in control of the memory for strict, long-term technical projects.
We’re already seeing devs use it to document complex codebases and maintain consistent logic across sprints.
What kind of development project are you currently working on that requires deep context? I’d love to hear about it!