gdad
Joined 9 months ago
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42
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25
Member since
Jan 15
Recent submissions
Comment · 2 months ago
Haha! I am going to put this one up as a win!
Thanks for reading! Hope you found it useful.
Comment · 2 months ago
Truly. Doing this for coding agents is an interesting and different shaped problem.
Comment · 2 months ago
Thanks Samyakk!
1. Yes, works on docs, agent conversations, human-conversations from different sources (Slack, JIRA, etc.). We have connectors for some of these as well; so it is plug and play 2. conventional RAG recall accuracy is quite low (50-60%) and latency is pretty high (seconds). But worst is the precision; you end up context stuffing to get acceptable recall 3. We do offer on-prem deployments, but only on sizeable annual contracts
Comment · 2 months ago
Interesting. Where can I read more about this?
Comment · 2 months ago
Apologies. Should be working now.
Comment · 2 months ago
I also wrote a shorter preview here: https://www.maximem.ai/blog/agentic-context-management-paper
Comment · 4 months ago
Aiden, Claude Code
Comment · 8 months ago
Building in the same space (Maximem Vity, cross-LLM memory as a Chrome extension and OpenClaw plugin). The SMTP analogy really resonates with me. I wrote a comparison a few weeks ago of how ChatGPT, Claude, and OpenClaw actually implement memory under the hood [1] and the architectures are so different that interop feels almost accidental when it works.
One thing I keep running into: the hard part isn't storage or retrieval. It's qualification. Deciding what's worth remembering from a conversation vs. what's throwaway context. ChatGPT takes the "summarize the last 15 chats" approach, Claude does on-demand search, and both have real failure modes. We went with a semantic graph that tries to capture relationships between memories (your preference for serverless connects to your AWS project connects to your cost constraints) rather than flat key-value pairs. Still iterating on it honestly.
Re: Claude's import-memory launch yesterday, I think the timing validates the category but the approach is fundamentally migration, not sync. You import once, and from that moment your contexts diverge again. Anyone using 3+ tools daily (which is basically everyone I talk to) is back to fragmented memory within a week.
Curious about your retrieval approach. Are you doing hybrid search or pure semantic?
[1] https://www.maximem.ai/blog/ai-apps-memory
Comment · 8 months ago
Author here. I have been learning about how ChatGPT, Claude, and OpenClaw handle persistent memory; what actually gets stored, how it is retrieved, etc.
Quick summary of the findings: - ChatGPT pre-computes lightweight summaries of your ~15 most recent conversations and injects them into every prompt. No vector search, no RAG. Simpler than I expected. - Claude takes an on-demand approach; it has search tools it can invoke to query your past conversations, but only fires them when it judges the context is relevant. More flexible, less consistent. - OpenClaw stores memory as plain Markdown on your local machine with hybrid search (semantic + BM25, 70/30 weighting). Fully transparent, but single-platform.
Full disclosure: I'm building in this space (Maximem Vity — a private, secure vault for cross-LLM memory). The comparison stands on its own, but that context motivated the research.
Happy to discuss the architectural differences or answer questions.