Joined November 2017
72 Photos and videos
Coming soon: GenoMark. Autoresearch at home. ZK verification for every result. Train locally, submit confidently, prove it cryptographically. 🧬⚑ @karpathy @ensue_ai @MinaFoundation @MinaDevelopers @MinaProtocol @minacryptocom
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Are you curious? Follow me for more..
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Every AI memory system I tested fails at contradiction detection. - Structured_memory 36% - Mem0: 29% - OpenAI RAG: 0% We built a benchmark to understand why. The problem isn't implementation. π“π‘πž π€πˆ 𝐬𝐭𝐚𝐜𝐀 𝐒𝐬 𝐦𝐒𝐬𝐬𝐒𝐧𝐠 𝐚 π₯𝐚𝐲𝐞𝐫. 🧡
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Example failure: store: "user is vegetarian" store: "user ate steak" RAG result: "User ate steak" Correct result: truth_state = BOTH confidence = 0.52 conflict_detected = True
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Claude Code just added structured auto-memory. That’s a big step forward for agent memory. But it also highlights something important: most AI systems still treat memory as stored notes. The harder problem is belief maintenance. 🧡 1/10
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If you're experimenting with Claude Code’s new memory system: you can layer belief maintenance on top. I wrote a guide showing how to integrate MnemeBrain with Claude Code: mnemebrain.ai/blog/mnemebrai…
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Example of belief maintenance: belief: "user is vegetarian" evidence: "user ate steak" truth_state: BOTH confidence: 0.52 conflict_detected: true
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This is the layer we’re exploring with MnemeBrain. Evidence β†’ Belief node β†’ Truth state β†’ Confidence decay Belief maintenance instead of retrieval. 9/10
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Claude Code is pushing the ecosystem forward with structured memory. The next step is belief systems. Memory β‰  beliefs. 10/10
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If you're building AI agents that remember things over months: the missing layer isn't retrieval. it's belief state. mnemebrain.ai/

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BMB is open. The MemorySystem interface is public. Any system can implement an adapter. We'd love to see adapters from: @mem0ai @langchain @zep_ai @llama_index If you beat 100% β€” great. If you don't β€” now we know exactly why `pip install mnemebrain python run_bmb_benchmark.py`
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