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AI Research
MemMachine: Why Storing Raw Conversations Beats Extracting Them
A new agent memory paper from April 2026 argues that the dominant pattern — extract facts with an LLM, store the facts — bleeds truth and tokens. MemMachine stores the raw episodes instead, hits 0.9169 on LoCoMo, and spends ~78% fewer input tokens than Mem0.
AI Research
Karpathy's LLM Wiki: Building a Second Brain with Obsidian and AI
Andrej Karpathy shifted his token budget from code to knowledge. His LLM Wiki system uses AI agents to build self-maintaining markdown knowledge bases browsable in Obsidian.