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AI · May 31, 2026

jatrapaladigital

AI memory architectures beyond context

jatrapaladigital launched on May 31, 2026 with 3 votes, #3,449 of 20,538 launches that month and more than 50% of that year's launches.

What it does

Discover how AI memory architectures help LLMs remember using vector databases, RAG, and persistent memory systems.

Does a similar job

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    Built this for my LLM workflows - needed searchable, persistent memory that wouldn't blow up storage costs. I also wanted to use it locally for my research. It's a content-addressed storage system with block-level deduplication (saves 30-40% on typical codebases). I have integrated the CLI tool into most of my workflows in Zed, Claude Code, and Cursor, and I provide the prompt I'm currently using in the repo. The project is in C++ and the build system is rough around the edges but is tested on macOS and Ubuntu 24.04.

  • AI memory with biological decay (52% recall)Apr 2026 · github.com · ▲98

    Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning. This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned. To solve the…

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    Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc. - CaviraOSS/LongMemory

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