Biologically decaying AI memory points to a leaner long-context stack

Persistent memory does not need to be permanent. A decay-based graph + vector design cut token waste by 84% and hit 52% Recall@5, making selective forgetting a practical product angle.

[ KEY POINTS ]

Uses the Ebbinghaus forgetting curve to assign memory strength; repeated recalls flatten decay, stale items get pruned.
Adds a graph layer on top of the vector store so logically related nodes can surface even when embeddings are not semantically close.
Reported 52% Recall@5 on LoCoMo, nearly 2x stateless vector stores; enough to treat memory quality as a measurable feature.
Built as a local-first MCP server on DuckDB, which fits agent workflows that need cheap long-running context without endless accumulation.

Originalgithub.com/sachitrafa/YourMemoryRead original →

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#0001
#0001Idea signals GeekNews3 hours ago
Forge UX Gap Around Pure `Git` Workflows
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The debate shifts forge design away from SPA vs SSR and toward how repositories expose version-control behavior. A niche but real opening for developer tools.
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#0002
#0002Idea signals r/SideProject5 hours ago
`ratemydick.ai`: CV-based adult health check hidden behind a rating app
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#0003
#0003Idea signals Google AI Forum6 hours ago
`Antigravity` Feature Request: Edge-Pruned Local-Cloud Context
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Source: discuss.ai.google.dev/t/feature-request-hybrid-local-cloRead original →
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Biologically decaying AI memory points to a leaner long-context stack

// related

Forge UX Gap Around Pure `Git` Workflows

`ratemydick.ai`: CV-based adult health check hidden behind a rating app

`Antigravity` Feature Request: Edge-Pruned Local-Cloud Context