silentsvn

8 ms·

1.
by silentsvn 6mo ago
Thanks for the response The determinism trade-off is genuinely interesting — auditability over fuzziness is a real design philosophy, not just a limitation. We've been building something that tries to avoid forcing that choice. Engram
2.
by silentsvn 6mo ago
The inspectability angle is genuinely useful, being able to trace exactly why something was retrieved is something vector search can't offer, and the tag-receipt approach is clean for structured knowledge. One thing I'm trying to
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by silentsvn 6mo ago
The /compact trigger is a clean pattern — agent-initiated but human-confirmed. Makes the interface feel more like a review than an interruption. The retroactive feeding of recorded sessions is underrated. That's basically supervis
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by silentsvn 6mo ago
Interesting approach with the hierarchy. I went the opposite direction with engram-mcp — flat structure, 5 tools, semantic search via Ollama (nomic-embed-text) with keyword fallback when Ollama isn't running. SQLite-backed. The bet is
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by silentsvn 6mo ago
> I’m sticking with humans for the moment Haha totally get this statement. The HitL fine-tuning angle is exactly right. The labeled dataset you're building (good/bad/stylistically-wrong memory events) is probably worth mo
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by silentsvn 6mo ago
Been saying this for a while. Vector similarity is the wrong primitive for agent memory. It finds things that sound related, not things that are actually relevant given current context and what the agent already knows. The "ditching ve
7.
by silentsvn 6mo ago
Concurrent writes are the right problem to be solving. Most agent memory implementations assume a single writer and fall apart the second you have parallel tool calls or multi-agent setups hitting the same store. Curious how you're han
8.
by silentsvn 6mo ago
You're right, and it's the part that keeps me up. We handle it with versioned writes — each memory has a createdAt, observedAt, and a validUntil that can be set explicitly or inferred from context. Temporal scope gets embedded as
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by silentsvn 6mo ago
Human-driven compaction is interesting — you sidestep the "what's worth keeping" problem by putting a person in the loop. The tradeoff I've hit is that agents running autonomously need it to happen automatically or coher
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by silentsvn 6mo ago
The scoring layer sits between ingestion and storage. Incoming items get evaluated on a few axes: source reliability (did the agent observe this directly or was it told?), semantic distance from existing memories, and recency weighting for
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by silentsvn 6mo ago
One thing I've been wrestling with building persistent agents is memory quality. Most frameworks treat memory as a vector store — everything goes in, nothing gets resolved. Over time the agent is recalling contradictory facts with equa
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by silentsvn 13y ago
This is coming from someone whose father just bought them their first Mac. Hmmmmm