[warthog]

imo it would be better to carry the whole memory outside of the inference time where you could use an LLM as a judge to track the output of the chat and the prompts submitted

it would sort of work like grammarly itself and you can use it to metaprompt

i find all the memory tooling, even native ones on claude and chatgpt to be too intrusive

[namanyayg]

I've been building exactly this. Currently a beta feature in my existing product. Can I reach out to you for your feedback on metaprompting/grammarly aspect of it?

[warthog]

yep hit me up

[elfenleid]

Totally get what you're saying! Having Claude manually call memory tools mid-conversation does feel intrusive, I agree with that, especially since you need to keep saying Yes to the tool access.

Your approach is actually really interesting, like a background process watching the conversation and deciding what's worth remembering. More passive, less in-your-face.

I thought about this too. The tradeoff I made:

Your approach (judge/watcher): - Pro: Zero interruption to conversation flow - Pro: Can use cheaper model for the judge - Con: Claude doesn't know what's in memory when responding - Con: Memory happens after the fact

Tool-based (current Recall): - Pro: Claude actively uses memory while thinking - Pro: Can retrieve relevant context mid-response - Con: Yeah, it's intrusive sometimes

Honestly both have merit. You could even do both, background judge for auto-capture, tools when Claude needs to look something up.

The Grammarly analogy is spot on. Passive monitoring vs active participation.

Have you built something with the judge pattern? I'd be curious how well it works for deciding what's memorable vs noise.

Maybe Recall needs a "passive mode" option where it just watches and suggests memories instead of Claude actively storing them. That's a cool idea.

[westurner]

Is this the/a agent model routing problem? Which agent or subagent has context precedence?

jj autocommits when the working copy changes, and you can manually stage against @-: https://news.ycombinator.com/item?id=44644820

OpenCog differentiates between Experiential and Episodic memory; and various processes rewrite a hypergraph stored in RAM in AtomSpace. I don't remember how the STM/LTM limit is handled in OpenCog.

So the MRU/MFU knapsack problem and more predictable primacy/recency bias because context length limits and context compaction?

[westurner]

OpenCogPrime:EconomicAttentionAllocation: https://wiki.opencog.org/w/OpenCogPrime:EconomicAttentionAll... :

> Economic Attention Allocation (ECAN) was an OpenCog subsystem intended to control attentional focus during reasoning. The idea was to allocate attention as a scarce resource (thus, "economic") which would then be used to "fund" some specific train of thought. This system is no longer maintained; it is one of the OpenCog Fossils.

(Smart contracts require funds to execute (redundantly and with consensus), and there there are scarce resources).

Now there's ProxyNode and there are StorageNode implementations, but Agent is not yet reimplemented in OpenCog?

ProxyNode implementers: ReadThruProxy, WriteThruProxy, SequentialReadProxy, ReadWriteProxy, CachingProxy

StorageNode > Implementations: https://wiki.opencog.org/w/StorageNode#Implementations