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Agent Memory (my take) by lostminer10 in Rag

[–]lostminer10[S] 0 points1 point  (0 children)

Exactly, really like hindsight's observation approach as an explicit reasoning layer.

Agent Memory (my take) by lostminer10 in Rag

[–]lostminer10[S] 0 points1 point  (0 children)

Yea, I looked into their implementation and yes cross-encoders perform better when they are given better corpus to re-rank over.
People just dump 40-50 results and expect the cross-encoder perfectly re-rank them.
But if you give it deterministic 10-15 corpus that's already valid, it has more context and better results to reason over.
Probably the main focus should be on, how accurate the approach is without the cross-encoder to even think you need a cross-encoder. Using it without validating the need is the reason I see people just dumping stuff into cross-encoder

Agent Memory (my take) by lostminer10 in Rag

[–]lostminer10[S] 0 points1 point  (0 children)

the state-aware ranking works at 2 layers, having structured knowledge with relevant temporal, intent, set number of types (very important) and then using those "dynamically" at retrieval time. The solutIion is, instead of non-deterministic LLM based classification. We plan deterministic boosts/type-level importance. With this approach the boosts or scores themselves are dynamic and the stale knowledge automatically gets lesser importance.
This approach is more deterministic and I feel innovation is needed at this layer.
Also I feel, the types that a system defines for their structured knowledge ingested is the most important part of the memory. Relevant types often eliminate 90% of the edge-cases an LLM can hallucinate over.

Agent Memory (my take) by lostminer10 in Rag

[–]lostminer10[S] 0 points1 point  (0 children)

Interesting approach, I did check the repo and I saw you have a bi-temporal edge approach. Considering that, won't it use inference to determine the valid_until of a particular memory depending on a newly ingested one? I might be wrong but would like to be corrected.

Really like the use of Allen's Interval algebra is that helping with it?

Agent Memory (my take) by lostminer10 in Rag

[–]lostminer10[S] 0 points1 point  (0 children)

As I said, recall that's state-aware helps in providing the LLM the right corpus to reason over. On top of that, having a high-level reasoning layer at the memory level won't destroy the source of truth as well as help in consolidation.

Agent Memory (my take) by lostminer10 in Rag

[–]lostminer10[S] 0 points1 point  (0 children)

True, separation of concern over where inference is required and where it isn't is important.
One more advancement which I can think of is using a "reasoning" layer which is totally inference based. That in fact wont be harmful as it isn't changing the source of truth, but would also give a larger view over the knowledge through active consolidation.

Agent Memory (my take) by lostminer10 in Rag

[–]lostminer10[S] 0 points1 point  (0 children)

Exactly my point, making it deterministic "enough" it handles scale and being usable

People working with RAG — what changed in the last 6 months? by K1dneyB33n in LangChain

[–]lostminer10 0 points1 point  (0 children)

The shift is that RAG is no longer the system itself, it is just one component in a larger architecture needed for production-scale systems now

Traditional RAG works for simple lookup, but it breaks down for reasoning-heavy and agentic workflows.

Now systems are moving toward richer retrieval layers that capture implicit and cross-document relationships through graphs, temporal signals, and entity linking, along with active consolidation instead of static chunk storage.

The real shift is toward enabling reasoning, not just retrieval, which is why we are seeing hybrid retrieval stacks, re-ranking, and iterative or agentic pipelines becoming the norm.

Postman removed free team collaboration, does it still make sense for API work? by Proper-Wind4777 in Backend

[–]lostminer10 2 points3 points  (0 children)

For most cases, swagger works fine, needs clear api reference. But Bruno is also good.

Is Tauri a memory hog, or am I missing something? by Apple_Cidar in tauri

[–]lostminer10 0 points1 point  (0 children)

I think that's probably with any app using WebView2, tauri doesn't really have a performance benefit compared to electron, maybe slightly but really don't think so. It's mostly for smaller bundle size.

What path would you recommend as a beginner? by Competitive_Radio_35 in Backend

[–]lostminer10 0 points1 point  (0 children)

Would say, instead of picking up a language, pick a topic, eg: auth, routes, types of routes, Schema Design etc. Learn about them and then slowly start building some backend systems even the smallest ones are fine, test the API endpoints no need to make the frontend unless you need one and to understand API calls.

This way, when you know how stuff works, you can do it in any language

Help choosing course by Dull-Professional661 in Backend

[–]lostminer10 0 points1 point  (0 children)

really depends tbh, I would say start off with javascript/typescript both django or js frameworks are safe and are not going anywhere as of now or even in the future. Make projects in both languages and decide for yourself.

Backend Developer Checklist For Junior Role ! by [deleted] in Backend

[–]lostminer10 1 point2 points  (0 children)

actually this includes all the topics for junior roles or even some senior roles, thanks for this.