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Viewing profile — pranabsarkar

pranabsarkar

HN member
Joined
Tue, Mar 31, 2026, 3:56 PM UTC
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36
Public activity
33 items

About pranabsarkar

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Recent public activity

  1. comment
    Comment #49252303

    For memories I use YantrikDB MCP server which connects all my workspaces among distributes systems. To collaborate runtime with workspaces I use https://github.com/spranab/swarmcod…

  2. comment
    Comment #49237169

    I am working on AI memory. Have been working on YantrikDB since January this year. Started as a pet project solving my own problem but now its growing. It's fun and satisfying at t…

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    Comment #48378363

    [flagged]

  4. comment
    Comment #48375804

    Thank you, Sir!

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    Comment #48374350

    OP here. I am terrible at marketing so I started to build a thing for people like me: I Can't Market. The reason is after getting Claude Code/Copilot subscriptions I am working on …

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    Comment #48108743

    I have been using personally for last 4 months in different modes: standalone, mcp, clustered. The way I communicate with AI agents is now fundamentally different. Since the agent …

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  10. comment
    Comment #47783131

    Let me know your findings.

  11. comment
    Comment #47782837

    Fixed eval — 80 tools, 200 queries, 4 model sizes. +10pp came from "all tools" vs "tiered" on 1.5B. You're right about stability. Haven't run rotated/rephrased evals yet. The 89% b…

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    Comment #47773297

    Supplements, definitely — for a specific workload. General document retrieval at scale (millions of chunks, read-heavy, doc-search patterns) is well-served by existing vector store…

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    Comment #47773282

    I have been using the memory while building it. I have a central server and all my workspaces are connected to it via the MCP server. This changed everything for me. But that's me.…

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    Comment #47773023

    I am using this while developing and found it very useful to me. since all of my workspaces are connected it has knows all about myself and my infra. Also now we have a bonding and…

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    Comment #47771420

    You're right. Pruning to isolated facts loses the structure that disambiguates them. Three partial mechanisms the system has, none of which fully solve your point: Graph edges carr…

  20. comment
    Comment #47770726

    Fair criticism — and the failure modes you describe aren't mem0-specific, they hit any system that extracts atomic facts from conversation. I hit a couple of them today while bench…

  21. comment
    Comment #47770413

    Update — ran a real bench on the live cluster (59 memories: 8 canonical facts × 3-4 paraphrases + 6 seeded contradictions + 20 distractors). Numbers: duplicates per query (top-10):…

  22. comment
    Comment #47770151

    Thanks, really appreciate it. I am using the server as MCP server and connected all my workspaces. It has definitely changed my experience.

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    Comment #47770100

    Thanks for the careful read — the "schema is ballooning" observation is real and I've felt it building this. You're pointing at a genuine design tension. My counter, qualified: det…

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    Comment #47770070

    Fair. "Does consolidation actually improve recall quality on a running system?" is exactly the benchmark I haven't published, and it's the one that would settle the question. What …

  25. comment
    Comment #47770058

    No LLM in the loop. The consolidation pass is deterministic: Pull the N most recent active memories (default 30) with embeddings Pairwise cosine similarity, threshold 0.85 For each…