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das-bikash-dev

HN member
Joined
Sat, Feb 21, 2026, 1:36 PM UTC
HN karma
3
Public activity
9 items

About das-bikash-dev

Building cuneiform.chat — AI agent platform with RAG. Python/FastAPI, microservices.

Recent public activity

  1. comment
    Comment #47165336

    the worktree isolation per agent is clever. i've been running claude code as my sole dev partner on a production platform (10 repos) and the biggest unlock was treating context as …

  2. comment
    Comment #47165122

    the DAG decomposition approach is interesting — curious how it handles goals that span multiple services/repos. i build a multi-service platform solo with claude code and the harde…

  3. comment
    Comment #47164365

    the context isolation approach is smart — cascading drift between agents is a real problem. i run 10 microservices with claude code and solved a similar issue by maintaining curate…

  4. comment
    Comment #47141419

    Interesting to see the evolution mapped out like this. For those building on top of these models (RAG systems, agent frameworks), the real inflection point wasn't just model count …

  5. comment
    Comment #47141404

    How does Emdash handle state management when running multiple agents on the same codebase? Particularly interested in how you prevent conflicts when agents are making concurrent mo…

  6. comment
    Comment #47137189

    This matches my experience. I work across a multi-repo microservice setup with Claude Code and the .env file is honestly the least of it. The cases that bite me: 1. Docker build ar…

  7. comment
    Comment #47137106

    The multi-agent budget problem you're describing gets even harder when the services are heterogeneous. In a RAG pipeline, a single user query might hit: query analysis (LLM call), …

  8. comment
    Comment #47135085

    Re: when to add a WebSocket gateway vs keeping it in the monolith — I've built multi-channel chat infrastructure and the honest answer is: keep the monolith until you have a specif…

  9. comment
    Comment #47135051

    Nice project, especially given the VRAM constraints. A few things I've learned building production RAG that might help: 1. Separate your query analysis from retrieval. A single LLM…