das-bikash-dev

4 ms·

1.
by das-bikash-dev 7mo ago
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 infrastructure — curated reference docs the agent reads
2.
by das-bikash-dev 7mo ago
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 hardest part isn't the coding, it's knowing which
3.
by das-bikash-dev 7mo ago
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 curated reference docs that agents read on-demand per task area in
4.
by das-bikash-dev 7mo ago
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 but the shift from completion-only to reasoning and str
5.
by das-bikash-dev 7mo ago
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 modifications to dependencies or config files. Also, does it s
6.
by das-bikash-dev 7mo ago
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 args — tokens passed to Dockerfiles for private package instal
7.
by das-bikash-dev 7mo ago
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), embedding generation (different model/pricing),
8.
by das-bikash-dev 7mo ago
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 specific scaling bottleneck, not a theoretical one. One patt
9.
by das-bikash-dev 7mo ago
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 call can classify the query type, decide whether to