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Report abuseAI engineer in Kathmandu, Nepal. I build retrieval (RAG) and agent systems, and I measure how well they work. Open to remote roles.
| Project | What it is | Proof |
|---|---|---|
| Production RAG (private, code on request) | Document search and QA: hybrid dense and BM25 search in Qdrant, LLM reranking, per document access control, cited answers | nDCG@10 0.795 on the public BEIR SciFact benchmark (dense only: 0.646, BM25: 0.667) |
| Fixgate | Agent that reproduces a GitHub issue in a sandbox, writes and checks a fix, and opens a PR only after a person approves the exact patch | Resolved 22 of 31 SWE-bench Verified issues (71%, partial run) at about $0.02 each |
| vault2okf | Converts Obsidian vaults into Open Knowledge Format bundles for AI agents, then validates and repairs them | Published on PyPI, with a GitHub Action and pre-commit hook |
| RAG Gateway | Local server that speaks the OpenAI, Anthropic and Gemini APIs, for building and testing RAG apps | Used to run the Production RAG benchmark through local OpenAI compatible endpoints |
GitHub issue agent with guardrails: triage, reproduce in a sandbox, fix, verify, and open a draft PR only after human approval.
Python
An intelligent customer service assistant powered by Retrieval Augmented Generation (RAG), Gemini LLM, and real-time intent classification. Built with Django REST Framework and React.
Python 1
The Anthropic, OpenAI and Gemini APIs on your machine, answered by the Claude Code, Codex and Antigravity CLIs you're already logged in to
Python 1
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