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utsav1033/README.md

hello, i am utsav khairnar

backend engineer working on ai infrastructure agent evals, harness engineering, multi-agent systems, persistent memory, and inference on apple silicon.

occasionally i work on fpga's and risc-v

Gmail LinkedIn Website LeetCode


projects

project about
carmen an ai writes gpu kernels for apple silicon, and a judge it can't fool decides which ones are real. fused 8 mlx kernels into 2 per layer of qwen 2.5: 1.03x faster decode, identical output. the judge caught 75/75 planted bugs; a standard one-shape check caught 40.
flowgap finds what callers wanted that your voice agent can't handle, straight from raw call transcripts, and drafts the missing flow nodes. in 298 real calls, 49% asked for something the flow couldn't do.
agent-mark eval harness for agents you can't see inside: traces every tool call, scores each run, flags regressions. one adapter runs composio's trustclaw against a live inbox.

stack

ml / ai PyTorch TensorFlow scikit-learn MLX
backend Python FastAPI Spring Boot
cloud / infra AWS Azure Docker Terraform Nginx
databases MySQL SQLite Supabase

Pinned Loading

  1. agent-mark agent-mark Public

    agent eval harness, tested on a light weight ai agent Trustclaw. the tool is open source

    Python 1

  2. flowgap flowgap Public

    CLI that reads your voice agent's call logs, finds the requests your flow can't handle (the calls quietly transferred to a human), and drafts the missing nodes.

    Python

  3. feynman-vol3 feynman-vol3 Public

    HTML 1

  4. carmen carmen Public

    An AI writes Apple Metal GPU kernels, a judge it can't fool proves them, and the winners run inside a real model: Qwen 2.5 0.5B decode 3% faster with identical output; 75/75 planted bugs caught.

    Python