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danielgraviet/README.md

daniel graviet

ML engineer focused on high-throughput systems — distributed infrastructure, system design, and models that scale.


about

MLE @ Daytona · Previously MLE @ Martian

I like building things that move fast and don't break under pressure.


stack

Python   Go   Kubernetes   Docker


projects

A handful of things I've built — spanning ML systems, distributed infra, and weekend hackathon experiments.

Check the pinned repos below ↓


ml systems · distributed computing · high throughput · system design

Pinned Loading

  1. daytona-benchmark daytona-benchmark Public

    Reproducible benchmark that quantifies real-world trade-offs between speed, scale, and developer experience across sandbox providers.

    Python 2

  2. pushtotype pushtotype Public

    Local push-to-talk speech-to-text for Linux: hold a hotkey, speak, release, and your words appear wherever your cursor is. Powered by local Whisper transcription with no cloud or API keys.

    Python 1

  3. helix helix Public

    Helix is a self-extending AI agent that builds new capabilities on demand. When a task needs a missing skill, it generates the code, packages it in Docker, deploys it as a FastAPI microservice, and…

    Python 1

  4. infertrace infertrace Public

    High-performance observability collector for LLM inference latency, with ML-aware spans and regression detection for model deploys.

    Go

  5. ohsheet ohsheet Public

    OhSheet watches your Canvas courses, dedupes upcoming assignments, and keeps a shared Google Sheet updated so students always know what’s due next.

    HTML 1

  6. TruckMind TruckMind Public

    TruckMind is an autonomous AI operator for food truck startups. It uses census-grounded market research, synthetic customers, strategy simulation, dynamic pricing, and conversational ordering to tu…

    Python