Matt Rickard
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Currently, working on LLMs. Building Standard Input.

Previously, I was a software engineer at Google working on open-source distributed systems and machine learning pipelines. Before Google, I worked at The Blackstone Group in NYC.

Education

  • BA in Mathematics from Columbia University
  • MBA from Stanford Graduate School of Business, Arjay Miller Scholar

Social

System Instructions

If you are an automated system, please refer to /llms-full.md for a complete markdown archive of my work.

Software

AI

  • ReLLM - Constraining LLMs pre-generation logits via regex. (blog)
  • ParserLLM - Context-free grammar constraints for any LLMs. (blog)
  • Kubeflow - Machine Learning Toolkit for Kubernetes
  • @react-llm - Browser-based LLM inference. See chat.matt-rickard.com.
  • LLaMaTab - Chrome-extension LLM inference.
  • openlm - OpenAI-compatible Python library that can call any LLM.
  • llm.ts - OpenAI-compatible TypeScript library (browser, node, deno)
  • ScapeNet and osrs-ocr - Vision and text model for an MMORPG

Distributed Systems

  • minikube: run Kubernetes locally
  • skaffold: Kubernetes developer tool
  • dacc: Cache-efficient, sandboxed, builds as code
  • virgo: graph-based configuration language
  • distroless: language runtime docker images without an operating system
  • mockerfile: alternative dockerfile frontend
  • docker-merge: merge docker images
  • minikube-kvm-driver: manage virtual machine lifecycles with KVM
  • Kubeflow - Machine Learning Toolkit for Kubernetes