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About

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

**Apps**

- Standard Workout: AI Lift Log - web & iOS
- Standard Habits: Habit Tracker - web & iOS
- Standard Reader: AI TTS & Chat - web & iOS

**Social**

- X (@mattrickard)
- TikTok (matt.rickard)
- Instagram (matt.rickard)
- YouTube (mattrickard)

**Contact**

- Email (\[email protected\])
- GitHub (r2d4)
- LinkedIn (msrickard)

**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