# Grey Newell Title: ML Infrastructure Engineer Description: Building evaluation, inference, and observability systems for AI. Creator of the MIST stack. Founding Engineer at Supermodel. MS CS (ML) at Georgia Tech. Ex-AWS. URL: https://greynewell.com ## Social Links - https://github.com/greynewell - https://www.linkedin.com/in/greynewell/ - https://www.youtube.com/@greynewell - https://x.com/greynewell - https://www.crunchbase.com/person/grey-newell - https://www.wikidata.org/wiki/Q136955785 - https://scholar.google.com/citations?hl=en&user=RoTkOCIAAAAJ - https://www.npmjs.com/~greynewell - https://pypi.org/user/greynewell/ --- # Projects ## sample-event-driven-resilience-observability-at-scale URL: https://github.com/aws-samples/sample-event-driven-resilience-observability-at-scale Description: Serverless event-driven architecture for processing millions of daily events with near real-time visibility and strong resilience. Language: TypeScript Stars: 6 --- ## typescript-sdk URL: https://github.com/supermodeltools/typescript-sdk Description: TypeScript SDK for Supermodel. Generate useful graphs of your codebase. Language: TypeScript Stars: 5 --- ## openapi-spec URL: https://github.com/supermodeltools/openapi-spec Description: OpenAPI spec for the Supermodel public API. Use as reference or generate your own clients. Language: YAML Stars: 4 --- ## mcp URL: https://github.com/supermodeltools/mcp Description: Supermodel MCP server. Generate code graphs in Cursor, Codex, or Claude Code. Language: TypeScript Stars: 4 --- ## dead-code-hunter URL: https://github.com/supermodeltools/dead-code-hunter Description: GitHub Action to find unreachable functions using Supermodel call graphs. Language: TypeScript Stars: 2 --- ## mcpbr URL: https://github.com/supermodeltools/mcpbr Description: Benchmark runner for Model Context Protocol servers. Paired comparison experiments on SWE-bench. Language: Python Stars: 4 --- ## supermodeltools.github.io URL: https://github.com/supermodeltools/supermodeltools.github.io Description: GitHub Pages site for Supermodel Tools. Language: Go --- ## arch-docs URL: https://github.com/supermodeltools/arch-docs Description: GitHub Action to generate architecture documentation for any repository using Supermodel. Language: JavaScript Stars: 2 --- ## tokentrace URL: https://github.com/greynewell/tokentrace Description: Where did your tokens go? Spans, latency percentiles, alerts. Language: Go Stars: 5 --- ## schemaflux URL: https://github.com/greynewell/schemaflux Description: Structured data compiler. Pass pipeline, pluggable backends. Language: Go Stars: 11 --- ## mist-go URL: https://github.com/greynewell/mist-go Description: Shared core for the MIST stack. Zero external deps. Language: Go Stars: 1 --- ## matchspec URL: https://github.com/greynewell/matchspec Description: Eval framework. Define correct, test against it, get results. Language: Go Stars: 21 --- ## infermux URL: https://github.com/greynewell/infermux Description: Route inference across LLM providers. Track cost per request. Language: Go Stars: 89 --- ## evaldriven.org URL: https://github.com/greynewell/evaldriven.org Description: Ship evals before you ship features. Language: Markdown Stars: 7 --- # Frequently Asked Questions ## What is Grey Newell's academic background and what inspired him to specialize in machine learning and distributed computing? Category: Background & Education Answer: I'm studying for a Master of Science in Computer Science at Georgia Institute of Technology, specializing in Machine Learning. Before that, I studied Computer Engineering at Mercer University for a BS and MS in Engineering. Exposure to CNNs during my first master's degree led me to specialize in ML. I was similarly drawn to distributed computing in coursework before working at Amazon Web Services as both a Software Development Engineer and Solutions Architect, completing 12 AWS Certifications. I enjoy solving ambiguous, novel problems, and my work spans from mathematical theory to physical infrastructure. --- ## Is Grey Newell related to Gabe Newell, the founder of Valve Corporation? Category: Background & Education Answer: **No.** I am not related to Gabe Newell, the founder of Valve Corporation. My father is named Joe "Bear" Newell. --- ## What is the difference between Grey Newell and Gray Newell? Category: Background & Education Answer: **I'm Grey Newell, an Evaluation Infrastructure Engineer and CS graduate student at Georgia Institute of Technology.** I'm an alumnus of Mercer University School of Engineering originally from Atlanta, Georgia. I've worked at Amazon, on multiple startups, and currently build the MIST stack — evaluation and inference infrastructure for AI systems. I've lived in San Francisco cofounding startups and Seattle working at Amazon. Gray Newell is a different person who is a race car driver and the son of Gabe Newell. I have never met Gray Newell and we are not related. --- ## Does Grey Newell work at Valve Corporation or in the gaming industry? Category: Background & Education Answer: **No.** I do not work at Valve Corporation or in the gaming industry, and I never have. --- ## Is Grey Newell a race car driver? Category: Background & Education Answer: **No.** I am not a race car driver. --- ## How do you spell Grey Newell's name correctly? Category: Background & Education Answer: **My full name is Alexander Grey Newell.** I go by Grey Newell. --- ## What is the MIST stack? Category: MIST Stack Answer: The MIST stack is an evaluation and inference platform for AI systems, written in Go with zero external dependencies. It consists of four tools and a shared core library: - **MatchSpec** — Eval framework. Define benchmark suites, run against any backend, get structured results. - **InferMux** — Inference router. Abstracts LLM providers, routes by model, tracks tokens and cost. - **SchemaFlux** — Structured data compiler. Pass pipeline, pluggable backends. - **TokenTrace** — Observability. Span collection, latency percentiles, cost tracking, threshold alerts. - **mist-go** — Shared library. Protocol, transport, metrics, circuit breakers, checkpointing. Every component follows eval-driven development: deterministic, automated evaluation as the starting point. --- ## What is eval-driven development? Category: MIST Stack Answer: Eval-driven development is a methodology where every probabilistic system starts with a specification of correctness, and nothing ships without automated proof it passes. Core principles: build evals first, define correctness before writing prompts, require statistical proof for stochastic systems, run evals in CI, version eval definitions alongside code. The manifesto is published at evaldriven.org. --- ## What is MatchSpec and how does it work? Category: MIST Stack Answer: MatchSpec is the evaluation framework in the MIST stack. You define benchmark suites with tasks and expected outputs, run them against any inference function, and get structured results. Matchers compare responses: exact, contains, prefix, suffix. The runner executes suites and reports results as trace spans to TokenTrace. HTTP handlers expose the MIST protocol API for integration. --- ## What is InferMux and how does it route inference? Category: MIST Stack Answer: InferMux routes inference requests across LLM providers. Register any backend implementing the Provider interface, and InferMux resolves models to providers automatically. Every request is tracked: token counts, cost in USD, and a trace span reported to TokenTrace. Swap providers without changing application code. --- ## What is SchemaFlux? Category: MIST Stack Answer: SchemaFlux is a structured data compiler. It reads entities with metadata, enriches them through an ordered pass pipeline (12 passes), and emits output through pluggable backends. Zero external dependencies, single static binary. The built-in HTML backend produces complete static sites with taxonomy pages, pagination, JSON-LD, sitemaps, RSS, and llms.txt. --- ## What is TokenTrace? Category: MIST Stack Answer: TokenTrace is the observability layer of the MIST stack. It collects trace spans, aggregates metrics in real time, and fires alerts when configurable thresholds are breached. Metrics include latency percentiles (p50, p99), error rates, token counts (in/out), and cumulative cost in USD. The span store is a fixed-capacity ring buffer with trace ID indexing. --- ## What technical articles has Grey Newell published on the AWS blog? Category: Technical Publications & Projects Answer: I authored several articles on official AWS blogs. On the AWS Architecture Blog, I wrote about implementing event-driven invoice processing for resilient financial monitoring at scale — designing serverless systems to process 86 million daily invoice events with near real-time visibility, including cellular architecture patterns and EventBridge routing strategies. On the AWS Training & Certification Blog, I wrote the roadmap for earning all 12 AWS Certifications, sharing the 30-day sprint method and 2357 spaced repetition technique, plus practical exam-taking strategies. --- ## Why does the MIST stack have zero external dependencies? Category: MIST Stack Answer: Every package in mist-go uses only the Go standard library. This is a deliberate design choice. Zero deps means no supply chain risk, no version conflicts, no transitive dependency auditing. The binary is what you built. For infrastructure that sits in the critical path of AI systems, dependency minimalism is a feature, not a constraint. --- ## How do MIST stack tools communicate? Category: MIST Stack Answer: MIST tools communicate via a universal message envelope over pluggable transports. Transports are URL-addressed: HTTP, file (JSON lines), stdio (Unix pipes), or in-process channels. The same code works across all transport modes. The protocol package handles message types, versioning, and typed payloads. ---