Full-stack AI engineer

github

Ryan Brown  • Peerlist

ThinHarness is a minimal, opinionated Python harness for purpose-built agents — it owns the agent loop and leaves the rest of your app to you.It exists for the gap between building the agent loop yourself and adopting a large runtime where the loop comes bundled with assumptions you don't need and can't easily change.Why ThinHarness:<ul><li>Focused scope: filesystem tools, structured output, tool retries, approvals, subagents, parallel LLM calls, skills, MCP, limits, streaming, and OTel tracing. Nothing more.</li><li>You keep your stack: orchestration, auth, storage, and deployment stay yours, because they become product-specific anyway.</li><li>Small enough to fork: full harness-level feature coverage in under 8k LOC, matching frameworks 5–10× its size; each major feature in its own file with no hidden dependencies.</li><li>Proven on real work: on a LongMemEval-V2 subset it outperformed the paper's task-tuned Codex harness (74.0% vs 72.4%) using ~46% fewer tokens, with only built-in tools.</li></ul>Pre-1.0, MIT. I'd love feedback from people building agent workflows in Python.

ThinHarness

In this project, I analyzed 4.5+ years of journal entries using NLP in python to derive unique, data-driven insights about my life experience.

Journal Analysis

Moodify is an interactive digital platform that uses your music to generate mood playlists customized to your specifications. It was created as a product for Texas Convergent’s Data Analysis build team, and on demo day it won “best tech” out of 16 teams.

Moodify

In this project, I analyzed 10 years of self-collected men's NCAA volleyball player height and team wins data to determine the importance of height for success.

NCAA Men's Volleyball Analysis

An academic research paper that explored the usage, potential, and limitations of content-based filtering for book recommendations using supervised and unsupervised machine learning models + NLP, proposing a strong case for their integration with existing media platforms.