Ethereum Fine-Tuning Cookbook
Open-source educational cookbook for fine-tuning language models on blockchain data using QLoRA/LoRA. Complete examples for extracting structured intents from Ethereum transactions with 90%+ accuracy.
John Young_
Chief Technology Officer | Full Stack Engineer | DeFi Systems Architect
With 12+ years in fintech, spearheaded multi-agent systems development, optimized workflow automation, and led
technical due diligence for portfolio investments. Fine-tuned LLMs for enhanced investigation efficiency and
built data infrastructure processing 100K+ daily events. Expertise spans agentic workflow automation, risk
management, and financial modeling.
Husband to the most beautiful woman in the world.
Runner-up in 6th grade spelling bee. Super-nerd.
Combine Capital
2023 - Present
Chief Technology Officer
Spearheaded multi-agent systems development, optimized workflow automation, and led technical due diligence for portfolio investments.
FRACTAL (Fractal Wealth)
2021 - 2022
Software Engineer
Designed and deployed automated crypto/DeFi trading strategies.
Cryptosheets
2018 - 2021
Founder & Chief Technology Officer
Founded and scaled cryptocurrency data platform to 30,000+ active users.
Spreadstreet.io
2016 - 2019
Founder & Chief Software Architect
Created Google Sheets integration for cryptocurrency market data.
Capital One
2010 - 2017
Principal Financial Analyst
Progressive roles: Finance Rotation Program Analyst → Senior Financial Analyst → Principal Financial Analyst.
Towson University College of Business and Economics
2008 - 2010
Bachelor of Science, Finance & Economics (Dual Concentration)
Activities: Towson University Investment Group
Thinkful
2018 - 2019
Engineering Immersion Program
Intensive 6-month program requiring 60-90 hours per week focused on full-stack development with JavaScript, React, Node.js, and PostgreSQL
StrategyV2 Developer
May 2024
Hummingbot Foundation
Developed trading strategy voted MVP by peers and instructors at demo day presentation
Open-source educational cookbook for fine-tuning language models on blockchain data using QLoRA/LoRA. Complete examples for extracting structured intents from Ethereum transactions with 90%+ accuracy.
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