Impact at a Glance

Regime AUC

0.925vs 0.631

Ridge logistic against Laya on market-snapshot numbers

Sentiment accuracy

71% → 88%

Qwen3-4B before and after QLoRA fine-tuning, on public sentiment data

Fine-tune

4-bit QLoRA

Qwen3-4B-Instruct in PyTorch (peft, bitsandbytes): a quarter of the weight memory of 16-bit

Building

1 year

Four projects built, two of them live products

0.925
Regime AUC, ridge logistic vs 0.631 for Laya
88%
Sentiment accuracy, up from 71% after QLoRA fine-tuning
4-bit
QLoRA fine-tune of Qwen3-4B-Instruct
1
Year building: four projects, two live

Featured Work

Featured

Agents · Analytics · AI

Fantasy Football Agent

Fantasy Football research took more time than I had, so I built an agent that does it and interprets the results the way I would. It saves me countless hours so I can focus on more important work.

PythonClaude APIAgents
Read case study ↗

AI · Finance · Local models

Tick Forward

A trading bot that trains its own local models (Qwen and Laya) to read financial data fast and cheaply, with hard risk limits before any real money is involved.

PyTorchQLoRAPaper trading
Read case study ↗

Game · Web app · Supply chain

Farmstead World

A free-to-play farming game where you plant, harvest and discover your way through the real food supply chain.

Farms
13 active
Eggs
10,355 doz
Corn
148 bu
Web appFree to playSupply chain
Read case study ↗

Reader · Audio · Signing

Lamp Light

A personal document reader that reads aloud and signs without leaving the page, usable on the web or as its own app.

Audio
playback
Signing
in-page
Price
free
Document readerAudioSigning
Read case study ↗

Core Stack

Python PyTorch AWS Cloud Services PostgreSQL Claude Code/API PowerBI Excel