Hi, I'm Jesse Moses (@Cre4T3Tiv3)

AI Software Engineer combining research rigor, mathematical depth, and innovative tool building to advance AI engineering through novel approaches and comprehensive developer workflows.

⇒ Research Rigor - Statistical validation, mathematical analysis, and reproducible methodologies
⇒ Mathematical Foundations - Deep expertise in core ML mathematics (SVD, linear algebra, statistical modeling)
⇒ Innovation Leadership - Temporal Intelligence and novel approaches to AI system analysis
⇒ Developer Tools - Building comprehensive workflows that solve real engineering problems at scale
⇒ R&D & Innovation - 10+ years across fintech, ad-tech, and enterprise SaaS | MS AI/ML, MS CS (in-progress)

I build AI systems that bridge mathematical rigor with practical engineering. My work spans temporal pattern analysis, rigorous AI agent benchmarking, foundational ML mathematics, and creating tools that solve real problems developers face daily.

Specializing in research-driven innovation, mathematical ML foundations, comprehensive developer tooling, and statistical validation. I build in public, treat open source like product, and advance the field through rigorous analysis and thoughtful engineering.

Mission: Advancing AI engineering through research rigor, mathematical depth, and innovative tools that empower both humans and machines.

Current Research & Innovation

Edge AI Performance Optimization - Power-performance analysis, CUDA optimization, and hardware efficiency validation.
Temporal Intelligence - Analyzing AI/ML systems and code evolution patterns over time.
AI Agent Architecture - Rigorous benchmarking and mathematical validation of agent systems.
Neural Network Training & Optimization - Advanced fine-tuning methodologies, QLoRA implementations, and efficient training pipelines.
Mathematical ML Foundations - Bridging core mathematics with modern AI applications.
Next-Gen AI/ML Tools - Building developer workflows for the future of AI/ML engineering.

What Sets This Work Apart

Research Rigor - Statistical validation (95% CI, Cohen's h), reproducible methodologies, mathematical analysis
Mathematical Foundations - Deep expertise in core ML mathematics applied to modern AI systems
Innovation - Temporal Intelligence and novel approaches that advance the field
Practical Engineering - Tools that solve real problems developers face daily
Open Innovation - Building in public with complete transparency and rigorous validation

Your support helps me ship polished, practical, and developer-first AI tools that actually get used.

Let’s build the future of AI/ML software together, one clean, open tool at a time.

Research rigor • Mathematical depth • Innovation • Building next-generation AI/ML engineering