Become a sponsor to Jesse Moses
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
Temporal Intelligence - Analyzing AI systems and code evolution patterns over time
AI Agent Architecture - Rigorous benchmarking and mathematical validation of agent systems
Mathematical ML Foundations - Bridging core mathematics with modern AI applications
Next-Gen AI Tools - Building developer workflows for the future of AI 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 *
Featured work
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Cre4T3Tiv3/gocmitra
High-performance AI-powered Git commit assistant with pluggable architecture. Cross-platform compatibility with zero-dependency binary and intelligent commit analysis. Written in Go. Built for ever…
Go 29 -
Cre4T3Tiv3/llm-prompt-debugger
Clean UI for LLM development workflows with prompt versioning and model selection. Built for engineers, not hype. Streamlined prompt → model → tag → export workflow. Currently supports OpenAI, Clau…
TypeScript 34 -
Cre4T3Tiv3/unsloth-llama3-alpaca-lora
Advanced 4-bit QLoRA fine-tuning pipeline for LLaMA 3 8B with production-grade optimization. Memory-efficient training on consumer GPUs for instruction-following specialization. Demonstrates cuttin…
Jupyter Notebook 25 -
Cre4T3Tiv3/gitvoyant
Temporal Code Intelligence platform analyzing Git history patterns to predict quality evolution and maintenance burden. Conversational AI agent reveals complexity trends, decay forecasting, and cod…
Python 57 -
Cre4T3Tiv3/agent-academy-labs
Experimental framework for multi-agent coordination and collaborative learning architectures. Research platform exploring agent-based learning systems, coordination protocols, and emergent behavior…
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Cre4T3Tiv3/ai-agents-reality-check
Mathematical benchmark exposing the massive performance gap between real agents and LLM wrappers. Rigorous multi-dimensional evaluation with statistical validation (95% CI, Cohen's h) and reproduci…
Python 40