Walt Burge — AI & Software for Local Business · Oxford, MS

I build custom AI and software for Oxford and North Mississippi businesses — a 24/7 AI receptionist that answers every call and books the job, automated follow-up that works every lead, estimating, and custom tools built around how you already run things. You own what I build; you don’t rent it. The first call and the estimate are free, and you talk to the person who builds it, not a sales rep. Already running for MHP Construction in Oxford.

Services

AI Receptionist — answers every call 24/7, books the job, and turns happy customers into reviews.
Lead Follow-Up Automation — works every lead so none go cold and slip to whoever answered first.
Custom Software & AI Models — tools and models trained for your work, built around how your business already runs.

Engineering background

ML / AI — PyTorch, LLM fine-tuning (Llama 3.1), distillation, RAG, embeddings, LangChain, Ollama, MCP
Data Science — dataset curation, synthetic data generation, evaluation, data pipelines
ML Infra — RunPod, vast.ai (GPU), serverless inference, model ops
Languages — Python, TypeScript, Java, Rust
Backend & APIs — Spring Boot, Node.js, REST, WebSocket, PostgreSQL, SQLite
Frontend — React, Next.js, React Native (Expo), Tailwind CSS

Contact: jamesburge.mcm@gmail.com · (662) 292-5533 · github.com/Aphrodine-wq

Selected Work

FairTradeWorker — Two-sided construction marketplace connecting homeowners with vetted contractors. Next.js, Java/Spring Boot, React Native. QuickBooks-native payments and AI-powered estimation.
MsHomePros — Contractor business platform with AI-powered line-item estimation, professional proposals, and job tracking. Live client: MHP Construction, Oxford, MS.
ConstructionAI — Fine-tuned Llama 3.1 8B for construction cost estimation. 18,000+ training examples. Deployed on RunPod Serverless at ~$0.002 per estimate.
AEON — A formal-verification and code-analysis platform: 73 engines (22 cybersecurity) that prove code correct and hunt vulnerabilities before it ships. Python.
Engram — MIT-licensed, local-first screen memory: on-device OCR plus an MCP server, installable with one pip command. Python.
Tessera — A markdown-native programming language for AI agents. Agents are written in .t.md files as substrate-tagged code fences, formally verified by AEON before they run. Python.
W.A.L.T. — Distributed AI platform across three machines. 64 MCP tools for screen understanding, 73-engine formal verification, autonomous overnight execution.

Blog — The Build Log

Tessera: Why I'm Writing AI Agents in Markdown — Building an AI agent shouldn't require five frameworks, three vendor SDKs, and a vector DB you babysit. It should require markdown and a compiler that takes the substrate boundaries seriously. The case for .t.md files as the source of truth for agents.
Substrates: Giving an Agent Named Modes of Thinking — Tessera agents are built from typed code fences — logic, agent, memory, prompt, tool, neural. The compiler enforces the boundaries between them, so an agent's architecture becomes legible and auditable instead of a blob of Python.
Verifying AI Agents Before They Ever Run — Tessera lowers each agent to a Substrate IR and runs AEON's 73 formal-verification engines against it before execution, catching capability leaks and PII egress at compile time. No other agent framework does formal verification before run.
Cognitive Traits: Channeling Maladaptive Patterns into Better Reasoning — Tessera lets you install reasoning postures — productive doubt, cross-domain scanning, compulsive verification — as first-class, inspectable code in a tsr:traits block, instead of burying them in prompt strings.
From Construction Sites to Codebases: 7 Months Self-Taught
Fine-Tuning a Construction Estimation LLM from Scratch
Designing a Three-Sided Construction Marketplace

Tech Stack

Next.js, TypeScript, Kotlin, Spring Boot, React Native, Python, Rust, Bevy, PostgreSQL, Tailwind CSS, PyTorch, Llama, RunPod

Contact

Email: jamesburge.mcm@gmail.com

GitHub: github.com/Aphrodine-wq