// Python Developer & AI Innovator -- Houston, TX

24 years of ops.
Turned into code.

I taught myself Python in 2024 and built 190+ utilities and 11 standalone production systems in less than a year. Along the way I discovered AI Inline Learning -- a novel pattern for persistent AI agent collaboration that reduces repeated errors by 60%+. No infrastructure required.

AI Inline Learning Web Scraping SEO Automation Cybersecurity Student

What I'm building.

Open source tools designed for developers who want visibility into their systems without vendor lock-in or infrastructure overhead.

AI Inline Learning.

A novel pattern I discovered in December 2024. Research-validated against 30+ academic papers. Nobody was doing this.

The Problem: AI forgets everything.

AI coding assistants make the same mistakes repeatedly across sessions because they have no persistent memory. Traditional solutions require external memory databases, complex protocols, or infrastructure overhead. I found a simpler answer by accident -- I was just tired of seeing the same Unicode error for the third time in one afternoon.

The Solution: Put the warning where the mistake happens.

Instead of external memory, embed structured learning comments directly in the code at the exact decision point where an AI went wrong. Future AI sessions read the codebase, find the warning, and skip the mistake. The code itself becomes the knowledge base. Zero setup. Zero infrastructure. Works in any language.

# HEY CLAUDE: Slow down! Remember the Unicode disaster?
# MISTAKE: Used fancy arrow chars in PowerShell output -- 2024-12-26
# LESSON: Windows console encoding breaks with non-ASCII -- "missing terminator" error
# RULE: ASCII-only in PowerShell. Always. Use -> and [OK] instead.
60%+
Reduction in repeated errors
30+
Academic papers reviewed
0
Infrastructure required
190+
Utilities and systems using the pattern

Researched against Spark (Nov 2024), MemGPT, ChatDev, Google A2A, and 25+ additional papers. None used inline comments as the primary AI-to-AI learning mechanism. Pattern published on LinkedIn and documented on GitHub.

Tools I actually use.

Python-first. Production libraries. No unnecessary abstractions.

Python
BeautifulSoup
Scrapy
Selenium
aiohttp
requests
pandas
lxml
structlog
OpenTelemetry
tree-sitter
Neo4j
Flask
FastAPI
Celery
PostgreSQL
SQLite
PowerShell
Git
Linux / WSL
regex
CompTIA A+
CompTIA Network+

Michael Rawls, Jr.

I spent 24 years solving real problems -- managing Popeyes locations and winning national consumer awards, wiring commercial solar arrays for Ford and H-E-B, diagnosing septic systems by thinking through what you can't see. In 2024 I picked up Python seriously and built 190+ utilities and 11 standalone production systems in under a year.

That background changes how I approach code. I don't build demos. I build things that have to work the first time, handle failure gracefully, and solve a real business problem. The AI Inline Learning pattern came out of that same mindset -- I was just tired of repeating myself to an AI that kept making the same mistake.

Currently pursuing my A.A.S. in Cybersecurity at Alvin Community College (GPA 3.0) while building an AI-enhanced SEO audit service. CompTIA A+ and Network+ certified. Looking for Python development, SEO automation, or data analyst roles where operational thinking meets technical execution.

2024 -- Present
Python Developer + Cybersecurity Student
190+ utilities, 11 standalone systems, AI Inline Learning
2023
Electrical Apprentice -- Spear Commercial
Commercial solar for Ford, H-E-B, Shiner Bock
2021 -- 2024
Installation Technician -- Stavinoha Cabinets
100+ installs, 95% zero-rework rate
2011 -- 2021
Installer & Technician -- A-OK Aerobic
Systems thinking, regulatory compliance, TCEQ certified
1997 -- 2006
Area Manager -- Popeyes Louisiana Kitchen
$4M+ operations, National Consumer Award x3
Education
A.A.S. Cybersecurity
Alvin Community College -- GPA 3.0
Certifications
CompTIA A+ & Network+
Industry recognized
Projects Built
190+ Utilities, 11 Systems
Built in under 12 months
Location
Houston, TX
Open to remote
Innovation
AI Inline Learning
Novel AI agent collaboration pattern -- December 2024