From Web Tinkerer to Problem Solver

I've been tinkering with web development for as long as I can remember. When I started learning HTML, CSS, and JavaScript, I was excited about innovations like Flexbox. But despite all the experimentation, I never built anything beyond individual components - I simply didn't have a compelling use case for a full website, nor the time to invest properly.

When I needed to create an ecommerce store quickly, Wix became my solution. I was genuinely proud of the online catalog I built for my personal brand Sebastien Beau (a luxury line of matching heels and handbags). That experience taught me the value of using the right tool for the job.

As a tech enthusiast, I continued building small projects with various frameworks and no-code builders like Framer and Webflow. Each project refined my understanding of design and user experience.

Being completely self-taught, these mini-projects shaped how I approach problems. In every company I worked for, I spotted opportunities to improve client or internal processes. But my suggestions rarely moved beyond "great idea!" and a pat on the back - managers were intimidated by the perceived IT costs of implementation.

Everything changed during a painful CRM cleanup project. I discovered that Google Apps Script uses the same JavaScript I already knew from web development. So I decided to take matters into my own hands and started building mini solutions, ultimately stitching them together. The results we achieved with a very simple tool stack - without having to use precious dev capacity - were beyond anyone's expectations. It was like finding a hidden door in a familiar room.

The "Aha!" Moment

Instead of learning backend development from scratch, I could repurpose my existing web skills for business automation. The breakthrough was reframing familiar tools:

• Google Sheets as my database - Familiar, accessible data storage with Google Workspace APIs and generous free tier
• Web interfaces as my control panel - User-friendly interaction points via simple interfaces
• JavaScript as my automation engine - The same language, different application

The missing piece was AI assistance. With AI helping translate business requirements into code, I could build functional solutions without years of additional study.

Context Is Everything

AI assistance quality directly correlates with context depth. Vague requests yield generic solutions. Specific requests yield targeted automation.

I learned to provide:

• Current workflow details - Tools, processes, and pain points
• Data structure information - Formats, sources, and destinations
• Time waste identification - Specific inefficiencies and their frequency
• Desired outcome clarity - Exact automation goals

Think of it like giving directions. "Take me downtown" gets you basic help. "Take me to 123 Main Street using the fastest route during rush hour" gets you exactly where you need to go.

Start With Daily Friction Points

Complex systems come later. I began by documenting repetitive tasks:

• Manual data transfers - Same information, different spreadsheets, every morning
• Report formatting - 30 minutes weekly on identical layout adjustments
• Batch communications - Dozens of similar emails with minor variations

Each automation started small - maybe 3-5 minutes saved per task. But frequency matters. A 5-minute daily task automated saves over 20 hours annually.

Two Years Later: Unexpected Skills Development

This journey developed capabilities I didn't anticipate:

Process optimization mindset. I automatically evaluate inefficiencies rather than accepting "that's how we've always done it." Every workflow becomes a potential improvement opportunity.

Technical communication ability. IT collaboration became genuinely productive. Understanding automation basics allows me to contribute meaningfully to technical requirements discussions.

Systems thinking development. I take detailed notes when stakeholders describe problems, then map workflows and process interdependencies to anticipate downstream effects of changes. I visualize how data flows and what the user experience should be - focusing on maximum achievement with minimal effort. One automation often reveals opportunities for related improvements.

Market Relevance

The current job market increasingly values problem-solving ability over narrow technical expertise. High-performing professionals identify inefficiencies, architect solutions, and leverage available resources for implementation.

The goal isn't becoming a traditional software developer. It's developing the ability to recognize optimization opportunities and execute solutions using AI assistance, existing tools, or collaborative partnerships.

Why I Started This Series

I work with many talented people who are younger than me. They each have their own unique potential and standout qualities - some are natural problem-solvers, others have incredible attention to detail, and many have fresh perspectives I genuinely value.

What I've noticed is that they often come to me with the same questions: "How did you build that?" "Can you give me some recommendations on getting started?" They see the automations or systems I've created and want to begin their own journey into what I call "vibe coding" - building practical solutions that actually solve real problems.

This series exists because of those conversations. Rather than explaining the same concepts repeatedly, I want to document the path I took and the lessons I learned. Not because I'm an expert, but because I've been where they are - looking at a problem and wondering if there's a better way to solve it.

Quick Implementation Strategy For Those Getting Started

For those interested in this approach, here's a practical framework that will get you started right now:

1. Document workflow friction - Track daily annoyances for one week
2. Prioritize quick wins - Select repetitive tasks consuming 2-5 minutes
3. Provide detailed context to AI - Include current tools, data formats, and desired outcomes
4. Request clear explanations - Ask for concepts to be explained at your technical level
5. Iterate through experimentation - Most automation requires testing and refinement

Business operations need more people who can bridge the gap between identifying problems and implementing solutions. The intellectual satisfaction of seeing your automation work is considerable.

What repetitive task consumes 5 minutes of your day? That might be your first automation opportunity.