The gap between "I need a tool that does X" and "I can build a tool that does X" used to require either coding skills or a budget. AI development tools have collapsed that gap—but not in the way most people think.

If you've been job searching recently, you know this is one of the roughest markets in years. Application fatigue is real. The AI resume tools promise to help, but most just generate generic schlock—slightly different words saying the same thing, optimized for ATS systems that may or may not exist the way we imagine them.

What I actually needed was different: a way to genuinely tailor my resume to each position without spending 30 minutes per application. Not AI rewrites, but intelligent reorganization based on what each role required. I could pay for a tool that kind of did this, or build exactly what I needed.

I chose to build. Not because I'm a developer (I'm not), but because I was curious whether AI tools had crossed the threshold from "technically possible" to "practically possible" for someone like me.

Turns out, they have. Here's what the first 100 feet of that journey looked like.


The Unexpected Unlock: Development Without the Dev Environment
I've tried local development environments before. Multiple times. It always ended the same way: hours troubleshooting dependencies, fighting with terminal commands, or getting stuck when neither I nor the AI assistant had the expertise to debug whatever broke.

The blocker was never the coding—it was all the infrastructure around it.

This time was different. I used a browser-based development environment where I could work directly with AI-generated code. I'd experimented with a few platforms, but for this iteration I settled on Claude, which I found particularly effective for translating what I wanted into working code. No local setup. No dependency hell. Just: the AI writes code, I copy it to the browser environment, see it run, iterate.

The first time something actually worked—when I uploaded my resume and watched the tool generate a tailored version—I had a genuine "holy shit, that worked" moment.

This removes the biggest barrier for non-technical builders: getting to the point where you're actually running code instead of fighting with your environment.

What Building Teaches You That Using Doesn't
Using AI tools is easy. Building with AI forces you to think clearly about what you actually want and why.

Clarity drives everything. From the start, I knew I wanted resume tailoring, not generation. I had dozens of carefully crafted resumes that surfaced relevant experience—I just needed it reorganized and emphasized differently for each role. Any PM knows this lesson: a clear, aligned vision drives success. The same holds true when working with AI.

Designing for trust became central. Black-box AI solutions fail in high-stakes scenarios like job applications. You need to see what the AI did and why.

So I built transparency in: the tool shows exactly how it mapped your experience to job requirements using the STAR method. For example, if a job emphasizes "stakeholder management," you see:

Situation: Cross-functional project with competing priorities
Task: Align stakeholders on unified roadmap
Action: Facilitated workshop series, established decision framework
Result: Reduced planning cycle by 40%, achieved stakeholder consensus

You can verify it makes sense. You can override if needed. The AI does the tedious reorganization work while you maintain judgment and control.

Real-world data is messier than you think. Early parsing attempts revealed inconsistent formatting, unexpected structure, edge cases everywhere. I learned to debug iteratively: test with real data, identify what broke, fix it, test again. The AI didn't magically solve these problems, but it made the iteration cycle fast enough to work through them.

Clear frameworks make AI more effective. The STAR method isn't just good for resumes—it's good for AI prompting. "Make this sound better" gets generic improvements. "Extract the Action and Result and emphasize the measurable outcome" gets useful output

What's Actually Hard (And What Isn't)
I expected blockers that would exceed our capabilities. That rarely happened.

Easier than expected: Getting something functional, basic features, iterating based on use, working through daunting problems

Harder than expected: Reliable data parsing, handling edge cases, knowing when to stop adding features, deciding what should be automated vs. manual

The real skill isn't coding—it's product judgment. Scoping, prioritizing, understanding the user perspective. These are PM skills, not engineering skills.

The AI handles implementation. I handle "what should this do and why?"

From Project to Product: What's Next
There's a useful PMBOK distinction: projects have defined endpoints—you deliver scope, close out, move on. Products are continuous—you release, learn, iterate, improve.

This tool is somewhere in between. The initial project is functionally complete. I can upload a resume, paste a job description, get useful output. Project success criteria: met.

But thinking like a product manager reveals gaps. I want mechanisms to collect user feedback systematically, parse usage patterns, refine logic based on real outcomes. The backlog is already forming: better edge case handling, more sophisticated matching, expanded formats.

This is genuinely different from traditional development. In the past, building meant either paying for ongoing development or accepting version 1.0 as final. Now? Version 1.0 is just the first sprint. I can continue iterating—limited only by time and attention, not technical capability or budget.

That changes the calculus for what's worth building.