How Jay Built an AI Tool That Multiplied His Job Interviews

Jay (GitHub @jayharish) was a job seeker who turned his own pain into a shipped AI tool. He built ats-resume-crafter, an agentic system that reads any job description and auto-tailors an ATS-optimized resume and cover letter. The result: roughly 10x more interviews in the same amount of time.

What does it take to prove you can actually build?

Most job seekers list skills on a resume and hope someone believes them. Jay did something stronger: he shipped working software, in public, that anyone can inspect. His focus is AI and Python, and instead of waiting for permission, he built the tools that solved his own problems.

That is the difference between claiming a skill and demonstrating it. A live tool and a public GitHub repo are evidence. Evidence is what convinces schools, hiring managers, and anyone deciding whether to bet on you.

Jay built these projects the StepAhead way: by prompting AI with coaching, turning ideas into shipped, verifiable software. If you want to start the same way, the $100 bundle of 13 build projects at /ship is the on-ramp.

How did one tool multiply Jay's interviews?

The job hunt has a brutal math problem. Every application needs a tailored resume and cover letter, and doing that by hand for dozens of roles is slow. Most people either send the same generic resume everywhere or burn out after a handful of custom ones.

Jay's answer was ats-resume-crafter, an agentic tool that reads any job description and automatically tailors an ATS-optimized resume and cover letter to match it. The agent does the matching work that used to eat his evenings.

The payoff was concrete: roughly 10x more interviews in the same amount of time. Same hours, same person, far more conversations with employers. That is what leverage looks like when you build the tool instead of grinding the task by hand.

Why does an agentic tool beat a template?

Plenty of people download a resume template and call it done. Jay's tool is a different category. It does not just fill in blanks. It reads, reasons about, and responds to each specific job description, then produces tailored output keyed to that role.

Building something agentic taught Jay skills that show up on no checklist but matter enormously:

• Breaking a messy real-world problem into steps an AI agent can execute.
• Designing prompts and logic that produce reliable, ATS-friendly output every time.
• Shipping a tool other people could actually use, not just a demo that works once.

Those are the muscles employers and admissions readers are quietly looking for. Jay grew them by building, with coaching, the same loop StepAhead runs you through inside the 13 shippable projects at /ship.

What else did Jay ship?

One tool can be luck. A portfolio is proof of a pattern. Jay did not stop at the resume crafter. His public GitHub shows a builder who keeps turning problems into working software:

• vector-search: a semantic search MCP tool for Claude Code, extending an AI coding agent with the ability to search by meaning rather than keywords.
• Regulytics: a healthcare regulatory data pipeline with a live executive dashboard, taking raw regulatory data and turning it into something a decision-maker can read at a glance.
• music-ai: an AI project in the music space, another domain to learn in public.

Notice the range: job tooling, developer tooling, healthcare data, and music. That breadth tells a story no single line item can. It says Jay can walk into an unfamiliar domain, understand the problem, and ship something that works.

Why is a public GitHub the portfolio that actually counts?

A resume bullet says "experienced with Python and AI." A public repo says "here is the running code, read it yourself." One is a claim. The other is verifiable.

When an admissions reader or a hiring manager can click into Jay's GitHub and see live tools, real commits, and working dashboards, the conversation changes. They are no longer guessing whether you can build. They can see it.

This is exactly the kind of evidence that separates a serious applicant from the crowd. It is concrete, it is public, and it cannot be faked with a polished paragraph.

How can you build a portfolio like this?

Here is the encouraging part: Jay did not do this by spending years in a classroom first. He built by prompting AI, coached by AI and by mentor Sahil Modi. He started with a real problem he cared about, his own job hunt, and shipped a tool that solved it.

You can follow the same path:

1. Pick a real problem you actually have. Personal pain makes the best first project.
2. Build the tool by prompting AI, with coaching to keep you unstuck.
3. Ship it. Put it live, push it to a public GitHub, make it inspectable.
4. Repeat, and let the portfolio compound into proof.

The hardest part for most people is the first ship. That is precisely what StepAhead is built to get you across. The $100 bundle at /ship gives you 13 build projects, each designed to take you from idea to shipped, with the coaching that kept builders like Jay moving.

What is the real lesson from Jay's story?

Jay's job search did not improve because he applied to more places by hand. It improved because he built leverage. He turned a frustrating, repetitive task into an agentic tool, and that tool gave him roughly 10x more interviews in the same time.

That mindset, build the thing instead of grinding the task, is what makes a builder valuable anywhere. The resume crafter solved his immediate problem. The portfolio behind it, vector-search, Regulytics, music-ai, proves the skill is real and repeatable.

You do not need to be a job seeker to learn from this. You need a real problem, the willingness to ship in public, and a way to keep moving when you get stuck.

Ready to build your own proof? Start with the $100 bundle of 13 build projects at /ship and ship your first real AI tool, the same way Jay did: by prompting AI, with coaching, until it is live and public for the world to see.