I’m 19. No team. No VC funding. Just me and a laptop.
Last week reality hit me hard. I applied to 127 jobs and got 0 interviews. I know I'm a solid dev, so I figured something was broken technically.
I dug into the parsing logic of legacy ATS systems like Workday and Taleo. Turns out my "modern" resume was being read as total gibberish because I used columns.
I wanted to build a tool to fix this for everyone else. Usually building a full SaaS takes months. I gave myself 4 days.
Here is how I built InterviewGhost.us by acting as the Architect and using AI as my engineering team.
The Stack
The goal was speed without sacrificing technical rigor.
Frontend: Next.js + Tailwind. I iterated this via Claude 3.5 Sonnet using "Linear-style" design tokens.
Backend: Node.js + Puppeteer. Used this for the forensic PDF generation.
Logic: DeepSeek-V3. Used it to optimize the heavy text extraction logic.
Deployment: Vercel.
Database: None. I built a zero-retention architecture for privacy.
The Hardest Part: Forensic PDF Generation
Generating a PDF in Node.js usually sucks. You have to mess with pdf-lib or jspdf and fight with CSS print rules.
I didn't want to write every CSS class by hand. So I fed the LLM a strict design system. I told it to create a forensic report template that looks like a McKinsey audit document mixed with a terminal log.
It generated a grid-based layout that dynamically visualizes the "parsing scramble" effect.
The first version actually looked "too clean" and didn't show the error.
So I forced the parser into "dumb mode" to strip layout data. This exposed exactly how the robot sees scrambled text.
The Result: A 3-page forensic audit that shows you exactly what the robot sees when it reads your resume.
Page 1: The Score Gauge.
Page 2: The "Evidence Locker" (Raw, scrambled text).
Page 3: The Fix Plan.
Why "No-Code" wasn't enough
I could have used Bubble or Framer. But I needed raw processing power.
I needed to rip apart a PDF file byte-by-byte to find hidden header layers that break older parsers.
You can't do that with a drag-and-drop builder. You need Node.
I used AI to write the boilerplate and handle the edge cases. This let me focus entirely on the parsing logic.
The "Ghost" Protocol
I call it InterviewGhost because it’s about reverse-engineering the silence.
Most candidates think they are being rejected by humans. They aren't. They are being archived by a regex script from 2012.
I built this tool to prove it.
The outcome:
Cost to build: $0 (Time + existing API credits).
Time to launch: 4 days.
Value: It catches formatting errors that $50/month tools like Jobscan miss.
If you are a dev getting ghosted, stop tweaking your keywords. Check your parsing.
I put the tool up here: https://interviewghost.us
(P.S. I’m currently running this on a $10/day ad budget. Bootstrapping is alive and well.)
More...
Last week reality hit me hard. I applied to 127 jobs and got 0 interviews. I know I'm a solid dev, so I figured something was broken technically.
I dug into the parsing logic of legacy ATS systems like Workday and Taleo. Turns out my "modern" resume was being read as total gibberish because I used columns.
I wanted to build a tool to fix this for everyone else. Usually building a full SaaS takes months. I gave myself 4 days.
Here is how I built InterviewGhost.us by acting as the Architect and using AI as my engineering team.
The Stack
The goal was speed without sacrificing technical rigor.
Frontend: Next.js + Tailwind. I iterated this via Claude 3.5 Sonnet using "Linear-style" design tokens.
Backend: Node.js + Puppeteer. Used this for the forensic PDF generation.
Logic: DeepSeek-V3. Used it to optimize the heavy text extraction logic.
Deployment: Vercel.
Database: None. I built a zero-retention architecture for privacy.
The Hardest Part: Forensic PDF Generation
Generating a PDF in Node.js usually sucks. You have to mess with pdf-lib or jspdf and fight with CSS print rules.
I didn't want to write every CSS class by hand. So I fed the LLM a strict design system. I told it to create a forensic report template that looks like a McKinsey audit document mixed with a terminal log.
It generated a grid-based layout that dynamically visualizes the "parsing scramble" effect.
The first version actually looked "too clean" and didn't show the error.
So I forced the parser into "dumb mode" to strip layout data. This exposed exactly how the robot sees scrambled text.
The Result: A 3-page forensic audit that shows you exactly what the robot sees when it reads your resume.
Page 1: The Score Gauge.
Page 2: The "Evidence Locker" (Raw, scrambled text).
Page 3: The Fix Plan.
Why "No-Code" wasn't enough
I could have used Bubble or Framer. But I needed raw processing power.
I needed to rip apart a PDF file byte-by-byte to find hidden header layers that break older parsers.
You can't do that with a drag-and-drop builder. You need Node.
I used AI to write the boilerplate and handle the edge cases. This let me focus entirely on the parsing logic.
The "Ghost" Protocol
I call it InterviewGhost because it’s about reverse-engineering the silence.
Most candidates think they are being rejected by humans. They aren't. They are being archived by a regex script from 2012.
I built this tool to prove it.
The outcome:
Cost to build: $0 (Time + existing API credits).
Time to launch: 4 days.
Value: It catches formatting errors that $50/month tools like Jobscan miss.
If you are a dev getting ghosted, stop tweaking your keywords. Check your parsing.
I put the tool up here: https://interviewghost.us
(P.S. I’m currently running this on a $10/day ad budget. Bootstrapping is alive and well.)
More...