I built a resume roaster in 2 hours without writing a single line of code
How vibe coding with MeDo changed the way I think about building products
I once sent out 47 job applications and heard back from 2.
Not because I wasn't qualified. Because my resume was quietly, invisibly terrible.
"Responsible for managing cross-functional teams." "Assisted in the development of key initiatives." "Worked collaboratively with stakeholders."
Translation: I did stuff. I think. Please hire me.
Every resume tool I tried gave me green checkmarks and told me I was doing great. I wasn't. I needed someone to tell me the truth.
So I built the tool that does exactly that — and I did it entirely through conversation, no code written by hand.
What I built
RoastMyResume — an AI-powered resume coach with two modes:
- Roast Mode: line-by-line brutal critique of your resume. Vague bullets, passive voice, buzzword soup, missing metrics — all called out with a Roast Score out of 100.
- Fix Mode: every weak line gets rewritten instantly. Strong verbs. Quantified impact. Recruiter-friendly language.
- Job Match Mode: paste a job description, get your resume rewritten to match that specific role's language and ATS keywords.
Try it live: [your-app-url-here]
The tool: MeDo
I built this for the Build with MeDo Hackathon using MeDo — an AI app builder where you describe what you want in plain English and it generates, deploys, and hosts the full-stack app.
No IDE. No terminal. No Stack Overflow at 2am.
Just conversation.
The actual prompts I used
Here's exactly how I built it, turn by turn.
Turn 1 — Core scaffold
Build a full-stack web app called "RoastMyResume". Users paste their resume
text into a text area. Two buttons: "Roast It" and "Fix It".
Roast It: AI critiques every weak line — vague bullets, buzzwords, passive
voice, missing metrics. Give a Roast Score out of 100. Show each critique
as a callout card with the original line and the burn.
Fix It: Rewrites every weak line with strong action verbs, quantified
results, recruiter-friendly language. Show original vs improved side by side.
UI: clean, modern, dark header with orange accent. Deploy with one click.
MeDo generated the entire app. Frontend, backend, AI logic, deployment. In one shot.
Turn 2 — Deepen the roast engine
Make the critique more specific. Detect: passive voice, unquantified impact,
buzzword density, generic responsibilities that could belong to anyone.
Add severity weights — not all bad lines are equally bad.
This is where it got interesting. MeDo rewired the prompt engineering under the hood and added pattern classification logic I would have spent days building manually.
Turn 3 — Job Match Mode
Add a third mode: user pastes a job description. AI rewrites the resume
to mirror that role's language and surface the most relevant experience.
ATS-optimized output.
Turn 4 — Polish & virality
Add: animated Roast Score meter on results, shareable image card of the
funniest roast lines (1200x630 for X/LinkedIn), PDF export, mobile
responsive layout, counter showing "X resumes roasted today".
Turn 5 — Ship
One-click deploy. Public URL. Done.
Total time: ~2.5 hours.
What surprised me
Vibe coding is a real skill — and it's not about being lazy. The constraint of describing everything in plain English forces you to think like a product manager before you think like a developer. Fuzzy thinking produces fuzzy apps. Clear thinking produces clean apps.
Multi-turn context is the actual superpower. The app I shipped isn't the app from Turn 1. It's the app that emerged from five rounds of "yes, but also...". That's not how traditional coding works. It's closer to design thinking — iterative, responsive, human.
Honesty is a product differentiator. I checked every resume tool on the market while building this. They're all optimistic to the point of uselessness. There's a real gap for something that just tells the truth.
The math behind the Roast Score
The score isn't a fake "AI confidence" number. It's a transparent weighted penalty function:
$$S_{roast} = 100 - \frac{1}{n}\sum_{i=1}^{n} w_i \cdot p_i$$
Where:
- $n$ = number of resume lines analysed
- $w_i$ = severity weight of the issue type detected in line $i$
- $p_i \in [0, 10]$ = penalty for that line
A score below 40 means your resume is on fire. Literally.
The biggest challenge: tone
The hardest problem wasn't the code. It was calibrating the roast voice.
Early versions were mean. Not "Gordon Ramsay calling out a burnt dish" mean — just demoralising. The kind of feedback that makes you close the tab. Getting MeDo to nail the voice of a sarcastic-but-constructive senior engineer took several prompt iterations. The key phrase that unlocked it:
"Roast the resume like you genuinely want this person to get hired. You're harsh because you care, not because you enjoy it."
That one sentence changed the entire output character.
What's next
- Interview mode — practice answering questions about your newly fixed resume
- Industry benchmarking — where does your score rank in your field's percentile?
- Chrome extension — highlight any job posting, get an instant resume gap analysis
- Before/after public gallery — the best roast transformations are genuinely funny
Try it
Live app: https://app-aysruew5kv7l.appmedo.com
Built with MeDo for the Build with MeDo Hackathon. If you want to try MeDo yourself, use this invite link for 300 free credits: medo.dev
Have a resume that needs roasting? Drop it in the comments. I won't be polite.
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