DEV Community: Talorr AI

I Stopped Optimizing My Resume. I Started Reverse-Engineering Job Descriptions Instead.

Talorr AI — Sat, 18 Jul 2026 16:22:51 +0000

TL;DR: I analyzed thousands of software engineering job descriptions and realized that most resume advice focuses on the wrong problem. The issue usually isn't ATS software—it's relevance.

A few months ago, I was applying for software engineering jobs.

Like most developers, I had spent hours polishing my résumé. It had quantified achievements, modern formatting, links to projects, and every technology I'd worked with over the last few years.

I thought it was solid.

The results said otherwise.

I kept applying.

Rejections.

Ghosting.

An occasional interview.

Then more ghosting.

At first, I blamed the market.

Then recruiters.

Then ATS software.

Eventually I asked myself a different question:

What if my résumé wasn't bad? What if it simply wasn't relevant to each job?

That changed everything.

Developers measure everything—except resumes

As engineers, we measure almost everything.

We profile slow APIs.
We benchmark databases.
We optimize Docker images.
We monitor production latency.

But when it comes to resumes?

Most of us follow generic advice from blog posts written years ago.

Keep it to one page.
Use action verbs.
Add keywords.
Make it ATS-friendly.

None of that is bad advice.

It's just incomplete.

Nobody explains which keywords matter, why they matter, or how much they affect your chances.

So I decided to stop guessing.

Turning resumes into a data problem

Instead of reading career blogs, I started collecting software engineering job descriptions.

Backend.

Frontend.

DevOps.

AI.

Full-stack.

Startups.

Enterprise companies.

Eventually I had a dataset containing more than 2,000 job descriptions.

Then I built a parser that extracted:

Required technologies
Preferred skills
Seniority
Responsibilities
Frequently repeated keywords
Relationships between technologies

Once everything was normalized, interesting patterns started appearing.

The biggest surprise

Everyone talks about ATS optimization.

But after analyzing the data, I came to a different conclusion.

The biggest problem wasn't formatting.

It was relevance.

Imagine a company hiring for a backend engineer using:

NestJS
PostgreSQL
Docker
AWS

Now imagine receiving a résumé that spends half a page talking about:

React animations
Tailwind CSS
Figma
Mobile development

Those skills aren't bad.

They're just not what this company is looking for.

The strongest resumes naturally reflected the language and priorities of the job description without looking like keyword spam.

More keywords don't automatically help

Another interesting pattern was keyword stuffing.

Many resumes repeated technologies several times, hoping ATS software would rank them higher.

Something like:

React • React.js • React Developer • React Framework

Modern hiring systems are becoming increasingly semantic.

Just like search engines, they're getting better at understanding context rather than simply counting repeated words.

Showing where and how you used a technology is far more valuable than repeating its name multiple times.

Companies hire outcomes—not technology lists

This might have been my favorite realization.

Many resumes look like this:

JavaScript
TypeScript
React
Node.js
Docker
AWS
Redis
MongoDB

Compare that with:

Reduced deployment time by 60%.
Built APIs serving over 100,000 requests per day.
Automated CI/CD pipelines.
Improved application performance by 40%.

Both candidates may know exactly the same technologies.

Only one demonstrates impact.

Recruiters hire people who solve problems—not people who can list frameworks.

Why I built Talorr AI

Originally, this analysis was just for me.

I wanted a tool that could answer questions like:

Which important skills am I missing?
Which parts of my résumé don't match this role?
Which keywords actually matter?
How closely does my experience align with this job?

That eventually became Talorr AI.

Instead of simply rewriting resumes with AI, it compares your résumé against a specific job description, identifies missing skills, highlights weak areas, explains why they matter, and generates a tailored ATS-friendly version if you want one.

The goal isn't to make every résumé look the same.

It's to make every résumé more relevant.

What do you think?

I'm curious how other developers approach job applications.

Do you customize your résumé for every application?
Do you rely on AI tools?
Have you noticed better results after tailoring your résumé?

I'd genuinely love to hear your experience.

If you'd like to see the tool that came out of this experiment, you can check it out at https://talorr.com/blog/how-to-tailor-resume-to-job-description.

I'm especially interested in feedback from recruiters and hiring managers—I'd love to compare your real-world experience with the patterns I found while analyzing thousands of job descriptions.

How I Built an AI Interview Preparation Platform

Talorr AI — Fri, 17 Jul 2026 02:11:46 +0000

Landing interviews is difficult. Passing them is even harder.

Over the past year, I noticed the same pattern among developers and job seekers:

They knew the technology.
They had solid projects.
Their resumes were decent.
Yet they struggled during interviews.

Most interview preparation tools focused on quizzes or generic questions. I wanted to build something that felt closer to a real technical interview—one that adapts to the role, asks relevant questions, and provides actionable feedback.

This led me to build an AI-powered interview preparation platform.

The Problem

Traditional interview preparation has several limitations:

Generic question banks
Little personalization
No understanding of your resume
No adaptation to the job description
Limited feedback beyond "correct" or "incorrect"

Real interviews are dynamic. Every interviewer asks different questions based on your experience, projects, and the role you're applying for.

I wanted to recreate that experience using AI.

The Goal

The platform should be able to:

Analyze a job description
Understand the candidate's resume
Generate role-specific interview questions
Conduct conversational mock interviews
Evaluate answers
Identify weak areas
Recommend improvements

Instead of memorizing hundreds of questions, candidates practice the questions they're actually likely to receive.

Choosing the Tech Stack

I wanted something scalable and easy to iterate on.

Frontend

Next.js
React
TypeScript
Tailwind CSS

Backend

NestJS
PostgreSQL
Prisma
Redis

AI

Large Language Models
Prompt engineering
Structured JSON outputs
Streaming responses

The backend exposes APIs for interview generation, scoring, feedback, and progress tracking.

Step 1: Understanding the Candidate

The first challenge was building context.

Rather than asking random questions, the AI first understands:

Resume
Skills
Experience
Projects
Target role
Company
Seniority level

This context becomes the foundation for every interview session.

Step 2: Understanding the Job

Every job description is different.

The AI extracts:

Required skills
Preferred skills
Responsibilities
Industry
Seniority
Technologies

This allows interview questions to closely match the actual position.

Step 3: Dynamic Question Generation

Instead of storing thousands of hard-coded questions, the platform generates them on demand.

For example, a backend engineer might receive questions about:

System design
REST APIs
Database optimization
Docker
Kubernetes
Authentication
Scaling

A frontend developer would instead receive questions about:

React
Performance
Accessibility
State management
Rendering
Testing

Each interview becomes unique.

Step 4: Evaluating Answers

The most challenging part wasn't generating questions.

It was evaluating answers fairly.

The AI scores answers based on multiple factors:

Technical accuracy
Completeness
Communication
Depth of explanation
Confidence
Missing concepts

Instead of simply giving a score, it explains why the answer could be improved.

This type of feedback is far more useful than a simple pass/fail result.

Step 5: Personalized Feedback

At the end of an interview, candidates receive a detailed report covering:

Strengths
Weaknesses
Knowledge gaps
Recommended topics
Practice suggestions

This transforms interview preparation into a continuous learning process rather than a one-time assessment.

Lessons Learned

Building AI products taught me several important lessons.

Context is everything

Large language models perform dramatically better when given structured, relevant context.

Prompt engineering matters

Small prompt changes often produced significant improvements in response quality.

Users value feedback more than scores

People don't just want to know how they performed.

They want to know how to improve.

AI should assist, not replace

The goal isn't to replace human interviewers.

It's to help candidates practice more effectively before the real interview.

What's Next?

I'm continuing to improve the platform with features such as:

Voice interviews
Coding interviews
Behavioral interview simulations
Company-specific interview preparation
Progress tracking over time
Personalized learning recommendations

Final Thoughts

Building this platform has been one of my favorite AI projects because it combines software engineering, machine learning, and user experience into a tool that solves a real problem.

If you're interested in AI-powered interview preparation, you can check out Talorr AI at https://talorr.com.

I also write about software engineering, AI, and startup development on my personal website: https://zakkasmi.com.

If you've built an AI product yourself, I'd love to hear what challenges you faced.