This is a submission for the "Hacktoberfest Weekend Challenge: Build for a Friend" (https://dev.to/challenges/hacktoberfest-weekend-2026-10-01)
What I Built
A few weeks ago, my friend Kunal was going through placement season.
He had been applying to different companies, taking online assessments, and waiting for interview calls.
But after doing this again and again, he kept facing the same problem:
OA → wait → no interview call.
The frustrating part wasn't just getting rejected.
It was not knowing where his placement journey was actually breaking.
Was it his resume?
Was he applying to the wrong roles?
Was he struggling in a particular part of the online assessment?
Or was there something else that only the recruiter could know?
That made me think:
What if students could actually learn from the applications they have already given?
So I built PlaceTrace.
PlaceTrace is a placement journey diagnostic tool for engineering students.
Instead of treating every application as an isolated event, students can record their:
- Applications
- Eligibility
- Online assessments
- OA performance
- Shortlists
- Interviews
- Offers
- Job descriptions
- Feedback and notes
PlaceTrace then turns that history into a simple placement funnel.
For example:
12 Applications → 9 OAs → 2 Shortlists → 0 Interviews
The important question becomes:
Where is the funnel breaking?
But I didn't want PlaceTrace to make up an answer.
It separates the diagnosis into three parts:
OBSERVED
What the student's own data actually shows.
POSSIBLE
Factors that could explain the pattern and are worth investigating.
UNKNOWN
Things that only the employer can really know.
That distinction matters because an AI shouldn't confidently tell a student:
«“You were rejected because of your resume.”»
when we simply don't know that.
Instead, PlaceTrace turns uncertainty into something more useful: an experiment.
For example, if a student is repeatedly getting OAs but very few shortlists, they can start tracking coding, aptitude, SQL, and CS performance across their next few assessments.
Or they can test a resume specifically targeted toward the roles they're applying for.
Then they can come back and see whether the outcome actually changes.
The core idea is:
TRACK → DIAGNOSE → EXPERIMENT → LEARN
And eventually, I want this to grow into anonymous cohort intelligence—so students can learn from patterns across similar profiles without exposing anyone's private placement history.
The goal isn't to predict whether a company will select you.
It's to help you stop repeating the same placement cycle without learning from it.
Demo
🎥 Watch the demo:
https://youtu.be/s_rD6ikpJcg?si=A514_O2dkdMUiCG9
🌐 Try PlaceTrace:
https://placetrace.onrender.com
Code
The project is open source:
https://github.com/devsomya28/PlaceTrace
The project is built with React, TypeScript, Vite, Tailwind CSS, Recharts, and a TypeScript/Node backend.
How I Built It
I started with a simple question:
What information would actually help a student learn from their placement journey?
The application history is treated as the source of truth.
The basic funnel calculations are deterministic rather than being handed to an AI model. This keeps things like application counts, conversion rates, and observable drop-offs transparent and reproducible.
The AI layer is intended for the parts that require interpretation:
- Understanding messy notes and feedback
- Summarizing patterns
- Separating observations from hypotheses
- Suggesting experiments
- Explaining what remains unknown
This separation was important to me.
I didn't want to build another chatbot that simply gives students generic placement advice.
I wanted to build a system where the student's data creates the context, deterministic analytics finds the measurable pattern, and AI helps interpret what to investigate next.
Why Does Open Innovation Matter?
Placement problems are extremely personal, but the underlying pattern is something many students experience.
One student may have ten applications.
Another may have thirty.
Some may get many OAs but few shortlists. Others may struggle to even reach the OA stage.
Open innovation makes it possible to build tools around these real-world problems without keeping the entire idea inside a closed system.
More importantly, I want PlaceTrace to eventually learn from anonymous, aggregated patterns rather than individual private records.
Imagine being able to say:
«“Students with similar profiles tend to see their biggest drop at this stage.”»
without revealing who those students are.
That could turn isolated placement experiences into shared learning.
But privacy has to come first.
Your rejection is private. Your learning doesn't necessarily have to be.
My Agent Session
I used AI-assisted development while building PlaceTrace and iterating on the product.
[Add your DevRelay agent session here if you decide to submit one.]
Prize Categories
No partner category claimed.
Placement season shouldn't have to be:
Apply. Get rejected. Forget. Apply again.
It can be:
Apply. Record. Diagnose. Experiment. Learn.
That's the idea behind PlaceTrace.
Trace your placement journey. Find where it breaks.
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