// readme / case study

README

A case study of Stack Trace

stack_trace.sh

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Stack Trace v1.0

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Founder · Product Manager · Product Designer

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Live and shipping ·

New guests added regularly

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// 01 / overview

An interactive career discovery platform that makes tech careers beyond software engineering easier to explore.

20+

career-specific modules to explore

45 mins -> <3 mins

full interviews to focused answers

core features shipped

// 02 / the problem

Careers in tech beyond software engineering are not talked about nearly as much. While people have skills that can translate across tech, many default to the SWE route due to three recurring barriers:

Visibility Gap

Individuals with strengths in writing, strategy, marketing or design may not understand how their skills translate into tech since traditional engineering roles appear to be the clearest point of entry.

Information Fatigue

Many existing career resources are presented as long-form content. Finding one relevant answer can require searching through an entire conversation, increasing the time and effort required to explore a career.

Industry Jargon Barrier

Unexplained terminology can make unfamiliar roles harder to understand, prompting users to leave the experience to search for context.

// 03 / discovery

Students are already looking beyond SWE, but uncertainty remains.

~40%

of applications from Class of 2022 CS majors targeted software engineering roles. Recent cohorts are distributing applications across IT, cybersecurity, finance, marketing, and project management.

Source: Handshake, Class of 2026 Spotlight

70%

of Class of 2026 CS majors report feeling at least somewhat pessimistic about their career prospects.

Source: Handshake, Class of 2026 Spotlight

PERSONAL OBSERVATION

I experienced this problem myself.

While making my own transition into tech, I found that understanding roles outside traditional software engineering paths often required piecing together information across LinkedIn profiles, job descriptions, portfolios, and long-form content.

THE OPPORTUNITY

Make career exploration easier to navigate by connecting students with firsthand experiences.

As people explore careers beyond software engineering, there is an opportunity to help them understand where their existing skills can translate across tech.

→ Stack Trace

A career discovery platform built around turning conversations with individuals working across tech into explorable answers.

// 04 / the solution

V1 of Stack Trace was built around five core product decisions.

[ screenshot placeholder ]

Modular Interviews

Explore the conversation on your terms.

Each interview is split into question-specific modules under three minutes, in a swipeable carousel users can move through freely.

Addresses: Information Fatigue

Contextual Glossary

Understand the lingo without leaving the experience.

Users get immediate in-context definitions of unfamiliar terms that are AI-identified and human-reviewed before publishing, allowing them to build context without leaving the experience.

Addresses: Jargon Barrier

[ screenshot placeholder ]

Resource Toolkit

Turn curiosity into action.

Each interview ends with four career-specific resources encouraging continued career exploration.

Addresses: Visibility Gap

Guest Directory

Browse before you commit.

Guest cards show a short description of each interview, letting users scan what’s available and decide what’s relevant before diving in.

Addresses: Decision Fatigue

[ screenshot placeholder ]

User Feedback

Shape Stack Trace as it grows.

A homepage mechanism captures career stage, interests, and content preference. A post-interview question measures whether the experience made the career clearer. Both provide direct input into the content Stack Trace prioritizes.

Addresses: Prioritization Uncertainty

// 05 / prioritization

Two paths were considered for distributing these interviews:

OPTION A: YOUTUBE

Built-in distribution, less development.

OPTION B: STACK TRACE (CHOSEN)

Interactive, contextual, centered on the user.

YouTube wouldn’t solve the visibility gap or jargon barrier — users would still search, evaluate, and leave to look things up.

DEFERRED TO V2

FEATURE

WAITING ON

Explore By Question

Bigger interview library

Request Company + Role

Demand + scalable pipeline

Saved Notes

Storage + privacy plan

// 06 / validation plan

This is V1 of Stack Trace. Behavioral data and direct user feedback collected after launch will help evaluate the experience and inform product decisions for V2.

Exploration Hypothesis

If question-specific modules make relevant information easier to find, users who begin an interview will explore more than one module.

Signals: Modules viewed per session ·% of sessions viewing 2+ modules · most-viewed modules

Clarity Hypothesis

If focused answers and contextual definitions improve understanding, users will report greater career clarity after exploring an interview.

Signals: Self-reported career clarity rate · glossary interaction rate · most-viewed glossary terms

Action Hypothesis

If the resource toolkit provides useful next steps, users will engage with the recommended resources.

Signals: Resource toolkit click-through rate · resources clicked per session

User Feedback Hypothesis

If enough users submit feedback, their responses will show which careers and topics Stack Trace should prioritize next.

Signals: Feedback submission rate · user journey stage · career interests selected · insight preference distribution

// 07 / behind the build

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My Trace

Hi, I’m Mahek.

I’m a graduate student at the University of Pennsylvania studying computer science. I’m drawn to understanding what users value and building products that make everyday experiences more meaningful.

Research

Interview

Strategy

Design

Build

Launch

I built Stack Trace independently, owning each stage from the initial research and guest interviews through product strategy, experience design, development and launch.

Building Stack Trace showed me that launch is not the end of product development. User needs, markets, and available tools continue to evolve, creating new opportunities to learn and improve. That does not mean building every possible feature, rather it means using evidence to decide what is worth changing next.