Kunal Choubey@kunal_choubey480
2
Events attended
1
Submissions made
India
1 year of experience
About me
I'm Kunal Choubey, a Biotechnology undergraduate at NIT Raipur and a multidisciplinary builder working at the intersection of AI, software, data, and biotechnology. I love turning ambitious ideas into functional products—from agentic AI and financial intelligence platforms to healthcare and predictive systems. I'm particularly drawn to hackathons because they let me experiment across disciplines, move quickly from problem to prototype, and build technology that solves meaningful real-world problems.
🤝 Top Collaborators
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🤓 Latest Submissions
Quantum Decision
Every meaningful decision — a job offer, a relocation, a career pivot — gets the same flawed treatment from most AI tools: one prompt, one answer, delivered with false confidence and no visibility into the reasoning behind it. People weighing high-stakes choices don't need a single opinion; they need to see multiple angles argued out, compared, and reconciled — the way a good mentor, a risk-averse friend, and your own future self might each weigh in differently. Quantum Decision solves this by turning decision-making into a visible, parallel reasoning process. Users type in a real dilemma they're stuck on — for example, "Should I move for a 40% pay increase or stay near family for a smaller local promotion?" The system splits the decision into two contrasting paths, then runs two specialized AI persona agents in true parallel using LangGraph.js's native graph execution: a "Future You" agent that simulates the lived, emotional reality six months down each path, and a "Risk Analyst" agent that stress-tests concrete downside risks, reversibility, and hidden tradeoffs. Both branches execute simultaneously, and their outputs converge into a single synthesized verdict — a clear recommendation, a confidence score, a side-by-side tradeoff matrix, and an immediate 24-hour action step. What makes this unique isn't just the personas — it's that the entire LangGraph execution graph is visualized live on screen using React Flow, streamed in real time via Server-Sent Events. Users watch the decision literally fork into parallel branches, see each agent "thinking," and watch the branches reconverge — making the AI's reasoning process transparent rather than a black box. Target users are anyone facing a consequential personal or career decision who wants structured, multi-perspective reasoning instead of a single flat answer — job seekers, career changers, founders, or anyone stuck between two real options. The result is a tool that doesn't just answer a question, but shows its work: parallel intelligence, visibly reasoning, converging into one clear next step.
27 Sep 2026
