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Mumbai, India

Vihang Chheda

Built a tool used by 57,000 people. Wrote a book that reached India's Commerce Minister. Thinking about AI governance, public policy and trade.

Work

Things I've built that solve real problems.

Product

NM Attendance

Built a student attendance tool during my final year of college. 57,000 users later, I still haven't spent a rupee promoting it.

My uni's attendance system was hard to make sense of. Students had no good way to check their standing, so I made a tool that parses the college data and shows you exactly where you are, subject by subject.

The part students actually care about: forecasting. The tool tells you exactly how many classes you need to attend to hit the 75% requirement, and lets you simulate outcomes. Skip the next 3 lectures? Here's what your attendance would look like. Attend every class this week? Here's where you'd land.

I shared it with a few classmates. Within hours, it had spread through every WhatsApp group in college. Two years later, I still work on it regularly, adding features, fixing edge cases, trying to make it better. The whole thing grew one student telling another.

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Writing

MACRO & micro

Research and writing on trade, AI governance, economics, and whatever else I'm curious about.

My first piece was on the EU-India FTA. I wanted to understand why the deal was happening at all, not just what was in it. That led to weeks of reading treaty texts, trade data, and geopolitical strategy. The answer turned out to have very little to do with trade.

Automation

Workflow Automation

Automated the entire meeting workflow for a 100-franchise education company. Scheduling, approvals, attendance, certification, all coordinated through a chatbot.

The process was entirely manual: scheduling meetings across franchises, getting approvals, tracking attendance, generating certificates. Each step involved someone copying data from one place to another. I built a system that handles the full pipeline, with a natural language chatbot interface so franchise managers don't need to learn new software. They just type what they need.

It saves about 8 hours of manual work every week and is still in daily use across all franchises.

Schedule Approve Meet Certify
Writing

The AI Starter Kit

I published a book on practical AI in September 2023. Somehow, it ended up on a Union Minister's desk.

This was early in the ChatGPT wave. Most people were still figuring out whether AI tools were a gimmick or something real. I thought the best way to answer that was to just show people how to use them. So I wrote a practical guide: how to get useful output, how to think about prompts, how to integrate AI into everyday work. 141 pages.

It was recognized by the Maharashtra Business Training Board. Separately, it was presented to Union Minister Piyush Goyal. The book I wrote to help friends make sense of ChatGPT ended up in front of India's Minister for Commerce and Industry. That part was not planned.

The AI Starter Kit by Vihang Chheda

Presented to Union Minister Piyush Goyal (Commerce & Industry)

Personal Tool

Final Progress

Two friends preparing for CA Finals, one dashboard, and a simple rule: hit start when you study. Live hours. Live status. If you're idle and your friend is working, everyone knows. 25 weeks, 1,239 hours.

The idea was pure accountability. You sit down to study, you press start. The dashboard shows both players in real time: who's working, who's idle, and exactly how many hours each person has put in. Hours tick up live. At the end of the week, whoever logged more hours wins the point. That's it.

I also built a simple Android app that pulled phone screen time data into the dashboard. So if you said you were studying but your screen time told a different story, that was visible too. Accountability through mild shame. It worked better than any productivity advice I've ever read.

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About

Lost in Translation

Specialization is the greatest invention of civilization. It's also its most dangerous blind spot.

The accountant sees cash flows. The engineer sees architecture. The policymaker sees incentives. Each of them is right. None of them is complete. And the problems that matter most almost always live in the spaces between them.

This isn't a new observation. Adam Smith saw it in 1776: the same division of labor that multiplied productivity by orders of magnitude also made individual workers, in his words, "as stupid and ignorant as it is possible for a human creature to become." Expertise narrows. That's not a moral failing. It's a structural one. Your training teaches you what to see, and in doing so, it teaches you what to ignore.

The more I read, the more I notice the same structures showing up in places that aren't supposed to be related. Rousseau asked where legitimate authority comes from. Two and a half centuries later, when algorithms decide who gets credit and what content gets seen, we are asking his exact question. Someone working on AI governance who hasn't grappled with Rousseau is solving a problem they haven't fully understood. Someone who has read Rousseau but doesn't understand how a language model works can't see where the old answers break.

I believe one of the most valuable skills for the coming decades will be translation: the ability to move between specialized worlds fluently enough to see what each one is missing. Not knowing everything. Recognizing that the same structures, feedback loops, incentive gradients, information asymmetries, keep appearing in different costumes, whether in a balance sheet, a codebase, or a trade agreement.

I'm still learning how to do this. But it's the direction everything I've done so far seems to point.

Most interesting problems live at intersections.

Contact

Always happy to talk.

Let's talk.

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