Inspiration

I lost money while betting on FIFA Finals 2026 !

What it does

Lemons allows users to enter a question about a potential outcome on platforms such as Kalshi or Polymarket. It analyzes relevant news, articles, historical statistics, databases, and public opinions from sources such as Reddit. The platform then runs approximately 3,500 simulations and produces a research-backed prediction in under 15–20 seconds.

How we built it

Lemons was built primarily using Next.js and TypeScript, with multiple APIs connecting it to real-time information sources. Its prediction engine uses predictive analytics models and Monte Carlo simulations to evaluate thousands of possible outcomes. These systems work together to transform large amounts of scattered information into a clear and understandable result.

Challenges we ran into

The greatest challenge was building enough accuracy for users to trust the product. Predictive analytics products depend heavily on credibility because even a polished platform has little value when its results are unreliable. To improve the system, we ran more than 35,000 simulations, repeatedly tested its predictions, and refined how it analyzes different sources.

Accomplishments that we're proud of

We turned the betting losing experience into a working platform capable of researching an outcome and delivering an analysis within seconds. We are especially proud of combining thousands of simulations, live information, statistics, and public sentiment into one simple interface.

What we learned

We learned that accuracy is the most important part of any predictive analytics product because user trust depends on the quality of every result. We also learned how important it is to manage API usage and implement strict rate limits, even before launching to consumers. Running automated simulations every minute consumed far more tokens than expected and taught us to build infrastructure with efficiency in mind from the beginning.

What's next for Lemons

The next step for Lemons is its public launch today. After launch, the focus will be on measuring prediction performance, improving accuracy, and learning how users interact with the platform. Those insights will help us expand its data sources and continue strengthening the prediction engine.