Polylens

Inspiration

The idea came from a frustrating experience: browsing Polymarket and seeing an interesting bet, but having no context to actually evaluate it. The odds are right there, but the why behind them isn't. You're essentially betting blind. That friction pointed to something familiar — Yahoo Finance. When researching a stock, you don't just see the price; you get news, analyst sentiment, volume context, and commentary all in one place. Prediction markets had nothing like that. PolyLens is the attempt to build it.

What it does

PolyLens lets you pick any active Polymarket bet and instantly get the full picture. Select a market, and it pulls in the key details — current odds, volume, and liquidity — then goes out and finds relevant news sources to surface what's actually driving the market. From there, it synthesizes everything into a plain-English summary so you can understand what's happening and make a more informed bet.

How I built it

It started with Next.js as the foundation, building out the UI so users could search and browse active Polymarket events cleanly. From there the challenge was getting real data into it, which meant plugging into the Polymarket Gamma API to pull live odds, volume, and liquidity for any given market. The interesting part was the research layer. Rather than just pulling a static news feed, browser-use was brought in as an AI web agent that actually goes out and queries relevant articles and sites in real time, the same way a person would research a bet before placing it. That raw intelligence then gets synthesized into a clean summary surfaced back in the UI.

Challenges we ran into

The biggest challenge was connecting all the pieces together into a coherent pipeline. Each part worked in isolation but getting Next.js, the Polymarket API, and browser-use to talk to each other cleanly took a lot of wiring. browser-use was its own battle. It is an incredibly capable tool but that versatility cuts both ways. Because it can do so many things, narrowing it down and specializing it for one specific task was genuinely difficult. On top of that, building an AI agent was entirely new territory. The whole concept of writing markdown prompts and structured instructions for an agent to follow was something that had to be learned on the fly, and getting that prompt structure right to reliably guide the agent toward the right results took a lot of iteration.

Accomplishments that I am proud of

Getting to a working MVP as a solo developer is something worth celebrating on its own. But beyond just shipping, having a fully functional web scraping pipeline powered by browser-use is genuinely powerful, the kind of feature that takes real effort to get right and opens up a lot of doors for where PolyLens can go. Being a one person team also meant wearing every hat, writing all the code, recording and editing the demo video, and handling everything in between. Getting all of that across the finish line solo made the end result that much more meaningful.

What I learned

The biggest takeaway has nothing to do with any specific technology. It is that the gap between a team of one and a team of four is closing fast, and the differentiator is not headcount, it is how well you can leverage AI as a force multiplier. The engineers who are pulling ahead are not necessarily the ones with the most experience or the biggest teams. They are the ones who know how to use AI better than everyone else, the ones who can ship faster, build cleaner, and stay productive across every layer of the stack without needing to delegate. That is the new definition of a 10x engineer. PolyLens was built by one person. And that felt like enough. AI has supercharged engineers to save a lot of time and do a lot more things because of it.

What's next for Polylens

integration into polymarket for live trading, creating an optimized trading bot for polymarket that is able to make decisions based on the web-scraping results plus a multi agent system for decision making regarding bets on specific predictions.

Integrating this tool to be used with otehr trading platforms such as IBKR, where the data form poly market could be used as another feature to training models based on the public's predictions on events that may or may not affect xyz stocks.

Built With

• browser-use
• google-gemini-1.5-flash-api
• google-news-rss-engine
• next.js-16
• node.js-22
• polymarket-clob-api
• polymarket-gamma-api
• postcss
• react-19
• tailwind-css-4.0
• typescript
• vercel