What it does
Questanda took a plain-language question, answered it with Gemini, and stored the question and answer in MongoDB so the answer had a page of its own.
How I built it
Next.js and Tailwind on the front end, the Gemini API for answers, MongoDB for storage, and a generated sitemap so the answer pages could be indexed.
Why I built it
This was my first project on a large language model. I wanted to learn the API end to end, from prompt to stored output to a page a search engine could read.
Where it went
Retired. The domain is offline and the code is still on GitHub. What I learned here about storing and serving model output turned into later work: Mender, which patches other agents' prompts and checks the fix against evals, and Actuary402, which buys from agent services and grades what comes back.
Built With
- gemini
- html5
- javascript
- mongodb
- next.js
- tailwind
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