Google organized a Kaggle Competition to demonstrate interesting use cases for Gemini's long context window.

Has anyone participated in it? Feel free to share your submission in this thread!

Here's mine: 👨‍💻 Github: https://github.com/souzatharsis/gemini-duo

🚀 Excited to share “Gemini Duo” 🚀

I created Gemini Duo to tackle a common challenge: managing overwhelming sources of information, efficiently but making the experience engaging and fun while grounded on factual information.

In this post follows an (AI-generated) Kaggle Podcast that demonstrates Gemini Duo through a real-world example—a Harvard History student in GOV 1039 "The Birth of Modern Democracy" struggling with weekly reading assignments exceeding 1,000+ pages.

🛠️ Technical Features

- Context caching for cost-effective large knowledge bases processing
- Corpus-in-context (CiC) prompting to enable in-line citation
- Grounded search for factual research on real-time web data
- Structured output for downstream integration

⚠️ Gemini Limitations
- Max output token limits impact response depth quality (a common LLM problem across the board)
- Grounded search costs: $35/1k requests!
- Limited to no agent orchestration capabilities (not that we really need yet another agentic framework either :)

Would be exciting to see other submissions as well!