This is a submission for the MLH x DEV Writing Challenge
WildQuest:
What if we could make exploring the world a little more fun?
Inspired by collecting animals for my Compendium in Red Dead Redemption 2, I decided to bring that same joy to real-world wildlife exploring through the application named WildQuest. This tool allows users to embark on their own quests, discover wildlife, gain points, snap pictures, and create their very own Compendium.
However, I was not satisfied with simply allowing users to collect animals. Using WildQuest's Explore tab users can discover species reported to be in their area and seek them out in person, as well as find natural landmarks to explore using ESA WorldCover for habitat information, GBIF for species occurrences, and OpenStreetMap to find natural sites near the player's location. Additionally, through the utilization of Gemma, Google's open-weight language model, which I accessed through their hosted Gen AI API, the program is able to analyze wildlife photos and suggest species based on context provided by the application.
All of this is powered by a Python-powered backend utilizing
Flask to access these services and parse their JSON data for the frontend.
What did I learn?
I learned a ton while creating this project, including working with various APIs and integrating a gemma model into my application. One of the most important lessons I learned while developing WildQuest, however, was that AI can often be much more helpful when it has real-world data to work with, instead of relying on its own generated data.
Demo:
https://youtu.be/JihFtNCWTHU?si=g9SC7uVOP0md39eR
Partner Tech: Gemma, ElevnLabs(demo narration)
While I didn't host Gemma myself, I was able to utilize this open-weight model through Google's hosted Gen AI API to allow the application to analyze photos and suggest species based on context provided by the application. The backend would send the photo and context to the model, analyze the response, and send the relevant data to the frontend, allowing me to focus on incorporating other features, rather than developing my own large language model.
Hackathon Experience:
While creating WildQuest, I participated in the Hacktoberfest Online Week 1 Challenge. Creating this project was an excellent opportunity to participate in the event by bringing an idea inspired by a video game to life while also learning how to integrate AI, biodiversity tracking, and mapping services into one cohesive application.
The idea is simple: make people curious about the world around them, because every journey can be a quest, and every discovery can be added to their Compendium.
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