Bird Quest is a weekly bird mission that you can only finish by going outside. It runs on open-source AI, on your own laptop, with no internet.
Every week it gives you one small mission, like "Find 5 different birds by sound" or "Record a bird before 8 AM". You go outside and record bird sounds with any phone. Then:
BirdNET (open-source bird-sound AI) names the birds it hears and says how sure it is.
A small local language model writes a 2-sentence field note for each bird: one fact, and one tip to recognise it next time.
Birds the AI is sure about tick off squares on a 3x3 bingo card and count toward the mission.
The mission and the card cannot be completed from the sofa. The only way to make progress is to walk out and listen. The screen is only used for a few seconds at the end, and on Windows --speak can read the field note aloud, so the phone can stay in your pocket.
It is for beginners who like birds but find it hard to leave the house on a normal weekday.
Code:
How I Built It
Bird Quest does not contain any AI of its own. It is the glue between two open tools:
Part Role Licence
BirdNET-Analyzer
Identifies birds from audio
Code: MIT. Models: CC BY-NC-SA 4.0 Ollama + Qwen2.5 1.5B Runs the small model that writes the notes, locally Qwen2.5 1.5B: Apache 2.0
My code is plain Python with no extra libraries, and it is MIT licensed. The flow is simple: Bird Quest starts BirdNET as a command, reads the CSV file it writes, keeps the best result per species, and sends the bird name to Ollama on localhost. Missions, the bingo card, and the sightings log are small Python files and one CSV file.
Three design choices I made on purpose:
Honest uncertainty. Birds under 50% confidence are shown as "maybe" and do not count. A tool that gets people outside should not reward wrong answers.
A walk is a day you recorded. If you hear nothing, it still counts, because going out is the point.
Everything degrades gracefully. If Ollama is not running, Bird Quest still works and marks the note "[no AI]".
What I learned while building it:
I read BirdNET's source code to find the exact columns of its result file, instead of guessing. That is one real benefit of open source.
BirdNET needs the internet once, to download its model on the first run. I added a warmup command and a check command so a user finds this out at home, not on the trail.
I wrote 26 automated tests. They use fake BirdNET and Ollama answers, so they check my logic, not bird-detection quality. That is what the field test is for.
Why Does Open Innovation Matter?
Three things Bird Quest does would not work with a closed API:
It works with no signal. Parks and hills often have poor reception. Both models run on the laptop, so the tool works on the trail.
Your location stays with you. BirdNET uses latitude, longitude, and the week of the year to hide birds that do not live near you. With a hosted service, my walking habits would go to a company. Here nothing leaves the laptop.
Anyone can run it. There is no account, no API key, and no bill, so a student can use it too.
Open source also let me check how the tool really works instead of trusting a guess, and the open licences tell me exactly what I may do. BirdNET's models are non-commercial, so Bird Quest is a non-commercial project, and I say so openly.