This past December, Shockoe hosted our annual Hackathon. Seven teams gathered with the goal of creating a working and fully-functional application within a 48-hour window. This year’s theme was to create an application related to any common daily activity. But here’s the catch: the technology had to anticipate an action, prove that it could be scalable, and create a delightful experience.
Check out what our teams created at yet another successful Hackathon:
MNQN
Mornings are hectic, and they’re something most everyone has in common. Keeping track of what’s on your schedule can be difficult to memorize, much less align to your wardrobe for the day. To cut back on the decision fatigue that is often exacerbated when getting dressed every day, or just ensure we are dressed appropriately, our team — WHAMM — created MNQN (pronounced Mannequin). Our app applies machine learning around color theory, weather conditions, and your daily plans to determine the right outfit choices for the day, based on your own wardrobe.
Stickr
Stickr is an app that helps people give self-injections with less pain and anxiety. The app monitors your shot rotation, collects feedback from the user on the experience, and provide recommendations for where to administer their next shot.
Archangel
Over 130 people die every day from the opioid epidemic. Archangel is here to change that. While there are thousands of apps to help try to reduce use, none anticipate behavior before it happens. Using machine learning, we aligned technology to recovery behavior to anticipate relapses before they happen.
The patient downloads the app and either individually or working with a counselor identifies encouraging or risky contacts and locations. Then the app monitors and analyzes behavior, to provide support notifications IN THE MOMENT, creating the appropriate response from the patient and giving the counselor an opportunity to help.
The app has a daily, almost continuous, involvement in the lives of patients while they are in treatment. It anticipates risky behavior via contacts and location. It can easily scale to other addictions as well as incorporate additional monitoring (biofeedback, email, etc).
BaeWatch
A satisfying romantic relationship is important for both partners’ quality of life and health status, but in today’s world, couples are finding they’re too busy to connect. Or, one partner is in the mood when the other isn’t
There’s no one-size-fits-all when it come to sex. BaeWatch is an app that learns individual preferences for you and your Bae, to better predict when one or both of you might be in the mood. BaeWatch also uses your love language to help your bae better understand and meet your needs.
Using integrations with Apple Health, social media, and users’ calendars, BaeWatch is able to predict whether you or your partner are in the mood based on amount of sleep, activity levels, social media sentiment, and stress based on busyness.
BaeWatch will then use GPS to anticipate when the baes are about to reunite, and send a text with their Bae Alert. They might both be a GO, or one partner might receive tips on how to put bae in a better mood based on their love language.
Board L.O.R.D
We were trying to solve the problem of Jira being… kind of a pain in everyone’s butt. In the sense that it is cumbersome, time-consuming, and leads to a lot of task-switching for developers and PMs alike.
Our app was actually a Slackbot that integrated with your Jira board and would give you updates that are routinely handled at stand-up. His name was Board L.O.R.D., for Labor Optimization of Routine Data, he was super cute and helpful (and polite), and a picture of him is attached to this email.
FridgeDaddi
FridgeDaddi helps people take control of their groceries, minimizing the financial and environmental impact of food waste. The app and in-fridge camera provide photo recognition and inventory management, as well as notifications and recipe suggestions to encourage users to consume items that will be expiring soon.
BellyFul
BellyFul tried to satiate a need in the marketplace to remove the need for choice/deliberation over what to EAT. This app initially simplified the choice aspect of getting take out – you tell it what you like and when you tell it that you’re hungry, *poof* food that you will like will arrive through an integration with postmates. Then using machine learning, eventually it will learn when you usually get hungry and start ordering your favorite food for you in advance of you ever needing to consider feeling hunger!
Our Hackathon is one of the best parts about Shockoe that make our culture what it is. We have a great time collaborating with one another, working in roles we don’t normally get to try, and creating something really neat.
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