We encountered several issues throughout the last 64 hours, mainly the steep learning curve as junior hackers with no neuroscience/neurotech experience. On top of using each of the following technologies, we had to start by learning all of them. After learning how to use them, we implemented the technologies to use the data given by the Muse S through Petal, which was also the first time we used neurotech hardware. After all that effort, we finally present to you our project. The REACT frontend allows users to navigate different pages and make POST requests to the Flask API. The user data, stored in MongoDB, provides authentication services through REACT. Upon successful validation, users can upload eeg data from the Muse S to the application. This data would be used to wake users up at the optimal sleep stage. During every sleep cycle, there is a 10 minute window known as the “Alpha Dropoff'', or the N1 sleep stage. After this stage, the brain goes back into slow wave sleep, or the N3 sleep stage. Waking up during slow wave sleep leaves people groggy and tired, but by waking up during the N1 stage, that grogginess can be minimized. Tabis takes brain waves, Fourier Transforms the brain waves and finds the most intense brain wave at a given time. Higher frequencies relate to more shallow sleep, and lower frequencies relate to slow wave sleep. When Tabis detects the N1 sleep stage near the designated wakeup time, it would then set off an alarm for the user.