appendly

A team of 4-5 undergraduate students from Madurai, spanning the years 2018 to 2021, won 12+ hackathons.

Notable wins:

  1. Winner, Atlassian App Development Hackathon — Feb, 2021
    • An app that allows you to schedule your day, keep focused on your TODOs, and alerts you if you're distracted.
    • An app that suggests health tips to keep you energized throughout the day, ensuring you're at your best.
    • Focus. Unplug. Stonks.
    • [GitHub] [LinkedIn]
  2. Winner, MTX HackOlympics — Feb, 2021
    • A wrapper API that helps you build a web service to upload and download files from S3 bucket and Google Cloud Storage.
    • It also allows deployment to the cloud as FaaS, while enabling communication with multiple SDKs such as AWS S3 and Google Cloud Storage.
    • [GitHub] [LinkedIn]
  3. Winner, IndoDataWeek — Dec, 2020
    • A data-driven evaluatory model that analyzes a region's vitals and quantifies them based on its Food Insecurity Level.
    • A suggestive model that considers various crop diversity factors in a region and recommends the best fit crop for cultivation.
  4. Winner, AGBI Hackathon — Nov, 2020
    • A cross-platform OCR mobile application that converts printed or handwritten text into digital text and automatically uploads it to the database.
    • A web application that stores patient and drug data in one place and performs data visualization and targeted marketing.
    • A cross-platform mobile application that consolidates hospital messages and facilitates interaction with the hospital.
  5. Winner, GraVITas — Oct, 2020
    • A cross-platform application that retrieves data sent by a Python server and ML model, displaying analytics, insights, and suggestions.
    • An LSTM time series prediction ML model to detect possible deeper issues from vital signs.
    • A Python server that acts as a mock fitness watch, sending data to Firebase.
  6. Winner, Hackerrupt 2020 — Mar, 2020
    • We built a speech-controlled voice assistant powered by real-time video analysis to assist visually challenged individuals in performing day-to-day tasks and ensuring their security from different threats.
  7. Winner, SRM Aarush Hack Summit — Aug, 2019
    • Team Albuquerque made a podium finish at the SRM Aaruush Hack Summit, under the theme of Healthcare Tech.
    • We developed an emotional stress monitoring API using Python, Flask stack, multiway classification neural networks, and Heroku.
  8. Winner, HSBC Hackathon, IIT Madras — Jan, 2019
    • We cross-posted a project, removed the hardware part of it, and made it to the podium.
  9. Winner, Honeywell Hackathon, IIT Madras — Jan, 2019
    • Team appendly made a podium finish at the Honeywell Hackathon, IIT Madras.
    • We developed a solid waste segregation image processing model powered by convolutional neural networks embedded with Raspberry Pi and its add-ons.
    • We used TensorFlow, OpenCV, AWS, Django Framework, Google Firebase, and Raspberry Pi for this model.
  10. Winner, IBM Trusted AI Hackathon, IIT Madras — Jan, 2019
    • We developed a news article sentiment analysis project using JavaScript for both front and back end (Vue JS and Node JS).
    • This project won the #1 prize in a hackathon at IIT Madras with around 200 teams participating from across India.
    • We received a cash prize of Rs. 30,000 as a reward.
    • [GitHub] [LinkedIn]
  11. Winner, TiE Hackathon — Sept, 2018
    • True to its nature, the city has always found solutions to its problems on its own.
    • The second edition of the TiE Hackathon was an effort to further boost this culture of solving civic problems.
    • We developed a solid waste segregation image processing model powered by convolutional neural networks embedded with Raspberry Pi and its add-ons.
    • We used TensorFlow, OpenCV, AWS, Django Framework, Google Firebase, and Raspberry Pi for this model.
  12. Winner, MadurAI Hackathon conducted by Guvi and HCL — Aug, 2018
    • Known as possibly "The Biggest AI Hackathon in India in terms of the number of participants" (as of August 2018).
    • We developed a solid waste segregation image processing model powered by convolutional neural networks embedded with Raspberry Pi and its add-ons.