HackStack

Project Info

Winner

Best Use of CockroachDB Serverless

LifeLine Aid

Devpost

This project did not submit a demo video on Devpost.

Inspiration

In light of the ongoing global conflict between war-torn countries, many civilians face hardships. Recognizing these challenges, LifeLine Aid was inspired to direct vulnerable groups to essential medical care, health services, shelter, food and water assistance, and other deprivation relief.

What it does

LifeLine Aid provides multifunctional tools that enable users in developing countries to locate resources and identify dangers nearby. Utilizing the user's location, the app alerts them about the proximity of a situation and centers for help. It also facilitates communication, allowing users to share live videos and chat updates regarding ongoing issues. An upcoming feature will highlight available resources, like nearby medical centers, and notify users if these centers are running low on supplies.

How we built it

Originally, the web backend was to be built using Django, a trusted framework in the industry. As we progressed, we realized that the amount of effort and feasibility of exploiting Django were not sustainable; as we made no progress within the first day. Quickly turning to the in-depth knowledge of one of our team member’s extensive research into asyncio, we decided to switch to FastAPI, a trusted framework used by Microsoft. Using this framework had both its benefits and costs. Realizing after our first day, Django proved to be a roadblock, thus we ultimately decided to switch to FastAPI. Our backend proudly uses CockroachDB, an unstoppable force to be reckoned with. CockroachDB allowed our code to scale and continue to serve those who suffer from the effects of war.

Challenges we ran into

In order to pinpoint hazards and help, we would need to obtain, store, and reverse-engineer Geospatial coordinate points which we would then present to users in a map-centric manner. We initially struggled with converting the Geospatial data from a degree, minutes, seconds format to decimal degrees and storing the converted values as points on the map which were then stored as unique 50 character long SRID values. Luckily, one of our teammates had some experience with processing GeoSpatial data so drafting coordinates on a map wasn’t our biggest hurdle to overcome. Another challenge we faced were certain edge cases in our initial Django backend that resulted in invalid data. Since some outputs would be relevant to our project, we had to make an executive decision to change backend midway through. We decided to go with FastApi. Although FastApi brought its own challenge with processing SQL to usable data, it was our way of overcoming our Jango situation. One last challenge we ran into was our overall source control. A mixture of slow and unbearable WiFi, combined with tedious local git repositories not correctly syncing create some frustrating deadlocks and holdbacks. To combat this downtime, we resort to physically drafting and planning out how each component of our code would work.

Accomplishments we're proud of

Three out of the four in our team are attending their first hackathon. The experience of crafting an app and seeing the fruits of our labor is truly rewarding. The opportunity to acquire and apply new tools in our project has been exhilarating. Through this hackathon, our team members were all able to learn different aspects of creating an idea into a scalable application. From designing and learning UI/UX, implementing the React-Native framework, emulating iOS and Android devices to test and program compatibility, and creating communication between the frontend and backend/database.

What we learned

This challenge aimed to dive into technologies that are used widely in our daily lives. Spearheading the competition with a framework trusted by huge companies such as Meta, Discord and others, we chose to explore the capabilities of React Native. Joining our team are three students who have attended their first hackathon, and the grateful opportunity of being able to explore these technologies have led us to take away a skillset of a lifetime. With the concept of the application, we researched and discovered that the only best way to represent our data is through the usage of Geospatial Data. CockroachDB’s extensive tooling and support allowed us to investigate the usage of geospatial data extensively; as our backend team traversed the complexity and the sheer awe of the scale of said technology. We are extremely grateful to have this opportunity to network and to use these tools that would be useful in the future.

What's next

There are a plethora of avenues to further develop the app, which include enhanced verification, rate limiting, and many others. Other options include improved hosting using Azure Kubernetes Services (AKS), and many others. This hackathon project is planned to be maintained further into the future as a project for others who may be new or old in this field to collaborate on.

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • DjangoIn code
  • FastAPIIn code
  • JavaScriptIn code
  • PythonIn code
  • ReactIn code
  • SQLIn code
  • TypeScriptIn code
  • Node.jsClaimed
  • PostgreSQLClaimed

7 of 9 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

41 KB

Source files

48

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

0 stars