## Inspiration

The process of renting is often tedious, repetitive, and exhausting for both renters and landlords. Why not make it efficient, fun, and enjoyable instead? For renters, no more desperately sifting through Marketplace, Craigslist, and shooting messages and applications into an abyss. For landlords, no more keeping track of prospective tenants' references and rent history through a series of back-and-forth messages.

## What it does

Rent2Be is a mobile application that borrows from Tinder's iconic concept of swiping left and right, effectively streamlining the renting process for both renters and landlords. Find your perfect match, truly **rent to be**!

From the renter's perspective, we query their potential matches based on their preferences (e.g. budget, location, move-in date, lease length, beds/baths, amenities, commute preferences, etc.). In their feed, renters can then swipe right for listings they're interested in and left for listings that don't fit their criteria.

On the other end, landlords can create their listings for the current rental database. The landlord's feed will be populated with the profiles of renters interested in that particular listing - if the landlord also swipes right on the renter, it's a match!

Upon matching, the pair will have an open chat session for further discussion and access to additional tenant details such as reference contact information.

## How we built it

* The frontend is built with React Native and expo.
* Our backend is powered by CockroachDB Serverless with "global locality" rows.
* The UI/UX design is created with Figma.

## Challenges we ran into

* Dependency discrepancies across each of our commits would occasionally lead to merge conflicts.
* Integrating CockroachDB for the first time.
* Considering the privacy and security risks of an app that handles highly confidential information (e.g. occupation, salary, credit score, etc.).

## Accomplishments that we're proud of

* With CockroachDB we're setting a “global” locality for low-latency reads and high-latency writes. We saw this as a key benefit given there are significantly more read operations (repeated viewings of each profile/listing) than write operations (creation of these profiles/listings), there are typically more renters than landlords. We may sacrifice fewer users for greater users, though this provides an overall better user experience for all types of users (without excluding those that are regionally further, such as internationals that may be doing market research before immigration or moving abroad).
* Using the benefits of serverless CockroachDB to automatically scale (and shard) for more popular geographic reasons. We would have to monitor and perform this manually otherwise.
* Leveraging CockroachDB's details and integrations to enhance user experience and minimize engineer efforts where possible.
* Creating a UI design that balances fun, energetic vibes with a professional, trustworthy feeling.

## What we learned

* Testing live on our mobile devices with expo and React Native
* How to set up and use CockroachDB Serverless, creating clusters and importing data in the database
* How to throw Water-LOO gxng signs (via will.i.am)

## What's next for Rent2Be

There are so many features in the future of Rent2Be!

A big part of the renting process is the viewings - Rent2Be renters will be able to book an appointment as soon as there is a match. Landlords will fill in their calendar availability ahead of time and renters will be able to book directly in-app as soon as there's a match.

Community feedback is crucial and often a great source for making decisions - as such, Rent2Be will also have features for leaving reviews on both renters and landlords. Whether it's a review from previous tenants on the apartment listed by a landlord or a space for landlords to leave their tenants a reference letter once their lease ends, feedback from both ends will help to improve the overall user experience.

Rent2Be also has the potential to handle areas such as payment. Since this app already aims to mitigate the inefficiencies of the current rent processes, combining pay onto one platform will only make lives easier. This would also be an opportunity to work with payment or banking APIs as well as Security practices.
## Inspiration

Finding a good place to live is time consuming and hard.
Let's make it a little better.

## What it does

It enables its users to transfer their lease on a very intuitive and simple platform thanks to high web technologies.

## How we built it

We built it using Google App Engine and its built-in nosql datastore.
All webpages are constructed in jsp allowing more exposure to the backend functionalities.
User management comes with them as a package and thus it currently supports only google account users.

## Challenges we ran into

We were working on different environments and the difference in our setup often broke the project setup and even the whole app..!
Also, we had to learn new concepts to implement some of the essential features such as search, pagination, multiple file uploading, and dealing with web technology libraries to display the UI pretty.

## Accomplishments that we're proud of

Albeit it may seem ugly, our first prototype is out at 'leasetransfer.tech' and it has all the basic functionalities running that we intended to have from the beginning.

## What we learned

We are learning from our painful mistakes: we should try our best to work in a same development environment.
Also, we learned that McHacks16 is awesome.

## What's next for leasetransfer

After users will have used it, we will verify their feedbacks and figure out where to improve and we will keep improving its UI and make it ready for bigger scale.
Inspiration
The genesis of LeaseEase lies in the escalating housing crisis in Canada, where landlords have increasingly exploited students and other vulnerable groups. Recognizing the urgent need for accessible legal resources, we envisioned LeaseEase as a beacon of support and empowerment. Our goal was to create a tool that simplifies the complexities of tenant rights under the Canadian Residential Tenancy Act, making legal protection accessible to those who need it most.

What It Does
LeaseEase is a groundbreaking application that combines a Large Language Model (LLM) with Retrieval-Augmented Generation (RAG) to interpret and apply the Canadian Residential Tenancy Act. It transforms user queries into actionable advice and automatically generates crucial legal documents, such as T1 and N7 forms. This functionality ensures that underprivileged groups are not only informed but also equipped to assert their rights effectively.

How We Built It
Our journey in building LeaseEase was a blend of innovative technologies and user-centric design. We utilized Streamlit for an intuitive front-end experience, integrating OpenAI and Cohere for the NLP and LLM functionalities. The backbone of our data operations was ChromaDB, a vector database, and we leveraged LangChain to seamlessly connect all these components.

Challenges We Ran Into
Developing LeaseEase was not without its hurdles. Integrating the backend with the frontend to accurately display the agent's thought process and RAG citations was a significant challenge. Additionally, creating the vector database and formatting the Residential Tenancy Act document appropriately required considerable effort and ingenuity.

Accomplishments That We're Proud Of
We take immense pride in LeaseEase's combination of aesthetic design and sophisticated technology. The implementation of the function calling feature and the streaming capability are particular highlights, demonstrating the effective use of RAG and LangChain agents. These features not only enhance the user experience but also validate our technological choices.

What We Learned
This project was a profound learning experience. Beyond mastering technical tools like Streamlit, LangChain, and various aspects of LLM technologies, we gained insights into the social implications of technology. We understood how the inaccessibility of legal resources can disadvantage vulnerable populations, reinforcing our commitment to tech for social good.

What's Next for LeaseEase
Looking forward, we aim to expand the range of forms LeaseEase can produce and enhance the reasoning capabilities of the LLM. We are excited about potential collaborations with government bodies or tribunals, which could include direct submission features for the forms generated. The future of LeaseEase is not just about technological advancement but also about deepening our impact on social justice and community empowerment.