## Inspiration

Being a student of the University of Waterloo, every other semester I have to attend interviews for Co-op positions. Although it gets easier to talk to people, the more often you do it, I still feel slightly nervous during such face-to-face interactions. During this nervousness, the fluency of my conversion isn't always the best. I tend to use unnecessary filler words ("um, umm" etc.) and repeat the same adjectives over and over again. In order to improve my speech through practice against a program, I decided to create this application.

## What it does

InterPrep uses the IBM Watson "Speech-To-Text" API to convert spoken word into text. After doing this, it analyzes the words that are used by the user and highlights certain words that can be avoided, and maybe even improved to create a stronger presentation of ideas. By practicing speaking with InterPrep, one can keep track of their mistakes and improve themselves in time for "speaking events" such as interviews, speeches and/or presentations.

## How I built it

In order to build InterPrep, I used the Stdlib platform to host the site and create the backend service. The IBM Watson API was used to convert spoken word into text. The mediaRecorder API was used to receive and parse spoken text into an audio file which later gets transcribed by the Watson API.

The languages and tools used to build InterPrep are HTML5, CSS3, JavaScript and Node.JS.

## Challenges I ran into

"Speech-To-Text" API's, like the one offered by IBM tend to remove words of profanity, and words that don't exist in the English language. Therefore the word "um" wasn't sensed by the API at first. However, for my application, I needed to sense frequently used filler words such as "um", so that the user can be notified and can improve their overall speech delivery. Therefore, in order to implement this word, I had to create a custom language library within the Watson API platform and then connect it via Node.js on top of the Stdlib platform. This proved to be a very challenging task as I faced many errors and had to seek help from mentors before I could figure it out. However, once fixed, the project went by smoothly.

## Accomplishments that I'm proud of

I am very proud of the entire application itself. Before coming to Qhacks, I only knew how to do Front-End Web Development. I didn't have any knowledge of back-end development or with using API's. Therefore, by creating an application that contains all of the things stated above, I am really proud of the project as a whole. In terms of smaller individual accomplishments, I am very proud of creating my own custom language library and also for using multiple API's in one application successfully.

## What I learned

I learned a lot of things during this hackathon. I learned back-end programming, how to use API's and also how to develop a coherent web application from scratch.

## What's next for InterPrep

I would like to add more features for InterPrep as well as improve the UI/UX in the coming weeks after returning back home. There is a lot that can be done with additional technologies such as Machine Learning and Artificial Intelligence that I wish to further incorporate into my project!
## 💡 Inspiration

Generation Z is all about renting - buying land is simply out of our budgets. But the tides are changing: with Pocket Plots, an entirely new generation can unlock the power of land ownership without a budget.

Traditional land ownership goes like this: you find a property, spend weeks negotiating a price, and secure a loan. Then, you have to pay out agents, contractors, utilities, and more. Next, you have to go through legal documents, processing, and more. All while you are shelling out tens to hundreds of thousands of dollars.

Yuck.

Pocket Plots handles all of that for you.

We, as a future LLC, buy up large parcels of land, stacking over 10 acres per purchase. Under the company name, we automatically generate internal contracts that outline a customer's rights to a certain portion of the land, defined by 4 coordinate points on a map.

Each parcel is now divided into individual plots ranging from 1,000 to 10,000 sq ft, and only one person can own a contract to each plot to the plot.

This is what makes us fundamentally novel: we simulate land ownership without needing to physically create deeds for every person. This skips all the costs and legal details of creating deeds and gives everyone the opportunity to land ownership.

These contracts are 99 years and infinitely renewable, so when it's time to sell, you'll have buyers flocking to buy from you first.

You can try out our app here: <https://warm-cendol-1db56b.netlify.app/>

(AI features are available locally. Please check our Github repo for more.)

## ⚙️What it does

### Buy land like it's ebay:

![](https://i.imgur.com/PP5BjxF.png)

We aren't just a business: we're a platform. Our technology allows for fast transactions, instant legal document generation, and resale of properties like it's the world's first ebay land marketplace.

We've not just a business.

We've got what it takes to launch your next biggest investment.

### Pocket as a new financial asset class...

In fintech, the last boom has been in blockchain. But after FTX and the bitcoin crash, cryptocurrency has been shaken up: blockchain is no longer the future of finance.

Instead, the market is shifting into tangible assets, and at the forefront of this is land. However, land investments have been gatekept by the wealthy, leaving little opportunity for an entire generation

That's where pocket comes in. By following our novel perpetual-lease model, we sell contracts to tangible buildable plots of land on our properties for pennies on the dollar.

We buy the land, and you buy the contract.

It's that simple.

We take care of everything legal: the deeds, easements, taxes, logistics, and costs. No more expensive real estate agents, commissions, and hefty fees.

With the power of Pocket, we give you land for just $99, no strings attached.

With our resell marketplace, you can sell your land the exact same way we sell ours: on our very own website.

We handle all logistics, from the legal forms to the system data - and give you 100% of the sell value, with no seller fees at all.

We even will run ads for you, giving your investment free attention.

So how much return does a Pocket Plot bring?

Well, once a parcel sells out its plots, it's gone - whoever wants to buy land from that parcel has to buy from you.

We've seen plots sell for 3x the original investment value in under one week. Now how insane is that? The tides are shifting, and Pocket is leading the way.

### ...powered by artificial intelligence

**Caption generation**

*Pocket Plots* scrapes data from sites like Landwatch to find plots of land available for purchase. Most land postings lack insightful descriptions of their plots, making it hard for users to find the exact type of land they want. With *Pocket Plots*, we transformed links into images, into helpful captions.

![](https://i.imgur.com/drgwbft.jpg)

**Captions → Personalized recommendations**

These captions also inform the user's recommended plots and what parcels they might buy. Along with inputting preferences like desired price range or size of land, the user can submit a text description of what kind of land they want. For example, do they want a flat terrain or a lot of mountains? Do they want to be near a body of water? This description is compared with the generated captions to help pick the user's best match!

![](https://i.imgur.com/poTXYnD.jpg)

### **Chatbot**

Minute Land can be confusing. All the legal confusion, the way we work, and how we make land so affordable makes our operations a mystery to many. That is why we developed a supplemental AI chatbot that has learned our system and can answer questions about how we operate.

*Pocket Plots* offers a built-in chatbot service to automate question-answering for clients with questions about how the application works. Powered by openAI, our chat bot reads our community forums and uses previous questions to best help you.

![](https://i.imgur.com/dVAJqOC.png)

## 🛠️ How we built it

Our AI focused products (chatbot, caption generation, and recommendation system) run on Python, OpenAI products, and Huggingface transformers. We also used a conglomerate of other related libraries as needed.

Our front-end was primarily built with Tailwind, MaterialUI, and React. For AI focused tasks, we also used Streamlit to speed up deployment.

### We run on Convex

We spent a long time mastering Convex, and it was worth it. With Convex's powerful backend services, we did not need to spend infinite amounts of time developing it out, and instead, we could focus on making the most aesthetically pleasing UI possible.

### Checkbook makes payments easy and fast

We are an e-commerce site for land and rely heavily on payments. While stripe and other platforms offer that capability, nothing compares to what Checkbook has allowed us to do: send invoices with just an email. Utilizing Checkbook's powerful API, we were able to integrate Checkbook into our system for safe and fast transactions, and down the line, we will use it to pay out our sellers without needing them to jump through stripe's 10 different hoops.

## 🤔 Challenges we ran into

Our biggest challenge was synthesizing all of our individual features together into one cohesive project, with compatible front and back-end. Building a project that relied on so many different technologies was also pretty difficult, especially with regards to AI-based features. For example, we built a downstream task, where we had to both generate captions from images, and use those outputs to create a recommendation algorithm.

## 😎 Accomplishments that we're proud of

We are proud of building several completely functional features for *Pocket Plots*. We're especially excited about our applications of AI, and how they make users' *Pocket Plots* experience more customizable and unique.

## 🧠 What we learned

We learned a lot about combining different technologies and fusing our diverse skillsets with each other. We also learned a lot about using some of the hackathon's sponsor products, like Convex and OpenAI.

## 🔎 What's next for Pocket Plots

We hope to expand *Pocket Plots* to have a real user base. We think our idea has real potential commercially. Supplemental AI features also provide a strong technological advantage.
## Inspiration

I really related to the problem the career councilors described. Reflecting on things can be hard. Especially when you're reflecting on yourself. Our goal is to make this process fun and easy, with a little help from our friends.

## What it does

Meerar compares HOW OFTEN you demonstrate a skill, and how much you ENJOY demonstrating it. To elaborate:

* When ever you engage in an event, Meerar will prompt you take a little note, describing your experience and how you felt.
* We then use the Lexalytics API to decide how much you enjoyed the event.
* Your reflection is posted anonymously on ExperienceMine, along with other people's reflections.
* Here's the twist: other students read your reflection and assign a "skill" that they feel you demonstrated.
* The more you review and post reflections, the more points you earn. You can earn achievements and compete with your friends like this!
* At this point, we have 2 pieces of info: how you felt about the event, and what skill you demonstrated at the event. Once you have enough data, we plot a graph that compares HOW OFTEN you demonstrate a skill, and how much you ENJOY demonstrating it, and provide an analysis on you strengths and weaknesses, and recommends events to attend to play to your strengths.

## How I built it

I built Meerar with a PHP backend, which is used to store and process user information. The front end was built on the trusty Twitter Bootstrap framework, with bunch of JavaScript libraries sprinkled in (i.e. materialize.js, chart.js). I used the Lexalytics API in order to gauge the sentiment in user's descriptions. I also wrote a little scraper to scrape from LinkedIn profiles of users which I execute with a bash script (what can I say, I'm learning). It's a bit patchwork-ish, but it works!

## Challenges I ran into

I came to McGill not knowing anyone, and being a beginner, it was challenging to find a team that would take me in. I managed to find another team of beginners but unfortunately, my team decided to leave the hackathon just after dinner was served on Saturday. As a result I had to do the project all myself: a pretty daunting task! This is my first major hackathon so I was a little bummed out. Still, I followed through because I thought I had an interesting idea. Hopefully the judges agree!

## Accomplishments that I'm proud of

I almost ran into a dead end with the LinkedIn API. Essentially, I wanted to get job information about a user from their profile. However, you needed to register your app with Linkedin and must have an official website (which I did not have) in order to qualify to recieve the access key to use their API. Since I didn't have this, I found a workaround: scrape the page manually with Python, and then exec() a bash script to scrape! Though it may not seem like much, I proud of finding the "way" with my "will".

## What I learned

Tech wise, I learned how to use chart.js. It's a really cool way to visualize data with beautiful graphs in the browser. Will definitely be using it again in the future. But what I really learned is that I have a lot left to learn. Everyone at McGill really knows there stuff. Next year, that'll be me.

## What's next for Meerar

Hopefully, if the judges like it, we can work on making it a full fledged app available online for student at McGill.