## Inspiration

eCommerce is a field that has seen astronomical growth in recent years, and shows no signs of slowing down. With a forecasted growth rate of 10.4% this year, up to $6.3 trillion in global revenues, we decided to tackle Noibu’s challenge to develop an extension to aide ecommerce developers with the impossible task of staying ahead amongst the fierce competitions in this space, all whilst providing a tremendous unique value to shoppers and eCommerce brands alike.

## What it does

Our extension. ShopSmart, aim to provide developers and brands with an accurate idea of how their website is being used, naturally. Unlike A/B testing, which forces a participant to use a given platform and provide feedback, ShopSmart analyzes user activity on any given website and produces a heatmap showing their exact usage patterns, all without collecting user identifying data. In tandem with the heatmap, ShopSmart provides insights as to the sequences of actions taken on their website, correlated with their heatmap usage, allowing an ever deeper understanding of what average usage truly looks like. To incentivize consumers, brands may elect to provide exclusive discount codes only available through ShopSmart, giving the shoppers a kickback for their invaluable input to the brand partners.

## How we built it

ShopSmart was built using the classic web languages HTML, CSS, and Javascript, keeping it simple, lightweight, and speedy.

## Challenges we ran into

We ran into several challenges throughout our development process, largely due to the complexity of the extension in theory being limited in execution to HTML, CSS, and Javascript (as those are the only allowed languages for use in developing extensions). One issue we had was finding a way to overlay the heatmap over the website so as to visually show the paths the user took. Whilst we were able to solve that challenge, we were sadly unable to finish fully integrating our database within the given timeframe into the extension due to the frequency of data collection/communication, and the complexity of the data itself.

## Accomplishments that we're proud of

Our team is very proud in being able to put out a working extension capable of tracking usage and overlaying the resulting heatmap data over the used website, especially as neither of us had any experience with developing extensions. Despite not being able to showcase our extensive database connections in the end as they are not finalized, we are proud of achieving reliable and consistent data flow to our cloud-based database within our testing environment. We are also proud of coming together and solving a problem none of us had considered before, and of course, of the sheer amount we learned over this short time span.

## What we learned

Our hackathon experience was truly transformative, as we not only gained invaluable technical knowledge in Javascript, but also cultivated essential soft skills that will serve us well in any future endeavors. By working together as a team, we were able to pool our unique strengths and collaborate effectively to solve complex problems and bring our ideas to life.

## What's next for ShopSmart

The next steps for ShopSmart are to focus on expanding its capabilities and increasing its reach. One area of focus could be on integrating the extension with more e-commerce platforms to make it more widely accessible to developers and brands. Another area for improvement could be on enhancing the heatmap visualization and adding more advanced analytics features to provide even deeper insights into user behavior. With the help of Machine Learning, developers and brands can utilize the data provided by ShopSmart to better recognize patterns within their customer's usage of their site to make better adjustments and improvements. Additionally, exploring partnerships with e-commerce brands to promote the extension and offer more exclusive discount codes to incentivize consumers could help increase its adoption. Overall, the goal is to continuously improve the extension and make it an indispensable tool for e-commerce businesses looking to stay ahead of the competition.
# 🎉 CoffeeStarter: Your Personal Networking Agent 🚀

Names: Sutharsika Kumar, Aarav Jindal, Tanush Changani & Pranjay Kumar

Welcome to **CoffeeStarter**, a cutting-edge tool designed to revolutionize personal networking by connecting you with alumni from your school's network effortlessly. Perfect for hackathons and beyond, CoffeeStarter blends advanced technology with user-friendly features to help you build meaningful professional relationships.

---

## 🌟 Inspiration

In a world where connections matter more than ever, we envisioned a tool that bridges the gap between ambition and opportunity. **CoffeeStarter** was born out of the desire to empower individuals to effortlessly connect with alumni within their school's network, fostering meaningful relationships that propel careers forward.

---

## 🛠️ What It Does

CoffeeStarter leverages the power of a fine-tuned **LLaMA** model to craft **personalized emails** tailored to each alumnus in your school's network. Here's how it transforms your networking experience:

* **📧 Personalized Outreach:** Generates authentic, customized emails using your resume to highlight relevant experiences and interests.
* **🔍 Smart Alumnus Matching:** Identifies and connects you with alumni that align with your professional preferences and career goals.
* **🔗 Seamless Integration:** Utilizes your existing data to ensure every interaction feels genuine and impactful.

---

## 🏗️ How We Built It

Our robust technology stack ensures reliability and scalability:

* **🗄️ Database:** Powered by **SQLite** for flexible and efficient data management.
* **🐍 Machine Learning:** Developed using **Python** to handle complex ML tasks with precision.
* **⚙️ Fine-Tuning:** Employed **Tune** for meticulous model fine-tuning, ensuring optimal performance and personalization.

---

## ⚔️ Challenges We Faced

Building CoffeeStarter wasn't without its hurdles:

* **🔒 SQLite Integration:** Navigating the complexities of SQLite required innovative solutions.
* **🚧 Firewall Obstacles:** Overcoming persistent firewall issues to maintain seamless connectivity.
* **📉 Model Overfitting:** Balancing the model to avoid overfitting while ensuring high personalization.
* **🌐 Diverse Dataset Creation:** Ensuring a rich and varied dataset to support effective networking outcomes.
* **API Integration:** Working with various API's to get as diverse a dataset and functionality as possible.

---

## 🏆 Accomplishments We're Proud Of

* **🌈 Diverse Dataset Development:** Successfully created a comprehensive and diverse dataset that enhances the accuracy and effectiveness of our networking tool.
* Authentic messages that reflect user writing styles which contributes to personalization.

---

## 📚 What We Learned

The journey taught us invaluable lessons:

* **🤝 The Complexity of Networking:** Understanding that building meaningful connections is inherently challenging.
* **🔍 Model Fine-Tuning Nuances:** Mastering the delicate balance between personalization and generalization in our models.
* **💬 Authenticity in Automation:** Ensuring our automated emails resonate as authentic and genuine, without echoing our training data.

---

## 🔮 What's Next for CoffeeStarter

We're just getting started! Future developments include:

* **🔗 Enhanced Integrations:** Expanding data integrations to provide even more personalized networking experiences and actionable recommendations for enhancing networking effectiveness.
* **🧠 Advanced Fine-Tuned Models:** Developing additional models tailored to specific networking needs and industries.
* **🤖 Smart Choosing Algorithms:** Implementing intelligent algorithms to optimize alumnus matching and connection strategies.

---

## 📂 Submission Details for PennApps XXV

### 📝 Prompt

You are specializing in professional communication, tasked with composing a networking-focused cold email from an input `{student, alumni, professional}`, name `{your_name}`. Given the data from the receiver `{student, alumni, professional}`, your mission is to land a coffee chat. Make the networking text `{email, message}` personalized to the receiver’s work experience, preferences, and interests provided by the data. The text must sound authentic and human. Keep the text `{email, message}` short, 100 to 200 words is ideal.

### 📄 Version Including Resume

You are specializing in professional communication, tasked with composing a networking-focused cold email from an input `{student, alumni, professional}`, name `{your_name}`. The student's resume is provided as an upload `{resume_upload}`. Given the data from the receiver `{student, alumni, professional}`, your mission is to land a coffee chat. Use the information from the given resume of the sender and their interests from `{website_survey}` and information of the receiver to make this message personalized to the intersection of both parties. Talk specifically about experiences that `{student, alumni, professional}` would find interesting about the receiver `{student, alumni, professional}`. Compare the resume and other input `{information}` to find commonalities and make a positive impression. Make the networking text `{email, message}` personalized to the receiver’s work experience, preferences, and interests provided by the data. The text must sound authentic and human. Keep the text `{email, message}` short, 100 to 200 words is ideal. Once completed with the email, create a **1 - 10 score** with **1** being a very generic email and **10** being a very personalized email. Write this score at the bottom of the email.

## 🧑‍💻 Technologies Used

* **Frameworks & Libraries:**

	+ **Python:** For backend development and machine learning tasks.
	+ **SQLite:** As our primary database for managing user data.
	+ **Tune:** Utilized for fine-tuning our LLaMA3 model.
* **External/Open Source Resources:**

	+ **LLaMA Model:** Leveraged for generating personalized emails.
	+ **Various Python Libraries:** Including Pandas for data processing and model training.
## Inspiration

As a team, we had a collective interest in sustainability and knew that if we could focus on online consumerism, we would have much greater potential to influence sustainable purchases. We also were inspired by Honey -- we wanted to create something that is easily accessible across many websites, with readily available information for people to compare.

Lots of people we know don’t take the time to look for sustainable items. People typically say if they had a choice between sustainable and non-sustainable products around the same price point, they would choose the sustainable option. But, consumers often don't make the deliberate effort themselves. We’re making it easier for people to buy sustainably -- placing the products right in front of consumers.

## What it does

greenbeans is a Chrome extension that pops up when users are shopping for items online, offering similar alternative products that are more eco-friendly. The extension also displays a message if the product meets our sustainability criteria.

## How we built it

Designs in Figma, Bubble for backend, React for frontend.

## Challenges we ran into

Three beginner hackers! First time at a hackathon for three of us, for two of those three it was our first time formally coding in a product experience. Ideation was also challenging to decide which broad issue to focus on (nutrition, mental health, environment, education, etc.) and in determining specifics of project (how to implement, what audience/products we wanted to focus on, etc.)

## Accomplishments that we're proud of

Navigating Bubble for the first time, multiple members coding in a product setting for the first time... Pretty much creating a solid MVP with a team of beginners!

## What we learned

In order to ensure that a project is feasible, at times it’s necessary to scale back features and implementation to consider constraints. Especially when working on a team with 3 first time hackathon-goers, we had to ensure we were working in spaces where we could balance learning with making progress on the project.

## What's next for greenbeans

Lots to add on in the future:
Systems to reward sustainable product purchases. Storing data over time and tracking sustainable purchases. Incorporating a community aspect, where small businesses can link their products or websites to certain searches.
Including information on best prices for the various sustainable alternatives, or indicators that a product is being sold by a small business. More tailored or specific product recommendations that recognize style, scent, or other niche qualities.