## Inspiration

Imagine this: You’re overwhelmed, scrolling through countless LinkedIn profiles, trying to figure out which clubs or activities will help you land your dream job. It feels like searching for a needle in a haystack!

Here’s where UJourney steps in: We simplify your career planning by providing personalized paths tailored specifically to your goals. UJourney uses LinkedIn data from professionals in your dream job to recommend the exact clubs to join, events to attend, skills to acquire, and courses to take at your university. Our mission is to transform career exploration into a clear, actionable journey from aspiration to achievement.

## What it does

UJourney is like having a career GPS with a personality. Tell it your dream job, and it will instantly scan the LinkedIn career cosmos to reveal the paths others have taken. No more endless profile scrolling! Instead, you get a curated list of personalized steps—like joining that robotics club or snagging that perfect internship—so you can be the most prepared candidate out there. With UJourney, the path to your dream job isn’t just a distant vision; it’s a series of clear, actionable steps right at your fingertips.

## How we built it

The UJourney project is built on three core components:

1. Gathering Personal Information:
We start by seamlessly integrating LinkedIn authorization to collect essential details like name and email. This allows users to create and manage their profiles in our system. For secure login and sign-up, we leveraged Auth0, ensuring a smooth and safe user experience.
2. Filtering LinkedIn Profiles:
Next, we set up a MongoDB database by scraping LinkedIn profiles, capturing a wealth of career data. Using Python, we filtered this data based on keywords related to company names and job roles. This process helps us pinpoint relevant profiles and extract meaningful insights.
3. Curating Optimal Career Paths:
Our AI model takes it from here. By feeding the filtered data and user information into an advanced model via the Gemini API, we generate personalized career paths, complete with timelines and actionable recommendations. The model outputs these insights in a structured JSON format, which we then translate into an intuitive, user-friendly UI design.

## Challenges we ran into

Problem: LinkedIn Scraping Restrictions.
Our initial plan was to directly scrape LinkedIn profiles based on company names and job roles to feed data into our AI model. However, LinkedIn’s policies prevented us from scraping directly from their platform. We turned to a third-party LinkedIn scraper, but this tool had significant limitations, including a restriction of only 10 profiles per company and no API for automation. While we utilized automation tools like Zapier and HubSpot CRM to streamline part of our workflow, we ultimately faced a significant roadblock. Despite these challenges, we adapted our approach to continue progressing with the project.

Solution: Manual Database Creation.
To work around these limitations, we manually built a database focused on the top five most commonly searched companies and job roles. While this approach allowed us to gather essential data, it also meant that our database was initially limited in scope. This manual effort was crucial for ensuring we had enough data to effectively train our AI model and provide valuable recommendations.

Despite these hurdles, we adapted our approach to ensure UJourney could deliver accurate and practical career insights.

## Accomplishments that we're proud of

1. Rapid Development:
We successfully developed and launched UJourney in a remarkably short period of time. Despite the tight timeline, we managed to pull everything together efficiently and effectively.
2. Making the Most of Free Tools:
Working with limited resources and relying on free versions of various software, we still managed to create a fully functional version of UJourney. Our resourcefulness allowed us to overcome budget constraints and still deliver a high-quality product.
3. University-Specific Career Plans:
One of our standout achievements is the app’s ability to provide personalized career plans tailored to specific universities. By focusing on actionable steps relevant to users' educational contexts, UJourney offers unique value that addresses individual career planning needs with precision.

## What we learned

1. Adaptability is Key:
Our journey taught us that flexibility is crucial in overcoming obstacles. When faced with limitations like LinkedIn's scraping restrictions, we had to quickly pivot our approach. This experience reinforced the importance of adapting to challenges and finding creative solutions to keep moving forward.
2. Data Quality Over Quantity:
We learned that the quality of data is far more important than sheer volume. By focusing on the most commonly searched companies and job roles, we ensured that our AI model could provide relevant and actionable insights, even with a limited dataset. This underscored the value of precision and relevance in data-driven projects.
3. Resourcefulness Drives Innovation:
Working within constraints, such as using free software and limited resources, highlighted our team’s ability to innovate under pressure. We discovered that resourcefulness can turn limitations into opportunities for creative problem-solving, pushing us to explore new tools and methods.
4. User-Centric Design Matters:
Our focus on creating university-specific career plans taught us that understanding and addressing user needs is essential for success. Providing tailored, actionable steps for career planning showed us the impact of designing solutions with the user in mind, making the tool genuinely useful and relevant.

## What's next for UJourney

What exciting features are on the horizon?

1. Resume Upload Feature:
To kick things off, we’re introducing a resume upload feature. This will allow users to gather personal information directly from their resumes, streamlining profile creation and reducing manual data entry.
2. Real-Time University Information:
Next, we’ll be scraping university websites to provide real-time updates on campus events and activities. This feature will enable users to see upcoming events and automatically add them to their calendars, keeping them informed and organized.
3. Enhanced Community Involvement:
We’ll then roll out features that allow users to view their friends' dream jobs and career paths. This will facilitate connections with like-minded individuals and foster a community where students can share experiences related to jobs and university clubs.
4. Automated LinkedIn Web Scraping:
To improve data collection, we’ll automate LinkedIn data scraping. This will help expand our database with up-to-date and relevant career information, enhancing the app’s ability to provide accurate recommendations.
5. AI-Driven Job Recommendations:
Finally, we’ll leverage real-time market information and AI to recommend job opportunities that are ideal for the current year. Users will also be able to apply for these jobs directly through the app, making the job application process more efficient and seamless.

These upcoming features are designed to enhance the UJourney experience, making career planning, networking, and job applications more intuitive and effective. Stay tuned for these exciting updates!
## Inspiration

Selin's journey was the spark that ignited the creation of our platform. Originally diving into the world of chemistry, she believed it was her calling. However, as time unfolded, she realized it wasn't the path that resonated with her true passions. This realization, while enlightening, also brought with it a wave of confusion and stress. The weight of expectations, both self-imposed and from the university, pressed down on her, urging her to find a new direction swiftly. Yet, the vast expanse of potential careers felt overwhelming, leaving her adrift in a sea of options, not knowing which shore to swim towards. Selin's story isn't unique. It's a narrative that echoes across university halls, with countless students grappling with the same feelings of uncertainty and pressure. Recognizing this widespread challenge became the cornerstone of our mission: to illuminate the myriad of career paths available and guide students towards their true calling.

## What it does

Our platform is an AI-powered career mapping tool designed for students navigating the tech landscape. Utilizing advanced machine learning algorithms combined with psychology-driven techniques, it offers a dynamic and data-driven representation of potential career paths. Each job node within the map is informed by LLM and RAG methodologies, providing a comprehensive view based on real user trajectories and data. Beyond mere visualization, the platform breaks down tasks into detailed timelines, ensuring clarity at every step. By integrating insights from both AI and psychology algorithms, we aim to provide students with a clear, strategic blueprint for their ideal tech career.

## How we built it

We integrated advanced machine learning algorithms with psychology-driven techniques. The platform's backbone is built on LLM and RAG methodologies, informed by real user trajectories. We also incorporated various APIs, like the Hume AI API, to enhance user experience and data collection.

## Challenges we ran into

Embarking on this journey, we were rookies in the arena of hackathons, stepping into uncharted territory with a blend of enthusiasm and trepidation. The path was riddled with unexpected hurdles, the most formidable being a persistent bug in the RAG model from MindsDB. Hours that could have been spent refining and enhancing were instead consumed in troubleshooting this elusive issue. As if the technical challenges weren't daunting enough, the spirit of the hackathon was challenged as friends, one after another, decided to step away despite having amazing ideas. The weight of their absence, combined with the mounting pressure of having to reconstruct a new model (the bug turned out to be withing MindDB's RAG handler which we had no control over) in a race against time, was palpable. With the clock ticking, sleep became a luxury we could scarcely afford, operating on a mere three hours. Yet, in the face of these adversities, it was our shared vision and unwavering determination that became our beacon, guiding us through the darkest hours and reminding us of the potential impact of our creation. The true essence of our challenge wasn't just in navigating technical glitches or decreasing excitement in the event; it was about resilience, adaptability, and the relentless pursuit of innovation.

## Accomplishments that we're proud of

Successfully integrating the Hume AI API to translate user opinions into actionable data was a significant win and as well as connecting it into a spreadsheet for further analysis. Despite the hurdles and it being our inaugural hackathon, our team's perseverance saw us through to the end.

## What we learned

We gained a lot of insignts into the nature of LLM models and the intricacies of the RAG model. The experience also taught us the importance of adaptability and persistence in the face of unforeseen challenges.

## What's next for Orna

Our immediate goal is to finalize the MVP, refining the suggestion system. With these enhancements, we aim to secure seed investment to propel Orna to new heights.
## Inspiration

There should be an effective way to evaluate company value by examining the individual values of those that make up the company.

## What it does

Simplifies the research process of examining a company by showing it in a dynamic web design that is free-flowing and easy to follow.

## How we built it

It was originally built using a web scraper that scraped from LinkedIn which was written in python. The web visualizer was built using javascript and the VisJS library to have a dynamic view and aesthetically pleasing physics. In order to have a clean display, web components were used.

## Challenges we ran into

Gathering and scraping the data was a big obstacle, had to pattern match using LinkedIn's data

## Accomplishments that we're proud of

It works!!!

## What we learned

Learning to use various libraries and how to setup a website

## What's next for Yeevaluation

Finetuning and reimplementing dynamic node graph, history. Revamping project, considering it was only made in 24 hours.