## Inspiration

As students and programmers we can spend countless iterations and follow ups prompting ChatGPT to give us the answer that we are looking for. Often before we reach a working solution we've already used up our tokens for ChatGPT 4.0. Not only is prompting time consuming, but also token-consuming. Thus, we decided to create something convenient and practical that we can use to help us engineer our prompts for large language models.

## What it does

Promptli is a prompt engineering chrome extension that provides personalized suggestions and improves your prompts as you type them live in the chatbot. By using frameworks from our research into prompt engineering, Promptli analyzes the prompt on the following dimensions:

1. Identify any ambiguities, vague terms, or areas that could benefit from more detail or context.
2. Assess the logical flow and organization of the prompt.
3. Evaluate the word choice, vocabulary precision, and overall readability.
4. Determine whether the prompt is appropriately focused and scoped for its intended purpose.
5. Making prompts more "AI-friendly"

Make the most out of AI with Promptli, your personal prompt engineer.

## How we built it

We built a chrome extension using:

* Javascript for the back-end server, scripting, and content rendering
* HTML & CSS for a user-friendly UI/UX that seamlessly integrates with the chat interface
* Gemini API for Promptli’s prompt engineering capabilities and JSON file rendering
* 100ms processing time for real time prompt improvement
* Effortless rendering of new prompts
* Interactive and easy-to-use UI/UX with single click per prompt

## Challenges we ran into

\We began this project without prior experience with creating web extensions. Our first step was looking at resources from the Chrome Developers site. Specifically focused on chrome extension sample code and familiarizing ourselves with their API.

Our second challenge was integrating our extension with AI platforms in a seamless fashion that was easy to use and wouldn’t block users’ workflow. This posed greater difficulties than we had initially anticipated as we needed to inject our JS into the website's dynamic layout. After several iterations of designs and troubleshooting our extensions compatibility with the AI site’s code, we were able to create something that works alongside the website’s ever changing layout and is intuitive to use.

Lastly, having to work in a time-crunch with little to no sleep was definitely challenging but at the same time thrilling to build something with our friends that we truly would use and love as a product.

## Accomplishments that we're proud of

We're proud of building an app that we could use ourselves and provides practical value to any daily user of a chat bot. A lot of the time, the world seems overcrowded with artificial intelligence and machines so we are also are proud of creating a more human friendly experience that allows better communication between machine learning models and the human being.

## What we learned

I think the best learning experience was about ourselves and our capabilities when working with friends for something we were passionate about. This was a really fun experience and we loved hacking our persimmon app: Promptli.

Other things we learned:

* how to code a chrome extension
* how to create a back end server
* how to utilize javascript to call API and parse it through
* how to prompt engineer
* how to prompt chatbots to output file structure
* how to sleep under a table

## What's next for Promptli

We're currently working on integration with a wider range of generative AI services and hope to make the product more accessible with future partnerships. We also hope for a custom prompting model for long term sustainability fine tuned for writing good prompts.
## Inspiration

Two days ago, as we were standing in the long line to check-in to Calhacks, we were competing to see who had the most emails in their inbox. Surprisingly enough someone had more than 150k unread emails! As fate would have it when it was our turn to check-in we were franticly searching through our inboxes for our tickets. That's when we realized there are so many ways e-mail can be improved on to make life easier, one major one being organization of emails. But, no one in their right mind is going to manually categorize and organize more than 150k e-mails. That's where MailMorph comes in!

## What it does

MailMorph is a smart e-mail tool which employs the use of AI and AI-Agents to make life with e-mail easier in five main areas. First, MailMorph can automatically create user specified categories and organize the e-mail in their inbox into the categories. Second, MailMorph can automatically identify mails sitting in the sent folder of you inbox and recommend you to follow up with the other person, and automatically create a draft email for you. Third, MailMorph can automatically identify mails coming into your inbox with important events or dates and automatically add it to your calendar. Fourth, MailMorph can create a daily digest of your emails of that day and summarize it all for you, as well as highlighting the most important details. Last but not least, MailMorph can identify emails with pdf files which need to be filled, and auto fill them for you if there is sufficient information in the email itself.

## How we built it

For MailMorph, we combined the capabilities of Gemini AI and AutoGen AI Agents to build a powerful email and PDF automation solution. To integrate with Gmail and Google Calendar, we secured access to their APIs, set up user authentication, and configured credentials to extract the necessary data. Depending on the use case, we retrieved key information: for categorizing emails, we identified relevant details; for adding events to calendars, we extracted dates and times; and for summaries, we collected the full content. We ensured PII redaction before sending any data to the LLMs and used prompt engineering with Gemini AI to generate accurate outputs. On the backend, we structured individual APIs to handle different functionalities, allowing the frontend—built with React, Node.js, and Swift—to communicate seamlessly with the Python backend through API calls. For PDF automation, we leveraged AutoGen AI to process both fillable and scanned PDFs. With fillable PDFs, the agent identified fields and matched them with data from a JSON dataset before generating code to fill the PDF. For scanned PDFs, we incorporated OCR to detect fields and injected fillable inputs at the appropriate locations, following the same logic as the fillable PDFs. This architecture enabled us to efficiently manage emails, automate PDFs, and integrate everything into a smooth, user-friendly interface.

## Challenges we ran into

One of the biggest challenges was setting up access to the Gmail API and Google Calendar API, as it involved navigating OAuth authentication and configuring user credentials securely. Another hurdle was ensuring PII redaction before sending any sensitive information to the LLMs, which required careful preprocessing to avoid leaking personal data. Implementing OCR for scanned PDFs was also complex, especially when accurately detecting fields and injecting fillable elements into the correct locations. Additionally, integrating multiple technologies—React, Node.js, Swift, and Python—into a seamless frontend-backend system took considerable effort to ensure smooth communication between all components.

## Accomplishments that we're proud of

We are proud of the fact that we got most of our features to perform in less than one second. This is huge for users with very large inboxes since they won't have to wait copious amounts of time. Also, it was our first time integrating flask API endpoints with node.js front end so figuring out how the endpoints worked and integrating everything and seeing it all come together was definitely something we were proud about,

## What we learned

Throughout the development of MailMorph, we learned the importance of efficient API integration and data security when working with sensitive user information. We also gained hands-on experience in prompt engineering for LLMs to optimize outputs based on different tasks. Another key takeaway was learning how to leverage AI agents to automate complex tasks like processing PDFs and identifying contextual information in emails. Furthermore, working with multiple programming languages and frameworks helped us improve our collaboration and technical integration skills.

## What's next for MailMorph

For now MailMorph is limit to Gmail, so in the future we look forward to implementing in outlook, yahoo, and other email services. Also, we look forward to adding more functionalities such as auto flight check-in, better spam detection, voice activated email, etc. Also, we look forward to implementing a mobile application to make an even more convenient way to organize e-mail.
## Inspiration

Imagine a world where learning is as easy as having a conversation with a friend. Picture a tool that unlocks the treasure trove of educational content on YouTube, making it accessible to everyone, regardless of their background or expertise. This is exactly what our hackathon project brings to life.

* Current massive online courses are great resources to bridge the gap in educational inequality.
* Frustration and loss of motivation with the lengthy and tedious search for that 60-second content.
* Provide support to our students to unlock their potential.

## What it does

Think of our platform as your very own favorite personal tutor. Whenever a question arises during your video journey, don't hesitate to hit pause and ask away. Our chatbot is here to assist you, offering answers in plain, easy-to-understand language. Moreover, it can point you to external resources and suggest specific parts of the video for a quick review, along with relevant sections of the accompanying text. So, explore your curiosity with confidence – we've got your back!

* Analyze the entire video content 🤖 Learn with organized structure and high accuracy
* Generate concise, easy-to-follow conversations⏱️Say goodbye to wasted hours watching long videos
* Generate interactive quizzes and personalized questions 📚 Engaging and thought-provoking
* Summarize key takeaways, explanations, and discussions tailored to you 💡 Provides tailored support
* Accessible to anyone with an internet 🌐 Accessible and Convenient

## How we built it

Vite React,js as front-end and Flask as back-end. Using Cohere command-nightly AI and Similarity ranking.

## Challenges we ran into

* **Increased application efficiency by 98%:** Reduced the number of API calls lowering load time from 8.5 minutes to under 10 seconds. The challenge we ran into was not taking into account the time taken for every API call. Originally, our backend made over 500 calls to Cohere's API to embed text every time a transcript section was initiated and repeated when a new prompt was made -- API call took about one second and added 8.5 minutes in total. By reducing the number of API calls and using efficient practices we reduced time to under 10 seconds.
* **Handling over 5000-word single prompts:** Scraping longer YouTube transcripts efficiently was complex. We solved it by integrating YouTube APIs and third-party dependencies, enhancing speed and reliability. Also uploading multi-prompt conversation with large initial prompts to MongoDB were challenging. We optimized data transfer, maintaining a smooth user experience.

## Accomplishments that we're proud of

Created a practical full-stack application that I will use on my own time.

## What we learned

* **Front end:** State management with React, third-party dependencies, UI design.
* **Integration:** Scalable and efficient API calls.
* **Back end:** MongoDB, Langchain, Flask server, error handling, optimizing time complexity and using Cohere AI.

## What's next for ChicSplain

We envision ChicSplain to be more than just an AI-powered YouTube chatbot, we envision it to be a mentor, teacher, and guardian that will be no different in functionality and interaction from real-life educators and guidance but for anyone, anytime and anywhere.