## Inspiration

The inspiration for our auto-scheduling project emerged from the **increased need for automated and personalized recommendations** in travel planning tools. We recognized that multi-agent LLMs hold immense potential to tackle complex tasks such as **personalized scheduling**. This potential drove our team to want to explore how these models could offer solutions that can integrate into daily life problems. We were also inspired to create this app because of the tediousness of scheduling, and we recongized that our tool could save lots of time.

## Our Goal

We aim to simplify the process of planning trips and transport for important trips. We also want to introduce users to popular and fun locations along the way. DayGenie **automatically generates recommendations** based on user preferences and user schedule.

## How we built it (Structure of our model)

### Agent Workflow

DayGenie is constructed as a **decentralized multi-agent large language model**. We defined 5 LLM Agents (InfoAgent, MapAgent, RedditAgent, SummaryAgent, Feedback Agent), each with specific prompting for their purpose.

**User Input**: user preference(str), google calendar schedule(google calendar API call)

* **InfoAgent**: Fetches User Input, generate prompts for MapAgent and RedditAgent.
* **MapAgent**: Find the location of events happening in the calendar.
* **RedditAgent**: Call Reddit API, fetch and analyze reviews based on the important keyword input.
* **SummaryAgent**: Calculates the transportation(time, cost)/ recommendations based on locatison and preferences.
* **FeedbackAgent**: Get physical feedback from the user, if negative run model again/ else output is provided to the user.

**Model Output**: A list of personalized recommendations of Transportation, Restaurant/Cafe/Event places.

### Conversation construction

We used Fetch AI to facilitate conversations and communication between our agents. We designed each agent to solve their own specific tasks and then pass results onto other in our workflow.

### Website construction

Our website is constructed using Next.js and Tailwind CSS. We built an intuitive and attractive user interface using these tools. While we have not deployed the website yet, we plan on doing so in the future.

## Challenges we ran into

* **Number of Agents:** How many Agents should we define?
* **Reducing Hallucinations of Agents:** How do we handle hallucinations generated by large language models..?
* **Understanding Fetch AI** We had some issues understanding the structure of Fetch AI and how to use it most effectively.

## Accomplishments

During this Hackathon, the major accomplishment was successfully **generating conversations between the agents** we designed. By calling Google Calendar API, user input, Google Map API, and reddit API, we could successfully generate output recommendations for users.

Towards the end of the project, we were also able to **add a feedback agent**, which plays a crucial role in ensuring that users receive the exact information they are seeking from our application.

Finally, our team cooperation made all of this possible, which we could learn a lot from each other and have fun participating on this project.

## What's next for DayGenie

* **Feedback on website**: We did not have enough time to add the feedback sign on the frontend website. We will work further to generate getting the feedback from the users so that we can call the feedbackAgent in the website.
* **Speech to text generation**: DayGenie can only get input by text till now. The next step is to recognize speech from the user and input it as user preference.
* **Addition of ScoringAgent**: To ensure quality recommendations, we make a ScoringAgent to evaluate the generated output.
* **Fine-tune** the DayGenie model to foster better abilities.
## What it does

Lil' Learners is a fun new alternative to learning tools for students in grades ranging from kindergarten to early elementary school. Allow for Teachers to create classes for their students and take note of the learning, strengths and weaknesses of their students as well as allowing for teachers and parents to track the progress of students. Students are assigned classes based on what each of their teachers needs them to practice and are presented with a variety(in the future) of interactive and fun games that take the teachers notes and generates questions which would be presented through the form of games. Students gain points based on how many questions they get right while playing, and get incentive to keep playing and in turn studying by allowing them to own virtual islands that they can customize to their liking by buying cosmetic items with the points earned from studying.

## How we built it

Using OAuth and a MongoDB database, Lil' Learners is a Flask based web application that runs on a structural backbone that is the accounts and courses class hierarchy. We created classes and separated all the types of accounts and courses, and created functions that check for duplicate accounts through both username and email and automatically save accounts to the database or courses to teachers and students or even children to their parents upon instantiation. On the front end, Lil' learners makes use of flask, html and css to create a visually appealing and interactive GUI and web interface. Through the use

## Challenges we ran into

Some challenges were making auth0 work with our log in system that we developed, along with one of the biggest setbacks being with 3.js model that we wanted to create to show off the island that each student owns in an interactive and cool looking way, but despite working at it for several hours, the apis and similar documentation for displaying the 3d models in a flask and html environment seemed to be a lost cause.

## Accomplishments that we're proud of

We are super proud of Lil Learners because despite the various different types of softwares and new/old skills that needed to me learned and merged together for it to work, we managed to create something that we could show off and works to convey the proof of concept for our idea

## What we learned:

We learned a lot about the interactions between various different software and how to integrate them together. Through the process of making Lil' learners we had the opportunity to try out the data management and back end development, and general software development skills with MongoDB, OAuth and GoDaddy and learn how they work and interact with other elements in a web application.

## What's next for Lil' Learners

We are hoping to be able to expand Lil learner's capacities further such as finishing up the 3.js models, fully integrating the OAuth with our account systems, launching our web app onto our go daddy domain, creating a larger variety of games and also providing better visualizations for the statistics for students along with better employments of the points and adaptive learning systems.
## Inspiration

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I recently read Ben Sheridan's paper on Human Centered Artificial Intelligence where he argues that Ai is best used as a tool that accelerates humans rather than trying to *replace* them. We wanted to design a "super-tool" that meaningfully augmented a user's workday. We felt that current calendar apps are a messy and convoluted mess of grids flashing lights, alarms and events all vying for the user's attention. The chief design behind Line is simple, **your workday and time is linear so why shouldn't your calendar be linear?**. Now taking this base and augmenting it with *just the right amount* of Ai.

## What it does

You get a calendar that tells you about an upcoming lunch with a person at a restaurant, gives you some information about the restaurant along with links to reviews and directions that you can choose to view. No voice-text frustration, no generative clutter.

## How we built it

We used React Js for our frontend along with a docker image for certain backend tasks and hosting a small language model for on metal event summarization **you can self host this too for an off the cloud experience**, if provided the you.com API key is used to get up to date and accurate information via the smart search query.

## Challenges we ran into

We tackled a lot of challenges particularly around the interoperability of our tech stack particularly a potential multi-database system that allows users to choose what database they wanted to use we simply ran out of time to implement this so for our demo we stuck with a firebase implementation , we also wanted to ensure that the option to host your own docker image to run some of the backend functions was present and as a result a lot of time was put into making both an appealing front and backend.

## Accomplishments that we're proud of

We're really happy to have been able to use the powerful you.com smart and research search APIs to obtain precise data! Currently even voice assistants like Siri or Google use a generative approach and if quizzed on subjects that are out of their domain of knowledge they are likely to just make things up (including reviews and addresses), which could be super annoying on a busy workday and we're glad that we've avoided this pitfall, we're also really happy at how transparent our tech stack is leaving the door open for the open source community to assist in improving our product!

## What we learned

We learnt a lot over the course of two days, everything from RAG technology to Dockerization, Huggingface spaces, react js, python and so much more!

## What's next for Line Calendar

Improvements to the UI, ability to swap out databases, connections via the google calendar API and notion APIs to import and transform calendars from other software. Better context awareness for the you.com integration. Better backend support to allow organizations to deploy and scale on their own hardware.