## Inspiration

The inspiration for T-Error came from the common frustration that tech leads and developers face when debugging problems. Errors can occur frequently, but understanding their patterns and seeing what is really holding your team up can be tough. We wanted to create something that captures these errors in real time, visualizes them, and lets you write and seamlessly integrate documentation making it easier for teams to build faster.

## What it does

T-Error is a terminal error-monitoring tool that captures and logs errors as developers run commands. It aggregates error data in real-time from various client terminals and provides a frontend dashboard to visualize error frequencies and insights, as well as adding the option to seamlessly add documentation. A feature we are really excited about is the ability to automatically run the commands in the documentation without needing to leave the terminal.

## How we built it

We built T-Error using:
Custom shell: we implemented a custom shell in c++ to capture stderr and seamlessly interface with our backend.
Backend: Powered by Node.js, the server collects, processes, and stores error data in mongoDB.
Frontend: Developed with React.js, the dashboard visualizes error trends with interactive charts, graphs, and logs, as well as an embedded markdown editor:).

## Challenges we ran into

One of the main challenges was ensuring the terminal wrappers were lightweight and didn’t disrupt normal command execution while effectively capturing errors. We spent hours trying to get bash scripts to do what we wanted, until we gave up and tried implementing a shell which worked much better. Additionally, coming up with the UX for how to best deliver existing documentation was a challenge but after some attempts, we arrived at a solution we were happy with.

## Accomplishments that we're proud of

We’re proud of building a fully functional MVP that successfully captures and visualizes error data in real-time. Our terminal wrappers integrate seamlessly with existing workflows, and the error analysis and automatic documentation execution has the potential to significantly speed up development.

## What we learned

Throughout this project, we learned about the complexities of error logging across multiple environments and how to efficiently process large volumes of real-time data. We also gained experience with the integration of frontend and backend technologies, as well as diving into the lower layers of the tech stack and smoothly chaining everything together.

## What's next for T-Error

Going forward, there are a few features that we want to implement. First is error reproduction - we could potentially gain more context about the error from the file system and previous commands and use that context to help replicate errors automatically. We also wanted to automate the process of solving these errors - as helpful as it is to have engineers write documentation, there is a reason there are gaps. This could be done using an intelligent agent for simple tasks, and more complex systems for others. We also want to be able to accommodate better to teams, allowing them to have groups where internal errors are tracked.
## Inspiration

Data analytics can be **extremely** time-consuming. We strove to create a tool utilizing modern AI technology to generate analysis such as trend recognition on user-uploaded datasets.The inspiration behind our product stemmed from the growing complexity and volume of data in today's digital age. As businesses and organizations grapple with increasingly massive datasets, the need for efficient, accurate, and rapid data analysis became evident. We even saw this within one of our sponsor's work, CapitalOne, in which they have volumes of financial transaction data, which is very difficult to manually, or even programmatically parse.

We recognized the frustration many professionals faced when dealing with cumbersome manual data analysis processes. By combining **advanced machine learning algorithms** with **user-friendly design**, we aimed to empower users from various domains to effortlessly extract valuable insights from their data.

## What it does

On our website, a user can upload their data, generally in the form of a .csv file, which will then be sent to our backend processes. These backend processes utilize Docker and MLBot to train a LLM which performs the proper data analyses.

## How we built it

Front-end was very simple. We created the platform using Next.js and React.js and hosted on Vercel.

The back-end was created using Python, in which we employed use of technologies such as Docker and MLBot to perform data analyses as well as return charts, which were then processed on the front-end using ApexCharts.js.

## Challenges we ran into

* It was some of our first times working in live time with multiple people on the same project. This advanced our understand of how Git's features worked.
* There was difficulty getting the Docker server to be publicly available to our front-end, since we had our server locally hosted on the back-end.
* Even once it was publicly available, it was difficult to figure out how to actually connect it to the front-end.

## Accomplishments that we're proud of

* We were able to create a full-fledged, functional product within the allotted time we were given.
* We utilized our knowledge of how APIs worked to incorporate multiple of them into our project.
* We worked positively as a team even though we had not met each other before.

## What we learned

* Learning how to incorporate multiple APIs into one product with Next.
* Learned a new tech-stack
* Learned how to work simultaneously on the same product with multiple people.

## What's next for DataDaddy

### Short Term

* Add a more diverse applicability to different types of datasets and statistical analyses.
* Add more compatibility with SQL/NoSQL commands from Natural Language.
* Attend more hackathons :)
### Long Term
* Minimize the amount of work workers need to do for their data analyses, almost creating a pipeline from data to results.
* Have the product be able to interpret what type of data it has (e.g. financial, physical, etc.) to perform the most appropriate analyses.
## Inspiration

At the University of Toronto, accessibility services are always in demand of more volunteer note-takers for students who are unable to attend classes. Video lectures are not always available and most profs either don't post notes, or post very imprecise, or none-detailed notes. Without a doubt, the best way for students to learn is to attend in person, but what is the next best option? That is the problem we tried to tackle this weekend, with notepal. Other applications include large scale presentations such as corporate meetings, or use for regular students who learn better through visuals and audio rather than note-taking, etc.

## What it does

notepal is an automated note taking assistant that uses both computer vision as well as Speech-To-Text NLP to generate nicely typed LaTeX documents. We made a built-in file management system and everything syncs with the cloud upon command. We hope to provide users with a smooth, integrated experience that lasts from the moment they start notepal to the moment they see their notes on the cloud.

## Accomplishments that we're proud of

Being able to integrate so many different services, APIs, and command-line SDKs was the toughest part, but also the part we tackled really well. This was the hardest project in terms of the number of services/tools we had to integrate, but a rewarding one nevertheless.

## What's Next

* Better command/cue system to avoid having to use direct commands each time the "board" refreshes.
* Create our own word editor system so the user can easily edit the document, then export and share with friends.

## See For Your Self

Primary: <https://note-pal.com>
Backup: <https://danielkooeun.lib.id/notepal-api@dev/>