## Inspiration

We love spending time playing role based games as well as chatting with AI, so we figured a great app idea would be to combine the two.

## What it does

Creates a fun and interactive AI powered story game where you control the story and the AI continues it for as long as you want to play. If you ever don't like where the story is going, simply double click the last point you want to travel back to and restart from there! (Just like in Groundhog Day)

## How we built it

We used Reflex as the full-stack Python framework to develop an aesthetic frontend as well as a robust backend. We implemented 2 of TogetherAI's models to add the main functionality of our web application.

## Challenges we ran into

From the beginning, we were unsure of the best tech stack to use since it was most members' first hackathon. After settling on using Reflex, there were various bugs that we were able to resolve by collaborating with the Reflex co-founder and employee on site.

## Accomplishments that we're proud of

All our members are inexperienced in UI/UX and frontend design, especially when using an unfamiliar framework. However, we were able to figure it out by reading the documentation and peer programming. We were also proud of optimizing all our background processes by using Reflex's asynchronous background tasks, which sped up our website API calls and overall created a much better user experience.

## What we learned

We learned an entirely new but very interesting tech stack, since we had never even heard of using Python as a frontend language. We also learned about the value and struggles that go into creating a user friendly web app we were happy with in such a short amount of time.

## What's next for Groundhog

More features are in planning, such as allowing multiple users to connect across the internet and roleplay on a single story as different characters. We hope to continue optimizing the speeds of our background processes in order to make the user experience seamless.
## Inspiration

As the imperative to reduce carbon emissions intensifies, businesses are increasingly adopting innovative approaches to shrink their carbon footprint. One such approach, carbon offsetting, has grown into a [$5.5 billion industry](https://journals.library.columbia.edu/index.php/cjel/article/view/10442), garnering support from both fossil fuel companies and environmental advocates alike. Rather than curbing emissions directly at their source, carbon offsets mitigate carbon pollution by either preventing potential emissions or directly extracting carbon dioxide from the atmosphere, often employing technologies such as carbon capture and storage (CCS) or direct air capture (DAC). Presently, numerous critical sectors—such as the aviation industry—face challenges in achieving decarbonization due to financial constraints, material limitations, and a lack of innovative solutions. **As such, the combination of decarbonization strategies and carbon offsetting is often the most economical; by reducing easily avoidable emissions and offsetting the rest, companies can achieve net-zero emissions without the financial pressures of complete emissions reductions.**

However, there are three existing barriers to achieving this diversified net-zero strategy. First, reducing emissions is often a lengthy process, often taking years to decarbonize key components of a company’s business model fully. Second, reducing low-cost emissions is dependent on the equilibrium price of carbon offset—a price that often needs to be calculated far in advance to successfully execute decarbonization. Third, the financial tradeoff between the price of offsets and emissions reductions is blurry, with no clear brightline between offset and reduced emissions.

## What does our project do?

Our product solves these issues by providing key insights about decarbonization strategy through forward pricing curves for carbon offsets. By calculating the expected demand and supply of carbon offsets for a given time in the future, the market equilibrium—the going price of carbon offsets at that time—can be estimated and given to the user in the present. Our results are displayed on a website dashboard.

Our project contains two central components: the market price of carbon credits over time, based on calculated demand and supply curves, and a carbon-zero pathway based on these curves (based on user inputs and other external data). The demand and supply curves are generated for each year until the user’s target year, this allows them to see how the market changes year-by-year, allowing them to optimize their offset buying distribution over a certain period. The carbon-zero pathway allows

After inputting information about a company’s carbon footprint, emissions targets, and the marginal cost of abatement, financial metrics about the best carbon-zero path are computed. The output of this function relies on market equilibrium outputs only available after computing the forward price curves. The output gives the user valuable information about the percentage breakdown between decarbonized emission reductions and credit offsets, and the financial projections of both strategies.

This tool, importantly, allows companies to see how the market price of carbon credits changes over time based on user-given inputs such as the type of carbon removal technology, the year of their net-zero goal, and the scope of emissions they are considering. This tool emphasizes the importance of decarbonization by reorienting the goal of maximizing profits towards environmental advocacy. This is key in assisting companies plan their emissions-reduction strategy in the most economical way—two priorities that have often been at odds with each other.

## How we built our project

We built all of our projection models in Python and used the Reflex framework to display our outputs in a user dashboard. Each of the three system functions—the demand curve, supply curve, and decarbonization-offset tradeoff scheme—are integrated into the user dashboard, with various inputs related to the computation of the base futures curve model and tradeoff model being imputed as fields. Each of the three system functions are described below:

The demand curve uses the emissions profiles of the top 2,000 largest companies in the world to project their future carbon emissions based on their emissions reduction goals. Using generated GICS sector data for industry-specific emissions—combined with company revenue—we project yearly emissions—including scope 1, 2, and 3 emissions—depending on carbon reduction goals. Using an exponential Pareto distribution—tuned to represent companies buying offsets closer to their consumption date—we generalize the year-to-year demand for carbon permits on a per-company basis. This data returns the expected demand for carbon credits per year for a theoretical global market. Using this data, combined with decarbonization prices generalized from half-normal, sector-level decarbonization costs, our model produces the expected carbon demand for a given period for each company. As each company faces a different cost of decarbonizing their business, the marginal benefit of each company is unique. We fit a demand curve to these projected quantity and price demands from individual companies using polynomial regressions from this data. This process is done for each year in the model.

The supply curve is interpolated from the technology pathway of the carbon credit and price range. Based on our research, most companies pay anywhere from $50/ton CO2 to $700/ton CO2 for a range of carbon credits. This data is combined with the cost of implementing the selected pathway for a single ton of CO2 and the ranking of the industries based on their relative CO2 emissions. A coefficient is calculated on these values that inform the supply curve on the scale of 50$/tCO2 to 700$/tCO2. We create a supply curve for each year until the user-specified target year with the values being adjusted for the following years accordingly.

The decarbonization-offset tradeoff graph is produced by mapping another exponential Pareto distribution to the marginal cost of reducing carbon emissions. The resulting intersection of the market price of carbon offsets for a given year with the CDF returns the percentage of total emissions for which it is more economical to reduce the production of emissions over buying offsets, and vice versa. The total cost associated with purchasing carbon offsets, alongside the quantity, can be calculated for a given company’s carbon footprint.

## Challenges we ran into

The biggest challenge we ran into was the lack of data for building the supply curves. One way in which we handled this was by taking data corresponding to the maximum and minimum prices, as well as the range of carbon credits companies were buying in bulk. We used these to generate a pseudo-supply curve that would allow us to reasonably estimate the curve as if we had the data. We expect this problem to be solved as more data becomes available.

## What we learned and accomplished

We learned a lot about time-series analysis, data analysis, and synthesizing, as well as how to use Reflex to implement our website. We made a working model and website on a project that aimed to encapsulate the entire carbon credits market for the next few decades.

## What's next for CarbonInsights

Short Term: The biggest thing we would like to change is to make the website dynamically update. Although the models we wrote *are perfectly suited to update dynamically*, the Reflex framework we are using has made that difficult.

Long Term: Improve the statistical models to create the projection for the supply-demand curves and use ARIMA, ETS, and ML models like Random Forests to validate and provide a more detailed analysis of the trends.
## Inspiration

**The Tales of Detective Toasty** draws deep inspiration from visual novels like **Persona** and **Ghost Trick** and we wanted to play homage to our childhood games through the fusion of art, music, narrative, and technology. Our goal was to explore the possibilities of AI within game development. We used AI to create detailed character sprites, immersive backgrounds, and engaging slide art. This approach allows players to engage deeply with the game's characters, navigating through dialogues and piecing together clues in a captivating murder mystery that feels personal and expansive. By enriching the narrative in this way, we invite players into Detective Toasty’s charming yet suspense-filled world.

## What It Does

In **The Tales of Detective Toasty**, players step into the shoes of the famous detective Toasty, trapped on a boat with four suspects in a gripping AI-powered murder mystery. The game challenges you to investigate suspects, explore various rooms, and piece together the story through your interactions. Your AI-powered assistant enhances these interactions by providing dynamic dialogue, ensuring that each playthrough is unique. We aim to expand the game with more chapters and further develop inventory systems and crime scene investigations.

## How We Built It

Our project was crafted using **Ren'py**, a Python-based visual novel engine, and Python. We wrote our scripts from scratch, given Ren'py’s niche adoption. Integration of the ChatGPT API allowed us to develop a custom AI assistant that adapts dialogues based on player's question, enhancing the storytelling as it is trained on the world of Detective Toasty. Visual elements were created using Dall-E and refined with Procreate, while Superimpose helped in adding transparent backgrounds to sprites. The auditory landscape was enriched with music and effects sourced from YouTube, and the UI was designed with Canva.

## Challenges We Ran Into

Our main challenge was adjusting the ChatGPT prompts to ensure the AI-generated dialogues fit seamlessly within our narrative, maintaining consistency and advancing the plot effectively. Being our first hackathon, we also faced a steep learning curve with tools like ChatGPT and other OpenAI utilities and learning about the functionalities of Ren'Py and debugging. We struggled with learning character design transitions and refining our artwork, teaching us valuable lessons through trial and error. Furthermore, we had difficulties with character placement, sizing, and overall UI so we had to learn all the components on how to solve this issue and learn an entirely new framework from scratch.

## Accomplishments That We’re Proud Of

Participating in our first hackathon and pushing the boundaries of interactive storytelling has been rewarding. We are proud of our teamwork and the gameplay experience we've managed to create, and we're excited about the future of our game development journey.

## What We Learned

This project sharpened our skills in game development under tight deadlines and understanding of the balance required between storytelling and coding in game design. It also improved our collaborative abilities within a team setting.

## What’s Next for The Tales of Detective Toasty

Looking ahead, we plan to make the gameplay experience better by introducing more complex story arcs, deeper AI interactions, and advanced game mechanics to enhance the unpredictability and engagement of the mystery. Planned features include:

* **Dynamic Inventory System**: An inventory that updates with both scripted and AI-generated evidence.
* **Interactive GPT for Character Dialogues**: Enhancing character interactions with AI support to foster a unique and dynamic player experience.
* **Expanded Storyline**: Introducing additional layers and mysteries to the game to deepen the narrative and player involvement.
* *and more...* :D