Project Info

How To ANYTHING

This project did not submit a demo video on Devpost.

Inspiration

We recognized the overwhelming amount of disparate information scattered across the web. This made it hard for individuals to find concise, reliable instructions for a wide array of tasks. "How To ANYTHING" was born out of the desire to streamline this process, turning any query into a detailed guide.

What it does

"How To ANYTHING" transforms user queries into step-by-step guides akin to WikiHow articles. By leveraging advanced text and image generation techniques, our platform creates uniquely tailored instructions with associated images, ensuring that even the most intricate tasks become straightforward.

How we built it

The foundation of our platform lies in the integration of state-of-the-art text generation and image synthesis technologies. We utilize LLMs for generating detailed, accurate textual content, while sophisticated algorithms create corresponding images that enhance comprehension. We used Together AI, Aleo, zillisDB, and HUME AI The system is built on the Next.js / FastAPI framework, ensuring a seamless and responsive user experience.

Challenges we ran into

Token Limitations: When we initially built our "How to Anything" tool, we faced significant challenges due to token limitations in our text-to-image model. Many complex tasks and detailed instructions required a large number of tokens to convey accurately. This limitation hampered our ability to generate comprehensive and detailed step-by-step instructions, which was a roadblock for providing valuable user experiences. Transition to Together AI: To address the token limitations and other challenges, we made the strategic decision to transition to Together AI. While this transition was beneficial in many ways, it also presented its own set of challenges, such as adapting our existing infrastructure and workflows to a new platform.

Accomplishments we're proud of

Achieving a seamless blend of generated text and images that genuinely aid users in understanding complex tasks is our hallmark. The intuitive interface and the reliability of our guides have garnered positive early feedback, reinforcing our belief in the platform's potential.

What we learned

The project was a deep dive into the intricacies of text and image generation technologies. We learned about different LLMs and Stable Diffusion models. We learned how to deploy a backend and a frontend. We learned about prompt engineering, tokenization and token count, and much more!

What's next

Our vision is to continually refine and expand the range of topics available. We're exploring the integration of videos and interactive elements, and we're also looking into fostering a community where users can contribute, modify, and rate guides, making "How To ANYTHING" a collaborative knowledge hub powered by Generative AI.

This is a Next.js project bootstrapped with create-next-app.

Getting Started

First, run the development server:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Open http://localhost:3000 with your browser to see the result.

You can start editing the page by modifying app/page.js. The page auto-updates as you edit the file.

This project uses next/font to automatically optimize and load Inter, a custom Google Font.

Learn More

To learn more about Next.js, take a look at the following resources:

Next.js Documentation - learn about Next.js features and API.
Learn Next.js - an interactive Next.js tutorial.

You can check out the Next.js GitHub repository - your feedback and contributions are welcome!

Deploy on Vercel

The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.

Check out our Next.js deployment documentation for more details.

Analysis

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Metric

JayYeung
9
Nathan Shum
2

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only

CSSIn code
HTMLIn code
JavaScriptIn code
Next.jsIn code
OpenAIIn code
PythonIn code
ReactIn code
Tailwind CSSIn code
TypeScriptIn code
FastAPIClaimed

9 of 10 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

28 KB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.