How I Create and Code AI Startup Ideas in 24 hours - OpenAI

Today, I'm excited to share my journey of building an AI business in just 24 hours! My goal was to demonstrate what’s possible when you put your mind to it, so I dove straight in. The very first step for me was brainstorming ideas and sketching them down. Here's how my creative process unfolded:

• Idea #1: A Chrome Extension - I considered using AI for autocomplete features in text fields; however, I quickly realized that established companies like Grammarly have cornered that market.
• Idea #2: Documentation Chatbot - I thought about creating a startup that would leverage AI to search through documentation of popular libraries and languages. Imagine a chatbot that provides answers based on that documentation!
• Idea #3: Image Processing - I then explored using AI for image processing, but again, big players like Midjourney and Adobe are already dominating this space.

With so much competition in AI, it was clear I needed to pivot. Startup culture is about failing fast and adapting quickly, so I remembered a frustration I had just a few weeks ago.

While working through a lengthy tutorial on FreeCodeCamp, I got stuck. I realized the solution could lie in efficiently searching for specific information within a video transcript. So, I thought: what if I could use the YouTube API to download the transcript of a video? I could then use GPT to help find answers within that transcript!

I had previously encountered issues with the YouTube Captions API, but I was determined to explore again. After some troubleshooting, I was able to use a script called YouTube Caption Scraper, but it didn’t always work reliably. So, I turned my attention to a new tool: the YouTube Transcripts API, which successfully fetched the transcript along with timestamps.

Feeling energized, I connected this to OpenAI's API. I used the transcripts to formulate questions; for example, “What is this video about?” To my relief, GPT provided accurate answers, validating my idea's potential. I also tested follow-up questions, like “What’s the biggest takeaway from this video for web designers?” The responses were insightful, even pointing out elements the video lacked.

With the core components of my project working, I knew I needed to store the data effectively. A vector database was essential for processing larger AI models. After researching, I chose Astra DB because they recently introduced vector databases, which are perfect for my use case.

After creating a free account on DataStax, I set up my new Astra database named YouTube Transcripts. I used a boilerplate project from Astra that seamlessly integrated a JSON API with Mongoose, tailored to movies. I modified it to fit my YouTube transcripts project.

Building the Core Features

I merged my local project with Astra’s template, ensuring each function had a specific role for clarity and maintainability. I created a model in Mongoose to capture video titles, descriptions, URLs, transcripts, and vectors.

Next, I wrote a script to collect video URLs and their unique IDs, but initially encountered a frustrating issue where no data was saved. The original template dropped collections when re-running the app, which was problematic. After commenting out that line, my videos started saving correctly to the database.

In no time, I had a functioning API! I could store basic video details and additional metadata like thumbnails and video lengths, but I focused on the essentials.

Creating a User Interface

With the backend solid, I moved on to designing a simple web user interface using Tailwind CSS. The interface allows users to input a YouTube URL and retrieves corresponding details from the Astra database.

Here’s how it works:

1. Input a YouTube URL.
2. The system pulls the details from the Astra database.
3. The transcript allows for questions, like “What’s this video about?”

For instance, when I asked about a video on deploying a Next.js project, I learned that it wasn’t actually a tutorial, much to my surprise!

By integrating chat GPT into the system, users can now chat about the transcripts of videos directly through the interface. While it’s functioning well, larger video transcripts may exceed GPT’s input limits, so I plan to enhance it by breaking transcripts into manageable sections for more effective querying.

Conclusion

And there you have it! In just 24 hours, I successfully built an MVP that functions as intended. If you're interested in exploring this project further, you can find it on GitHub.

A big thank you to Astra DB for sponsoring this video! Their support makes projects like this possible. Be sure to check them out through the link below for great database solutions!

If you're ready to build your own startup, sign up for Astra vector database and get free credits to kickstart your idea!