How I Create and Code AI Startup Ideas in 24 hours - OpenAI
TLDRIn this video, the creator demonstrates the process of developing an AI-powered startup within 24 hours. Starting with brainstorming ideas, the creator explores various concepts such as a Chrome extension for auto-completion, a documentation search startup, and an image processing AI. After considering the competition in the AI space, the creator pivots to a personal problem: difficulty finding specific information in long tutorial videos. The solution involves using the YouTube API to download video transcripts and integrating them with a chatbot powered by GPT to provide answers. The creator successfully connects to the YouTube API, retrieves transcripts, and uses them in conjunction with the GPT API to answer questions about the video content. The project is further developed by storing video data in a vector database, Astra DB, and creating a web interface for user interaction. The final product is a functional MVP that allows users to input YouTube URLs, retrieve detailed video information, and ask questions about the video content using chat GPT, providing a unique and engaging user experience.
Takeaways
- 🚀 The speaker aims to build an AI business in just 24 hours to demonstrate the feasibility of rapid development.
- 💡 Initial ideas included a Chrome extension for auto-completion and an AI chatbot for coding documentation, but these were dropped due to market saturation.
- 🔍 The pivot came from a personal problem of searching through long tutorial videos, leading to the idea of using AI to parse video transcripts.
- 📚 The YouTube API was utilized to download video transcripts, which posed some initial technical challenges.
- 🔗 Connecting the YouTube API directly to an AI model like Chat GPT was explored as a potential solution.
- 💻 Various coding and API integration steps were taken, including using a YouTube caption scraper and dealing with errors.
- 📈 The successful use of the 'YouTube transcripts' library provided a full list of transcripts with timestamps.
- 🗃️ A vector database, Astra DB, was chosen for storing and managing the video transcripts and data.
- 🛠️ A boilerplate template from Astra DB was adapted to fit the project's needs, including refactoring and creating a model for video data.
- 🌐 A simple web interface was developed to interact with the backend, allowing users to input YouTube URLs and receive information about the video content.
- 🔑 Limitations were acknowledged, such as the inability to process very long video transcripts in a single AI message.
Q & A
What was the initial idea for the AI startup?
-The initial idea was to create a Chrome extension that uses AI for auto-completion in text fields.
Why was the Chrome extension idea not pursued?
-The idea was not pursued because large companies like Grammarly already cover the space of auto-completion in text fields.
What was the second idea that the creator considered?
-The second idea was to create a startup that searches through documentation of popular libraries and languages and uses AI as a chatbot to provide answers from that documentation.
What was the main challenge in developing the AI startup within 24 hours?
-The main challenge was finding a unique and unoccupied niche in the AI space that was not already covered by large companies.
How did the creator decide to pivot the idea?
-The creator decided to pivot by recalling past problems they faced, specifically the difficulty in searching for specific information in long tutorial videos.
What was the final idea that the creator implemented?
-The final idea was to use the YouTube API to download video transcripts and then use a chatbot like GPT to find answers within those transcripts.
How did the creator overcome the initial difficulties with the YouTube Captions API?
-The creator overcame the initial difficulties by using a different library called 'YouTube Transcripts' which provided a working solution for downloading transcripts with timestamps and durations.
What is the role of the vector database in this project?
-The vector database, specifically Astra DB, is used to store and manage the video transcripts and vectors generated from them, allowing for efficient search and retrieval of information.
What was the main limitation encountered when integrating with the chat GPT?
-The main limitation was that if the video was a few hours long, the entire transcript might not fit into a single chat GPT message, requiring a solution to split the transcript into sections.
How does the final project interface allow users to interact with the AI?
-The final project interface allows users to input a YouTube URL, which then retrieves details and stores them in the Astra DB. Users can ask questions about the video, and the system uses the transcript to provide answers via chat GPT.
What was the main goal of the project?
-The main goal of the project was to create a Minimum Viable Product (MVP) that could download YouTube video transcripts, store them in a database, and use AI to answer questions about the video content.
What is the future direction for the project?
-The future direction includes improving the transcript handling for long videos by splitting them into sections and enhancing the user interface for a better user experience.
Outlines
🚀 Building an AI Business in 24 Hours
The speaker embarks on a challenge to create an AI business within a day. Initially, they brainstorm ideas, considering a Chrome extension for autocomplete and a chatbot for programming documentation, but these are dismissed due to market saturation. They then recall a personal problem of finding specific information in long videos and decide to use the YouTube API to download video transcripts. The transcripts are to be integrated with a database that a chatbot, powered by GPT, can query to provide answers. After overcoming initial technical hurdles, they successfully retrieve transcripts and use them to query GPT, receiving accurate responses. The speaker then plans to store this information in a vector database, specifically choosing Astra DB for its recent addition of vector databases.
🔍 Creating a Vector Database for Video Transcripts
The speaker explores how to use Astra DB with vector databases by watching a tutorial by Anna. They create a new Astra database called 'YouTube Transcripts' and set up the necessary configurations. Facing the challenge of integrating different components, they find a boilerplate template provided by Astra DB and adapt it for their project. The speaker refactors the code, creating a model in MongoDB for storing video details and transcripts. They ensure that the database stores only the necessary information and generates embeddings using OpenAI. A simple web interface is developed to interact with the backend, allowing users to input YouTube URLs and receive information about the video content through questions posed to the chatbot. The front-end uses Tailwind CSS and JavaScript to render the UI and communicate with the backend. The project concludes with a minimum viable product that allows users to query video transcripts using chatbot technology.
Mindmap
Keywords
💡AI
💡Chrome Extension
💡Startup
💡YouTube API
💡Transcript
💡Chat GPT
💡Vector Database
💡API Key
💡MVP (Minimum Viable Product)
💡Astra DB
💡Tailwind CSS
Highlights
The speaker attempts to build an AI business in just 24 hours to demonstrate the feasibility of rapid development.
Initially, the idea was to create a Chrome extension with AI for auto-completion in text fields, but similar solutions by Grammarly were already available.
A second idea involved creating a startup that uses AI as a chatbot to provide answers from the documentation of popular libraries and languages.
The third idea was to use AI for image processing, but this space is well covered by companies like Mid Journey and Adobe.
The speaker decided to pivot and change the idea after considering the competition in the AI space.
A past problem of searching for specific content in a long video tutorial inspired the next idea.
The speaker thought of using the YouTube API to download video transcripts and then using a chatbot to find answers within those transcripts.
The YouTube captions API was initially problematic, but the speaker found a solution by directly connecting to the YouTube API.
The YouTube Transcripts library was used to successfully download a full list of the transcript with timestamps and durations.
The speaker integrated the transcript with OpenAI's chat GPT to answer questions about the video content.
The speaker faced challenges in building a database to store and communicate with the video transcripts and decided to use a vector database.
Astra DB was chosen for its recent introduction of Vector databases and its compatibility with large language models.
The speaker created a simple web user interface to interact with the backend and retrieve information from the database.
The project's front end was built using Tailwind CSS and JavaScript to render UI based on messages from the backend.
The backend was designed to handle video URL inputs, communicate with Astra DB and chat GPT, and update the front end accordingly.
The final result is a working project that can download YouTube transcripts, store them in a database, and interact with chat GPT to answer questions about the video content.
The speaker acknowledges limitations, such as the inability to process very long video transcripts in a single chat GPT message.
The project serves as a minimum viable product (MVP) that demonstrates the potential of combining AI with databases and user interfaces.
Astra DB is thanked for sponsoring the video, highlighting the importance of such tools in enabling the development of innovative projects.