How I Create AI Demo Videos for My Hackathon Projects

ration. Microsoft Edge TTS to turn the script into spoken audio. Other AI tools to help create supporting visuals, diagrams, subtitles, or scene ideas. Screen recording and video editing tools to show the real project and assemble the final video.

The goal is not to make an AI-generated video pretend to be the I run a language model locally and serve it through vLLM. I can send I use the model to: Turn rough project notes into a structured video script. Explain technical features in simpler language. Write an opening hook and a concise conclusion.

Break the narration into scenes. Refine wording so the voiceover sounds natural when spoken aloud. vLLM provides an OpenAI-compatible API, which makes it convenient to Documentation: vLLM documentation A practical note: local inference is not automatically free. You After the script is ready, I pass the narration text to Microsoft Edge to generate speech.

The Python package edge-tts makes Why I use it: I can generate narration without recording my own voice. I can choose from available voices and languages. I can regenerate a section after changing the script. I can save the output and use it in my video editor.

Important: edge-tts is an unofficial interface to Microsoft's I can use other AI tools where they genuinely help---for example, to I keep the actual product walkthrough authentic: screenshots and screen I start with the material I already have: Project name and one-line summary. Problem being solved. Target users. Main features.

Technology stack. A short list of actions to demonstrate. The result or impact I want viewers to remember. The more concrete the input, the less generic the script will be.

I ask the local LLM to structure the video into a few clear sections: Hook: introduce the problem or surprising idea. Problem: explain why the problem matters. Solution: introduce the project. Live demo: show the main user journey.

Technology: briefly explain the architecture. Closing: summarize the value and next steps. I review the script before generating audio. AI-generated claims should I send the approved narration to Edge TTS and save the resulting audio I keep the script divided into scenes rather than generating one huge I record the application running and capture the most important A useful demo usually shows: The starting screen.

One complete user journey. The most distinctive feature. The result produced by the application. A brief view of the architecture, if it helps explain the project.

I avoid long loading screens, unnecessary cursor movement, and repeated I combine the screen recording, narration, and supporting assets in a I also check that the voiceover matches the action on screen. A polished The following diagram shows the content-production flow. The project flowchart TD A[Project notes, features, and tech stack] --> B[Local LLM served by vLLM] B --> C[Scene-by-scene demo script] C --> D[Review and edit script] D --> E[Microsoft Edge TTS] E --> F[Voiceover audio] G[Real application] --> H[Screen recording] I[Other AI tools] --> J[Supporting visuals and assets] F --> K[Video editor] H --> K J --> K K --> L[Final hackathon demo video] Reusable: I can follow the same process for different projects. Flexible: I can change the model, voice, visuals, or editor independently.

More control: I can run the LLM locally instead of sending every script prompt to a hosted LLM API. Faster iteration: I can regenerate only the narration section that changed. Clearer storytelling: a structured script helps keep the demo focused on the problem and the product. There are trade-offs.

vLLM needs compatible hardware and setup. Edge TTS There is room to take this workflow further: Generate a first-draft script from a repository README. Turn the script into a scene list with suggested screen actions. Generate subtitles automatically.

Match narration sections to recorded clips. Use FFmpeg to automate repetitive editing tasks. Export versions with different lengths for a presentation, social post, or project page. The long-term idea is a pipeline where project information and a screen A hackathon project deserves a demo that makes its value easy to The important part is the balance: use AI to speed up scripting and Build the project.

Tell the story. Show the proof. vLLM documentation Microsoft Edge TTS Python package (edge-tts) FFmpeg documentation