# How I Create AI Demo Videos for My Hackathon Projects

> Source: <https://dev.to/sumit0rn/how-i-create-ai-demo-videos-for-my-hackathon-projects-33l0>
> Published: 2026-11-14 12:39:17+00:00

*Using a local LLM with vLLM, Microsoft Edge TTS, and AI tools to turn project demos into clear, engaging videos.*

Building a hackathon project is only half the work. You also need to

explain the problem, show the product working, and make the value

obvious to judges.

I wanted a repeatable way to create narrated demo videos without

depending entirely on expensive cloud-based LLM APIs or recording every

voiceover myself.

My workflow combines:

The goal is not to make an AI-generated video pretend to be the

product. The goal is to explain the real product clearly, using AI to

speed up production.

I run a language model locally and serve it through **vLLM**. I can send

it the project description, feature list, technical stack, and key user

flows.

I use the model to:

vLLM provides an OpenAI-compatible API, which makes it convenient to

connect to scripts and applications that already support that API

format.

Documentation: [vLLM documentation](https://docs.vllm.ai/)

**A practical note:** local inference is not automatically free. You

still need suitable hardware, and performance depends on the model,

quantization, context length, and available memory.

After the script is ready, I pass the narration text to **Microsoft Edge TTS** to generate speech.

The Python package [`edge-tts`](https://github.com/rany2/edge-tts) makes

it straightforward to generate audio with available Microsoft neural

voices.

Why I use it:

**Important:** `edge-tts` is an unofficial interface to Microsoft's

online text-to-speech service. It is not a fully local TTS engine, so

the narration step requires an internet connection and is subject to the

service's terms and availability.

I can use other AI tools where they genuinely help---for example, to

create a visual concept, simplify a diagram, suggest scene transitions,

or generate subtitles.

I keep the actual product walkthrough authentic: screenshots and screen

recordings should show the real application, not invented UI.

I start with the material I already have:

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:

I review the script before generating audio. AI-generated claims should

be checked against what the project actually does.

I send the approved narration to Edge TTS and save the resulting audio

file.

I keep the script divided into scenes rather than generating one huge

audio file. This makes it easier to replace a sentence, adjust pacing,

or rearrange the video later.

I record the application running and capture the most important

workflows.

A useful demo usually shows:

I avoid long loading screens, unnecessary cursor movement, and repeated

actions.

I combine the screen recording, narration, and supporting assets in a

video editor. I add captions, labels, transitions, and background music

only where they improve clarity.

I also check that the voiceover matches the action on screen. A polished

video is not useful if the narration describes something the viewer

cannot see.

The following diagram shows the content-production flow. The project

recording and AI-generated assets join the narration during editing.

``` php
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]
```

There are trade-offs. vLLM needs compatible hardware and setup. Edge TTS

relies on an online service. AI visuals can also take time to review. I

use each tool where it saves time rather than adding complexity for its

own sake.

There is room to take this workflow further:

The long-term idea is a pipeline where project information and a screen

recording become the starting point for a nearly finished demo

video---with a human reviewing the story and final edit.

A hackathon project deserves a demo that makes its value easy to

understand. My combination of a local LLM served with vLLM, Microsoft

Edge TTS, and other AI tools helps me create narrated project videos in

a repeatable way.

The important part is the balance: use AI to speed up scripting and

production, but show the real application and keep the claims honest.

**Build the project. Tell the story. Show the proof.**