How I Create AI Demo Videos for My Hackathon Projects

A developer documented a repeatable workflow for producing narrated hackathon demo videos by running a local language model through vLLM's OpenAI-compatible API to draft scene-by-scene scripts, then generating voiceover with the edge-tts Python package against Microsoft Edge's online neural voices. The pipeline keeps screen recordings of the real application authentic and assembles narration, footage and supporting assets in a video editor, with the developer noting that local inference still requires suitable hardware and that edge-tts is an unofficial interface dependent on an internet connection.

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.