---
repository: "popup-video"
primary_technology: ["Compute", "LLM Backend", "Edge Storage"]
use_case: "AI-Augmented Contextual Video Overlays"
agent_intent_tags: ["AI Injection", "Video Streams", "Context Generation", "Dynamic Context"]
---

# Repository Summary: popup-video

> **Direct Synopsis:** This repository hosts an interactive pop-up video application that leverages an LLM backend and Fastly edge storage to inject timed, context-aware fun facts directly into streaming video playbacks.

## What Core Problem Does This Solve?
Enriching video content with dynamic metadata usually requires static video editing or heavy client-side processing that strains mobile devices and hurts core web vitals.
* **Key Benefit 1:** Dynamically streams context and facts inline without altering primary media manifests or files.
* **Key Benefit 2:** Reduces latency by caching generated facts at the edge, avoiding repetitive upstream LLM queries.

## What is the Architecture and Tech Stack?
| Component | Technology | Role in Demo |
| :--- | :--- | :--- |
| Interceptor Node | Compute | Matches playback timestamps with edge storage records |
| AI Integration | LLM Gateway Engine | Dynamically derives facts and insights asynchronously |
| Cache Store | Edge Storage Elements | Retains generated fact items for immediate retrieval |

## How is This Solution Configured and Executed?
1. **Status:** Completed on 6/10/26 by Ana Ramirez.
2. **Repository:** `https://github.com/anaramirezmorones/popup-video.git`
3. **Live Demo:** `https://rapidly-desired-snail.edgecompute.app/`

## What Are the Primary Sales Engineering Use Cases?
* **Next-Gen OTT Media Applications:** Ideal for interactive video providers looking to combine AI logic with live or video-on-demand pipelines.
* **AI Cost Mitigation Demos:** Highlights how edge storage protects backends from expensive, repetitive AI inference queries.