ZeroPrep

The description states:

• ZeroPrep listens while you speak and creates a live visual presentation in real time.
• It uses OpenAI’s Realtime API to understand the speaker and decide how visuals should evolve.
• The presentation is rendered with Next.js and React, and background imagery is generated by Gemini.
• It allows users to just speak naturally, with no need for pre-prepared slides or manual interface interaction.
• At the end of a talk, the generated presentation can be downloaded as PDF or PowerPoint.

Inference The tool appears to be a proof-of-concept or prototype built in a single day during a hackathon. It is not evidenced to be a commercial product with ongoing functionality or user base.

The description states:

• The core idea came from the problem of time spent preparing presentations vs. speaking content.
• The positioning is that users can "just speak naturally" and have the presentation build itself around them.
• It is described as a tool for live, real-time presentation generation with near real-time images and clean card layouts.

Inference The positioning is focused on reducing friction in presentation creation by automating visual elements based on speech input. The claim is that it streamlines the process of preparing and delivering presentations.

The description does not state any specific customer or ICP.

It only mentions that the idea came from two individuals who met at a hackathon, and that they built it for "the problem we both understood" — which is not defined in detail.

Inference The target audience is likely speakers or presenters who want to reduce time spent preparing slides, but no explicit customer segment or persona is described.

There is no evidence of a business model or pricing structure in the description.

The project is presented as a hackathon submission with no indication of monetization, subscriptions, or sales.

Inference No commercial model is evident beyond the initial idea and prototype.

The description states:

• Built using Next.js, React, OpenAI’s Realtime API, and Gemini.
• Uses Codex as a pair programmer during the hackathon.
• The system handles continuous speech input, dynamic visual updates, and image generation delays.
• It prevents older responses from changing newer scenes and supports flexible layouts.

Inference The technical stack suggests a modern web-based application with AI integration. However, no evidence of production deployment or scalability is provided.

The description states:

• The project was built in one day during a hackathon.
• It was submitted to the OpenAI 2026 hackathon on Devpost.
• No revenue, customer adoption, or usage metrics are mentioned.

Inference There is no evidence of traction, user base, or commercial maturity beyond the hackathon submission.

The description does not mention any competitors or market context.

It does not state whether similar tools exist or how ZeroPrep differentiates from them.

Inference No competitive positioning or landscape is described in the self-report.

• The project is a hackathon prototype with no evidence of commercialization or user adoption.
• No revenue, customers, or product-market fit data are provided.
• The tool is described as built in one day — suggesting it may not be production-ready or scalable.
• No mention of IP, legal, or technical sustainability beyond the hackathon.

Inference The lack of any commercial evidence raises significant risk that this is a non-operational idea or prototype with no demonstrated path to market traction.

1. What is the current status of ZeroPrep beyond the hackathon? Is it being used by anyone?
2. Have you validated the core use case with real users or potential customers?
3. Do you have any plans for monetization or commercial deployment?
4. How do you plan to scale the technology beyond a single-day prototype?
5. What are the technical limitations of the current system, and how do you plan to address them?