Pass Forward

What the company appears to be: Pass Forward is a self-reported tool built for soccer players aiming to improve their long passes using AI-powered coaching. The author describes it as a personal project developed with GPT-5.6 Terra and other technologies, focused on one specific movement (a stationary-ball long pass) and one correction per attempt.

What changed: The project is described as a prototype built in the context of an OpenAI hackathon. It does not appear to have evolved into a commercial product or service beyond its initial development phase.

Single most important open question: Is there any evidence that this tool has been used by users outside of the author’s own practice, or that it has moved beyond a personal prototype?

The description states that Pass Forward is a browser-based tool for soccer players to improve their long passes. It allows users to record or upload a fixed side-view clip and receive one personalized coaching cue and visual target for the next attempt.

• The system samples five low-resolution frames from the clip and sends them to OpenAI’s gpt-5.6-terra model.
• If the clip is coachable, it returns a structured output with either one correction or a retake instruction.
• A replay loop shows an observed trace (off-white) and target trace (orange), synchronized to the selected frame.
• The full video remains local in the browser; only five JPEG samples are sent to the API.
• It uses React, TypeScript, Next.js, and browser APIs for media handling.

This is a self-reported prototype built by one person using AI agents and tools like Codex. No evidence of commercial use or user adoption exists beyond the author’s own description.

The author positions Pass Forward as a modest tool addressing a specific gap in coaching: private coaching is inaccessible, generic tutorials don’t respond to individual movement, and ordinary video review gives no actionable next step.

• The product claims to offer an interactive loop for players across experience levels who want to work on the same supported pass.
• It narrows its scope intentionally — focusing only on one type of pass (long pass from a fixed side view) and one correction per attempt.
• The author emphasizes that it does not try to teach all of soccer or rate ball flight, but instead stays focused on one movement.

This is a self-reported positioning claim. There is no evidence of market testing, customer feedback, or competitive positioning beyond the author’s own narrative.

The description states that Pass Forward targets “players across experience levels” who want to work on the same supported pass — specifically, long passes from a fixed side view.

• It is designed for individuals practicing alone, particularly those returning to sport after a break.
• The interface is intended to be usable both on phones at practice fields and desktops.
• It is not described as targeting coaches, teams, or institutions.

No evidence of segmentation beyond "players" or any indication of how the product would scale to other users or use cases exists in the description.

There is no evidence provided about a business model or pricing strategy. The author describes the tool as a personal prototype built during a hackathon, with no mention of monetization, subscriptions, licensing, or any commercial structure.

• The product does not appear to have moved beyond its initial development phase.
• No revenue streams, customer acquisition plans, or pricing tiers are described.

The author describes building the tool using:

• React, TypeScript, Next.js
• Browser media and canvas APIs for camera input and video decoding
• OpenAI’s gpt-5.6-terra model via structured output (Zod validation)
• Codex as a development system with bounded task sessions and agent delegation

Key technical details:

• Only five low-resolution frames are sent to the API; full clip stays local.
• Strict schema enforcement prevents incomplete or invalid responses from being used.
• No fallbacks, retries, or local results if API fails.
• The runtime path is: browser clip → five JPEG samples → same-origin route → structured response → validation → replay.

The author reports using autonomous agents (Codex, GPT-5.6 Terra) for development and management of the project, with human oversight for final decisions.

There is no evidence of traction or maturity beyond the author’s own development efforts:

• The tool was built by one person (Chase Ripley).
• It was submitted to a hackathon.
• No users, customers, or adoption data are mentioned.
• No production deployment, usage metrics, or feedback loops are described.

The project is described as a prototype with no commercial or user-facing evolution.

No competitive landscape is described. The author does not reference existing tools for soccer coaching or biomechanics, nor do they compare their solution to others in the market.

• The tool is positioned as addressing a niche gap — between generic tutorials and private coaching.
• No mention of competitors, substitutes, or market positioning relative to other platforms.

Several risks are implied by the self-reported nature of the project:

• No external validation: The entire description is self-reported with no third-party verification.
• Prototype-only status: There is no evidence that this has evolved into a product or service used by others.
• Single-person development: The tool was built by one individual, suggesting limited scalability or institutional support.
• AI dependency without fallbacks: The system fails closed if the API returns invalid data or fails — which may limit usability in real-world conditions.
• Limited scope: While intentional, the narrow focus may hinder broader adoption or product evolution.

1. Has this tool been tested with actual users beyond yourself?
2. What is the current status of the prototype? Is it being used by others?
3. Are there any plans to expand beyond soccer or long passes?
4. How does the system handle edge cases where the model fails to return a valid correction?
5. Have you considered integrating with existing coaching platforms or devices?
6. What are your thoughts on privacy and data handling in a commercial setting?