Football AI Inspector

The description states that Football AI Inspector is a tool for inspecting football AI decisions, built as an offline HTML artifact using Unreal Engine telemetry. The author claims it allows engineers, AI designers, and QA to share evidence of AI decision-making by replaying and diffing decisions, showing rejected options and ranked candidates with scores.

The project appears to be a prototype or hackathon submission (submitted to the OpenAI 2026 hackathon), built during a single Build Week. It is not evidenced to have any revenue, customers, traction, or production use.

The single most important open question: Is this tool intended for internal AI debugging or as a commercial product? The description does not clarify whether it's a proof-of-concept or a scalable solution.

• The description states that Football AI Inspector is a standalone offline HTML artifact.
• It uses Unreal Engine telemetry, specifically a versioned JSON schema (v1, draft 2020-12).
• It supports importing, replaying, and diffing football AI decisions.
• The tool walks through Scan, Evaluate, Choose, and Execute steps.
• It keeps rejected routes visible, ranks candidates with scores, and traces the strongest logged factors behind the winner.
• It allows comparing two snapshots and exporting deterministic Markdown review packets.
• Files stay in browser memory and are never uploaded.

Note: The tool is described as a prototype or hackathon submission. No evidence of production use or scalability is provided.

• The description states that the tool was built to help engineers, AI designers, and QA share evidence instead of debating final animation.
• It aims to resolve ambiguity in football AI decisions, such as whether a pass looks wrong due to perception, pressure, attributes, or variance.
• The author claims it provides “the same evidence” to different stakeholders.
• The tool is positioned as a debugging and review tool for AI decision-making, not a commercial product.

Inference: The positioning seems to be that of an internal QA or debugging tool, but the description does not clarify if this is intended for broader use or just for developers working on football AI simulations.

• The description states that the tool is intended for engineers, AI designers, and QA.
• It is built to help these users share evidence of AI decisions.
• No specific customer segment beyond these roles is identified.

Not evidenced: No information on whether this targets internal teams at game studios, AI research labs, or external partners. No evidence of a defined ICP or target persona beyond the stated roles.

• The description does not state any pricing model or business model.
• It is described as a standalone offline HTML artifact, with no mention of monetization or licensing.
• There is no indication that it is intended for sale, subscription, or commercial use.

Not evidenced: No evidence of revenue streams, pricing, or monetization strategy.

• The tool is built using HTML5, CSS3, JavaScript, and Playwright.
• It uses a versioned telemetry fixture (v1, draft 2020-12 JSON Schema).
• It has a strict adapter boundary for future Unreal exporter integration.
• The interface supports keyboard navigation, live announcements, reduced-motion support, forced-colors support, and responsive layout.
• It includes 49 automated checks covering schema, import safety, decision diff, export, content drift, runtime errors, network isolation, and Chromium interactions.
• It was built using GPT-5.6 through Codex for inspection, interface implementation, and documentation.

Inference: The tool is built with a focus on offline-first, testability, and accessibility, suggesting an emphasis on developer experience and robustness.

• The project was submitted to the OpenAI 2026 hackathon.
• It was built during a single Build Week.
• The Football Universe foundation existed only one day before Build Week.
• Most of the product, including decision and test extensions, were built during that time.

Not evidenced: No evidence of traction, adoption, or usage beyond the hackathon submission. No revenue, customers, or user feedback are mentioned.

• The description does not mention any competitors.
• It is unclear if there are existing tools for inspecting AI decisions in football simulations or game engines.
• The tool is described as a debugging and review tool, but no comparison to similar tools is made.

Not evidenced: No competitive landscape, market positioning, or differentiation from other tools is provided.

• The tool is described as a hackathon prototype with no evidence of production use.
• It is offline-only, and the description does not indicate plans for cloud or shared access.
• The tool is built to work with Unreal Engine telemetry, which may limit its applicability to other platforms or engines.
• No evidence of scalability, performance, or integration into larger systems is provided.

Inference: The risk of limited adoption or scalability is high due to the prototype nature and narrow scope.

1. Is this tool intended for internal use only, or are you planning to commercialize it?
2. What is the expected lifecycle of the telemetry format (JSON schema)? Are there plans for versioning or migration?
3. How does this tool integrate with Unreal Engine workflows? Is there a plan for a production exporter?
4. Do you have any early adopters or users who are testing this in practice?
5. What are your plans for expanding beyond passes to other types of AI decisions (e.g., positioning, substitutions)?
6. Are there any technical limitations that prevent broader adoption or scalability?