Catch the Mistake

Company: Catch the Mistake

Self-reported basis: The description is entirely self-reported and unverified, based on a Devpost submission for the OpenAI 2026 hackathon. No third-party corroboration or historical data exists.

What it appears to be: A learning tool that uses AI to generate one believable mistake in a math problem, prompting learners to identify and correct it through explanation and reapplication of the concept.

What changed: The project is described as an experiment in AI-assisted learning where the AI acts as an apprentice rather than an answer machine.

Most important open question: Is there evidence of any user testing, adoption or feedback from learners?

The description states that Catch the Mistake turns GPT-5.6 into an apprentice by making exactly one believable error in a math problem. Learners then select the faulty step, explain the repair in plain language, and prove the idea on fresh numbers.

It also supports uploading screenshots of percentage problems, which are processed using GPT-5.6 vision to apply the same "Find, Explain, Prove" loop.

The app is built with codex, gpt-5.6, javascript, openai, and openai-api. It has no accounts, analytics, or persistent learner records. Screenshots are only sent after explicit action.

This is a self-reported product description — it does not contain evidence of actual users, usage metrics, or commercial functionality beyond the author's own account.

The author claims that Catch the Mistake is designed to make learners do meaningful reasoning and finish with a feeling of accomplishment. It positions AI as an apprentice rather than an answer machine.

It also states that most AI tutors simply provide another answer, implying a contrast with existing tools in the space.

This is a claim, not a fact. The description does not include any evidence of how this differs from other educational tools or whether it has been validated by users or educators.

The author describes the product as intended for learners working on math problems, particularly percentage-related ones.

It supports both manual input and screenshot uploads of problems, suggesting a focus on students or learners who may be using digital tools to practice math concepts.

No specific customer segments or personas are named. The description does not indicate whether this is aimed at K-12, higher education, or professional development.

Not evidenced: No explicit target customer or ICP defined beyond general "learners".

The description states that the app has no accounts, analytics, or persistent learner records, and that screenshots are only sent after an explicit action.

There is no mention of pricing, monetization, or any business model. The project appears to be a hackathon submission with no indication of commercial intent or revenue streams.

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

The app is built using:

• GPT-5.6
• Codex
• JavaScript
• OpenAI API
• OpenAI Vision

It integrates structured output and validation, and includes accessibility tests.

The product is described as having a secure public deployment and no persistent data storage.

Not evidenced: No evidence of scalability, infrastructure robustness, or performance metrics.

The project was submitted to the OpenAI 2026 hackathon, indicating it is in an early-stage prototype or experimental phase.

There is no evidence of:

• Users
• Customers
• Revenue
• Product adoption
• Feedback loops
• Iteration history

It is described as a single-person project (team size: 1), and no mention of any traction beyond the hackathon submission.

The author states that most AI tutors simply provide another answer, implying a contrast with current tools in the educational space.

However, there is no evidence of:

• Competitor analysis
• Market positioning
• Product differentiation from existing tools
• Awareness of similar offerings

This is a claim, not a fact. The description does not include any competitive landscape or market data.

• No user feedback or adoption: The project is described as a hackathon submission with no evidence of real-world use.
• Single-person team: Limited capacity for development, iteration, and scaling.
• Unproven learning effectiveness: No evidence that the "Find, Explain, Prove" loop improves learning outcomes.
• No monetization strategy: No indication of how this would be commercialized or scaled.
• Highly experimental nature: The product is described as an experiment in AI-assisted learning.

1. What was the outcome of the hackathon submission? Did it receive any attention or follow-up?
2. Have you tested this with real learners or educators? If so, what feedback did you get?
3. How do you plan to scale beyond a single-person development effort?
4. Are there any plans for monetization or commercialization?
5. What is the intended user journey and how does it differ from existing educational tools?