Magicomancy

Note: This analysis is based solely on the self-reported, unverified project description provided by the author. No third-party data, revenue figures, customer names, or traction metrics are available.

The description states that Magicometry is a browser-based real-time PVP/PVE wizard duel game where players cast spells using mathematical functions. These functions can be expressed in Cartesian coordinates (e.g., x(t), y(t)) or polar coordinates (e.g., r(t), θ(t)). Players move, aim, and cast simultaneously within a wrapped arena.

Key technical components include:

• A custom tokenizer, parser, and bounded abstract syntax tree evaluator
• Use of Canvas 2D API for rendering
• WebSockets for online multiplayer via Digital Ocean
• Supabase for authentication and cloud saves (in development)
• Built-in expression editor with LaTeX-style input support

The game supports:

• Live trajectory and damage previews
• Destructible terrain that retains spell-created craters during rounds
• Time-varying barriers that fade between solid and intangible
• Starter spells for new users
• Health, mana, cooldowns, animated wizards, and combat feedback

Inference: The game appears to be a prototype built with minimal infrastructure beyond the developer's own tools and AI assistance (e.g., GPT-5.6). It is not evident whether it has been released publicly or tested by external users.

The author positions Magicometry as a dynamic, fast-paced alternative to existing browser-based games like GraphWars 2, which they describe as turn-based and limited to Cartesian graphs. The game aims to combine mathematical creativity with real-time combat in an engaging setting.

Claims made:

• It allows players to "pull spells out of your spellbook on the go"
• Designed to make graph sketching more interesting for college students preparing for Oxbridge interviews
• Emphasizes creative freedom without punishing complexity

Claim vs Fact: These are claims about intent and positioning, not proof of traction or adoption. The author does not state any external validation or market testing.

The description indicates that the game was inspired by teaching graph sketching to college students preparing for Oxbridge interviews. However, no explicit target customer segment is defined beyond this context.

Inference: The initial audience may be students or educators interested in interactive math learning. There is no evidence of broader targeting or segmentation beyond this origin story.

There is no mention of pricing, monetization strategy, or business model in the description.

Not evidenced: No indication of how users would pay for the product or what revenue streams are planned.

The author describes:

• Development using Next.js, React, TypeScript, Tailwind CSS, and Canvas 2D API
• Custom tokenizer/parser/evaluator with safety limits on expression size, AST complexity, numerical range, and evaluation work
• Deterministic simulation loop at 60 Hz for reproducibility and networking
• Use of GPT-5.6 to assist in product specification and implementation decisions
• Modular architecture separating parsing, spell evaluation, collision rules, and terrain deformation

Inference: The technical stack suggests a lightweight, portable prototype built with modern web technologies and AI-assisted development practices.

The description states that the project was submitted to an OpenAI hackathon and is currently in early-stage development. It includes:

• Solo challenges (planned)
• Shareable spells and replays (planned)
• Player profiles (planned)

No evidence of:

• Public release
• User base or retention metrics
• Revenue generation
• Customer feedback or usage data

Absence of evidence: There is no indication of traction, adoption, or user engagement beyond the developer’s own testing and development.

The author references GraphWars 2 as a prior art, describing it as turn-based and limited to Cartesian graphs. No other competitors are mentioned in the description.

Not evidenced: No competitive landscape analysis, no mention of similar products or platforms in the market.

• Single developer team: Only one member (Brendan Beh) is listed; lack of team structure may limit scalability.
• No verified traction: No evidence of users, customers, or adoption beyond personal use.
• Unproven monetization model: No indication of how the product will generate revenue.
• Limited external validation: The project was submitted to a hackathon and lacks independent review or testing.
• AI dependency: Heavy reliance on AI tools (e.g., GPT-5.6) may pose risks if those services change or become unavailable.

Inference: The lack of third-party verification, user data, or commercial traction raises concerns about viability as a scalable product.

1. What is the intended path from prototype to full product release?
2. Are there any plans for monetization or revenue generation?
3. How do you plan to scale beyond a single developer?
4. Have you conducted any user testing or gathered feedback from students or educators?
5. What are your long-term goals for the game beyond the current features?
6. Is there any intention to expand into mobile or desktop platforms?