Software MRI

The description states that Software MRI is a tool that:

• Takes a public GitHub repo URL.
• Runs static analysis using tools like dependency-cruiser, escomplex, and ts-prune.
• Produces a 3D structural scan rendered with React + Three.js.
• Shows modules as nodes on a rotatable sphere, sized by scope and colored by health.
• Includes layers for complexity (cyclomatic) and debt (dead code).
• Provides a deterministic, plain-language diagnosis readout based on analysis output.
• Does not use AI or LLMs at runtime.

The author claims the tool is built with React, Three.js, GSAP, Node.js, TypeScript, and several static-analysis libraries. It is described as a diagnostic instrument for developers to understand codebase structure and risk.

Inference This is a developer-facing visualization tool that aims to make codebase health visible through structured 3D scanning. The tool is self-reported as non-AI-powered, relying on deterministic analysis pipelines.

The description states:

• The product is positioned as an “MRI scanner for your codebase.”
• It aims to show structure, complexity, and dead code in one live 3D scan.
• It contrasts with tools like GitHub (which shows changes) or linters (which show style violations).
• It claims to offer a diagnostic view that no other tool provides — a single glance at the shape of a codebase.

Inference The positioning is aspirational, aiming to be a new kind of developer diagnostic tool. The claim evolution appears to center on the idea of visualizing code health in a way that’s more intuitive than raw metrics or dashboards.

The description states:

• The target audience is developers who open unfamiliar codebases and feel lost.
• It addresses pain points like identifying real entry points, load-bearing files, and rotting code.
• It is designed for use with public GitHub repositories.

Inference The ICP appears to be individual developers or small teams working with open-source or public repos. The tool is not described as targeting enterprise customers or internal tools.

The description states:

• No pricing information, business model, or monetization strategy is provided.
• The tool is presented as a hackathon project with no indication of commercial intent.

Inference There is no evidence of a business model or pricing structure. The product is self-reported as a solo-built hackathon submission.

The description states:

• Built with React, Three.js, GSAP, Node.js, TypeScript.
• Uses dependency-cruiser for import graphs and circular dependencies.
• Uses escomplex for cyclomatic complexity.
• Uses ts-prune for dead code detection.
• No LLMs or AI calls at runtime.
• The frontend uses 3D-force-graph via Three.js with GSAP for animations.
• The backend clones the repo and runs static analysis pipelines.

Inference The tool is technically self-contained, built as a solo project using open-source tools. It’s described as deterministic and non-AI-powered, which may be a key differentiator in its positioning.

The description states:

• The project was submitted to the OpenAI 2026 hackathon.
• No evidence of users, customers, or adoption beyond the author's own submission.
• No revenue, ARR, headcount, or usage metrics are mentioned.

Inference There is no evidence of traction or maturity. It is a solo-built hackathon project with no indication of real-world use or commercial viability.

The description states:

• The tool contrasts with GitHub (shows changes) and linters (style violations).
• It claims to be the first to offer a single 3D scan that shows structure, complexity, and dead code.
• No competitors are named or described.

Inference The competitive context is unclear. The author does not reference existing tools in this space, nor does the description provide any comparison data or market positioning.

• No traction or revenue: The tool is presented as a hackathon project with no evidence of adoption.
• Solo builder risk: The team size is listed as 1; solo projects often lack scalability or long-term viability.
• Unproven commercial model: No pricing, monetization, or business model is described.
• Limited scope: The tool only supports public GitHub repos and lacks enterprise features like private repo support or CI integration (mentioned in “What’s next” but not implemented).
• Self-reported claims: All evidence is self-reported; no external validation or data exists.

Inference The project is early-stage, with no commercial traction. It may be a proof-of-concept rather than a scalable product.

1. What is the current status of private repo support and CI integration?
2. Are there any users or teams currently testing the tool in real development workflows?
3. What are the plans for monetization or pricing, if any?
4. How does the tool handle large or complex codebases beyond what was demonstrated in the hackathon?
5. Has the author considered how to scale beyond a solo builder model?