SMC Multidegree Solver

The description states that SMC Multidegree Solver is a web-based tool built by one developer to help Santa Monica College (SMC) students plan efficient paths for earning multiple degrees. It allows users to import transcripts locally in-browser, identifies overlapping courses and degree requirements, and offers both greedy and SAT/Z3-optimized planning modes. The author reports building it using Rust/Dioxus with Tailwind styling, and integrating AI tools like Codex and GPT-5.6 for development support.

The project appears to be a personal or hackathon effort focused on solving a specific problem within SMC’s academic ecosystem. It does not appear to have any commercial traction, revenue, or customer base beyond the author's own use case. The tool is described as privacy-first and designed to supplement rather than replace academic counseling.

The description states that SMC Multidegree Solver is a web application that:

• Allows users to import transcripts locally in-browser.
• Removes personally identifiable information (PII) such as names, student IDs, and birthdates.
• Uses completion status to:
  • Rank the closest SMC degrees.
  • Recognize awarded degrees, certificates, transfer credit, and GE certification.
  • Build optimized multi-degree plans and show overlapping courses.
  • Suggest semester-by-semester schedules respecting prerequisites and unit limits.
  • Show popular degree paths based on the 2026 commencement program.
  • Offer certificate pathways when selected programs support a Certificate of Achievement.

It is built as a Rust/Dioxus web app styled with Tailwind, and uses a structured DSL for degree requirements, a prerequisite DAG, and either greedy or SAT/Z3 optimization modes. Transcript parsing and planner state are handled client-side in the browser.

Inference The tool appears to be a single-developer project focused on solving an internal academic planning challenge at SMC, not a commercial product.

The description states that the author was inspired by the number of students who earn multiple degrees and wanted to help them do so efficiently. It positions itself as a tool for students to visualize degree paths, overlapping courses, prerequisites, and GE requirements.

It claims to be:

• A privacy-first solution (transcripts parsed in-browser).
• A supplement to academic counseling, not a replacement.
• Capable of handling complex degree rules including transfer credit, prerequisites, and multiple program options.
• Designed with SMC-specific catalogs and degree structures in mind.

Inference The positioning is narrow and self-contained — focused on one college’s ecosystem. It does not claim broader market applicability or scalability beyond SMC.

The description states that the tool is for Santa Monica College (SMC) students, particularly those who are pursuing multiple degrees.

It is built to help users:

• Understand how to complete multiple degrees efficiently.
• Identify overlapping courses and shared requirements.
• Plan semester-by-semester schedules respecting prerequisites and unit limits.
• Navigate GE requirements, transfer credit, and certificate pathways.

Inference The ICP appears to be SMC students who are actively pursuing or considering multiple degrees. No evidence of broader targeting or institutional adoption is provided.

The description does not state any business model or pricing information.

It is described as a personal or hackathon project, with no mention of monetization, subscriptions, licensing, or paid features.

Inference There is no evidence of a commercial business model. The tool appears to be non-commercial in nature.

The description states that the tool was built using:

• Technology stack: Rust/Dioxus with Tailwind CSS.
• Data handling: Transcript parsing and planner state stored locally in-browser.
• Degree rules modeling: Structured DSL for degree requirements, prerequisite DAGs.
• Planning modes: Greedy overlap planner and optional SAT/Z3 optimizer.
• Development tools: Codex and GPT-5.6 used for development, debugging, testing, and communication.

It also mentions that the tool is designed to be responsive and handles complex constraints like prerequisites and unit limits.

Inference The technical approach is lightweight and client-side, with AI-assisted development. No evidence of enterprise-grade infrastructure or scalability beyond a single user or small group.

The description states that this was submitted as a project to the OpenAI 2026 hackathon, and that it was built by one developer (lolpro11).

There is no mention of:

• Users, customers, or adoption.
• Revenue or monetization.
• Product usage metrics.
• Institutional partnerships or integration with SMC systems.
• Any public deployment or live use beyond the author’s own testing.

Inference The project is at a very early stage — likely a prototype or proof-of-concept. No evidence of traction, user feedback, or institutional adoption.

The description does not mention any competitors or existing solutions in this space.

It is described as a tool for SMC students to plan multi-degree paths, which could overlap with academic planning tools or systems used by colleges and universities, but no comparison or market positioning is given.

Inference No competitive landscape is evident from the description. The tool appears to be unique within its narrow scope (SMC-specific).

• No institutional adoption or user feedback: The tool is described as a personal project with no evidence of real-world use.
• Single developer effort: The team size is listed as one, suggesting limited scalability or long-term maintenance capacity.
• Limited scope: It is built for SMC only and does not appear to be designed for broader institutional or market use.
• No commercial viability: No pricing, monetization, or business model is described.
• AI dependency: Heavy reliance on AI tools (Codex, GPT-5.6) for development may raise questions about long-term maintainability or reproducibility.

Inference The project lacks commercial viability and institutional traction. It is likely a prototype with no clear path to product-market fit beyond its author’s use case.

1. What is the actual adoption rate among SMC students? Have you received feedback from users or counselors?
2. How does the tool handle edge cases in degree requirements, especially those involving transfer credit or articulation with external institutions?
3. Is there any plan to expand beyond SMC, or is it intentionally limited to this one college?
4. What are the limitations of the current SAT/Z3-based optimization? Have you tested how well it scales with larger course selections?
5. How do you plan to maintain and update degree requirements as they change over time?
6. Are there any institutional or legal constraints around using AI tools like GPT-5.6 for development, especially in a student-focused academic environment?