A university course produced 69 ideas, not 69 guaranteed businesses
A July 7, 2026 Xinhua page republishing a China Youth Daily report described two university entrepreneurship courses. At Communication University of China, 18 projects presented at a final pitch session, and one student offered 100 trial invitations for an AI companion application. At Nanjing University, an AI-and-product course formed 69 creative projects during one semester and moved its final exhibition beyond the campus classroom.
Those figures document activity, not revenue, funding, or a success rate. The students, teachers, and universities in the report have no usage or partnership relationship with APIToken. The defensible takeaway is that AI has shortened the path to a visible prototype, while evaluation is moving from a paper business plan toward demonstrations, user feedback, engineering limits, and commercial constraints.
Faster prototyping makes problem selection more important
The same report describes an important limitation. Students can receive a quick burst of progress when AI generates an attractive interface and runnable code, but repeated patching may add redundant or incorrect code. Without enough engineering experience, the human operator can lose track of the expanding task tree. Speed at the start does not prove that the product is reliable or worth continuing.
A student project should therefore begin with a problem that has appeared repeatedly in study, research, an internship, or campus life. Slow literature organization, difficult experiment review, confusing club registration, or a repeated document-processing task can provide a stronger starting point than a fashionable model name. A concrete recurring problem gives the team something observable to test.
Keep the first experiment within a budget you can lose
Define one input, one useful output, one core path, and a small group of testers. A first-round budget such as RMB 50 or less can make a wrong assumption affordable for many student teams, but the exact limit should reflect the team's circumstances. Write stopping conditions before running the test: stop when the core path repeatedly fails, manual correction exceeds the expected effort, or early testers have no reason to return.
Compare only a few candidate models on the same task and record usable-output rate, response time, editing time, and total request cost. A broad model marketplace is useful because it supports controlled comparison, not because every model must be used. Failed attempts, retries, repeated deposits, tool switching, and manual debugging all belong in the cost of one completed result.
Protect research, resumes, and personal data by design
Student prototypes may contain papers, resumes, interview notes, campus identities, or test-user records. Remove phone numbers, student identifiers, government identifiers, unpublished research data, and other personally identifying details before sending material to a model. Use a separate, revocable, limited API key for each project instead of placing a long-lived key in demo code, group chats, or a public repository.
A model appearing in a list does not prove that the intended task will complete. Check current channel status and run one minimal real request before a batch job. Keep error and usage records so the team can stop, diagnose, or switch approaches. Stability means that failure remains observable and bounded; control means that data, keys, and budget remain with the project team.
Expand only after one result survives real testing
APIToken can serve as one example of a multi-model entry point for this workflow. Review the current marketplace and channel status, create an isolated key, set a small spending limit, and compare a few models on the same real student task. Add features, budget, or team members only after the core path works and real testers provide evidence that the result matters. Current models, prices, groups, and availability follow the live site pages.
This is not an entrepreneurship or income promise, and it does not imply that anyone in the Xinhua report used APIToken. AI can lower the cost of producing a first prototype, but a viable student project still depends on a real problem, user feedback, cost accounting, engineering judgment, privacy discipline, and a clear decision about when to stop.