Turn any Python function into a ready-to-assign math olympiad problem set in seconds — built for competitive programming coaches who hate writing problem sets from scratch.

Customer: Solo competitive programming coaches (USACO, Codeforces prep) who run 10–50-student cohorts online, charge $200–500/month per student, and currently spend 3–5 hours per week hand-crafting problems that test mathematical reasoning — not just coding syntax.

Problem: Writing problems that test WHY an algorithm works (the underlying math) rather than HOW to implement it is brutally slow. It requires dual expertise in math and CS, and coaches either recycle old Codeforces problems (students find answer keys) or spend hours writing originals by hand.

Pricing: saas-mrr — $500 MRR within 90 days (~17 paying coaches at $29/month)

Why now

Claude Sonnet’s reasoning quality crossed a threshold where problem synthesis feels original rather than templated — earlier LLMs produced shallow rewording. The multi-agent wave also means coaches are actively looking for AI tools that encode domain expertise, not just autocomplete. Early movers who build trust with the competitive-programming-coach niche can own the community before generic EdTech AI catches up.

Go-to-market

1. Post a free live demo on r/usaco and r/competitiveprogramming: ‘Paste any DP function, get 5 olympiad-style problems — built for coaches.’ Collect emails before charging anything.
2. DM 20 competitive programming coaches on Codeforces educator forums and Twitter/X who post about teaching algorithms; offer 3 months free in exchange for feedback and a testimonial.
3. Launch on ProductHunt with a ‘Free for the first 50 educators’ hook; the niche is tight enough that even modest PH traction surfaces the right buyers.
4. Partner with one small online bootcamp (e.g., a solo USACO prep coach with a Substack or YouTube channel) to co-create a ‘Problem Set Pack’ as a case study — they get content, you get social proof.

Moat (or lack thereof)

No real moat. This is a well-prompted Claude API call wrapped in Streamlit — any developer can replicate the core in a weekend. The defensible edge, if any, comes from (1) prompt quality tuned over hundreds of real problem sets and (2) community trust with a niche that is small, word-of-mouth-driven, and sticky once coaches build their curriculum around your output format. SymPy-based answer verification is a minor technical differentiator but not a lock-in. Treat this as a lifestyle business, not a VC play.