Instructors spend 5 hours writing project specs by hand. You can fix that in a weekend.
There's a thread on r/learnprogramming from a few weeks ago where someone asked for a platform that "forces you to build" with real acceptance criteria. 55 upvotes, 30 comments, everyone agreeing the same thing: tutorials are useless, toy projects teach nothing, and nobody knows how to find a real project that actually makes you think.
Instructors are posting the same frustration from the other side. How do you grade 40 capstone projects without going insane? How do you write test suites for every cohort's stack?
This is the gap the AI Project Spec & Auto-Test Generator idea lives in. And I think it's buildable in a weekend, at least the version that proves people will pay for it.
Forget the full vision for now. The CI integration, the cohort dashboards, the hint tier unlocking system. That's week five stuff.
Weekend one is one thing: an instructor types in a stack, a topic, and a difficulty level, and gets back a complete project spec in editable Markdown plus a test file they can actually run. That's it. That's the aha moment you're selling.
The stack for this is boring on purpose. Next.js frontend, Supabase for auth and storing generated packages, OpenAI API for the actual generation. GitHub OAuth so you can eventually add the repo scaffolding, but you don't need it on day one. No sandboxed execution yet. No student-facing portal. Just the generator and a download button.
A single GPT-4o call with a well-structured prompt will get you 70% of the way there on spec quality. The remaining 30% is your prompt engineering. Spend time on the system prompt. Be specific about what a good spec looks like: user stories, acceptance criteria per milestone, a grading rubric with point values. Make it generate the test file in the right language automatically based on stack input. This is where you actually earn the product.
Cost per generation at GPT-4o pricing is somewhere between $0.10 and $0.40 depending on spec length. At $79/month for the instructor tier, you have a lot of room.
Here's exactly what to do before touching Cursor or Lovable:
Spend two hours with GPT-4 manually generating three example project packages. One full-stack Node project, one Python data science project, one React project. Make them genuinely good. Write them up in a Notion doc that looks like a real product page, with before/after time estimates and a screenshot of the test file.
Then do two things simultaneously. Post in r/learnprogramming and r/bootcamp. And DM 20 bootcamp instructors on LinkedIn. The search query "coding bootcamp instructor" on LinkedIn returns thousands of people. Message them something honest: you're building a tool that generates project specs and test suites in under 5 minutes, you'll generate a free custom package for their next cohort, you want 20 minutes of their time.
The success metric is specific: 5 instructors say they'd pay $99/month for this, and at least 2 ask when they can actually use it. If you don't hit that, you don't build. If you do hit it, you have your first paying customers before you've deployed anything.
This takes a weekend. The LinkedIn outreach takes maybe three hours. If the signal is there, you spend the next weekend building.
I want to be honest about the things that can kill this, because they're non-trivial.
LLM-generated tests are wrong more often than you'd want. GPT-4o is probably hitting 60-70% correctness on non-trivial test cases, which means one cohort of students graded incorrectly by a hallucinated expected output could torch your reputation in a community that is small and talks to each other. The fix is to frame the product as "AI draft, human review" from day one. Never position it as fully autonomous grading. Build an explicit review step before tests go anywhere near CI. Eventually you add a validation layer that runs tests against a reference solution before showing them to the instructor, but even before that, the mental model you're selling is "saves you 80% of the time, you still approve."
GitHub Education is the other real threat. They already own the repo, the CI, and GitHub Classroom. A single product announcement from them could commoditize the core value proposition overnight. Microsoft has absorbed features like this before. The counterplay is to go deep on bootcamp-specific workflows fast: cohort dashboards, multi-milestone hidden reveals, the data that accumulates from instructors editing your generated tests over time. Generic GitHub features will be generic. Yours can be specific.
Three tiers. Free is two packages per month with no GitHub export. Instructor is $79/month for unlimited packages and GitHub export. Bootcamp is $199/month for five instructor seats, a student submission dashboard, and cohort analytics.
Break-even is 18 instructor-tier customers. That covers roughly $1,500/month in infrastructure and API costs. At a 15:1 LTV/CAC ratio through Reddit and direct LinkedIn outreach, the unit economics work if retention holds. Bootcamps that wire this into their GitHub workflow and build up a library of specs and test suites are not easy to churn. That library is the moat.
The $7.2M ARR ceiling from the bootcamp wedge alone (~4,000 active bootcamps globally at a $150/month average) is not a unicorn number. That's fine. Bootstrappable, acquirable, or pivotable into corporate L&D if the bootcamp market stays soft post-2022. One of those outcomes is very achievable.
If you're going to touch code this weekend, build in this order:
The GitHub Classroom CI integration is week three or four. The student-facing submission portal is a second product and a scope trap. Build the instructor side first, make it undeniably useful, and let the instructors tell you when they're ready to bring students into the platform.