Python Training for Professionals with Day Jobs

AI Prompts to Build This

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1. 1. Project Setup

   Build the weekend MVP of "Workframe": a profession-first Python course. This weekend there is one course, Python for Real Estate Agents, and one working module whose output the app checks.
   
   Stack: Next.js (App Router), TypeScript, Tailwind, Supabase (Postgres, Row Level Security, Storage, Auth with email magic link), MDX for the lessons. Students run their Python on their own computer and upload the file it writes, so no student code runs on the server. Deploy on Vercel.
   
   Tables (Row Level Security on; courses and modules are read-only to signed-in users, a student reads only their own enrollments and submissions):
   - courses(id, slug, profession, title, insider_name, published boolean)
   - modules(id, course_id, position, title, hours_saved_claim, dataset_path)
   - enrollments(id, user_id, course_id, created_at)
   - submissions(id, enrollment_id, module_id, passed boolean, checks jsonb, hours_saved, created_at)
   
   Screens: /login, /real-estate (the course page), /learn/[course]/[module] (the lesson, the dataset and the upload), /app/hours (the cohort chart).
   Env vars (names only): NEXT_PUBLIC_SUPABASE_URL, NEXT_PUBLIC_SUPABASE_ANON_KEY, SUPABASE_SERVICE_ROLE_KEY (server only).
   
   Do not build: billing, course or library pricing, a cloud code sandbox, an AI grader, a corporate pack, live data from any real site.
   Done when: npm run dev starts, you can sign in, and the four tables exist with Row Level Security on.

2. 2. Core Feature

   Build the one feature that proves Workframe: a first module that produces a tool the student can use on Monday, and a check they can trust.
   1. Seed the course with five module titles: MLS export, follow-up texts, open-house logs, CMA helpers and a weekly digest. Build module 1 fully.
   2. Module 1 lesson (MDX): a 200-row synthetic listings CSV (address, price, days_on_market, agent_email) generated by a seeded script, with example.com emails and a banner that says it is fake data. The student writes a Python script that keeps listings with more than 30 days on market and writes follow_up.csv with agent_email and a templated message.
   3. The student uploads follow_up.csv. The server checks it in code against the dataset: the header is exactly agent_email,message; the row count equals the number of listings over 30 days; every agent_email belongs to such a listing; there are no duplicates; each message is not empty and is 300 characters or fewer.
   4. Show each check as passed or failed in plain words ("Expected 63 rows, found 61"). On a pass, save the submission and show a 1 to 10 slider: "How many hours would this save you this week?"
   5. /app/hours charts the hours reported across the cohort, anonymized, and shows nothing until at least 5 students have reported.
   6. Provide a downloadable solution script for the instructor only, not shown to students.
   Rules: never use live listing data and never scrape a real site in an exercise. Never run student code on the server.
   Empty state: a student with no submissions sees module 1 and the dataset download.
   Done when: a correct follow_up.csv passes every check and unlocks the slider, a file with a missing row names exactly what is wrong, and the hours chart stays empty until 5 students have reported.

3. 3. Landing Page

   Build a one-page landing site for Workframe, Python courses that start with your profession.
   Hero: "Python that starts with your lead pipeline, not a weather app." Sub: "Build a tool in week one that you use on Monday. Real estate first." One button: Join the waitlist, with a choice of profession (real estate, legal or fitness).
   Sections: the five weekly tasks of the real estate course, an instructor bio placeholder for the practitioner who co-builds it, a week-one result shown as the follow-up file the student produces, and an FAQ on "do I need to become a developer" (no) and what the beta includes.
   Waitlist: store the email and the profession in a waitlist table in Supabase. No other service.
   Style: Geist, a warm off-white background, near-black type, one amber accent.
   Done when: the page renders on a phone and a submitted email with its profession appears in the waitlist table.

4. 4. Branding Package

   Use a design or image tool for this one. A coding agent cannot draw a logo.
   Brand for Workframe: a wordmark and an icon that suggest a square frame with a small code bracket inside it.
   Colors: warm off-white, near-black and one amber. Type: Geist, with Geist Mono for code.
   Deliverables: wordmark, icon, three profession tags (real estate, legal, fitness) that differ by shape and letter as well as color, a module card style, and one launch graphic.
   Done when: each deliverable is saved in one folder and the profession tags are distinguishable without color.