Self-Study Planner with Spaced Repetition for Dev Skills

Self-taught developers and career-changers face a specific pain: their learning backlog balloons faster than they can work through it. They bookmark ML courses, save coding tutorials, star GitHub repos, and queue up YouTube playlists, but they have no system to decide what to study today or how to retain what they already covered. Anki solves the retention half with spaced repetition flashcards, but it does nothing to prioritize a chaotic queue of skills and resources into a coherent daily plan.

Enter Study Queue (working name), a SaaS self-study planner that combines two mechanics: queue prioritization and spaced repetition logic. Users dump their entire learning backlog into the tool, tag items by skill area (ML, backend, frontend, systems) and urgency, and the planner produces a prioritized daily plan. Once a topic is studied, the tool schedules review sessions using spaced repetition intervals so that knowledge is reinforced rather than forgotten. The difference from Anki is that this is a study planner, not just a flashcard app. It manages the full pipeline from backlog to daily action to long-term retention.

The MVP is deliberately narrow: an intake form for the learning queue, a prioritization engine that outputs a daily plan, and a spaced repetition scheduler that resurfaces completed topics for review. No native mobile app, no content library, no social features at launch. The single testable hypothesis is whether developers overwhelmed by backlogs will keep coming back to a tool that tells them what to study each day.

Revenue is a straightforward B2C subscription, likely 6 to 12 dollars per month, with an annual discount. Given the target audience of career-changers who already spend on courses (Udemy, Coursera, bootcamps), a low monthly price is defensible as a productivity layer on top of that spend.

Go-to-market starts where the audience already congregates: YouTube study-routine and Notion-setup content, developer subreddits, and communities of self-taught coders. The growth loops are (1) shareable public study plans and progress streaks that create curiosity and referral, and (2) template sharing where a user's curated ML or web-dev roadmap becomes a lead magnet others can import and then subscribe to maintain.