Multi-Step Coding Project Trainer

Upgrade learners from single-function puzzles to multi-file, multi-step real-world projects

Problem

A developer points out that LeetCode-style single-function questions are disconnected from real work. Companies like Anthropic, OpenAI, Meta, Snowflake, and Databricks ask multi-step, multi-file questions closer to real problems. For example, building a key-value store: step 1 is get/set/delete/count, and only after tests pass does step 2 unlock more features. Yet existing practice platforms rarely offer this progressive experience, leaving learners without a proper training ground.

Solution

A multi-step project practice platform for AI/backend developers, where each problem is a staged implementation of a real system. Users complete multiple modules in one repo, and each passing step unlocks the next. Problems can come from open-source projects, open courses, or community contributions, with automated tests and explanations. The core is 'staged like a real project', not grinding puzzles.

Users

Developers preparing for AI lab or big-tech interviews, CS students, and self-learners transitioning from algorithm puzzles to system design.

MVP

Launch 3-5 multi-step project problems (e.g., key-value store, simple task queue, rate limiter); each provides a starter repo, step-by-step instructions, and one-click tests; a web UI showing progress and submission results; GitHub login and progress saving.

Revenue

Free access to first few projects; subscription unlocks full problem set and detailed solutions; enterprise offering for custom interview problems and evaluation dashboard.

Why now

AI job interviews increasingly emphasize system design and multi-step implementation, while existing platforms remain single-function. AI can help generate and verify problems, and indie developers can quickly build a problem bank and auto-grader.