Place queens without conflicts, then scale the solver from board scans to bitmasks
4 sections · 8 lessons
Define the N-Queens constraints, then build the backtracking mental model of partial solutions, branching, and undoing state.
2 lessonsImplement is_safe with board scans, then replace it with set-based column and diagonal tracking for faster checks.
3 lessonsBuild the compact bitmask formulation for columns and diagonals, and trace how shifts carry threats across rows.
1 lessonDiagnose state bugs in recursive solvers, then consolidate the N-Queens pattern into a reusable backtracking playbook.
2 lessonsN-Queens turns a simple chess rule into a search problem: place one queen per row so no two attack each other. This course builds the backtracking model and implementation patterns behind that solution, from checking conflicts to returning every valid arrangement. It is aimed at learners who want to understand why backtracking works and how to write efficient, correct code for it.
The course covers constraint-based search, depth-first backtracking with choose recurse undo, and conflict checking by board scan. It also covers full solution generation, set-based pruning, bitmask state tracking, and common debugging pitfalls in mutable recursive code.
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Three findings from decades of learning research shaped how every Fenzo lesson is built.
Average pass rate · Freeman et al., PNAS 2014
What the science says
Students in lecture-only classes are 1.5× more likely to fail.
How Fenzo does it
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You work through itAI does it for you
Skill retention over time · with vs. without practice
What the science says
Offloading thinking to AI erodes the very skills you’re trying to build.
How Fenzo does it
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Retrieval practiceRe-reading
Long-term recall · retrieval vs. re-reading
What the science says
Recalling information beats re-reading it for building durable understanding.
How Fenzo does it
Lessons weave in quizzes and recall prompts, so it sticks.