Debug and extend a maze solver through progressive graph search with follow-up questions like movement-rule, and state-tracking tasks. Recently seen in Meta AI-enabled interviews

Maze Solver

Build and optimize a subset solver that maximizes unique characters under Meta-style performance pressure.

Max Unique Characters Subset

Practice a progressive AI-enabled coding interview problem with runnable tests and structured parts.

Card Game Strategy

Friend Recommendation

Compiler Optimization

Card Hand Comparator

Delivery Cost Dashboard

Expense Rule Engine

Service Dependency Impact

Meeting Scheduler

LRU Cache Progressive

Crawler Frontier Queue

You are given a small card-table game where a legal move is to choose three visible cards whose values sum to 15.

Across the interview, you will fix move validation, implement a basic chooser, measure strategy quality by simulation, and improve the strategy.

engine.cpp

engine.h

piece.h

strategies.cpp

strategies.h

test_framework.h

test_helpers.h

test_part1_legality.cpp

test_part2_baseline_strategy.cpp

test_part3_evaluation.cpp

test_part4_improved_strategy.cpp

tests

Chooser.cs

Engine.cs

Mark.cs

Piece.cs

RoundState.cs

Strategies.cs

Assertions.cs

TestAttribute.cs

TestHelpers.cs

TestPart1Legality.cs

TestPart2BaselineStrategy.cs

TestPart3Evaluation.cs

TestPart4ImprovedStrategy.cs

TestRunner.cs

Chooser.java

Engine.java

Mark.java

Piece.java

RoundState.java

Strategies.java

Assertions.java

Test.java

TestHelpers.java

TestPart1.java

TestPart2.java

TestPart3.java

TestPart4.java

TestRunner.java

engine.js

pieces.js

strategies.js

helpers.js

part1.test.js

part2.test.js

part3.test.js

part4.test.js

engine.py

pieces.py

requirements.txt

strategies.py

__init__.py

test_helpers.py

test_part1_legality.py

test_part2_baseline_strategy.py

test_part3_evaluation.py

test_part4_improved_strategy.py

Fix the Card Selection Bug

Implement a Baseline Strategy

Measure Strategy Quality

Improve the Strategy