Build a Best-First Search Solver for a Delivery Startup's Test Lab
Overview
What this challenge is about.
Build a Python module exposing uniform-cost search and A-star search on a 2D grid with weighted cells (open road, traffic, no-go). Implement two admissible heuristics for A-star: Manhattan distance and a slightly smarter learned-from-shortest-paths offline table. Create 10 benchmark instances of increasing size (10x10 up to 200x200), measure expanded nodes and wall-clock time per algorithm, and produce a 5-page tutorial with code snippets, plots, and a short quiz. The first-week intern should be able to read the tutorial, run the code, and answer the quiz inside a 2-hour study session.
The Brief
What you'll do, and what you'll demonstrate.
Deliver a teaching-grade implementation of uniform-cost and A-star search with a benchmark and a beginner tutorial.
Earning criteria — what you'll demonstrate
- Implement classical search algorithms from first principles
- Design admissible heuristics and reason about their effect on node expansion
- Benchmark algorithms fairly across input sizes
- Write learner-friendly technical documentation
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
AI Engineer
Implementing core search algorithms cleanly and writing onboarding material is the kind of foundational craft an AI engineer is expected to have on day one at any AI-forward company.
This challenge sharpens
- search-algorithms
- python
- algorithm-benchmarking
Machine Learning Engineer
Clean Python and benchmark discipline transfer directly to the MLE's daily work of comparing model variants under fair conditions.
This challenge sharpens
- python
- algorithm-benchmarking
- data-structures