Starter track
Follow the authored sequence, or switch to recap mode for a faster review of the same path.
Start with visible list work, reuse that search language for binary search, and then carry the branch into one live graph bench for adjacency, BFS, DFS, and visited-state behavior.
Entry diagnostic
Reuse the sorting quick test and the binary-search challenge to decide whether to start from visible sorting work or jump ahead to the shared graph bench.
Check the list-to-search bridge first
No saved diagnostic checks are available yet, so the opening concept is still the best place to start.
Uses the same local-first quick tests, checkpoint challenges, and track history already saved in this browser.
Check whether comparisons, writes, and input order already feel readable on the live list.
No saved quick-test result yet.
Create a large ordered list with the target near the far-right edge, but still find it in five checks or fewer.
No saved checkpoint attempt yet.
About this track
Keep the first scan focused on the next lesson. Open the authored rationale and shared-framework notes only when you need them.
Why this order
Sorting and Algorithmic Trade-offs comes first because it turns the list itself into a live object with visible cost and disorder. Binary Search / Halving the Search Space then reuses that list language while ordered data, low-mid-high markers, and interval shrinking explain why binary search is fast. Graph Representation and Adjacency Intuition widens the branch from lists into neighborhoods, Breadth-First Search and Layered Frontiers keeps that bench shallow and queue-based, Depth-First Search and Backtracking Paths gives the same graph a stack-based contrast, and Frontier and Visited State on Graphs closes by showing how cycles become manageable through clean bookkeeping.
Shared concept pages
Compare mode, prediction mode, quick test, worked examples, guided overlays, challenge mode, and read-next cues stay on the concept pages. The track only decides the guided order and the next recommended stop.
Guided path
Checkpoint cards reuse the authored challenge entries already living on the concept pages.
Watch sorting as visible work on a live list so input order, comparisons, and writes stay concrete instead of collapsing into one final answer.
Start here before moving into Binary Search / Halving the Search Space.
Create a run where insertion sort finishes a nearly sorted list with very few writes.
Finish Sorting and Algorithmic Trade-offs first. This checkpoint ties together Sorting trade-offs through Make insertion pay off.
Pause here after Sorting and Algorithmic Trade-offs before moving into Binary Search / Halving the Search Space.
Keep an ordered list, the low-mid-high markers, and the shrinking interval visible together so binary search feels visual instead of procedural.
Builds on Sorting and Algorithmic Trade-offs before setting up Graph Representation and Adjacency Intuition.
Create a large ordered list with the target near the far-right edge, but still find it in five checks or fewer.
Finish Binary Search / Halving the Search Space first. This checkpoint ties together Sorting trade-offs and Binary search through Find an edge target fast.
Pause here after Binary Search / Halving the Search Space before moving into Graph Representation and Adjacency Intuition.
Keep one live graph, one local neighborhood, and one frontier cue visible together so graph structure feels readable before traversal rules get formal.
Builds on Binary Search / Halving the Search Space before setting up Breadth-First Search and Layered Frontiers.
Keep the queue frontier, visited count, and graph layers visible together so breadth-first search reads as a layered process instead of a procedure list.
Builds on Graph Representation and Adjacency Intuition before setting up Depth-First Search and Backtracking Paths.
Keep the stack frontier, current depth, and branch order visible together so depth-first search feels like disciplined backtracking instead of random wandering.
Builds on Breadth-First Search and Layered Frontiers before setting up Frontier and Visited State on Graphs.
Use one cyclic graph to see which nodes are waiting, which are finished, and why repeat edges are skipped instead of reopening loops.
Capstone step after Depth-First Search and Backtracking Paths.
