AlgoVision turns data structures, algorithms, and famous interview problems into step-by-step interactive visualizations. Edit the input, scrub the timeline, and watch the work happen.
Arrays, linked lists, stacks, queues, heaps, trees. Watch each one grow, shrink, balance, and break — and feel exactly what each operation costs.
Browse data structuresSearch, traverse, partition, relax. Run any algorithm with the same controls — play, step, scrub, replay — until the trick stops being a trick and starts being a pattern you recognize.
Browse algorithmsThe problems you’ll see in every interview — with the data structure visible at every step. Pause, rewind, edit the input, and ask “why does this work?” until you actually know.
The brute-force solution checks every pair in O(n²). The hash-map trick: as we scan left-to-right, for each nums[i] we look up its complement (target − nums[i]) in a map of values we've already seen. The map turns the inner loop's linear scan into an O(1) lookup, so the whole thing runs in a single O(n) pass.
Visualize it →Walk left-to-right with a stack. Every opener gets pushed; every closer pops and must match the top. If you ever pop the wrong opener — or pop from an empty stack — the string is invalid. After the loop, the stack must be empty for the string to be valid. The stack is the perfect data structure here because brackets nest like function calls.
Visualize it →The brute force is to try every (buy, sell) pair — O(n²). The trick is realizing you don't need to revisit history: for each day, the best sell-today profit is today's price minus the cheapest price seen so far. So track running min and running max-profit in one pass.
Visualize it →Every algorithm produces a stream of snapshots. Each one freezes the data structure in time — see exactly which cell was compared, which edge was relaxed, which pointer moved. No more squinting at pseudocode trying to picture what happens.
Scrub forwards. Scrub backwards. Hit reset. The execution is fully replayable — go back to the exact moment a value changed and ask why.
Edit the input as JSON. Change the array, swap the graph, set k=5 instead of k=3. Watch the algorithm rerun on your data in real time.
Stacks, queues, dp tables, visited sets, distance arrays — surfaced as first-class panels alongside the visualization, not hidden behind variable names.
When you can see why Dijkstra picks the cheaper edge, you don't need to recall the pseudocode under interview pressure — you can derive it.
Most algorithm explanations tell you what the algorithm does. They rarely let you watch it happen. AlgoVision exists so you can stop reading pseudocode and start watching the work.
AlgoVision is built in the open. New modules and improvements ship regularly. Pick a channel and follow whichever fits your style.
Read the code, file an issue, contribute a module, or watch the repo to get release notifications.
Module breakdowns, recorded sessions, and longer explanations that pair with the visualizations.