The simulator visualizes a Kademlia identifier space and lets you watch how XOR distance shapes each node's k-buckets, how a lookup narrows in on a target key hop by hop, and how passive updates from ordinary traffic keep routing tables fresh as nodes join and leave.
Add nodes to the network and pick a target key to look up. Step through the lookup one hop at a time to see which contacts are queried, how the candidate set shrinks toward the target, and how the remaining XOR distance roughly halves at each step. Toggle node churn on to watch buckets self-heal through passive updates and periodic refreshes.
Controls include adding or removing nodes, setting a target lookup key, stepping or auto-playing the iterative lookup, toggling simulated node churn (join/leave events), adjusting k-bucket size, and inspecting any node's routing table and distance ranges.
Because the XOR metric guarantees exactly one node exists at any given distance from you, no two nodes ever perceive the network's shape identically, yet every one of them can still reach the same correct answer to 'who is closest to this key' using only its own local, partial view.
The simulator visualizes a Kademlia identifier space and lets you watch how XOR distance shapes each node's k-buckets, how a lookup narrows in on a target key hop by hop, and how passive updates from ordinary traffic keep routing tables fresh as nodes join and leave.
The simulator visualizes a Kademlia identifier space and lets you watch how XOR distance shapes each node's k-buckets, how a lookup narrows in on a target key hop by hop, and how passive updates from ordinary traffic keep routing tables fresh as nodes join and leave.
Add nodes to the network and pick a target key to look up. Step through the lookup one hop at a time to see which contacts are queried, how the candidate set shrinks toward the target, and how the remaining XOR distance roughly halves at each step. Toggle node churn on to watch buckets self-heal through passive updates and periodic refreshes.
Because the XOR metric guarantees exactly one node exists at any given distance from you, no two nodes ever perceive the network's shape identically, yet every one of them can still reach the same correct answer to 'who is closest to this key' using only its own local, partial view.