Explora ejemplos curados de animaciones de Computer Science por tema. Abre cualquier hub temático para encontrar escenas reutilizables de Manim, pruebas visuales e ideas listas para enseñar.
28 animaciones · 7 temas
24 animaciones
Demonstrates binary search tree operations step by step: inserting nodes to build a BST, searching for a value with left/right comparisons shown, detecting an unbalanced tree, and performing an AVL left rotation to restore balance. Height and balance factor labels update throughout.
Explains the CAP theorem through an animated distributed system with 3 nodes. A network partition is triggered, and the animation shows the trade-off: choosing CP sacrifices availability while choosing AP allows divergence. Real-world systems are labeled with their CAP classification.
Demonstrates consistent hashing on a circular hash ring (0–360 degrees). Server nodes are placed on the ring, keys are assigned clockwise, and the animation shows that adding or removing a server only redistributes a minimal fraction of keys. Virtual nodes are introduced to improve load distribution.
Visualizes Dijkstra's algorithm on a weighted directed graph with 6 nodes. The animation shows the priority queue (min-heap), greedy node exploration, distance table updates at each step, and the final shortest path tree highlighted in gold.
8 animaciones
Demonstrates consistent hashing on a circular hash ring (0–360 degrees). Server nodes are placed on the ring, keys are assigned clockwise, and the animation shows that adding or removing a server only redistributes a minimal fraction of keys. Virtual nodes are introduced to improve load distribution.
Contrasts three approaches to computing Fibonacci numbers: naive recursion (exponential, showing repeated subproblems in red), top-down memoization (cached calls return instantly in green), and bottom-up DP table filling. Complexity labels O(2^n) vs O(n) are displayed prominently.
Visualizes a forward pass through a [3, 4, 4, 2] neural network. Input activations propagate layer by layer through weighted connections, showing the weighted sum computation and activation function at each layer. The output layer applies softmax. Active neurons glow to indicate high activation values.
Plots growth curves for the six major Big-O complexity classes on a shared set of axes. A ValueTracker animates n growing, showing how quickly each curve diverges. A reference table maps each complexity to example algorithms.
5 animaciones
Explains the CAP theorem through an animated distributed system with 3 nodes. A network partition is triggered, and the animation shows the trade-off: choosing CP sacrifices availability while choosing AP allows divergence. Real-world systems are labeled with their CAP classification.
Visualizes Dijkstra's algorithm on a weighted directed graph with 6 nodes. The animation shows the priority queue (min-heap), greedy node exploration, distance table updates at each step, and the final shortest path tree highlighted in gold.
Visualizes Git's underlying commit DAG structure. Commit objects are shown as circles with abbreviated hash IDs, branches as colored labels pointing to commits, and HEAD as a special pointer. Operations animated include: git commit (new node), git branch (new label), git merge (merge commit with two parents), and git rebase (commits replayed on new base).
Shows an 8-node undirected graph and demonstrates two traversal strategies: BFS spreads level by level using a queue, while DFS dives deep using a stack. Visited nodes are colored and the visit order is displayed for each strategy.
3 animaciones
Demonstrates backpropagation in a 3-layer neural network. A forward pass first computes activations left to right, then a loss is computed. Gradients flow backward through the network as a glowing wave, with the chain rule applied at each layer. The key partial derivative formula is displayed.
Visualizes a forward pass through a [3, 4, 4, 2] neural network. Input activations propagate layer by layer through weighted connections, showing the weighted sum computation and activation function at each layer. The output layer applies softmax. Active neurons glow to indicate high activation values.
Visualizes the scaled dot-product self-attention mechanism in a Transformer. Input tokens are projected to Q, K, V matrices, attention scores are computed via Q×K^T dot products shown as a heatmap, softmax is applied, and the output is computed as a weighted sum with V. The full attention formula is displayed.
2 animaciones
Walks through the RSA public-key cryptosystem: key generation from two primes, encryption using the public key, and decryption using the private key. Alice and Bob exchange an encrypted message, demonstrating the one-way trapdoor function. The security relies on the hardness of integer factorization.
Introduces binary numbers using the metaphor of light switches that are either off (0) or on (1). Students see how combinations of 4 switches represent decimal numbers and how counting works in binary from 0 to 3.
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