Layered Network Topology
Visualize input features, dense hidden layers, and output nodes with animated synaptic connections and activation pulses.
Technical & Neural Network Explainers
Make neural networks, complex algorithms, and deep learning crystal clear.
Vivipilot converts deep learning architectures, computational graphs, and mathematical transformations into structured diagrams, neuron activation flows, and guided camera transitions. Perfect for explaining how neural networks compute predictions, how data propagates through hidden layers, and how complex algorithms operate.
Forward Propagation & Flow
Animate glowing data pulses and weighted signals traveling through computation layers in strict causal order.
Dynamic Labels & Math Notation
Display inline activation functions (ReLU, Sigmoid, Softmax), weights, biases, and prediction confidence badges.
A practical workflow
From brief to editable project.
- 01Describe the model architecture (Input features -> Hidden computation layers -> Output classification).
- 02Select the Technical & AI Explainer style (/style tech).
- 03Review the sequential neuron activations, synaptic weight connections, and camera focus moves.
- 04Fine-tune neuron labels, timing, or color palettes directly on the canvas and export in 4K.
Example prompt
“Explain how a deep neural network processes data. Open on input feature nodes lighting up in green, propagate weighted signal pulses through two hidden computation layers with blue activation pulses, and glide the camera into the output layer revealing a 98.7% classification confidence badge.”
Typical deliverables
- ✓ AI & ML Explainer Videos
- ✓ Technical Documentation & Whitepapers
- ✓ Engineering Blog & Research Companions
- ✓ Developer Onboarding & Educational Content
Questions
What to know.
What technical subjects work best?
Deep learning neural networks, machine learning algorithms, distributed systems, mathematical transformations, and technical protocols work exceptionally well.
Can I edit individual diagram nodes and neuron weights without rebuilding the video?
Yes. Every node, neuron label, connector line, math label, and timing parameter is represented as editable native PixiJS objects on the canvas.