Interactive sandboxes for modern Artificial Intelligence: master Backpropagation, derive Attention mechanisms, optimize models with LoRA, build RAG systems, and explore AI Safety. Optimized for research and education.
Foundational calculus, linear algebra, and probability theory for machine learning research.
Computational graphs, tensor strides, autograd JVP/VJP, and CUDA execution models.
Universal approximation, weight initialization (He/Xavier), and rigorous backprop derivation.
Multi-head attention math, RoPE positional embeddings, KV caching, and architecture variants.
Low-Rank Adaptation (LoRA), quantization theory, FlashAttention, and knowledge distillation.
Vector indexing (HNSW), hybrid search, re-ranking algorithms, and GraphRAG architectures.
ReAct loops, function calling schemas, planning algorithms (ToT), and agentic memory.
Proximal Policy Optimization (PPO), Reward Modeling, and Direct Preference Optimization (DPO).
Adversarial attacks (GCG), mechanistic interpretability, and sparse autoencoders.
World models (JePA), neuromorphic computing, and energy-based probabilistic models.
An experimental environment for testing initial AI hypotheses and rapid prototyping.
A premium, responsive chat interface designed for large language model interactions.
Advanced visualization tools for high-dimensional data and model activations.