you can now build your own RL environment from scratch. watch your agent exploit a badly designed reward, and learn why reward design is the hardest part of RL

8 computer vision concepts worth understanding by implementing them yourself on TensorTonic: > 2D Convolution - slide a kernel across an image, and work out the output size from padding and stride. > Max Pooling - shrink a feature map by keeping only the strongest activation in each window. > Batch Normalization - normalize activations across the batch, then rescale them with learned gamma and beta. > Intersection over Union - score how well two boxes overlap by dividing the shared area by the combined area. > Non-Max Suppression - remove duplicate detections by keeping the top-scoring box and dropping the ones that overlap it too much. > Focal Loss - a cross-entropy variant that down-weights easy examples so the hard, rare ones drive the gradient. > Dice Loss - train segmentation models on mask overlap, so a tiny object isn’t drowned out by a huge background. > Receptive Field - compute how much of the input image each output unit can actually see through stacked layers. link in comments.

Every LLM is 8 layers of ideas stacked on top of each other which you can implement on tensortonic: Layer 1: Gradients Write a tiny autograd engine. Value nodes, expression graphs, a neuron. loss.backward() stops being a black box. Layer 2: Tokens Train BPE yourself and apply the merges by hand. Layer 3: The block RMSNorm, SwiGLU, RoPE, causal grouped-query attention. Layer 4: Training at scale FLOP estimates, mixed-precision memory, gradient accumulation. Layer 5: Attention variants MHA → MQA → GQA → MLA, then FlashAttention. Layer 6: Kernels Softmax, GELU and reductions in CUDA, then again in Triton. Layer 7: Fine-tuning QLoRA on Qwen3-4B with real medical Q&A. Layer 8: Serving Build your own Qwen3 inference server. All 8 are graded, runnable problems on TensorTonic. link in comments.