Attention is an information-routing operator

A conceptual walkthrough of attention as learned information routing. The post builds from tokens and Q/K/V projections to self-attention, multi-head attention, masking, positional encoding, and how the same ideas appear in robotics, VLMs, and VLA-style policies.

15 min read   ·   June 29, 2026

2026   ·   attention   network-architecture   transformers   self-attention     ·   paper-notes  

Understanding U-Net as a “what → where” machine

A visual walkthrough of U-Net for image segmentation, explaining how CNNs learn “what” is in an image but lose precise “where” information. The post breaks down the U shaped architecture in down-sample blocks, up-sample blocks, skip connections, transposed convolutions, and supervised training with segmentation masks.

13 min read   ·   June 26, 2026

2026   ·   u-net   network-architecture   segmentation   deep-learning     ·   paper-notes