#Transformers

State Space Models in 2026: Mamba-2 State Space Duality vs Transformer Attention

An authoritative mathematical and systems analysis of State Space Models. We examine Mamba-2 State Space Duality (SSD), Structured State Space (S4) recurrence, linear-time $O(N)$ inference scaling, and hybrid Mamba-Transformer production architectures.

AI Context Windows Explained: Working Memory, Attention Limits, and "Lost in the Middle" (2026)

Transformers Explained: Self-Attention Is Reading With Highlighters (2026)

The Model Architecture Behind Realistic AI Video (Explained Simply)

Demystifying Diffusion Transformers (DiT). How 3D spatial-temporal patches, self-attention, Flow Matching, and VAE latents generate realistic video in 2026.

1M Token Context Windows Are Table Stakes Now: So What Still Differentiates? (2026)

Context Window Extension: RoPE Scaling, YaRN, and LongRoPE Frequency Domain Mathematics (2026)

DeepSeek-V3 Multi-Head Latent Attention (MLA): Low-Rank KV Compression & 93% VRAM Reduction (2026)

Mixture-of-Depths (MoD) & Dynamic Token Routing: 50% FLOP Reduction in Transformer Compute (2026)