LLM
KV Caching: How This Optimization Makes LLM Inference Viable
Key-value caching is the hidden optimization that makes large language models practical. Learn how this technique eliminates redundant computation during inference.
transformers
Hugging Face
Hugging Face Transformers v5: Simplified APIs for AI Development
Hugging Face releases Transformers v5 with cleaner APIs, unified model loading, and breaking changes that simplify building AI applications across text, image, and video domains.
transformers
Positional Encoding Methods: Why Token Order Matters in AI
Transformers process tokens in parallel, losing sequence information. Four positional encoding methods—sinusoidal, learned, RoPE, and ALiBi—solve this fundamental challenge differently.
LLM
KV Cache Explained: The Hidden Engine Powering Fast LLM Inference
Understanding Key-Value caching in transformer architectures reveals how modern LLMs achieve fast token generation. This core optimization technique is essential for efficient AI inference.
NLP
Research Reveals How AI Transformers Distort Business Sentiment
New research exposes systematic sentiment bias in NLP transformers, showing how AI language models struggle to maintain neutral tone in business communications, raising concerns for automated content generation.
LLM research
New Research Maps LLM Embeddings Using Hamiltonian Physics
Researchers propose a physics-inspired framework treating LLM token embeddings as discrete semantic states governed by Hamiltonian dynamics, offering new insights into transformer interpretability.
multimodal AI
How 7 Key Breakthroughs Enabled Multimodal AI Systems
The human brain seamlessly integrates sight, sound, and touch. Replicating this took a decade of AI research and seven critical innovations that now power today's video and image generation systems.
AI Architecture
5 Essential AI Architectures Powering Modern Synthetic Media
From Transformers to GANs, these five foundational architectures form the backbone of AI video generation, deepfake creation, and synthetic media systems that every engineer should understand.
transformers
Transformers vs Mixture of Experts: Architecture Guide
Deep technical comparison of transformer and mixture of experts architectures, exploring how MoE models achieve computational efficiency while maintaining performance in modern AI systems including video generation.
GPT
Training GPT Models on MacBook Air M1: A Technical Guide
A detailed technical walkthrough of training transformer-based language models on consumer hardware, covering tokenization, architecture implementation, training optimization, and resource management on Apple Silicon.
transformers
Building Transformers from Scratch with Tinygrad
Learn to implement transformer components and mini-GPT models from the ground up using Tinygrad. This technical deep dive covers attention mechanisms, layer normalization, and neural network fundamentals to understand how modern AI systems work.
Generative AI
Understanding Generative AI Model Families and Architectures
A technical deep dive into the major families of generative AI models—from GANs and VAEs to diffusion models and transformers—that power today's synthetic media, deepfakes, and AI video generation tools.