Transformers
The original transformer had both an encoder and a decoder. GPT threw away the encoder entirely. Here's why decoder-only architecture became the backbone of modern generative AI — and what it means for the models powering today's synthetic media.
GANs
Generative Adversarial Networks power much of today's synthetic media, from deepfakes to AI-generated faces. Here's a technical breakdown of how GANs pit two neural networks against each other to learn the art of creating realistic new data.
Speculative Decoding
Speculative decoding lets AI models predict multiple tokens ahead using a small draft model verified by a larger one, dramatically cutting inference latency without sacrificing output quality — a technique reshaping how fast generative models run.
quantization
Extreme quantization techniques compressing models to under 2 bits per weight are rewriting the economics of AI infrastructure, enabling larger models to run on cheaper hardware without catastrophic accuracy loss.
Generative AI
A technical breakdown of Flow Matching, Optimal Transport, and Schrödinger bridges — the mathematical engines increasingly powering the generative models behind modern AI image and video synthesis.
AI Security
Malicious model weights and pickle-based exploits are emerging as serious threats to the AI supply chain. Here's how attackers weaponize model files, why safetensors matters, and what practitioners can do to verify the integrity of downloaded models.
latent space
Latent space is the mathematical foundation behind generative models that power deepfakes, face swaps, and AI video. Here's how these compressed representations enable realistic synthetic media generation and manipulation.
AI Text Detection
Team DACTYL's PAN 2026 submission combines Bayesian data mixing and empirical cross-risk minimization to build more robust AI-generated text detectors, tackling generalization across unseen generators and domains.
Sakana AI
Sakana AI's error diffusion method trains Dale-compliant dual-stream networks without backpropagation, hitting 96.7% on MNIST and 61.7% on CIFAR-10, pointing toward more biologically plausible learning.
deepfake detection
Researchers at Brazil's Unicamp have developed a deepfake detection approach that generalizes to synthetic media created with techniques the system has never seen, tackling one of the toughest problems in digital authenticity.
deepfake detection
A global deepfake detection competition reveals critical blind spots in current AI systems, exposing fairness gaps and accuracy failures across demographics that undermine real-world reliability.
LLM Training
A hands-on exploration of training GPT-style transformer models on a budget GTX 1050 GPU, revealing practical constraints, optimization tricks, and what hobbyists can realistically achieve with limited VRAM.