Neural Network Interpretability: Methods for Understanding What Models Learn
A technical survey of feature attribution, probing, and mechanistic interpretability methods, and an honest look at what they can and can't explain.
Read more →AI Research
Research explainers and surveys of advances in artificial intelligence — paper walkthroughs and the ideas shaping the field, translated for practitioners.
Reinforcement Learning from Human Feedback: How RLHF Actually Works
How RLHF actually works: the three-stage pipeline, why it exists, its failure modes, and why DPO is displacing it in production alignment pipelines.
Read more →
Diffusion Models Explained: How Denoising Generates Images
A technical deep-dive into diffusion models: forward noise addition, reverse denoising, training objectives, noise schedules, and latent diffusion—explained …
Read more →