Machine-Learning

• Inside Vector Databases: Building Retrieval-Augmented Systems that Scale · 2025-10-26

  How modern vector databases ingest, index, and serve embeddings for production retrieval-augmented generation systems without falling over.

• Learned Indexes: When Models Replace B‑Trees · 2025-10-04

  A practitioner's guide to learned indexes: how they work, when they beat classic data structures, and what it takes to ship them without getting paged.

• The Johnson-Lindenstrauss Lemma and the Geometry of High-Dimensional Data · 2025-09-05

  Explore the surprising geometry of high-dimensional spaces: the Johnson-Lindenstrauss lemma showing that random projections preserve pairwise distances, the concentration phenomena that make it work, and its profound applications in nearest-neighbor search, compressed sensing, and machine learning.

• Latency-Aware Edge Inference Platforms: Engineering Consistent AI Experiences · 2023-03-12

  A full-stack guide to designing, deploying, and operating low-latency edge inference systems that stay predictable under real-world constraints.

• Keeping the Model Awake: Building a Self-Healing ML Inference Platform · 2023-02-14

  A field report on taming production machine learning inference with proactive healing, adaptive scaling, and human empathy.

• Convex Optimization: Gradient Descent, Nesterov Acceleration, KKT Conditions, and the ML Stack · 2020-02-18

  A deep investigation of convex optimization—the engine of modern machine learning—from gradient descent and Nesterov momentum to KKT conditions and interior-point methods.

• Artificial Intelligence: A Modern Approach (4th ed.)