The bottleneck of modern vector retrieval. When working with high-dimensional embedding space-whether serving semantic search, recommendation engines, or Retrieval-Augmented Generation (RAG) pipelines - the fundamental challenge is nearest-neighbor search. Exact nearest-neighbor methods require calculating the vector distance (like Cosine or Euclidean) between a query vector and every single item in
Machine learning systems increasingly rely on data generated by devices operating in the real world: cameras capturing environments, sensors streaming telemetry, microphones collecting audio, and applications logging user interactions. These signals make excellent training data because they reflect the actual conditions models need to perform under. The moment a training
Retrieval-Augmented Generation has become the default architecture for practical AI systems because it separates reasoning from knowledge freshness. Models no longer need to contain every fact at training time, they can retrieve relevant information from search indices, document stores, internal knowledge bases, and user-generated corpora at runtime. That shift improves
Telemetry is supposed to be your safest window into production reality. But in device plus AI systems, it's also the quickest way to leak sensitive context, quietly, accidentally, and at scale. The pattern is familiar: a "temporary" debug field ships, raw sensor payloads sneak into logs,
AI systems are no longer passive observers. What began as chatbots, summarizers, and recommendation engines has evolved into systems that actively influence application behavior and persistent state. AI now classifies events, prioritizes alerts, triggers workflows, escalates anomalies, and shapes timelines that users and downstream systems rely on. This shift fundamentally