Recently, researchers at Anthropic published a study showing autonomous AI agents discovering and monetizing real software failures in simulated environments. The headlines made it sound dramatic, triggering unhelpful reactions ranging from dismissal (“this is just crypto”) to alarmism (“AI attackers are here”). Both miss the point. I care about this result not because it’s a…
Most people think dense vector search works like this: embed your documents store the vectors run cosine similarity Done. This is the biggest misunderstanding in modern AI systems. Dense vector search looks simple, but in real deployments it becomes one of the hardest layers to scale—and often the true bottleneck behind: slow RAG pipelines inconsistent…
A systems-level explanation for engineers, architects, and anyone building RAG, search, or agent infrastructure. Dense retrieval looks clean on paper. You take an embedding model, generate vectors, drop them into a vector database, and let an ANN index handle the rest. But once you go beyond a single machine, dense search becomes something very different:…
Why dense search becomes a routing, sharding, and distributed-systems problem Vector search looks simple when everything fits on one machine. It becomes a different discipline entirely when you need to serve: millions to billions of vectors, across multiple nodes, with predictable latency, and high recall, while RAG or agent pipelines depend on you staying under…
(Where Dense Search Becomes a Distributed Systems Problem) Most content about vector databases focuses on the glamorous part: fast queries, clever indexing, tight cosine similarity loops. But if you operate these systems in production, you learn something uncomfortable: Your system’s correctness, performance, and scalability are defined far more by the write path than by the…
The Hidden Failure Modes of Dense Vector Search, ANN Indexes, and RAG Infrastructure Most engineering teams learn this the hard way: vector databases don’t fail like relational systems or search indexes. They fail in quiet, geometric, and catastrophic ways that often go unnoticed until correctness, latency, or agent performance collapses. Dense vector search systems built…
Most AI failures don’t happen inside the model. They happen one layer earlier — in retrieval. If your RAG system, copilot, or agentic workflow is hallucinating, the LLM probably isn’t “dumb.” It’s operating on bad context. Dense search alone can’t fix that. Sparse search alone can’t fix that. Hybrid retrieval — the combination of semantic…
Introduction: navigating the digital revolution with Akka at the edge capabilities In the ever-evolving digital technology landscape, a significant shift is taking place, with edge computing emerging as a cornerstone of this transformation. This shift is fundamentally changing our approach to data handling and processing. Leading this change is Akka Edge, a key player in the…
Introduction Brief overview of the blog post Hello, fellow engineers! Looking to dive into the magic behind clustering in machine learning? Well, then you’ve come to the right place! In this post, we’re going to deep-dive into the colorful realm of K-means clustering, a powerful algorithm that might look deceptively simple but hides a beastly…
Introduction: Diving into the World of Neural Networks Welcome aboard, folks! We’re about to embark on a thrilling journey into the world of neural networks – an integral part of the exciting machine learning and deep learning landscape. What is a Neural Network? Imagine your brain. Now imagine it a lot simpler (no offense intended!)….