In Q3 2024, our 12-person platform team slashed log ingestion spend by 35% in 90 days, moving from a brittle Elasticsearch-based pipeline to a tuned Vector 0.30 and Loki 3.0 stack—without losing a single log or breaking our 99.95% SLA. GameStop makes $55.5B takeover offer for eBay (279 points) Talking to 35 Strangers at the Gym (144 points) Newton's law of gravity passes its biggest test (15
We Cut Compliance Costs by 40% Using Pulumi 3.140 and Chef 18 for Multi-Cloud AWS and GCP Modern multi-cloud environments offer unmatched flexibility, but they also introduce complex compliance challenges. For our team managing hybrid infrastructure across AWS and GCP, manual policy enforcement and fragmented tooling were driving up compliance costs by 22% year-over-year. By integrating Pulumi 3
Fixed-length chunking requires no external services, yet semantic chunking absolutely needs an Embedding API — why? The core idea of semantic chunking is to split text at semantic boundaries. Determining whether "two pieces of text belong to the same topic" requires converting text into vectors and computing similarity — that's exactly what the Embedding API does. Dimension Fixed-Length / Recur
RAG stands for Retrieval Augmented Generation. Why do we even need RAG?? To answer this lets take a look at What LLMs and SLMs are. LLM(Large Language Model). Data on several categories(generalized) will be given as input. From that, a model would be created. What is a model ? To understand this, lets take mathematical equation of a straight line y = mx +c Lets take x values to be 1, 2, 3, ... a
Why Do We Need Specialized Vector Databases? In the first five articles, we figured out how to chunk documents and generate embeddings. Now where do these vectors live, and how are they efficiently retrieved? You might wonder: "Can't I just store vectors in Redis or PostgreSQL?" No — traditional databases are designed for exact queries (e.g., WHERE id = 123), while vector retrieval is Approximat
In Q3 2024, our 12-person platform engineering team reduced confirmed security incidents by 41.7% (from 72 to 42 per quarter) after rolling out Trivy 0.50 for pre-deployment scanning and Falco 0.40 for runtime detection across 142 production microservices. We didn’t rewrite our CI/CD pipeline, we didn’t hire a dedicated security team, and we didn’t spend a dime on enterprise security tools. Here’s
In Day-1, we understood about the overview of a RAG system and what are its components and how it helps the LLM to generate more accurate and contextual responses. Now, lets see about the storage of the data using Vector Databases. Lets assume that we have a PDF with us and this would be considered as our private data. Now I want my LLM to have the context about this PDF, So that I could ask any q
Gen AI Based Chatbots, Its quite normal and people are doing it for couple of years now, So what’s Different that I am doing? Well the biggest issue with using AI models now is its Cost, even for a simple FAQ based chatbots. The Cost goes in Thousands.. The result is P.A.I.. It's a chatbot widget that lives in the corner of my portfolio site. Visitors The Architecture at a Glance Before diving