If you mostly live in .NET, the Java platform can look like a parallel universe: JVM, JDK, JARs, app servers, bytecode. The useful shortcut is to map each concept back to something you already know from C# and the CLR. This guide is a translation layer for .NET developers: what the JVM is, how the JDK compares to the .NET SDK, and what your real options are when a C# system needs to work with Java
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
💡 Problem: How do we ensure that a class has only ONE instance throughout the application? 💡 Common Use Cases: Logger Configuration Manager Database Connection 💡 Approach: We restrict object creation and provide a global access point. 💡 Key Idea: Private constructor Static instance Public method to access it 💻 Java Example: private static Singleton instance; privat
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
The drift problem Every project that ships a translated README has the same lifecycle: Someone writes README.md in English. A contributor opens a PR with README.zh.md. Great. Three months later, English has six new sections. Chinese has the original. A second translator opens README.es.md. Spanish gets translated from… which version? The current README.md? Or README.zh.md, by accident, because t
Apache Stateful Functions is one of the quietly powerful frameworks in the Flink ecosystem - durable per-key state, exactly-once messaging, polyglot remote functions, all on top of Apache Flink. It's also been functionally dormant since October 2024, and it doesn't run on Flink 2.x. We needed it on Flink 2.x. So we maintained the continuation: kzmlabs/flink-statefun. This post is the why and the h
How We Improved Payment System Throughput by 25% Using Apache Kafka at a Fortune 500 FinTech By Disha Sune — Java Backend Engineer | Spring Boot | Kafka | AWS | Fiserv At Fiserv, our payment processing platform handled millions of financial transactions daily for 600+ enterprise clients including McDonald's, Google, and Domino's. As transaction volumes grew, our legacy synchronous REST API archi