Why This Topic Matters OTP (One-Time Password) verification is a critical security feature in modern mobile applications. Whether you're building a fintech app, healthcare platform, or any service requiring user authentication, implementing OTP verification efficiently can be the difference between a smooth user experience and frustrated users abandoning your app. The react-native-otp-auto-verif
Introduction Building a mobile application that handles sensitive financial data — crypto transactions, KYC verification, gift cards — means security is not an afterthought. It is a core deliverable. During the development of a cross-platform fintech application, one of the non-negotiables on the security checklist was runtime application self-protection (RASP). After evaluating our options, we
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
React Native's New Architecture — JSI, Fabric, and TurboModules — has been "coming soon" for long enough that some teams wrote it off as vaporware. It shipped. It is now default in new React Native projects. And it meaningfully changes how the framework works at the performance-critical boundaries between JavaScript and native code. This post is not a getting-started guide. It is an honest account
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
It's a one-line item on the roadmap. "Send a push notification when X happens." Estimate is two days, three if the backend doesn't have FCM credentials yet. There's a library for it. The library is the visible part. The other 90% is platform lifecycle, registration state machines, race conditions with navigation, payload archaeology, and a half-dozen iOS and Android quirks. Nobody writes them down
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 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