Gayan's Personal Blog

 Have you ever wondered about the best way to integrate Flyway into your Gradle project? Let me break it down into two main approaches: using Flyway as a dependency and applying the Flyway plugin . The Dependency Approach: implementation 'org.flywaydb:flyway-core' Adding Flyway as a dependency essentially embeds it within your application. Think of it as equipping your application with a built-in toolbox to handle database changes during runtime. This approach is ideal when you want your application to take charge of migrations. When to Use It Your application needs to manage database changes while it’s running. You’re working with a Spring Boot application that integrates seamlessly with Flyway. Key Features Spring Boot Auto-Configuration: Spring Boot automatically detects the Flyway dependency and configures it. You can custo...

  "Backable" by Suneel Gupta explores how to inspire belief in your ideas, combining storytelling, resilience, and practical strategies to succeed.

 Recently I attended Analysis Design Architcture conference (https://adaconf.org/) where listened to talk named "Future Proof Software Architecture: Microservices". That reminded me my experience of using sidecars as part of a microservice architecture in one of my previous projects( Australian Bank). So I decided to write down some of the key points and insights that came out of that experience.  firstly, let's start with understanding why we might want to move beyond traditional API gateways. API gateways traditionally handle everything from TLS termination to authentication, authorisation, and traffic routing. They're like the swiss army knife of API management - doing lots of things in one place. But here's the problem - as our microservices architectures grow more complex, having all these responsibilities in a central gateway becomes a bottleneck. So let's see what are sidecars - think of them as your microservice's personal assistant. Instead of rou...

 Everyone talks about ChatGPT and revolution of GenAI. Ever wondered what powers the chatbot you use or how your favourite AI assistant seems to know just the right thing to say? It’s all thanks to LLM s , or Large Language Models. Let me break it down in plain English and make it easy to grasp. Key things to know about LLMs: Think of them like super-smart text predictors! (imagine writing "Hello, how are..." and it knows to complete with "you"... but way more advanced!) They work like a magical book predictor (they read MILLIONS of books, articles, websites... if you read non-stop, it would take 2600+ years to read what GPT-3 learned from!) Don't expect them to be human-like!!! (they're like a chef who memorised recipes but never tasted food) Cool Metaphors that helped me understand: The Library Detective Has read EVERY book in existence Can find info super fast Connects different facts together (like having the world's smartest librarian at yo...