System-Design

Introduction The last decade has seen a convergence of two once‑separate worlds: Microservice‑centric architectures that decompose business capabilities into independently deployable services, each exposing a well‑defined API. Action‑oriented AI—large language models (LLMs), reinforcement‑learning agents, and tool‑using bots—that can reason, plan, and execute tasks autonomously. Individually, each paradigm solves a critical set of problems. Microservices give us scalability, resilience, and clear ownership boundaries. Action‑oriented AI gives us the ability to interpret natural language, make decisions, and orchestrate complex, multi‑step procedures without hard‑coded logic. ...

Revolutionizing Microservices Security: Lessons from Uber’s Charter ABAC System In the sprawling ecosystem of modern microservices architectures, where thousands of services interact billions of times daily, traditional access control methods crumble under the weight of complexity. Uber’s engineering team tackled this head-on by developing Charter, an attribute-based access control (ABAC) system that delivers microsecond authorization decisions while handling nuanced policies based on user location, time, data relationships, and more. This innovation not only secures Uber’s vast infrastructure but offers a blueprint for any organization scaling microservices.[1][2] ...

In the era of social media and character limits, URL shorteners like Bitly and TinyURL have become essential infrastructure. While the core functionality—mapping a long URL to a short one—seems simple, building a system that can handle billions of requests with millisecond latency and 99.99% availability is a classic system design challenge. In this post, we will walk through the architectural blueprint of a scalable, resilient URL shortener. 1. Requirements and Goals Before diving into the architecture, we must define our constraints. ...

Introduction Designing systems around large language models (LLMs) is not just about calling an API. Once you go beyond toy demos, you face questions like: How do I keep latency under control as usage grows? How do I manage costs when token usage explodes? How do I make results reliable and safe enough for production? How do I deal with context limits, memory, and personalization? How do I choose between hosted APIs and self-hosting? This post is a zero-to-hero guide to system design for LLM-powered applications. It assumes you’re comfortable with web backends / APIs, but not necessarily a deep learning expert. ...

Introduction RSS and Atom feeds remain foundational for syndicating content across the web—from news and blogs to podcasts and enterprise integrations. Designing a robust feed system isn’t just about outputting XML; it’s about correctness, scale, freshness, discoverability, compatibility, and reliability. This article walks through a detailed system design for building and operating RSS/Atom feeds. We’ll cover data modeling, HTTP semantics, caching, pagination and archiving, push (WebSub) vs pull, security, observability, and practical implementation snippets. A comprehensive Resources section at the end provides standards, validators, and production-ready libraries. ...