This article details Alipay’s technical journey of refactoring the MyTab feature across Android, iOS, and HarmonyOS using Kotlin Multiplatform, covering architectural shifts, performance bottlenecks, stability fixes, and the resulting gains in code reuse and development efficiency.
This article demystifies Kotlin Multiplatform’s compiler architecture, explaining how shared Kotlin code is transformed into platform‑specific binaries via a common intermediate representation, the roles of Kotlin/JVM and Kotlin/Native backends, expect/actual contracts, memory model evolution, and practical build and integration steps for Android and iOS.
This article presents a KMP‑centric solution that uses Kotlin/JS to compute Compose UI on the JS side and Skia PictureRecorder on the native side, enabling block‑level, script‑driven dynamic UI embedding in native Compose pages without WebView or additional rendering stacks.
The "AI‑Led Cross‑Platform Technology New Paradigm" salon gathered experts from Kuaishou, Alipay, Tencent, and JetBrains to share deep technical insights on Kotlin Multiplatform, large‑scale multi‑device architecture, AI integration, and real‑world migration experiences across iOS, Android and HarmonyOS.
This article details how Alipay's engineering team built an AI‑Agent‑powered coding assistant for Kotlin Multiplatform (KMP) native cross‑platform development, covering architecture, UI generation from designs and images, RAG‑based knowledge retrieval, crash analysis, and future directions for AI‑driven software engineering.
This article details the end‑to‑end development of Alipay’s AI Travel Assistant, covering product conception, the migration to a unified xUI 1.0 framework, the adoption of KMP for cross‑platform UI, the shift from RPC + SYNC to gRPC streaming, and the challenges of card‑ecosystem migration, all achieved within a two‑month sprint by a four‑person client team.
This article details the rapid two‑month development of Alipay’s AI Travel Assistant, covering product goals, challenges of fragmented travel services, the adoption of the xUI framework, gRPC streaming, KMP cross‑platform code, and the resulting performance and engineering benefits.
This article analyzes high‑throughput sensitive‑word filtering by comparing brute‑force, KMP, Trie, double‑array Trie and Aho‑Corasick algorithms, presents their time and space complexities, shows Java implementations for Trie and AC automata, evaluates Netty deployment options, and offers practical optimizations such as asynchronous detection, hot‑reloading, tiered responses, logging and fuzzy matching.
This article explains the Knuth-Morris-Pratt (KMP) string‑matching algorithm, describes how the next array is built to capture prefix‑suffix information, shows Java implementations of the search and next‑array construction, and discusses common optimizations to reduce redundant comparisons.
This article introduces Kotlin Multiplatform (KMP), explains its core concepts and advantages, walks through a simple demo project with code snippets and build configurations, and discusses the underlying compilation process, Compose UI sharing, ecosystem libraries, and real‑world industry adoption.
This article introduces common graph concepts and several advanced string‑matching algorithms—including Brute‑Force, Rabin‑Karp, KMP, Boyer‑Moore, AC automaton, Trie, and Double‑Array Trie—explaining their principles, implementations, complexity analyses, and typical application scenarios for search systems.
This article introduces the KMP string‑matching algorithm, explains its practical scenarios, details the construction and purpose of the Next array, provides a step‑by‑step Python implementation, and analyzes its linear‑time complexity for efficient pattern searching.
This article explains the Brute‑Force (BF), Boyer‑Moore (BM), and Knuth‑Morris‑Pratt (KMP) string‑matching algorithms, illustrates their operation with step‑by‑step visual examples, discusses the inefficiencies of BF, the shift rules of BM, and the prefix‑suffix logic of KMP, and provides a complete Java implementation including the construction of the next array.
This article explains the KMP string‑matching algorithm, how it builds the prefix (next) table to avoid the O(M·N) worst‑case of naive search, provides JavaScript implementations, compares it with Boyer‑Moore and V8's indexOf strategy, and includes detailed code examples.
This article introduces several classic string‑matching algorithms—including Brute‑Force, KMP, Boyer‑Moore, BMH, Sunday, Shift‑And/Or, and bitmap techniques—explaining their principles, preprocessing steps, and practical considerations for improving text search performance.
