This article details a practical experiment that uses the Kuikly App Builder skill to let an AI agent automatically create, compile, and deploy multi‑platform mobile apps, highlighting the workflow, results, current limitations, and broader implications for future AI‑driven development tools.
Google AI Edge introduces two open‑source projects—Gallery and LiteRT‑LM—that let Android developers run large language models locally without network connectivity, offering offline inference, privacy protection, GPU/NPU acceleration, and streaming output for real‑time AI experiences.
PhoneClaw and PokeClaw are open‑source, on‑device AI agents for iOS and Android that run Gemma 4 locally, offering offline privacy, zero‑cost operation, and native tool calling through iOS APIs or Android Accessibility services.
Google’s AI Edge Gallery lets developers run open‑source large language models such as Gemma 4 directly on Android devices without network connectivity, offering an integrated framework with agent skills, thinking mode visualizations, multimodal interaction, and a prompt lab, thereby addressing privacy, latency, and offline AI needs.
Google has introduced an Advanced Installation Flow that tightens side‑loading permissions, requiring developer verification and a 24‑hour waiting period, which dramatically changes how Android users and developers install apps outside Google Play.
This guide walks through creating an Android project that combines Java and Kotlin, adding Kotlin dependencies via Maven, and implementing a simple controller with Kotlin syntax, illustrating variable definitions, method signatures, and basic service interaction.
This article explains the concept of edge computing, why models are deployed to devices, the advantages of using Python scripts on Android/iOS, and provides a step‑by‑step guide to implementing a Python interpreter, NumCpp integration, feature management, dynamic deployment, and business impact through real‑time strategy updates.
The article explains how mobile network performance issues—especially long connection‑pool wait times—cause slow page loads in Android apps, demonstrates how to interpret Alibaba Cloud RUM’s Resource event metrics and timing data, walks through a real‑world case study with detailed stage‑by‑stage analysis, and provides concrete diagnostic steps and optimization recommendations for OkHttp connection‑pool configuration and other common bottlenecks.
This comprehensive guide demystifies Android dependency injection, covering core concepts, manual implementation, the role of composition roots, and a detailed comparison of Hilt and Koin—including their architectures, advantages, trade‑offs, and practical tips for choosing the right framework in real‑world projects.
This article explains how mobile network diversity, device fragmentation, and limited visibility make performance troubleshooting hard, then introduces Alibaba Cloud RUM's Resource event model, walks through its attribute and metric fields, demonstrates a real‑world Android case study with step‑by‑step timing analysis, and provides concrete diagnosis and optimization guidelines for connection‑pool, DNS, SSL, and TTFB issues.
This article shows how to create a reusable GET request wrapper for WeChat Mini Programs, demonstrating the function that appends a base URL, passes query parameters, sets JSON headers, and handles success and failure callbacks.
XcodeBuildMCP, an open‑source Sentry project, lets AI agents such as Cursor and ClaudeCode fully automate iOS/macOS development by handling Xcode build management, device selection, log capture and error handling, eliminating the manual verify‑compile‑test loop.
This article details a systematic four‑phase optimization of React Native page startup—preloading resources, advancing initialization, reusing and pre‑creating containers—that shrank cold start time by 55 % (1.78 s → 0.80 s) and hot start time by 70 % (1.10 s → 0.33 s), while exposing the new pitfalls introduced by container reuse.
This article walks through the complete process of obtaining a WeChat Mini Program user's OpenID by using wx.login to get a code, calling the updated auth.code2Session API with appid and appsecret, and handling the server‑side Java implementation that extracts the OpenID and session key.
During the 2026 Chinese New Year, Xiaohongshu built a real‑time dynamic interaction system that combined adaptive scheduling, high‑performance particle rendering, and industrial‑grade ASR to deliver synchronized voice greetings and emoji fireworks to over a billion daily active users across heterogeneous mobile devices.
The article examines how careless AI integration can degrade mobile app performance, cause overheating, battery drain, and poor user experience, and proposes a systematic, user‑centric approach—including device fragmentation awareness, lightweight models, graceful degradation, and edge‑cloud collaboration—to ensure AI adds value rather than harm.
This article examines the practical challenges of adding AI features to mobile clients, highlighting device fragmentation, performance trade‑offs, user pain points, and a layered approach that balances lightweight models, graceful degradation, and edge‑cloud collaboration to keep the experience smooth for the majority of users.
This article explains the differences between D8 and R8, details R8’s advanced optimization techniques, discusses challenges like reflection and keep‑rules, and highlights recent tooling improvements and future features that make R8 essential for faster, smaller Android apps.
This guide walks through installing Termux, Ubuntu, and OpenClaw on a Xiaomi Android phone, configuring developer options, connecting via ADB, installing the burner‑phone skill, capturing screenshots, and attempting click actions, while highlighting limitations when click debugging is unavailable.
This article details how the Vivo Game Center team tackled slow page loads and resource failures in weak‑network environments by integrating the QUIC‑enabled Cronet library into OkHttp, defining precise network states, using custom interceptors, and conducting A/B tests that reduced failure rates by 40% and cut request latency by 7%.
