Official Baidu App Tech Account
This article explains how Baidu's iOS app analyzes Mach‑O binaries and applies systematic resource‑optimization techniques—including big‑resource detection, unused config removal, and duplicate asset elimination—to shrink package size by over a dozen megabytes.
The article details Baidu APP iOS package size reduction by identifying and removing unused images, converting resources to Asset Catalogs, and leveraging HEIC and appropriate compression, achieving a 9.75 MB saving while outlining scripts, regex extraction methods, and best‑practice recommendations.
This article examines why Baidu's super‑app needed a drastic reduction in its iOS package size, outlines the metrics that link bundle size to download conversion, compares the app‑size footprints of major domestic and overseas apps, and details the multi‑layered technical solutions—resource trimming, architecture safeguards, compiler tweaks, image compression, and code slimming—that together saved over 50 MB while preserving functionality.
The article traces Chromium’s prerender evolution from the resource‑heavy Prerender 1.0 to the lightweight, API‑driven Prerender 2.0 built on MPArch, explains its Speculation Rules usage, activation detection, implementation flow, and advises conservative deployment in high‑confidence navigation scenarios to boost perceived load speed.
This article explains the architecture of Mach virtual memory in iOS, details its page‑based management, describes the core data structures and kernel APIs, analyzes the Objective‑C alloc implementation, and walks through the memory‑pressure (Jetsam) mechanism that triggers low‑memory warnings.
This article details Baidu's systematic approach to reducing Android APK size by analyzing Dex annotation types, defining removal targets, implementing a Titan‑Dex based optimizer, and automating detection and whitelist management with custom Lint rules, ultimately saving about 1.2 MB of Dex bytecode.
The article explains Baidu App's memory‑control solution that monitors real‑time memory usage, predicts page‑level memory growth, defines safe and dangerous memory watermarks, and triggers proactive or reactive actions to avoid OOM crashes on low‑end iOS devices.
A SIGKILL crash indicates the OS forcibly terminated an iOS app, which cannot be caught directly, but MetricKit (iOS 14+) can automatically collect diagnostic payloads after restart, allowing developers to extract termination reasons, stack traces, understand codes like 0x8badf00d, and address common causes such as main‑thread watchdog timeouts and deadlocks.
The Thor Container Framework, created by Baidu for its App to meet Hook technology needs, offers standardized Java and Native Hook interfaces through an abstract layer, enabling lightweight, dynamically delivered plugins with hot‑swap capability, cloud‑controlled activation, and robust disaster‑recovery features, supporting use cases such as low‑end optimization, privacy compliance, OOM handling, and pipeline integration.
This article explains how to build a high‑precision Android startup performance pipeline by selecting the right tracing tool, extending Perfetto with custom Gradle instrumentation, handling edge‑case trace mismatches, and using Trace Processor’s Python API for automated wall‑time and CPU‑time regression detection.
