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Bilibili Tech
Bilibili Tech
Apr 22, 2025 · Operations

Client‑Side DCDN Disaster‑Recovery Drills and Automated Testing at Bilibili

Bilibili performed client-side DCDN disaster-recovery drills using a self-built HTTPDNS to simulate DNS, CDN, and SSL faults; automated scripts across Android, iOS, and Web injected errors, measured rendering latency, validated immediate downgrade to commercial services, refined fallback strategies, and demonstrated near-zero user impact during a real network incident.

BilibiliDCDNHttpDNS
0 likes · 13 min read
Client‑Side DCDN Disaster‑Recovery Drills and Automated Testing at Bilibili
TAL Education Technology
TAL Education Technology
Feb 10, 2022 · Operations

Client‑Side Circuit Breaking Strategies: State Machine, Google SRE Breaker, and Mitigation Techniques

This article explains why client‑side circuit breaking is essential, describes common state‑machine and Google SRE breaker strategies, provides practical pseudocode, and discusses mitigation methods such as Gutter mode, jittered exponential backoff, and graceful degradation to protect system stability.

Google SRERate Limitingcircuit breaking
0 likes · 14 min read
Client‑Side Circuit Breaking Strategies: State Machine, Google SRE Breaker, and Mitigation Techniques
ByteDance Dali Intelligent Technology Team
ByteDance Dali Intelligent Technology Team
Jun 20, 2021 · Frontend Development

Practical Refactoring Guidance for Client‑Side Development

This article offers practical engineering guidance on when and how to refactor client‑side code, emphasizing planning, incremental changes, systematic approaches, tooling, and team coordination to improve code quality while minimizing risk and maintaining system stability.

client-sidecode qualityrefactoring
0 likes · 19 min read
Practical Refactoring Guidance for Client‑Side Development
Xianyu Technology
Xianyu Technology
Apr 20, 2018 · Artificial Intelligence

Client‑Side Voice Recognition with TensorFlow Lite and MFCC Optimization

The paper presents a client‑side speech recognizer that uses a compact TensorFlow Lite Inception‑v3 CNN model combined with an optimized MFCC feature pipeline and ARM‑NEON‑accelerated, multi‑threaded processing, achieving low‑latency, high‑accuracy voice recognition on mobile and embedded devices.

Audio ProcessingMFCCTensorFlow Lite
0 likes · 14 min read
Client‑Side Voice Recognition with TensorFlow Lite and MFCC Optimization