Official Kuaishou tech account, providing real-time updates on the latest Kuaishou technology practices.
This article analyzes Kuaishou's development of a Java 17 transparent coroutine solution, detailing its architectural evolution, scheduling and I/O optimizations, performance gains in cloud‑native high‑load environments, and future directions for large‑scale production deployment.
KwaiCoder-23BA4-v1 is a 23B wide MoE code‑completion model that achieves state‑of‑the‑art performance on HumanEval, BigCodeBench and Fill‑in‑Middle benchmarks by using high‑quality data, a cost‑effective training pipeline that combines model pruning, knowledge distillation and fine‑grained merging, and extensive ablation studies.
Kuaishou has achieved a significant milestone with 7 papers accepted at AAAI 2025, covering diverse AI research areas including video processing, recommendation systems, and image restoration, demonstrating the company's strong research capabilities in artificial intelligence.
This article presents an overview of Kuaishou's system software team, detailing its core value, challenges, the "Four Horizontal Four Vertical" architecture, JVM innovations such as transparent coroutines and checkpointing, advanced compilation techniques, kernel isolation for cloud‑native workloads, and extensive performance‑boosting strategies that have dramatically improved resource utilization and stability.
The NeurIPS 2024 Auto‑Bidding competition attracted over 15,000 submissions and 1,500 teams, featuring two tracks—General and AI‑Generated Bidding—where Kuaishou’s commercial algorithm team secured first place in both by leveraging reinforcement‑learning‑based online exploration and a decision‑transformer‑driven generative approach, achieving more than a 5% lift in ad revenue.
This article presents a comprehensive analysis of page performance issues across Kuaishou's commercial front‑end projects, outlines the challenges of unified governance, B‑end experience measurement, and C‑end web‑native integration, and details the systematic optimization strategies and measurable results that significantly improved user experience and business metrics.
Kuaishou and Tsinghua University were honored with the first‑place Qian Weichang Chinese Information Processing Science and Technology Award for their collaborative content recommendation project, which achieved international‑level innovations in explainable recommendation, bias correction, and edge intelligence, and has been applied widely in Kuaishou's platform and top academic conferences.
This article examines how Kuaishou’s e‑commerce platform leverages AI to analyze and improve B‑side user experience, detailing current challenges, the CPS metric model, a self‑built intelligent inspection platform, workflow automation, and future prospects for AI‑driven UX optimization.
This article summarizes the challenges of distributed training for massive language models and presents a suite of solutions—including DP/TP/PP overlap, context parallelism, efficient recomputation, and a performance‑aware cost model—that together boost training throughput by over 30% on large GPU clusters.
This article examines the challenges and benefits of running stateful services such as Redis on Kubernetes, presents Kuaishou’s practical experience with cloud‑native migration, evaluates risks and performance impacts, and details the custom workloads, operators, federation and KubeBlocks solutions that enable large‑scale, reliable stateful service orchestration.
