Official Kuaishou tech account, providing real-time updates on the latest Kuaishou technology practices.
Apache Auron graduates from incubation to become a top-level project after 13 months, delivering 2x faster Spark query performance and 50% resource savings via its Rust-native vectorized execution engine, already proven at exabyte scale across companies like Kuaishou, DiDi, and LinkedIn.
Kuaishou's KwaiMind unifies general and e-commerce image editing via a four-agent data engine and DiffusionOPD multi-teacher distillation, achieving top scores on public benchmarks and the custom Ecom-Bench, with a 2.44% relative CTR increase in online A/B tests.
Kuaishou E-commerce's merchant team details their end-to-end AI delivery pipeline (Harness Engineering) combined with organizational restructuring, achieving 30% throughput gains and 40% delivery-time reduction by shifting from functional silos to full-stack 'super individuals' across requirements, spec, dev, test, and oncall phases.
The article examines how six Kuaishou engineering teams integrated AI across product, development, testing, e‑commerce, anti‑fraud and operations, using Agent‑First platforms and a Harness framework to convert individual coding speed gains into measurable organizational efficiency, merchant revenue growth, and faster risk detection.
This article presents Kuaishou’s 2026 H1 comprehensive review of its AI productivity system, detailing the three‑year evolution, the three critical bottlenecks that hindered scaling (skill polarization, workflow friction, and AI‑human coordination), the historical banking analogy that shaped a new AI‑productivity paradigm, a five‑stage roadmap with three parallel execution tracks, and concrete case studies that demonstrate a 10% rise in per‑person demand delivery, an 11%+ reduction in cycle time, and a doubling of AI code‑generation rates across the organization.
PlatformBid, the first benchmark designed from a unified advertising‑platform perspective, expands real‑time bidding research beyond DSP‑centric single‑advertiser goals by adding platform‑level constraints, three realistic evaluation settings, and a new Flow‑Matching‑based algorithm (BidFlow) that achieves state‑of‑the‑art performance both offline and in a live Kuaishou e‑commerce deployment, delivering a 0.68% consumption lift.
The Kuaishou technology team had 25 papers accepted at the prestigious KDD 2026 conference—including three oral presentations—covering generative recommendation, automated bidding, semantic ID learning, multi‑behavior modeling, and other AI‑driven advances that are already deployed at massive scale on the platform.
KAT-Coder-Pro V2.5 introduces a flagship Agentic coding model that expands long‑chain engineering ability, adds a universal Agentic framework, and leverages a large‑scale RL pipeline, achieving top scores on SWE‑Bench Pro, PinchBench and internal benchmarks while enabling developers to hand over complete issues without manual decomposition.
This article analyzes the growing pressure on software testing caused by rapid product iteration and complex business rules, then details a four‑stage evolution—from prompt‑engineered V1 to multi‑agent V2, knowledge‑enhanced V3, and agentic self‑evolving V4—showing how each stage improves generation rate, adoption, and defect coverage while outlining practical lessons for teams adopting AI‑driven testing.
The talk traces Kuaishou's AI coding journey from early tab‑completion with 5‑20% generation rates to a 90‑100% code‑generation ratio in recent weeks, analyzes the persistent ABAB serial‑execution friction, and proposes a spec‑driven long‑running Agent loop that externalizes state, unifies execution nodes, and enables true human‑agent parity through shared context and CLI integration.
