Alibaba’s Qwen3.7‑Max model tops multiple Arena leaderboards, achieves SOTA scores in programming, reasoning, and multilingual benchmarks, runs a 35‑hour autonomous coding task on a custom AI chip with 10× speedup, and demonstrates end‑to‑end desktop app creation and web‑search agents, illustrating a rapid monthly model‑iteration strategy.
Former Alibaba AI leader Lin Junyang is reportedly raising hundreds of millions for a new AI lab that could be valued at $2 billion, with venture firms Gaorong Capital and Sequoia Capital in talks and a team recruited from ByteDance, Tencent and overseas talent.
Alibaba’s HappyHorse 1.0, a native multimodal video generation model launched on April 27, combines audio‑video synthesis and editing in a single platform, tops several AI video benchmarks, offers low‑cost per‑second pricing, and demonstrates strong scene understanding through a series of prompt‑driven examples, while still showing minor glitches such as occasional text artifacts.
This guide shows how to set up a Spring Boot 3.x project with JDK 17, add the spring‑ai‑alibaba‑starter, configure an Alibaba Cloud Bailei API key, and implement a simple chat controller to call the model via Spring AI.
Alibaba has added four new open‑source weight versions to its Qwen3.6 series, featuring the 27‑billion‑parameter dense multimodal model Qwen3.6‑27B and the 35‑billion‑parameter sparse expert model Qwen3.6‑35B‑A3B, both designed for stable, real‑world coding tasks and outperforming their Qwen3.5 predecessors.
Alibaba’s Meoo tool, used by over 10,000 non‑technical employees, lowers the execution barrier of software creation but shifts the real challenge to expressing clear requirements, prompting a deeper look at AI‑driven software democratization and its impact on developers and cloud strategy.
Alibaba’s Meoo is a zero‑code AI tool that claims to turn a natural‑language idea into a complete front‑ and back‑end web application in about a minute, using four domestic large models, a swarm‑Agent architecture, and deep integration with Alibaba Cloud, while offering dramatic time‑saving compared to traditional development.
In the first week of April 2026, Alibaba’s Tongyi Lab launched three purpose‑built large language models—Qwen3.6-Plus for programming, Qwen3.5-Omni for multimodal tasks, and Qwen3 Coder Next for repository‑level coding—illustrating a strategic shift from pure benchmark races to targeted, cost‑effective deployment across distinct AI battlefields.
Alibaba’s HappyHorse-1.0, a new text‑to‑video and image‑to‑video model from the ATH team, claimed the #1 spot on Artificial Analysis’s video arena rankings, matches ByteDance’s Dreamina Seedance 2.0 in Elo score, supports four generation modes, and will open its API around April 30.
Meta, Alibaba and Tencent have turned AI token consumption into a competitive KPI, with over 85,000 Meta staff logging 60 trillion tokens in a month, earning badges and sparking industry debate about productivity, cost and corporate culture.
Alibaba's newly released Qwen3.5-Omni multimodal model adds an Audio‑Visual Vibe Coding feature that lets users describe a website or game with speech and gestures to generate code, while offering advanced audio comprehension, long‑duration media support, multilingual capabilities, fine‑grained voice control, and voice cloning, though its weights remain closed‑source.
This article explains Alibaba's multi‑site high‑availability architecture, covering its origins after Double 11 bottlenecks, core principles like decentralization and consistency‑availability trade‑offs, layered design from traffic routing to data storage, and a real‑world deployment example.
This article explains Alibaba Group’s AI‑driven code review benchmark, the agent‑based assistant that understands repository context, its real‑world impact on reducing null‑pointer exceptions, and how the open‑source AACR‑Bench dataset provides a multi‑language, context‑aware evaluation standard for AI code review.
In just 48 hours, Alibaba’s youngest P10 AI leader Lin Junyang resigned, exposing deep organizational and resource‑allocation challenges within the rapidly expanding Tongyi Qianwen project and sparking widespread industry debate over open‑source strategy, talent retention, and the future of large‑scale AI development.
On March 4, 2026, Alibaba’s youngest P10 technical leader Lin Junyang announced his resignation with a nine‑word tweet, just hours after releasing four Qwen 3.5 models that earned Elon Musk’s praise, while two other core researchers also left, leaving the future of China’s leading open‑source AI flagship uncertain.
RynnBrain, an open‑source unified spatiotemporal foundation model from Alibaba DAMO Academy, integrates perception, localization, physics‑based reasoning and planning across 2 B, 8 B and 30 B MoE scales, handles multimodal visual inputs, and outperforms existing models on over 20 embodied benchmarks.
Alibaba's Tongyi releases the Z-Image base model, a non‑distilled diffusion transformer that supports full classifier‑free guidance, negative prompts, higher diversity, and fine‑tuning, contrasting with the faster Turbo variant and providing detailed usage instructions and community resources.
The article details Alibaba's 2024 campus backend salary bands, compares them with peers, outlines the interview process and project presentation tips, and provides in‑depth technical tutorials on Redis Lua scripts, cache consistency, slow‑query logging, MySQL optimization, Java reflection, WebSocket vs polling, SSE, TCP/UDP, and HTTP vs HTTPS.
Spring AI Alibaba Admin is an Alibaba‑built platform that extends Spring AI to address three major engineering hurdles—inefficient prompt debugging, uncertain AI quality, and opaque production operations—by offering comprehensive prompt, dataset, evaluator, experiment, observability, and model‑configuration capabilities for enterprise AI agents.
A recent report comparing Alibaba’s home‑grown PPU AI accelerator with NVIDIA’s A800 shows the PPU’s 96 GB HBM2e memory, 700 GB/s bandwidth, PCIe 5.0×15 interface and 400 W power draw surpass the A800 and rival the H20, while large‑scale deployments in China’s telecom sector demonstrate rapidly growing compute capacity.
Amid escalating US export restrictions, Chinese tech giants Alibaba and Baidu are accelerating the development of their own AI chips—Alibaba's self‑designed processors and Baidu's Kunlun P800—to reduce reliance on Nvidia’s H100 and A100, signaling a potential shift in the global AI compute landscape.
Alibaba introduced the Qwen3‑Max‑Preview model, a trillion‑parameter LLM that boosts multilingual understanding, complex instruction handling, and tool use while cutting hallucinations, offers competitive benchmark scores, supports 262K context, and comes with tiered token‑based pricing that may limit broader adoption.
Over ten years, Alibaba’s data engineers transformed a modest Hadoop‑based system into a globally‑scalable, high‑performance big data platform—ODPS/MaxCompute—supporting massive offline and real‑time workloads, pioneering innovations like the 5K cluster expansion, Blink streaming, and the unified ‘Moon’ migration.
A recent GitHub study alleges that Huawei's Pangu Pro MoE and Alibaba's Qwen‑2.5 14B share an almost identical parameter structure with a 0.927 attention‑parameter correlation, prompting plagiarism accusations, while Huawei counters with a claim of novel MoGE architecture and strict open‑source compliance.
This article provides a comprehensive technical analysis of Alibaba’s chip portfolio—including the XuanTie RISC‑V processors, Hanguang 800 AI accelerator, Yitian 710 server silicon, and a novel compute‑in‑memory architecture—detailing their specifications, performance highlights, roadmap plans, and real‑world applications in cloud, AI, and IoT ecosystems.
While most eyes remain on familiar AI giants, China’s Alibaba and DeepSeek are unveiling open‑source video and inference models that run on consumer GPUs, sparking a regulatory scramble and threatening the dominance of closed‑source AI, heralding a rapid, disruptive shift across the industry.
Alibaba Mama’s advertising platform migrated from a monolithic Flink‑Kafka pipeline to a layered Paimon lakehouse, adding DWS upsert support and multi‑layer storage, which delivers minute‑level data freshness, cuts latency by 2.5 hours, reduces resource use over 40 %, halves development effort and achieves ≥99.9 % availability.
Alibaba has released the open‑source Qwen QwQ‑32B model, a 32‑billion‑parameter LLM that matches DeepSeek‑R1's performance while being deployable on consumer‑grade GPUs, and the announcement is accompanied by extensive promotional offers for AI‑related products and services.
Alibaba’s newly released 32‑billion‑parameter QwQ‑32B model matches the performance of 670‑billion‑parameter rivals like DeepSeek‑R1, integrates agent‑based reasoning, runs on consumer hardware, and has sparked strong open‑source community adoption, as shown by benchmark results and download statistics.
Alibaba has open‑sourced its new QwQ‑32B inference model, a 32.5‑billion‑parameter transformer that rivals top models like DeepSeek‑R1 and o1‑mini, features integrated agent abilities for tool use and critical thinking, and offers a low inference barrier with extensive technical specifications and RL‑based training details.
Chinese tech giants Huawei and Alibaba respond to President Xi’s call for stronger innovation, with Huawei showcasing its HarmonyOS and server‑grade Arm processor while Alibaba unveils the Qwen 2.5‑Max large language model that outperforms leading Western AI systems on multiple benchmarks, highlighting China’s accelerating AI ambitions.
Apple’s new Apple Intelligence platform, unveiled at WWDC24, will incorporate Alibaba’s Qwen 2.5 Max model to create China‑specific AI features for iPhone users, with a custom dataset and regulatory submission, marking a shift from overseas ChatGPT reliance to a domestic partnership.
Apple’s recent decision to partner with Alibaba as its AI collaborator in China reflects the iPhone’s sales slump, regulatory pressures, and Alibaba’s superior large‑model performance, cloud infrastructure, and compliance expertise, a move that could boost Apple’s market share while propelling Alibaba Cloud’s revenue and showcasing China’s rising AI capabilities.
Apple’s steep sales decline in China has driven it to partner with Alibaba’s Qwen AI platform, a move that blends cutting‑edge large‑model technology, cloud scalability, and local compliance to revive iPhone market share and showcase China’s rising AI prowess.
This article introduces the basic concepts of supervised and unsupervised machine learning, explains the core mechanisms of large language models such as Transformers, and provides a step‑by‑step guide with code to build a simple chatbot using Alibaba's Tongyi LLM via Spring Boot.
The cloud‑native large‑scale cloud‑edge collaborative computing platform jointly developed by Alipay, Zhejiang University, Xieyun Technology and Alibaba Cloud won the 2023 Zhejiang Science and Technology Progress First Prize, showcasing breakthroughs in unified resource scheduling, million‑node management, patents, papers and open‑source contributions.
This tutorial walks through creating an intelligent airline‑ticket customer‑service agent using Spring AI Alibaba, covering requirements, architecture, RAG integration, function calling, chat memory, core capabilities, code implementation with ChatClient, and a sample running result.
After OpenAI halted API access in China, Alibaba’s Tongyi Qwen‑2 quickly rose to the top of global open‑source LLM leaderboards, surpassing Meta’s Llama‑3 and other contenders, with detailed benchmark scores, performance gains over previous versions, and implications for China’s AI ecosystem.
The upcoming PC web version of Xianyu, announced by senior engineer Zhao Kun, will initially support browsing, searching and favoriting items, while the full transaction features are still under development, prompting developers to speculate whether the platform will continue using Flutter for its web front‑end.
On May 18, 2024, CCF and Alibaba Mama will unveil the CCF‑Alibaba Mama Technology Fund—a global academia‑industry platform targeting next‑generation AI algorithms and large‑model research through ten projects in e‑commerce, knowledge‑enhanced generation, and legal AI, with a livestreamed forum featuring a keynote by Prof. Zhang Qi on boosting task performance.
On May 18 in Ningbo, CCF and Alibaba Mama unveiled the CCF‑Alibaba Mama Technology Fund—a global academia‑industry platform targeting next‑generation AI algorithms and large‑model research through ten projects in e‑commerce, tool generation, and legal services—accompanied by a keynote on boosting large‑model task performance and open registration for scholars.
This article presents Alibaba's enterprise knowledge recommendation system, detailing its role in digital transformation, the challenges of long‑document recommendation, the multi‑layer architecture spanning feature, engine, ranking, and functional layers, various recall strategies, progressive ranking models, and the integration and evaluation of large language models for improved recommendation performance.
The paper introduces a real‑time controllable, multi‑objective re‑ranking framework for Taobao’s feed recommendation that combines actor‑critic reinforcement learning with hypernetworks to instantly adjust objective weights, handling diverse media and cold‑start constraints while delivering higher click‑through, diversity, and cold‑start ratios with only 20‑25 ms latency.
This article presents a comprehensive study of a scenario‑adaptive and self‑supervised multi‑scenario recommendation model (SASS) for Taobao, detailing its motivation, adaptive multi‑scenario architecture, two‑stage pre‑training and fine‑tuning, experimental validation, deployment in the recall stage, and practical challenges addressed through Q&A.
The 2023 AIGC Year-End Summit highlighted the integration of AI in the insurance industry, with Alibaba's CTO announcing the open-source release of their AI code assistant tool DevPilot to foster industry collaboration.
The article examines three major Alibaba service disruptions, analyzes how insufficient testing and a lack of psychological safety among engineers may have contributed to the failures, and suggests ways to improve testing practices and workplace transparency.
The 2023 Tmall Double 11 campaign saw Alibaba’s data‑intelligent tools boost market share to 63.14%, lift GMV by over 6%, and help more than 200 brands achieve double‑digit growth through platforms like ShowMAX, Super Live, and Wanxiang AI, illustrating the power of integrated digital marketing in e‑commerce.
In November 2023, Alibaba Cloud suffered a massive outage that disrupted client, web, and mobile access for nearly eight hours, affecting a wide range of Alibaba products, over 300,000 enterprise customers, and numerous global data centers, highlighting the fragility of large-scale cloud services.
This article explains why enterprises need custom low‑code platforms, introduces Alibaba’s open‑source Low‑Code Engine and the commercial UIPaaS product, compares generic and custom low‑code solutions, and outlines the architecture, capabilities, and real‑world cases that demonstrate cost and time savings.
This article presents Alibaba's experience in addressing large‑scale data stability challenges by outlining common problems, governance principles, baseline monitoring, team collaboration methods, practical implementations, and proactive measures to ensure reliable and accurate data production on the DataWorks platform.
Taobao’s IPv6 journey, from early mobile‑internet growth through five development stages, culminated in a full end‑to‑end architecture that now delivers over 95 % IPv6 traffic, 11 % latency reduction and billion‑user scale, while pioneering innovations such as precise stack detection, multi‑channel competition and future APN6/BIERv6 extensions.
In this event, a senior Alibaba Taobao Tech engineer introduces LangChain—a framework for large language models—explaining its core concepts, diverse applications, and future directions, and shows attendees how to leverage the platform to boost AI capabilities within their own businesses.
This article presents a comprehensive case study of Taobao's data model governance, detailing the background challenges, the four‑pillar solution framework, specific governance practices such as invalid table decommissioning, data handover, public layer operations, incremental control, productization, future plans, and a Q&A session.
This article presents a comprehensive overview of applying causal inference to Alibaba’s Business Intelligence platform, detailing fundamental concepts, the merchant intelligent diagnosis system architecture, key technologies such as hybrid causal network discovery (HCM) and deep attribution, and showcases the resulting operational impact.
The article shares a fresh graduate's experience interviewing for offline data‑warehouse roles at companies like Ctrip, Meituan and Alibaba, outlines the common interview pattern, and lists detailed Spark, Hadoop, and data‑warehouse questions used by these firms.
Alibaba has reorganized its technology leadership by eliminating the group CTO position, merging core tech teams into subsidiaries like Aicheng Technology, and shifting its "big middle platform" strategy toward more agile front‑end structures, signaling a broader move toward decentralized, product‑centric engineering.
The article critiques Alibaba’s recent “1+6+N” reorganization and explains why many digital and business middle‑platform projects fail in enterprises, arguing that true middle‑platform capabilities must stem from existing IT assets and should be treated as a shared service aggregation rather than a standalone product.
Chinese tech giants including Baidu, Tencent, Alibaba, ByteDance, Huawei, NetEase, and 360 have launched or are developing their own ChatGPT-like large language model products, detailing release dates, technical specifications, applications, and the challenges faced in the domestic AI landscape.
The article reports that Alibaba's Taobao and Tmall are undergoing a major organizational split after the departure of the industry leader Yang Guang (nickname “Chui Xue”), detailing the new departmental structure, historical context, strategic focus, GMV trends, and industry reactions.
This article presents a comprehensive study of multi‑scenario personalized recommendation, introducing a scenario‑adaptive and self‑supervised model (SASS) that jointly addresses data sparsity, domain adaptation, and recall‑stage deployment through a two‑stage training pipeline and extensive experiments on Alibaba’s Taobao platform.
This article details Alibaba Mama’s dynamic compute system—its architecture, offline and online tidal‑compute mechanisms, city‑level mutual backup, RT control, large‑scale promotion handling, metric integration, and recent infrastructure upgrades—showcasing concrete performance gains and future challenges in green, intelligent ad‑engine resource management.
LowCodeEngine, an open‑source low‑code engine from Alibaba DingTalk, follows strict protocol specifications, supports major browsers, offers a rich ecosystem of components and plugins, and provides detailed usage examples, CDN options, and UI panels for rapid front‑end development.
LowCodeEngine, an open‑source low‑code engine from Alibaba’s DingTalk team, follows Alibaba’s backend‑frontend standards, supports modern browsers, offers a minimal core with rich ecosystem elements, detailed protocol specifications, multiple CDN integration options, extensive UI panels, schema editing, and real‑world case studies, all illustrated with code snippets and screenshots.
This article analyzes how Alibaba’s large‑scale deployment of Direct Attach Cables (DAC) transformed data‑center physical networking by cutting costs, reducing power consumption, improving reliability and latency, and driving architectural innovations that address past adoption barriers and future challenges.
This article explains how Alibaba combines frontend development with data intelligence—covering massive internet data, market demands, three integration modes, key technologies like WhaleM, DataCook and PipCook, practical workflows, a k‑means clustering case study, and future directions such as WebNN, WebGPU and WASM—to enhance user experience and drive business growth.
LowCodeEngine is an open‑source low‑code engine released by Alibaba's DingTalk team that follows internal construction protocols, supports modern browsers, offers a minimal core with a rich ecosystem, provides TypeScript typings, and includes extensive UI panels, schema editing, and multiple CDN integration options for developers.
This presentation outlines Alibaba's digital supply chain strategy, detailing the data, analysis, and decision challenges, the multi‑layer digitalization and intelligent solutions, the evolution of the Falcon forecasting technology, and the Alibaba DChain Forecast SaaS product, with case studies and a Q&A.
In 2022 Taobao’s front‑end team was reorganized into business‑line groups plus a central “big front‑end” unit, establishing a three‑layer map—Engineering, R&D, Application—and delivering platforms such as O2, JSTracker, PHA, Weex 2.0, ICE, Midway, low‑code builders, and consumer and back‑office apps, thereby unifying tooling, strengthening the technical ecosystem, and ensuring continuous engineering excellence.
Alibaba Mama’s Secure Data Hub, a privacy‑preserving data clean‑room platform that uses multi‑party computation and federated learning for secure advertising analytics, successfully passed the China Academy of Information and Communications Technology’s Trusted Privacy Computing evaluation, confirming its strong security, compliance and industry‑recognized capabilities.
On January 9 at 7 p.m., Alibaba senior algorithm engineer Zhang Yuanliang will present a scenario‑adaptive, self‑supervised model for multi‑scenario personalized recommendation, discussing its background, technical details, experimental results, and real‑world deployment within Taobao’s recommendation system.
GBA (Global Batch Gradients Aggregation) introduces a training mode that seamlessly switches between synchronous and asynchronous learning for search‑advertising models by keeping a constant global batch size, using token‑controlled gradient aggregation and staleness management to retain synchronous‑level accuracy while preserving asynchronous efficiency and eliminating manual hyperparameter tuning.
This comprehensive guide explains why data modeling is essential for big‑data systems, compares relational and OLAP approaches, details ER, dimensional, Data Vault and Anchor methodologies, and walks through Alibaba’s multi‑stage data‑model practice, integration framework, dimension design, fact‑table strategies and aggregation techniques.
GaiaX is a cross‑platform template engine created for Alibaba’s Youku ecosystem that tackles multi‑terminal UI development bottlenecks by using a custom DSL with native rendering, offering high performance, dynamic updates, reusable components, containerized solutions, and integrated preview and design‑to‑code tools.
This article uses vivid analogies—professional craftsman, orchestra conductor, and film director—to explain Alibaba's technical job level system (P5‑P10), outlining core responsibilities, skill expectations, and the differences between each tier for developers, testers, and ops professionals.
This article examines common misconceptions about application layering, outlines Alibaba's multi‑layer architecture—including Open Interface, Terminal Display, Web, Service, Manager, and DAO layers—offers optimized layering practices, explains domain model transformations, and provides actionable guidelines for building maintainable, reusable backend systems.
The article examines how cloud‑native databases are reshaping the industry by combining historical database evolution, disruptive innovation, and Alibaba's PolarDB breakthroughs, illustrating why this shift is a strategic advantage over traditional database architectures.
This article examines Alibaba’s AliMama “Smart Font” project, detailing how AI‑driven pipelines, few‑shot generation, and open‑source toolkits are used to create commercial Chinese typefaces, enable font subsetting, and explore dynamic and variable‑font technologies for modern digital media.
Alibaba’s DaTaobao team unveiled a high‑definition live‑streaming guide that combines video processing algorithms, equipment recommendations, and standardized low‑cost solutions to dramatically improve picture quality for both top‑tier and novice streamers, while also offering free access and ongoing support.
This article presents a comprehensive overview of Taobao's data model governance, detailing the background and problems of the current data architecture, analyzing root causes, proposing a structured governance framework with DataWorks automation, and outlining future plans to improve efficiency, standardization, and product tooling.
This article presents an overview of data governance concepts, common enterprise challenges, and Alibaba's comprehensive data governance framework, covering theory, demand layers, practical solutions for stability, quality, standards, security, cost control, and the supporting platforms and operational practices.
Alibaba Open Source Week 2022, held online from August 22‑24, offers a main forum and five specialized forums—Operating Systems, Databases, Cloud Native, Big Data & AI, and Terminal—featuring over 30 technical presentations by more than 40 global experts on open‑source ecosystem development, emerging technologies, and real‑world applications.
This article explains how Alibaba leverages Flink to handle over 40 billion events per second across all business units, detailing risk‑control concepts, rule types, architectural stages, resource tuning, dynamic CEP, shared computing, and the FY23 roadmap for large‑scale streaming risk management.
This article summarizes Guo Jinshi's presentation on Taobao's data model governance, covering the current data landscape, identified problems, analysis of root causes, proposed governance solutions—including DataWorks intelligent modeling—and future plans, while also providing a Q&A session on practical implementation.
This article explains Alibaba's P7 engineering level, detailing its salary range, the differences between P6 and P7 roles, the technical, business, and soft‑skill competencies required, and provides practical guidance on how developers can progress to become a P7 senior expert.
This guide breaks down Alibaba’s P7 role, detailing the salary range, required technical breadth and depth, business acumen, leadership and soft‑skill expectations, and practical steps to advance from P6 to the coveted P7 position.
The article reviews the shift of big‑data computing from batch to real‑time, outlines the evolution of one‑stop real‑time data warehouses, introduces Alibaba's Hologres solution and its technical advantages, and shares the CCO department’s three‑generation architecture upgrades and practical use cases.
This article chronicles Alibaba's three‑stage evolution of Envoy Gateway—from the 1.0 incubation that powered Double‑11 traffic, through the 2.0 growth phase adding north‑south routing and hybrid‑cloud support, to the mature 3.0 release that unifies traffic and micro‑service gateways with high performance and community‑driven extensions.
PICASSO, Alibaba’s GPU‑centric sparse training engine for wide‑and‑deep recommender systems, merges identical embedding tables, interleaves data and kernel operations, and caches hot embeddings on GPU, eliminating the parameter server and delivering up to tenfold speedups over TensorFlow‑PS while maintaining model quality.
This article presents the DIRN model, which leverages product inclusion graphs and graph‑based embeddings to address the challenges of ranking both single‑item and complex travel products on Fliggy, demonstrating significant CTR, CVR, and GMV improvements through offline experiments and online A/B testing.
The XQUIC project, Alibaba’s high‑performance open‑source QUIC transport library that powers mobile Taobao and large‑scale gateway services with up to 20 % latency reduction, offers a standards‑compliant stack (QUIC‑Transport, QUIC‑TLS, HTTP/3, qlog) and multi‑path XLINK, and invites students in the Open Source Summer program to extend the code, improve diagnostics, and earn mentorship and rewards.
EasyCV is Alibaba’s open‑source, PyTorch‑based visual modeling platform that unifies self‑supervised learning and Transformer techniques, offering a comprehensive algorithm suite, pre‑trained models, high‑performance training/inference optimizations, extensible architecture, and seamless cloud deployment for a wide range of computer‑vision tasks.
OpenSergo, launched by Alibaba Cloud, bilibili, ByteDance and major open‑source communities, seeks to create a language‑agnostic, technology‑agnostic service‑governance specification and implementation that unifies control and data planes for heterogeneous microservices, easing enterprise complexity, reducing developer burden, and encouraging open‑source contributions.
This article introduces Alibaba's open‑source LowCodeEngine, outlines its key features, and provides a step‑by‑step tutorial for quickly building a low‑code development platform that generates React front‑end code, configures components, connects data sources, and exports the resulting code.
This guide from an Alibaba Mama algorithm team member outlines the six focus areas—recruitment emphasis, interview stages, concise résumé writing, pre‑interview setup, in‑interview conduct, and typical assessment topics such as machine‑learning fundamentals and ad‑system knowledge—to help candidates secure offers.
The article details Alibaba’s end‑to‑end online‑learning pipeline for real‑time ranking in the Taobao home‑decor channel, covering UT log parsing, full‑feature extraction, ODL sample creation, xDeepCTR model training, and deployment, which yielded up to 7.8% CTR improvement and demonstrates the value of rapid model adaptation.
This guide compiles Alibaba’s 2023 campus recruitment interview resources, covering resume tips, common technical topics, personal career stories, and detailed role analyses to help graduates prepare effectively for front‑end, back‑end, and algorithm positions.
NextRPC is a multi‑stage RPC model that delivers partial responses over multiple network channels, improving latency and conversion in Taobao’s transaction and recommendation services; it employs a hybrid asynchronous streaming and parallel execution architecture with client‑side orchestration and server‑side sub‑request handling, achieving over 5% UV lift and up to 25% recommendation uplift.
Alibaba’s MNN, a lightweight high‑performance deep‑learning inference engine, earned top honors in China’s 2022 “Science & Innovation China” awards, and delivers impressive gains such as 350% speedup on X86 CPUs, 2.1‑2.3× acceleration on ARM with sparse models, plus integrated OpenCV/Numpy functionality for edge AI deployment.
This article introduces the Alibaba Java Coding Guidelines plugin for IntelliJ IDEA, explains its rule set and detection modes, and provides step‑by‑step instructions for installing and using the plugin to automatically enforce coding standards in Java projects.
This article explains how Alibaba’s local‑life platforms leverage edge‑side AI to run machine‑learning inference on users’ devices, detailing the concept, advantages, technical architecture, and concrete implementations such as user feature extraction, intelligent recommendation, and smart push, while outlining future directions.
