hief Scientist of the Singularity Intelligence Research Institute; Chair of the Machine Learning Study Group of the ISO C++ Standards Committee; expert member of the ISO Artificial Intelligence Technical Committee. Chair of the C++ Embedded Systems Study Group (SG14) and the Machine Learning Study Group (SG19), and also Chair of the C++ Language Evolution group. Former Vice President of R&D at Codeplay and former CEO of OpenMP. Head of the Canadian delegation to the ISO C++ Standards Committee. Michael has extensive experience in C++ parallel computing, high-performance computing, and machine learning. He led the development of the SYCL standard, a C++ heterogeneous programming model for GPU application development, as well as the OpenCL standard. He has deep expertise and insight into low-level performance optimization for PyTorch and TensorFlow. His work spans parallel programming, neural networks, computer vision, and autonomous driving. Michael previously served as a Senior Technical Expert at IBM, where he led the development of the IBM XL C++ and XL C compilers.

Researcher at the institute, focusing on large-scale system software architecture evolution, domain modeling and design, AI technologies, and engineering productivity enhancement. He has been deeply involved in projects such as AI system software, deep learning engines, autonomous driving systems, and intelligent dialogue platforms. He has extensive project experience in applying AI technologies to improve R&D efficiency and in building AI-native software systems.

A research institute fellow and author of *C++ in Action: Core Techniques and Best Practices*, he is a well-known C++ expert in China. He previously served as a senior system architect at Intel’s Asia-Pacific R&D Center and has nearly 30 years of experience in C/C++ system-level software development and architecture. His expertise spans the C++ language (including C++98/C++11/14/17/20/23), software architecture, performance optimization, design patterns, and code reuse. He is dedicated to creating concise, maintainable code and architectures, and has deep knowledge of open-source platforms such as GNU/Linux. As a long-standing senior technical coach, he has provided training and guidance across multiple areas, including C++, software architecture, secure software development, and open-source software.

A senior C/C++ software engineer with 20 years of industry experience, he began his career contributing to the development of MiniGUI, a well-known open-source GUI framework in China. After joining Xiaomi, he was deeply involved in the development of MIUI, Quick Apps, and the Xiaomi Vela operating system. He has extensive expertise in popular GUI frameworks, including Flutter, LVGL, and MiniGUI, and has conducted in-depth research into multiple virtual machine technologies, including ART, V8, QuickJS, and WebAssembly. More recently, he has been involved in the core development of Super XiaoAI, Xiaomi’s intelligent AI assistant.

Senior Engineer, primarily responsible for research on intelligent computing power scheduling platforms, AI compilers, and AI chips. He has served as a technical lead and expert at several internet companies, engaging in AI research and development across both foundational technologies and practical applications. His representative achievements include the large-scale deployment of JD Logistics’ drones and autonomous vehicles, as well as the application of large models in short video scenarios at Kuaishou. He holds 13 granted invention patents.

Hongbin Zhang, Postdoctoral Researcher at the Institute of Software, Chinese Academy of Sciences, and a member of the Technical Steering Committee (TSC) of RISC-V International. His main research focuses on multi-level compilation technologies. He has participated in major national research projects in the field of foundational software and initiated and leads the open-source community of the Ruyi Buddy Compiler for AI compilation.

Clang Modules & Serialization Owner, LLVM Committer, C++ Standard Committee member, Development Engineer in Aliyun's Programming Languages and Compilers team, responsible for the maintenance and promotion of compilation toolchain as well as the implementation, maintenance, and application of new features in C++ (e.g., Concurrency and Modules).

Jian Yang is an Associate Professor at Beihang University and a Huawei Distinguished Young Scholar. His research focuses on large code models and intelligent agents, with particular interests in pre-training, post-training, collaborative generation, and evaluation. He has published more than 100 papers in leading international journals and conferences, including ICLR, NeurIPS, ACL, and EMNLP, and has served as the first or corresponding author on more than 30 publications. He has also served multiple times as SAC/AC for ARR (ACL, EMNLP, and NAACL), Program Chair of INLG 2026, and SPC and AC for the AIA Track of AAAI. His work has received more than 40,000 citations on Google Scholar, and he was honored with the WACI 2026 Yunfan Award. After completing his Ph.D., he joined Alibaba’s Qwen team through the Alibaba Star talent program. As a core contributor to the Qwen series of large language models and the QwenCoder series of code models, he was responsible for advancing Qwen’s code capabilities and leading the development of specialized QwenCoder models. After joining Beihang University, he led the development of LoopCoder, a recurrent code foundation model, as well as InCoder-32B for industrial applications, covering multiple model scales including 7B, 14B, 32B, and 40B parameters. In the practical application and deployment of large code models, he has received the First Prize for Innovation and Entrepreneurship from the China Association of Inventions and holds more than ten patents, application certificates, and vulnerability validation certificates.

Head of Model R&D for Tencent WorkBuddy, graduated from the University of Science and Technology of China. He currently leads model research and development for the WorkBuddy team, focusing on establishing a data flywheel for models in WorkBuddy’s agentic AI scenarios and advancing toward continual learning. His areas of expertise include large language models, data flywheels, and production-oriented model engineering.

Yingran Tan is a Research Scientist at the Guangqi Research Institute of MetaX. She graduated from the Department of Microelectronics, Tsinghua University, and has long focused on research related to GPU architecture and performance optimization.

Weicong Li works on the R&D of Qoder CLI, focusing on the system architecture and engineering implementation of AI Coding Agents. He leads the design of Qoder CLI’s tooling and permission governance systems, covering key areas including long-horizon task orchestration and Harness design, remote control pipelines, and permission management. He also explores the adoption of AI-Native DevOps tools throughout the full development lifecycle of Qoder CLI to improve engineering efficiency. His ongoing work focuses on the architectural evolution and performance optimization of Agent Harnesses, as well as exploring new possibilities for applying AI-Native tools to enhance productivity in production environments.

Wanpeng Niu graduated from Jilin University and currently serves as an R&D Manager at Baidu Comate. He is responsible for advancing Baidu’s intelligent R&D initiatives, with a focus on driving the adoption of AI-native development paradigms across large-scale engineering systems. His work explores human-AI collaborative development models centered around Coding Agents. He holds more than 10 domestic and international invention patent applications in the field of intelligent R&D and is a core member of a key research project under China’s Ministry of Industry and Information Technology, “Intelligent Development Tools for Industrial Applications Based on Large Language Model Technology.” Prior to his current role, he was responsible for the incubation and implementation of DevOps tools at Baidu and Gitee (Open Source China), covering the development and commercialization of platforms for project management, code management, CI/CD pipelines, artifact repositories, application deployment, and operations management.

Zhang Handong is a renowned Rust expert in China and a core advocate of the Rust Chinese community. With nearly 20 years of software development experience, he is one of the earliest Rust practitioners in China and one of the initiators of RustChinaConf. He is the author of the acclaimed book The Way of Rust Programming and has developed and taught multiple hands-on Rust courses. He has provided Rust project consulting and technical training services to leading companies including Huawei, ZTE, and Li Auto. He specializes in Rust systems programming and engineering implementation. In recent years, he has focused on the integration of Rust with AI foundation models, with extensive expertise in systems software, Rust UI engines, intelligent agent software factories, and open-source community development.

Jing Jin is an Expert Engineer at DiDi’s ABC Technology division and leads the AI Coding Platform, while also heading the safety and business technology for DiDi Qingju. He is responsible for the overall planning, architecture design, and implementation of DiDi’s internal AI Coding Platform, driving the deep integration of large language models across the entire software development lifecycle. He also leads the development of the safety and business technology framework for Qingju’s two-wheeler business, combining innovation in AI engineering platforms with real-world business applications.

Ren has extensive experience in game backend development, business security, data engineering, engineering productivity, and observability governance. As an AI Builder consuming approximately 3 billion tokens daily and 36 billion tokens monthly, he is working to evolve individual development loops into team-scale graphs. He leads the transformation of a game development organization of around 100 engineers, shifting from “Token Maxing” toward “Token Apocalypse,” while driving governance for the effective use of Agentic AI. He has designed and implemented clear human–AI interaction boundaries for specific development scenarios, enabling “super individuals” to operate effectively while supporting the stable iteration of online services. His team’s AI-assisted code productivity is approaching 100%, with costs reduced by more than 80%. He is also currently writing and preparing to publish a book on Loop Engineering.

Peilin Li is a Ph.D. candidate at Tsinghua University, under the supervision of Associate Professor Mingxing Zhang. He is involved in the development of the Qujing Technology engine, with research interests in GPU/CPU heterogeneous systems for inference and fine-tuning, as well as inference optimization for large-scale heterogeneous and homogeneous clusters. He is a core contributor to KTransformers, a framework designed for cost-efficient inference and fine-tuning of large-scale models, and currently leads the development of fine-tuning capabilities and the maintenance of its open-source ecosystem. The project has earned more than 19,000 stars on GitHub and has collaborated extensively with leading open-source large-model training and inference frameworks, including LlamaFactory and SGLang.

Jiachen Bai is Head of the Intelligent R&D Center at PingCAP (TiDB) and a Senior Engineer. He has long focused on the intersection of artificial intelligence and software engineering. He currently leads product development and team building in intelligent R&D, with a focus on applying large language models and agent technologies to enterprise software development. Previously, he worked at Amazon Web Services (AWS) and a Silicon Valley autonomous driving company, where he gained extensive experience in distributed systems, cloud infrastructure, and large-scale engineering. He later served as a Founding Engineer at a startup, where he built TabbyML, an open-source AI coding agent, from the ground up. The project has gained more than 30,000 stars in the global open-source community and has become a widely recognized open-source project in the AI coding agent space. He has extensive hands-on experience and deep insights into agent architecture, AI-native software development paradigms, and enterprise AI application deployment. He is committed to exploring how AI can evolve from a personal productivity tool into a core source of productivity at the organizational level.

Yingdi Shan is an Assistant Researcher in the Department of Computer Science and Technology at Tsinghua University and the founder of the open-source project AgentENV. He received his Ph.D. from Tsinghua University, with research interests focused on distributed systems and storage systems. His research has been published at leading international conferences, including OSDI, SOSP, USENIX ATC, and SIGMOD, and has received awards such as the Huawei Global Olympus Award. He has also led or participated in multiple national key R&D programs and industry collaboration projects.

Huasheng Li is a Senior Performance Optimization Engineer at ByteDance, an internal technical instructor, and the lead of a C++ high-performance foundational library project. He has long focused on high-performance foundational libraries, system performance optimization, and large-scale engineering practices, with deep expertise in server-side infrastructure components, resource efficiency, runtime performance optimization, and performance management for complex systems. He has led and contributed to multiple performance optimization initiatives for core business scenarios, driving the large-scale adoption of high-performance foundational library capabilities across various business lines. His work spans high-concurrency services, core infrastructure components, resource and cost optimization, and engineering governance, significantly improving system efficiency and resource utilization while delivering substantial cost benefits.

Zetian Qin is a core R&D engineer for ByteDance’s C++ server-side compiler toolchain and a contributor to the LLVM community. He has long focused on building ByteDance’s internal C++ compilation and build ecosystem, advancing compiler toolchains, and optimizing compilation efficiency for large-scale engineering projects. He has deep expertise in build systems, toolchain infrastructure, and compilation performance optimization for ultra-large-scale C++ codebases. He is responsible for driving the development of ByteDance’s C++ compilation and build ecosystem and the evolution of its compiler toolchain. His work focuses on continuously improving compiler toolchains, build infrastructure, and developer productivity through systematic analysis of compilation bottlenecks and ongoing optimization of the build pipeline, helping reduce compilation costs for large-scale C++ projects while improving build efficiency and the overall developer experience.

Xiaomeng Zhang is a maintainer of the C++ runtime libraries for NuttX, an embedded RTOS. He has ported and adapted the full LLVM libc++ / libc++abi / libunwind stack, as well as libgcc / compiler-rt, to resource-constrained environments, with support for lightweight runtimes such as libminiabi and uClibc++. He previously contributed to the maintenance of the LLVM 17 C++ libraries, integrating them with NuttX and the Infineon TASKING compiler. He also led the upgrade of NuttX’s three LLVM runtime libraries to LLVM 21, and enabled C++ exception handling for dynamically loaded modules across ARM, x86-64, and RISC-V architectures.

Wenjie Xu is an R&D Engineer at TsingJing Technology and a Mooncake Maintainer. He primarily contributes to Mooncake Store, engineering infrastructure, and the maintenance of the open-source community.