Articles

This article deeply explores the multi-turn dialogue mechanism in AI Agents, emphasizing its critical role in solving complex tasks, distinguishing it from traditional single-turn Q&A. The article first vividly illustrates the synergistic operation of the four core modules of multi-turn dialogue through a coffee ordering scenario: Natural Language Understanding (NLU), Dialogue State Tracking (DST), Dialogue Management (DM), and Natural Language Generation (NLG). Subsequently, the article directly points out the four common 'fundamental errors' in AI multi-turn dialogue: 'cannot remember,' 'misunderstanding,' 'illogical follow-up questions,' and 'superfluous responses.' To address these pain points, the article provides actionable optimization paths from three aspects: 'technical optimization, process design, and data refinement,' including expanding memory range, integrating large language models to enhance understanding, designing dialogue flowcharts, and reducing templated responses, aiming to make AI multi-turn dialogue understand users better and communicate more effectively.

The article from Stack Overflow Blog investigates developers' daily tasks and frustrations, leveraging a survey of over 800 developers. It reveals that while 84% of developers use or plan to use AI in their workflow (primarily for coding, searching, learning, and debugging), frustrations often stem not from AI's limitations, but from issues with documentation. Developers spend the most time on proprietary code, followed by architectural design and system maintenance. Tasks like budgeting and payroll are highly time-consuming and frustrating. Crucially, the analysis points out that documentation and deployments are among the least time-intensive tasks but contribute significantly to frustration, particularly when learning new codebases. While AI assists heavily in coding, its adoption for documentation is low. The article concludes that improving documentation quality and consistency is key to reducing developer frustration and enhancing efficiency, especially for early-career developers.

This article delves into OpenAI's current predicaments, tracing its journey from being hailed as AI's 'savior' to confronting substantial funding gaps and a crisis of trust. It highlights the diminishing impact of GPT-5 demonstrations and Google's forceful counterattack with its Gemini model, both technologically and across the industry chain. This creates an ironic parallel: OpenAI's current 'red alert' state mirrors Google's own challenges just two years prior. The article underscores OpenAI's projected financial deficit of up to $207 billion and the 'interdependent funding' dilemma exposed by its management's anxious responses to scrutiny. In contrast, Google's deep cash reserves, proprietary TPUs, and cloud infrastructure provide unparalleled advantages, suggesting the AI competition is now less about models and more about infrastructure and capital strength. Ultimately, the article posits that this emotional shift is a 'stress test' for AI expectations, indicating that AI technology will move from blind enthusiasm to an 'industrialization stage' focused on profitability and product quality, where those with robust infrastructure will hold a decisive edge.

This article provides an in-depth interpretation of Google DeepMind's latest research, "Evo-Memory," which introduces a new paradigm for continual learning without modifying model parameters. The article begins by identifying the limitations of traditional RAG systems' memory capabilities, specifically their static retrieval mechanism, which lacks self-reflection and filtering, making them easily overwhelmed by noise and unable to genuinely learn from experience. To address this, "Evo-Memory" introduces the core concept of "Context as Weights." Through the meta-reasoning (Refine), pruning, and reorganizing mechanisms within the ReMem framework, the model dynamically adjusts and optimizes context during inference, thereby endowing fixed-parameter LLMs with learning capabilities akin to weight adjustment. The article showcases the significant improvements in task success rate and problem-solving efficiency achieved by the ReMem framework through specific case studies, emphasizing the pivotal role of meta-reasoning. Ultimately, by integrating "Evo-Memory" with DeepSeek V3.2's planning capability, the article outlines a complete closed loop for next-generation AI's "System 2 Thinking," foreshadowing an AGI prototype where parameters remain constant but wisdom continually grows.

This article details Qunar's Edge AI architecture design philosophy and practical experience. It first elaborates on the advantages of Edge AI and how it overcomes the bottlenecks of cloud-based models, namely network latency and user privacy issues. Subsequently, it deeply analyzes the Edge AI architecture, which comprises an Engine Layer, an Infrastructure Layer, and an Application Layer. The Behavior Center and Model Management Platform within the Infrastructure Layer are highlighted as core components responsible for real-time feature collection and processing, and automated model training and deployment, respectively. Furthermore, taking user churn prediction and retention as an example, the article explains in detail how Edge AI leverages real-time feature capture and millisecond-level inference to overcome business pain points. It also shares strategies for addressing challenges such as real-time integration of complex multi-source features and data sparsity. Finally, it outlines future plans for platform enablement, deep perception, and the exploration of complex on-device capabilities.

This article discusses Andrej Karpathy's latest insights on Large Language Model (LLM) interaction strategies. Karpathy emphasizes that LLMs are essentially simulators, not autonomous, conscious entities. Therefore, when posing questions, one should avoid personified questions like 'What do you think?'. He suggests a more effective prompt strategy: ask, 'What kind of group of people would be suitable for exploring a certain topic? What would they say?', to guide the LLM in simulating multi-perspective answers. The article also explains the engineered nature of AI persona, clarifying that an LLM's 'personality' is deliberately constructed through techniques like SFT and RLHF, rather than emerging naturally. Finally, the article differentiates the impact of this new prompt strategy in factual and subjective domains. Through practical validation, by simulating the perspectives of Deep Learning Theorists, RLHF Engineers, Cognitive Scientists, and Advanced Prompt Engineers, the article further elaborates on and verifies the effectiveness of Karpathy's viewpoint, demonstrating that treating LLMs as simulators can significantly enhance their performance and answer quality. This piece is an interpretation, validation, and application of Karpathy's core ideas.

The article details the significant updates in NVIDIA CUDA Toolkit 13.1. Hailed as the largest in 20 years, this update introduces the core CUDA Tile programming model, which simplifies GPU programming through higher-level abstraction and better leverages dedicated hardware like Tensor Core. In addition, the runtime API opens up Green Context, allowing developers to perform more granular GPU resource partitioning and management. The article also covers enhancements to CUDA Multi-Process Service (MPS), performance optimizations for mathematical libraries such as cuBLAS, cuSPARSE, cuFFT, cuSOLVER, and CCCL, as well as new features for developer tools like Nsight Compute, Compute Sanitizer, and Nsight Systems. These updates aim to improve GPU programming efficiency, performance, and debugging capabilities, especially for AI workloads.

This report comprehensively reviews today's major events in the tech sector. OpenAI officially released its GPT-5.2 series models to counter Google Gemini 3, enhancing its capabilities in programming, mathematics, and multimodal processing. It plans to launch an adult mode in the first quarter of 2026, will update its knowledge base to August 2025, and reached a licensing agreement with Disney. Apple plans to equip the iPad 12 with the same A19 chip as the iPhone 17, breaking tradition. OPPO integrated its AI departments, establishing a Smart Product R&D Department to build an AI strategy with 'Super Xiaobu' as a unified entrance. Xiaomi's smart home appliance factory was completed and went into production, achieving end-to-end capabilities for home appliances. In addition, the report also includes ChatGPT topping the US iPhone free apps chart, Spotify launching AI-generated playlists, Quark AI glasses selling well, the new version of DingTalk integrating AI features, Android 17 natively supporting app locks, PS5 liquid metal aging issues, Attorneys General from multiple US states warning about AI risks, new achievements in MIT chip energy efficiency, Bill Gates discussing the AI bubble, Ayaneo releasing a slider phone, Tencent QQ launching AI summaries, Alibaba Tongyi releasing Qwen3-Omni-Flash, Adobe applications integrating ChatGPT, and Samsung S26 Ultra charging upgrades.

This article presents Zhao Junbo's discussion, a Senior Technical Expert at Ant Group, on Diffusion Language Models (dLLMs) at the MEET2026 conference. He noted that, unlike mainstream autoregressive models, dLLMs utilize a "cloze-filling" method instead of "token-by-token generation," allowing for direct modification and control of tokens during the inference process. Theoretically, this can lead to faster generation speeds and reduced computational costs. The article emphasizes that dLLMs, under computational constraints, are more 'data-hungry' and absorb data faster. Furthermore, similar to image diffusion models, they can be continuously trained without easily saturating, thereby overcoming the training saturation problem of autoregressive models and enabling continuous performance improvement. LLaDA 2.0, as a hundred-billion-parameter diffusion language model, showcases dLLMs' unique strengths in areas like code generation and literary creation, exemplified by efficiency gains from parallel decoding and more flexible editing capabilities. Although the training and inference ecosystem for dLLMs is still nascent, major players like Google and ByteDance are actively investing, signaling its future potential and calling for community-wide development.

This article details the evolution and real-world practices of Lalamove's Big Data storage architecture in a hybrid cloud environment. It covers stability enhancements and performance optimization for online storage, addressing HBase's pain points through business isolation, HA capabilities, and heterogeneous storage. For offline storage, it achieved a cumulative 54% storage cost reduction through hot/cold tiering and cost governance. Furthermore, it tackled challenges brought by large AI models in the AI era through bucket isolation and cache acceleration to support AI training. Finally, the article shares the practices of AI vector storage in multiple large model application scenarios, such as intelligent customer service and IT operations assistants. The article emphasizes business-driven technology selection and cross-cloud data stability assurance strategies, providing valuable experience for enterprise-level Big Data storage.