Original articles on artificial intelligence and machine learning, deep optimization. Less is more, life is simple! Shi Chunqi
The article analyzes how large‑model development has moved from reasoning to the agentic stage, compares open‑source and closed‑source capabilities, details Reasoning RL versus Agentic RL designs, and proposes skill‑centric data and verification mechanisms to close the performance gap.
The article explains how randomly masking a large portion of gradient updates during large‑model training—sometimes up to 99%—can accelerate convergence and even surpass traditional optimizers like Adam, supported by recent Google research and empirical observations.
Claude Opus 4.6 showcases unprecedented finance‑scenario analysis and a powerful Skills integration, prompting the author to argue that enterprises should adopt offline open‑source large language models to safeguard their proprietary prompts and maintain a robust competitive moat.
The article argues that embracing the Skills paradigm— a lightweight, low‑cost alternative to large‑scale model training—represents the third major compromise in the large‑model era, balancing reduced emergence and planning hallucinations against increased stability and engineering efficiency for enterprise AI deployments.
The article analyzes Google’s shift from scaling‑law to efficiency‑law, detailing how speculative decoding, language‑model cascades, distillation, CALM, accurate quantized training, and the Mixture‑of‑Recursions architecture together form a multi‑layered strategy to cut inference cost, boost throughput, and sustain the company’s AI moat.
The article analyzes how the AI explosion, breakthroughs like DeepSeek R1, and successful case studies such as Lemonade and AIG’s Underwriter Assistant are driving a shift in insurance from scale expansion to risk‑focused, AI‑native transformation in 2026, outlining strategic frameworks, agile tribe structures, modular delivery, and risk‑tolerant innovation processes.
The article examines Google’s AI resurgence after Sergey Brin’s comeback, detailing the evolution of TPU hardware from v1 to v7, the strategic focus on algorithmic efficiency, comparisons with Nvidia’s B200, the role of JAX/XLA, and how these advances create a powerful competitive moat for Google’s AI infrastructure.
Jeff Dean highlighted speculative decoding as a lossless inference acceleration technique that can boost large language model throughput by 2–3×, and the article breaks down its core concepts—including parallel token verification, draft‑target model collaboration, rejection sampling theory, and practical optimizations such as continuous batching and tree‑based verification.
This article explains how Paged Attention, introduced by the vLLM team, applies virtual‑memory techniques, non‑contiguous block mapping, copy‑on‑write reuse, distributed scheduling, and hardware‑level optimizations to improve KV‑cache efficiency and reduce memory fragmentation in large language model serving.
The article breaks down the principles of KV caching in large language models, explaining how Q/K/V behavior differs between training and inference, the role of prompts, cache size trade‑offs, and the complexities of concurrent inference, all backed by concrete examples and references.
