Articles

The article highlights the performance bottlenecks encountered in frontend development when handling large-scale CSV file imports in a React admin system. After trying React, FileReader, PapaParse, and even Web Worker, the author's team realized that JavaScript itself is not suitable for CPU-bound tasks. To address this, they turned to a solution combining Rust with WebAssembly. The article elaborates on the advantages of Rust+WASM, integration steps (including Rust environment setup, project configuration, core parsing function implementation, and WASM module building), and specific code examples for seamless integration into a React project. Through performance comparisons, it demonstrates that the Rust+WASM solution reduced the processing time for an 8MB CSV file from 3.8 seconds with JS to 700 milliseconds, with a smooth UI response. Finally, the author summarizes the immense potential of WebAssembly in solving frontend pure computation performance bottlenecks and shares practical insights.

Fei-Fei Li, a leading figure in AI, predicts that spatial intelligence will define the next decade of artificial intelligence. The article highlights that current AI primarily operates at the 2D pixel level, lacking a deep understanding of 3D spatial structures, physical relationships, and causal reasoning. In contrast, Coohom (Kujiale's parent company), with 14 years of dedicated effort, has accumulated vast amounts of structured 3D data that conforms to physical laws. This positions Coohom as a crucial enabler in providing the 3D data and foundational capabilities required for the physical world. Coohom has launched the Aholo Spatial Intelligence Open Platform, the LuxReal 3D AI Video Tool, and the SpatialTwin Industrial Twin Platform, aiming to open its core strengths to empower content creation and embodied AI. Through in-depth interviews with Coohom executives, the article also addresses prevailing industry issues such as debates over technical pathways and commercialization challenges, underscoring spatial intelligence as a critical infrastructure for the realization of embodied AI, and explores the evolution of the SaaS subscription model in the AI era.

This article details Gemini 3 Pro's performance in Pokémon Crystal Version, notably outperforming Gemini 2.5 Pro. Gemini 3 Pro not only completes the game with significantly higher speed and efficiency than its predecessor but also showcases advanced Agent capabilities when navigating complex challenges. It exhibits scientific thinking, testing and verifying hypotheses, and bypasses environmental constraints by autonomously creating tools. Utilizing multimodal input, such as visual perception, it solves problems that memory data alone cannot address, and employs sophisticated tactical reasoning in battles to achieve a flawless record. Despite current limitations like unverified assumptions, insufficient proactive planning, and difficulties with parallel planning, this leap in efficiency and intelligent behavior signifies substantial progress in AI Agent technology.

This article, the second in the “Ironies of Automation” series, expands on Lisanne Bainbridge’s work, applying her observations to the current landscape of white-collar job automation driven by LLMs and AI agents. It first stresses the need for a critical perspective, noting that while white-collar automation isn't always perilous, humans still face significant challenges in rapid decision-making when striving for superhuman efficiency under high-pressure conditions. The article then criticizes the poor user experience of existing AI agent interfaces, where verbose and overly confident outputs can easily obscure critical errors. The author proceeds to discuss the paradox of training: increasingly automated systems, though rarely failing, demand extensive operator training to handle unforeseen anomalies. Finally, it introduces a “leadership dilemma” not covered in Bainbridge’s paper, arguing that overseeing AI agents requires proactive guidance—a new leadership skill for which training is currently largely absent. The article concludes by emphasizing that resolving these ironies in AI automation demands profound reflection and sophisticated technical wisdom.

This article deeply explores how to utilize snapdom and jsPDF libraries to achieve high-quality HTML to PDF functionality. It begins by analyzing common HTML to PDF demand scenarios in web applications (e.g., customer service systems, e-commerce platforms) and the associated challenges (style preservation, pagination, clarity, performance, and compatibility). It then thoroughly introduces the features, advantages, and basic usage of the two core libraries: snapdom and jsPDF. The article's focus is on providing a comprehensive case study for exporting customer service message lists, detailing step-by-step DOM rendering, image pagination, PDF creation, and the main export function, accompanied by code implementation. Finally, by comparing it with the traditional html2canvas solution, it emphasizes snapdom's significant advantages in high-fidelity rendering and style preservation, and proposes future optimization directions such as Web Worker, segmented rendering, loading prompts, and PDF compression. The overall solution aims to provide a higher quality, more reliable frontend HTML to PDF solution.

The article, presented by Francisco Javier Arceo, a Senior Principal Software Engineer at Red Hat, delves into the complexities of building and deploying AI in production. It highlights that proprietary data is the sole true value differentiator in AI, and effective data management is crucial. Arceo introduces 'production AI' as a trinity of inference, data, and product, explaining why each component presents significant challenges, particularly data due to issues like consistency (training-serving skew), efficiency (re-implementing integrations), governance, and reliability. He stresses the importance of getting AI projects into production to avoid the high failure rates seen in the past. The article then positions Feast, an open-source feature store, as a solution to these data-related problems. Feast unifies data for serving and training, provides a centralized catalog for discovery and governance, and ensures data consistency, efficiency, and reliability in production AI systems. A detailed architectural overview and code demo illustrate how Feast handles various data sources and transformations, including its application in Retrieval-Augmented Generation (RAG), aiming to provide a common language and pattern for MLOps engineers and data scientists to bring AI projects to fruition.

The article opens by recounting the Pantum printers' expedition with the Chinese Antarctic Expedition Team, then transitions into an in-depth interview with Pantum Executive Director Yin Aiguo. It meticulously details Pantum's journey from confronting the humiliation in 2007, when international giants declared that 'China wouldn't even have access to printers,' to becoming the world's fourth and China's second-largest laser printer manufacturer today. The article reveals the immense challenges Pantum faced in technology research and development, patent circumvention, establishing an independent and controllable supply chain, and market encirclement. It also showcases how they achieved break-even and breakthroughs in high-end A3 products through continuous investment, product innovation, and embracing the domestic ecosystem. Special emphasis is placed on their efforts in localizing key components, solving the paper jam rate pain point, and even AI-powered intelligent upgrades, while also looking ahead to Pantum's competition strategy in the global market. Through the struggle of a specific enterprise, the article demonstrates the resilience and achievements of Chinese technology companies in breaking through 'bottleneck technologies.'

The article elaborates on a major partnership between OpenAI and Disney: OpenAI will sell $1 billion worth of company equity to Disney in exchange for the licensing of over 200 popular IP characters from Disney's portfolio for Sora and ChatGPT Images. This collaboration, set for a three-year term with initial exclusivity for the first year, covers classic characters, costumes, props, and scenes from Disney, Pixar, Marvel, and Star Wars, with an expected launch in early 2026. Notably, the **main driving force behind this cooperation is OpenAI's need to address growing copyright infringement issues and improve Sora's currently low user retention rate.** Furthermore, Disney will become an important enterprise client for OpenAI, utilizing its APIs to build new products and integrating ChatGPT into its internal workflows, thereby deepening its AI strategy.

The article vividly describes how AI giants, top talents, and massive capital are fiercely vying for Artificial General Intelligence (AGI) at an unprecedented speed, using Silicon Valley's "Tech Corridor"—a 50-60 kilometer route running through the heart of technology—as its central axis. It delves into various aspects of the AGI race, from NVIDIA, the computational heartland in Santa Clara, to Google DeepMind, the thinking core in Mountain View, to Stanford University, the talent incubator in Palo Alto, and finally to OpenAI, the front line of this sprint in San Francisco, detailing the key roles and challenges at each stop. At the same time, the article profoundly reflects on the potential social impact, ethical risks, and the inexperience of young teams that AGI might bring, and cites multiple viewpoints to discuss the industry's predicament and future direction in the absence of regulation.

This “LatePost Oral History” recounts Ele.me’s 17-year evolution, from its founding in a Shanghai Jiao Tong University dormitory in 2008 to its core business being integrated into and transformed by “Taobao Lightning Buy” in 2025, rather than simply replaced. Through candid accounts from Ele.me’s founder, co-founders, early team members, employees, and industry observers, the article illuminates its experiences in the campus market, the intense 'Group-buying War,' fierce rivalry with Meituan, funding challenges, and the subsequent integration following its acquisition by Alibaba. It not only portrays the entrepreneurial struggles and passion of Ele.me as a startup but also reveals the inevitable changes and pressures it faced amidst capital-driven growth and giant competition. Ultimately, while the Ele.me brand faded, the merchant network and delivery system it established became the cornerstone of “Taobao Lightning Buy.” The article concludes by highlighting the new ventures undertaken by several founders after their departure from the Alibaba ecosystem.