Articles

This article thoroughly examines Anthropic's Claude Skills, positioning them as the future standard for industrial-grade Agents. It defines Claude Skills as a file system-based, reusable knowledge package operating within a sandbox VM, crafted to inject procedural, deterministic Standard Operating Procedures (SOPs) into Agents in a standardized manner. Its core components include metadata, instructions, and resources. The article elaborates on how Agents perceive and load Claude Skills through a 'Progressive Disclosure' mechanism, which incrementally loads information on demand, effectively addressing the challenge of excessively large context windows. Furthermore, it analyzes the interplay between Claude Skills and MCP (Open AI Tool Standard), where both collaborate to furnish Agents with specialized domain knowledge and external tools. Finally, the article introduces open-source solutions for implementing Agent Skills and illustrates their application potential with an example of building a smart shopping guide AI assistant.

The article details the model and practices of AnyInt, an AI startup founded by Bryan Zhou, a former Google and YouTube senior technical expert. AnyInt positions itself as AI middleware, aiming to solve common challenges users face in large model usage, such as inconsistent API formats, numerous providers, and inconvenient payments. Its core product is a "decentralized" platform that uses intelligent routing algorithms to match users with the most suitable models. This platform also ensures data security and transparency through trusted computing and blockchain technology. The article emphasizes AnyInt's business strategy of "targeting paying users from day one" and highlights its technological moats: a "multi-model intelligent routing algorithm" and a "network effect." The team advocates an "AI-powered workforce" and uses its own platform internally, driving product iteration through community feedback, demonstrating an efficient startup model for small teams.

This article explores how Polymarket, a Web3 prediction market, has become one of the most accurate sources of information within the AI industry. The author showcases Polymarket's high accuracy in key event predictions with multiple examples, including the release of OpenAI's new model and the Gemini 3 launch date. The article delves into Polymarket's operational mechanism: users place bets on future events, and the price dynamically reflects the market's consensus on the probability of an event occurring. The author attributes this mechanism to the 'wisdom of the crowd' theory, emphasizing how the 'genuine financial commitment' required from participants effectively filters out noise and emotional judgments. Beyond tech news, Polymarket has accurately foretold Oscar Awards and political election outcomes. The author suggests that Polymarket's value lies in offering a more considered and rational perspective for information assessment, helping readers in an information-saturated era discern reliable information, rather than advocating for gambling or crypto speculation.

This article details how Google Cloud and App Orchid developed a multi-agent system to tackle the challenges of accurate forecasting in dynamic business environments. The system comprises three core agents: App Orchid's Data Agent for processing and integrating enterprise data, Google Cloud's Prediction Agent utilizing advanced models like TimesFM and PDFM for precise predictions, and a Business Forecasting Agent acting as an orchestrator for user interaction. This multi-agent approach, enabled by the A2A Protocol, Google's ADK, and Gemini models, significantly enhances forecasting accuracy, increases operational efficiency, provides faster insights, reduces costs, increases revenue, and offers greater agility and adaptability. The solution aims to help businesses achieve unprecedented prediction precision and is provided through Gemini Enterprise as a secure, enterprise-grade platform, laying the groundwork for future collaborative AI agents.

This article details how to achieve automatic deployment of an "AI phone" by optimizing its deployment documentation for direct comprehension and execution by AI, specifically Claude Code. The author collaborated with AutoGLM's developers to rewrite the deployment instructions, clearly guiding the AI on how to install dependencies, connect to the phone, configure models, and handle common errors. This strategic approach allows Claude Code to autonomously read the documentation and complete AutoGLM's deployment and setup with just one prompt, subsequently enabling it to operate phone applications (like WeChat) via commands. The article looks to the future, advocating that all open-source projects should provide AI-readable description files to promote the democratization of technology. It emphasizes the importance of adhering to open-source and open practices amidst current AI development trends.

The article points out that OpenAI is experiencing a "Code Red" crisis, with Sam Altman having halted projects like Sora, shifting the company's focus entirely to enhancing ChatGPT's market competitiveness. Facing Google's rapid rise and Anthropic's encroachment on the enterprise customer market, OpenAI is eager to release GPT-5.2 to reverse its decline. The article delves into OpenAI's adoption of a "pandering" training method to pursue user growth, which has triggered a mental health crisis, and touches upon the strategic disagreements between product managers and scientists within the company. Altman even suggested that Apple is OpenAI's true adversary, signaling that the future AI battlefield will shift to end-user devices. The article concludes that OpenAI's success is also its burden; capital pressure has forced it to pivot from AGI research to market competition, and the rushed release of GPT-5.2 is a concentrated manifestation of this anxiety, revealing the contradiction between the company's survival and its ideals.

This article offers a detailed analysis of the acclaimed "Doubao Phone's" innovative technology, emphasizing that it's more than just an app; it's an OS-level shadow system built within the Android Framework. Through black-box testing and academic reasoning, the article uncovers its core mechanisms: including System 1 (Intuition) and System 2 (Reasoning) Agent modes, hybrid perception routing via XML+Vision, an OS-level virtualized parallel runtime, and latency strategies integrated into heuristic engineering. It also highlights the phone's "task-level" physical isolation for privacy design and its precise invocation of memory and tools through the MCP protocol, ultimately demonstrating the AI Agent's remarkable task resilience. The article notes that while the "Doubao Phone" isn't flawless, its architecture establishes a new benchmark for mobile AI phones, and introduces ByteDance's open-source UI-TARS model, underscoring the immense potential of GUI Agents as a frontier in future AI interaction.

The author introduces the interesting phenomenon of "AI being unable to draw left-handed writing" as a starting point, verifying its widespread existence through practical testing of multiple mainstream AI image generation models. The article further cites the paper "Bias in Phenomenological Space Hinders Generalization of Text-to-Image Models" to explain the root cause: AI training datasets themselves contain significant statistical biases, leading models to struggle with learning the concept of 'left-handedness' due to its minority representation. Experimental results from the paper demonstrate that deficiencies in the "completeness" and "balance" of datasets severely impede the models' generalization capability. The article not only reveals this blind spot in AI but also extends the reflection to humans, suggesting that we are similarly constrained by biases formed by our 'training sets,' advocating for increasing the depth of experiences to broaden cognitive boundaries.

This article thoroughly analyzes the eight major economic tasks for 2026, unveiled at the 2025 Central Economic Work Conference, and contrasts them with past policies. It highlights the 'Year of Intensive Cultivation' characteristic of current Chinese economic development. The core transformation involves a strategic shift from short-term 'external support' to long-term 'self-sustaining growth,' from a pursuit of speed to a focus on quality, and from reliance on subsidies to the activation of internal dynamics. Specifically, domestic demand stimulation is evolving from 'consumption subsidies' to 'increasing residents' income'; investment is transitioning from 'government-led' to 'stimulating private sector vitality'; innovation-driven development is moving from 'dedicated research' to 'accelerated implementation,' with a regional and entity focus on the Beijing-Tianjin-Hebei region, the Yangtze River Delta, the Greater Bay Area, and enterprises; tackling tough reforms is progressing from 'establishing rules' to 'removing obstacles,' emphasizing a unified national market and addressing detrimental internal competition; opening up to the outside world is deepening from 'opening up projects' to 'opening up rules,' promoting institutional opening-up and service exports; coordinated development is shifting from 'each stabilizing independently' to 'linked restructuring,' with county towns as carriers and urban agglomeration collaboration; the dual carbon goals are advancing from 'industrial upgrading' to 'systemic restructuring,' building a new energy system and a carbon market; people's livelihood policies are elevating from 'ensuring basic needs' to 'investing in people,' focusing on population size, education upgrading, and high-quality employment; and risk prevention is transitioning from 'short-term stabilization' to 'long-term systemic reconstruction,' promoting real estate model transformation and resolution of local government debt. The article envisions a more equitable, predictable, and robust future growth model and identifies corresponding market opportunities.

This article focuses on the inference optimization of Large Language Models (LLMs), providing an in-depth analysis of the critical role and technical details of Key-Value Cache (KV Cache). It first introduces how KV Cache improves LLM generation efficiency by reusing the Key and Value matrices of historical tokens, thus avoiding redundant computations. It then explains why only K/V are cached, rather than Q, and distinguishes between Prefilling (computation-intensive) and Decoding (memory bandwidth-intensive) as two key stages. The article points out that KV Cache becomes a bottleneck for GPU memory bandwidth in scenarios with long sequences and multi-batch processing. Based on this, it compares the differences between traditional ML Infra and AI Infra in terms of hardware stability, memory bandwidth, and model hardware evolution. Finally, it introduces optimization techniques such as PD/PS Separation, Speculative Decoding, and micro-system-level optimizations like DeepSeek V3's CPU+GPU Hybrid Addressing to address the challenges and improve performance in AI Infra.