書き手に報酬が届くウィキ

没有什么敌人，是一支舞蹈搞不定的 如果有，那么就跳两支！

什么？每领一次盒饭，我的岁数就增加一岁？ 注：本期我刻意规避了深层的剧情，仅以第一人讲述一趟我的《师父》之旅，也给那些想玩本作的哥们留些悬念。

在童话般的小镇上，有一座电影院 对着电影院里的屏幕点一下~ 咦~我来到了DOMO制作组！

The opening of Computex 2026 in Taipei marks a definitive pivot in the trajectory of the personal computing industry. Industry leader NVIDIA has unveiled the RTX Spark, a specialized artificial intelligence superchip designed to transform the consumer PC from a passive tool into a proactive, autonomous agent. This announcement, delivered by CEO Jensen Huang, signals the end of the traditional operating system era and the beginning of agentic computing, where hardware is purpose-built to host and execute autonomous AI agents. By integrating advanced neural processing directly into the silicon, NVIDIA is aligning with the broader industry trend of shifting AI workloads from data center clouds to on-device edge environments. This shift addresses the escalating costs and latency constraints of current cloud-based AI infrastructure, promising a localized, privacy-focused, and highly responsive computing experience. The implications of the RTX Spark extend far beyond simple performance gains; they represent a fundamental reimagining of the human-computer interface, where users move from writing code or executing applications to expressing high-level intent. As major original equipment manufacturers begin to integrate this architecture into upcoming consumer hardware, the industry faces an inflection point that necessitates a wholesale restructuring of enterprise software, developer workflows, and hardware procurement strategies to accommodate a new class of intelligent, persistent digital assistants.

为什么会失忆？ 因为...我已经不是原来的我自己

什么，战斧能开机了？而且还被我发现了新用处？

At its annual I/O developer conference, Google unveiled an aggressive architectural transformation that fundamentally alters the nature of consumer computing, officializing the transition from the retrieval era to the agentic era. By modifying its iconic homepage search input mechanism for the first time in over two decades, Google has integrated its new Gemini model infrastructure directly into the primary Search pipeline utilized by three billion daily users. Rather than operating as an isolated chat interface or a standalone application, the upgraded Gemini framework now functions as an ambient, persistent intelligence layer native to both Google Search and the underlying Android operating system kernel. This platform deployment model, designated as Gemini Intelligence, establishes deep integration across the operating system. It marks a decisive shift away from reactive chatbots toward proactive, background-executing software agents capable of orchestrating multi-step workflows across disparate, third-party software ecosystems. To sustain the significant computational demands of this ubiquitous inference framework, Google concurrently introduced its bifurcated eighth-generation Tensor Processing Unit architecture, separating infrastructure pipelines into TPU 8t for globally distributed model training and TPU 8i for localized, low-latency edge execution. By leveraging an entrenched global distribution moat of three billion active Android devices, Google’s platform strategy poses an immediate infrastructural challenge to competitors like OpenAI, Microsoft, and Anthropic. While rival frontier models continue to iterate on isolated browser tabs and standalone applications, Google is systematically embedding ambient artificial intelligence into the existing, high-frequency digital workflows of the global population. This deep structural realignment signals the obsolescence of traditional keyword search optimization and application-switching paradigms, introducing a unified, screen-aware, and multimodal operating ecosystem that permanently recalibrates how humanity interfaces with digital infrastructure.

A profound shift is occurring within the enterprise technology landscape as companies aggressively transition from conversational AI to autonomous, multi-agent systems. Dubbed the "Agentic Paradox," this trend highlights a major systemic friction point: while autonomous AI agents drastically reduce human labor costs and streamline enterprise operations, their massive consumption of cloud-hosted frontier model APIs is driving corporate computational expenses to unprecedented heights. The architectural shift from single-prompt interactions to complex, recursive multi-agent workflows means a single customer inquiry or data-processing task can trigger dozens of automated iterative reasoning loops behind the scenes. This continuous token consumption is exponentially compounding API costs. Enterprises deploying autonomous agents for high-volume workflows, such as legal document synthesis, automated software engineering, and continuous healthcare monitoring, are facing a stark economic reality. The efficiency gains achieved by removing human touchpoints are increasingly being offset by skyrocketing computational line items. In response, the tech ecosystem is experiencing an intense period of restructuring. Startups and enterprise technology leaders are pivoting away from a pure reliance on centralized, closed-source models. Instead, they are adopting hybrid architectures that integrate local, highly specialized open-source models with orchestration frameworks designed to curb token overhead. Simultaneously, the physical footprint required to support this agentic compute boom is triggering intense geopolitical and environmental standoffs, illustrated by recent multi-billion-dollar infrastructure controversies across the United States. This deep analytical report examines the technical mechanics of the Agentic Paradox, its economic implications for enterprise software, the emerging architectural mitigations being deployed by developers, and the broader macroeconomic and environmental fallout of this next phase in the artificial intelligence gold rush.

要说日本漫画史上销量最高的那些作品，剑风传奇绝对算是其中之一，这套漫画总共销售了3500万套，三浦健太郎这家伙可以说是赚得盆满钵满，今天就跟大家聊聊这套独特的作品。

难度超高，新手教学还一概欠奉 就是要让每一个玩它的人吃苦 坚持下来后，才能体会它的好

高分少女里边有一幕，男主在操场上，跟朋友们聊一台在当时算很高级的主机，这台机器在日本都算是稀有物，国内就更加稀有了，今天就跟大家聊聊这台我很向往的机器。

An AI painting of 1 girl and 3 boys standing near the sea pointing yeah and facing sea with boats in the sunset

Ten stories that mattered in tech over the last 48 hours since Sep 13th, 2026

On May 19, 2026, GitHub detected and contained a severe security incident within its corporate estate that resulted in the exfiltration of approximately 3,800 internal software repositories. Driven by the notorious cybercriminal syndicate TeamPCP, also tracked as UNC6780, the breach was executed not via infrastructure zero-days or direct perimeter exploitation, but through a highly sophisticated developer supply chain vector. Specifically, the threat actors compromised an employee development workstation using a trojanized version of the Nx Console extension for Microsoft Visual Studio Code. This extension, which holds a verified publisher status and millions of baseline installations, was backdoored via an upstream dependency exploit linked to the systemic Mini Shai-Hulud campaign. The malicious update remained active on the Visual Studio Marketplace for an eighteen-minute window on May 18, 2026, before being identified and retracted. During this period, background auto-update mechanisms pushed the compromised package to the target workstation. Upon initiation, the extension executed an obfuscated, multi-stage credential harvester that scraped highly sensitive access tokens, environment secrets, and cloud credentials directly from the local development environment memory and disk. Utilizing these hijacked credentials, TeamPCP bypassed standard organizational perimeters to systematically clone thousands of internal code repositories housing GitHub proprietary source code, internal configurations, security tooling architectures, and developmental projects for core services like GitHub Actions and Copilot. While GitHub Chief Information Security Officer Alexis Wales confirmed that customer-facing enterprise accounts, production repositories, and core user data remain unimpacted, the stolen source code has been posted for sale on underground cybercrime networks with asking prices reaching ninety-five thousand dollars. This report provides an exhaustive, forensic exploration of the structural and mechanical failure points that permitted this compromise, mapping the technical pipeline from upstream open-source packages to the proprietary core of the software industry's central code repository.

94年的夏天，SEGA的土星SS和SONY的PS有快要发售了，我也从杂志上了解到它们的资讯，据说是两台非常强大的32位游戏机，可是我等不及了，买了另外一台32位机。

本期有双胞胎姐妹花预告，已经和她们老爸联系好了，15年后姐妹花故事可以开拍

一套是我追看了三十年的漫画 一个是我玩了三十年的游戏系列 这俩也能擦出爱情的火花？？怎么擦？

一路上，我都在帮助代表正义的妹子 然而最后却亲手葬送了王国的未来...... 我究竟做错了什么吗？

有座雄伟又重要的关卡 自古就是兵家的必争之地 武侠小说、电影电视剧中更是频繁出现它的身影 新天龙八部联动山西代县雁门关打造了6月全新主题版本来啦！上线时间:6月24日！

你们用拳头我也用拳头 你们拔枪我也拔枪 就是这么倔强(∩_∩)

那一天，他们没能控制住自己的脾气 然后我的3DS遭殃了......

九十年代初的时候，我第一次见到了世嘉的MD，也是第一次见到了烧录卡。

老孙精剪之荒野大镖客2剧情

Yann LeCun’s World Model Thesis: Why a Deep Learning Pioneer Believes LLMs Are a Dead End for AGI In a widely circulated long-form interview, Yann LeCun—Meta’s Chief AI Scientist and one of the foundational figures of modern deep learning—reiterates a sharply contrarian view of today’s AI trajectory. While large language models (LLMs) have demonstrated rapid progress across coding, mathematics, reasoning benchmarks, and conversational ability, LeCun argues they are structurally incapable of reaching human-level intelligence. His core claim is that LLMs lack “world models”: internal representations of reality that allow an intelligent agent to predict the consequences of its actions, reason about cause and effect, and plan through structured search rather than token-by-token prediction. Without such models, he argues, systems cannot generalize to novel environments or perform robust real-world decision-making. LeCun further critiques prevailing approaches in robotics and autonomous systems—particularly vision-language-action (VLA) models and imitation learning—arguing that they fail to achieve human-like data efficiency and generalization. He highlights the contrast between humans learning to drive in hours and autonomous systems requiring millions of training examples without achieving full robustness. A key pillar of his argument is that LLMs operate via autoregressive next-token prediction rather than planning through optimization or simulation. This, he claims, makes them inherently brittle outside training distributions and unable to guarantee safe behavior. He goes further, describing LLMs as “intrinsically unsafe” due to their inability to enforce hard constraints or consistently predict downstream consequences of actions. LeCun proposes an alternative paradigm he calls objective-driven AI or world-model-based systems, where agents simulate outcomes, evaluate them against goals, and select action sequences through search. He predicts that the limitations of current systems will become widely acknowledged by around 2027, triggering a broader paradigm shift in AI research and industry priorities.

On May 20, 2026, Space Exploration Technologies Corp. publicly filed its Form S-1 prospectus with the Securities and Exchange Commission, initiating the formal process for the largest initial public offering in the history of American capital markets. Targeting a listing valuation between 1.75 trillion and 2 trillion dollars under the ticker symbol SPCX, the document represents a profound shift in the corporate identity, financial structure, and risk profile of the aerospace pioneer. The prospectus reveals that SpaceX is no longer operating primarily as a launch provider or a satellite communications network. Through the integration of xAI, completed in February 2026 for 250 billion dollars, management has explicitly repositioned SpaceX as an artificial intelligence infrastructure enterprise. The company claims a total addressable market of 28.5 trillion dollars, of which 93 percent is anchored in enterprise and consumer AI applications powered by its flagship model, Grok. Financially, the prospectus exposes a stark dichotomy between the core communications business and the capital-intensive AI buildout. In 2025, SpaceX generated 18.7 billion dollars in revenue, primarily driven by Starlink, which yielded 11.4 billion dollars in sales and 4.4 billion dollars in operating profit. However, massive infrastructure spending, combined with 2.5 billion dollars in quarterly losses within the newly formed AI division, led to a net GAAP loss of 4.94 billion dollars for the full year, alongside an accumulated deficit of 41.3 billion dollars. Armed with 29 billion dollars in total debt, including a 20 billion dollar bridge loan executed in March 2026, SpaceX is seeking to raise up to 75 billion dollars from public markets. The capital structure establishes an unprecedented dual-class share framework that concentrates over 85 percent of voting control within the founder, effectively insulating management from public shareholder governance, derivative litigation, and traditional regulatory oversight.

话说丧尸围城4上市也有一段时间了，趁着最近PS4版打折，我也入手了一份，今天就和大家聊聊这款集合了全部DLC的作品.

女友叫凯瑟琳 咦，你怎么也叫凯瑟琳...

神马，当年大家挤着上机的《电精》 居然是块盗版的基板？