# Пов'язані статті щодо Copilot

Центр новин HTX надає останні статті та поглиблений аналіз на тему "Copilot", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

The World Cup has only been played for a few days, but some AI prediction models have already been crowned as oracles, while others have stumbled badly.

The 2026 FIFA World Cup has sparked significant interest not only on the pitch but also in AI-driven match prediction. Major models like Qwen, Copilot, and ChatGPT are being used to forecast outcomes, scores, upsets, red cards, and key player performances. Qwen gained early attention by accurately predicting Mexico's 2-0 win over South Africa (including a red card risk) and South Korea's 2-1 victory over the Czech Republic in the opening matches. Copilot's pre-tournament predictions had notable successes, such as correctly calling the Mexico 2-0 scoreline, South Korea's 2-1 win, and Brazil's 1-1 draw with Morocco. However, it also had clear misses, failing to predict upsets like Australia's 2-0 win over Turkey or Switzerland's draw with Qatar. ChatGPT provided detailed analytical reasoning, correctly predicting Mexico's 2-0 win, but its full-tournament predictions tended to favor favorites, missing several underdog results and draws. Tests pitting multiple models (ChatGPT, Gemini, Grok, Claude) against the same match, like Mexico vs. South Africa, showed varying predictions, with only some hitting the exact score. In summary, while AI models like Qwen have shown promising early results in specific match details, and others have had isolated successes, they collectively struggle to consistently identify upsets and underdog performances. AI is becoming an additional reference tool for prediction markets but is far from a definitive source.

marsbit06/16 03:53

The World Cup has only been played for a few days, but some AI prediction models have already been crowned as oracles, while others have stumbled badly.

marsbit06/16 03:53

The World Cup Has Only Just Begun, But AI Predictions Already Have Models Hailed as 'Godly' and Others Flipping Over

After only a few days of the World Cup, AI models are being widely used for match predictions, with mixed early results. These models analyze details like scores, upsets, red cards, and key players, offering users in prediction markets an extra layer of analysis beyond odds and news. Qwen gained early attention for its remarkably accurate calls on the opening day, correctly predicting Mexico's 2-0 win over South Africa and Korea's 2-1 victory over the Czech Republic, while also highlighting red card risks and match flow. Copilot had its own highlights, accurately forecasting the Mexico 2-0 result, the Korea 2-1 win, and a surprising 1-1 draw between Brazil and Morocco. However, it also misjudged several matches, like predicting a Swiss win that ended in a draw with Qatar and missing Australia's upset over Turkey. ChatGPT provided detailed pre-match analysis and correctly called the Mexico 2-0 score, explaining factors like home-field advantage. Yet, it struggled to anticipate upsets, often siding with the stronger team on paper, as seen in its missed calls for the Australia-Turkey and Japan-Netherlands matches. Social media tests pitted models like Gemini, Grok, and Claude against each other for the same games, revealing different predictive "scripts" even for the same fixture. Overall, while AI models like Qwen and Copilot have shown promising, high-profile successes in early matches, their consistency and ability to predict genuine upsets remain in question. As the tournament progresses, more data will be needed to determine which models offer the most reliable insights for prediction markets.

Odaily星球日报06/15 08:51

The World Cup Has Only Just Begun, But AI Predictions Already Have Models Hailed as 'Godly' and Others Flipping Over

Odaily星球日报06/15 08:51

GitHub, Transfixed by AI

On the night of February 9th, GitHub suffered a major outage caused by a simple configuration change—reducing a cache refresh interval from 12 to 2 hours—that triggered a cascade of failures. This was not an isolated event, but part of a broader pattern. In early 2026, GitHub experienced at least 8 major incidents, failing to meet its promised 99.9% availability. These outages stemmed from structural issues: explosive growth in load, tight service coupling, and insufficient protection against abnormal traffic. This unprecedented load is driven by AI Agents. In 2025, GitHub handled ~1 billion commits. By 2026, weekly commits reached 275 million, projecting to ~14 billion for the year—a 14x increase. AI tools like Claude Code now contribute 4.5% of all public repository commits, with weekly submissions surging 25x in just three months. AI-generated pull requests jumped from 4 million to 17 million per month in half a year. Unlike human developers, AI Agents work continuously, generating commits at a scale that overwhelms infrastructure designed for human rhythms. The surge also shattered GitHub's business model. Copilot's flat-rate pricing, based on assisting human developers, became unsustainable as Agentic AI sessions consumed resources worth hundreds of dollars for a few dollars in fees. In response, GitHub imposed usage limits and, by June 1st, shifted to a pay-per-use "AI Credits" system. Facing this new reality, GitHub realized a 10x scaling plan was insufficient. It announced a need to *redesign* its architecture for 30x current scale—decoupling services, adding fault isolation, and improving change management to prevent cascading failures. Other platforms like Stripe and AWS are facing similar challenges with AI Agents. Fundamentally, GitHub is transitioning from a human collaboration platform to an "exhaust pipe" for automated AI workflows. Its detailed post-mortem reports aim to maintain trust during this turbulent rebuild. The February outage was not just a technical glitch, but a signal of the software industry's entry into a new, AI-driven era.

marsbit06/04 10:40

GitHub, Transfixed by AI

marsbit06/04 10:40

Microsoft Halts Vibe Coding: "Burning Tokens" Is Now More Expensive Than Employees

Microsoft has halted the widespread internal use of Claude Code, withdrawing licenses from most employees by the end of its fiscal year, June 30, 2026. This reversal comes just six months after actively promoting the AI coding tool to boost productivity via "vibe coding"—where developers describe intent in natural language and let the LLM generate code. The core issue isn't the tool's effectiveness; internal reports suggest employees preferred Claude Code over Microsoft's own Copilot CLI. The problem is financial: the "copilot mode" adds a variable, consumption-based token cost on top of existing employee salaries without a proportional revenue increase. As usage grew, the token bills became unsustainable, leading to what sources describe as a cost-structure failure. Similar overruns have been reported at other firms like Uber. The article contrasts this with the approach of AI-native startups, exemplified by Y Combinator's philosophy. Here, high token consumption is strategic—it replaces, rather than supplements, human labor. Startups operate with tiny teams where AI agents handle work previously done by many, making the high token bill financially viable as it offsets much larger personnel costs. The conclusion is that "vibe coding" isn't dead, but its economics fail within traditional corporate structures that treat AI as a productivity add-on for existing staff. Success requires a foundational shift to an AI-native organization, where processes are built to be "legible to AI," and the company's core knowledge and assets reside in documented, AI-accessible systems rather than solely in employees' minds. The future divide will be between companies that merely add AI tools and those that redesign their organizations around them.

marsbit05/26 08:51

Microsoft Halts Vibe Coding: "Burning Tokens" Is Now More Expensive Than Employees

marsbit05/26 08:51

Has Microsoft Lost Its Way in the AI Race, and Can Copilot Bring It Back on Track?

Microsoft, once seen as an early AI frontrunner due to its investment in OpenAI, is navigating a strategic shift amid increased competition. Its initial reliance on OpenAI’s GPT models has been complicated by OpenAI’s growing ambitions as a direct competitor, rapid advancements from rivals like Claude and Gemini, and the disruptive rise of AI agents, which challenge its traditional SaaS business model. These factors contributed to stock declines and slower-than-expected adoption of its flagship Copilot products. In response, CEO Satya Nadella has taken a hands-on role in product development, signaling the urgency of change. Microsoft is pivoting from a model-centric strategy to a "model-agnostic" enterprise platform approach. It aims to become the foundational layer connecting various AI models—from OpenAI, Anthropic, or its own new "Superintelligence" team—with enterprise workflows, data, security, and cloud services. Recent organizational changes merged consumer and enterprise Copilot teams to accelerate innovation, exemplified by new products like Copilot Tasks and Copilot Cowork. However, this transformation comes at a high cost. Microsoft faces massive capital expenditures, potentially reaching ~$190 billion by 2026, to support AI infrastructure. While its platform strategy shows early signs of traction with growing Azure AI revenue, it must balance startup-like agility with the reliability expected by enterprise clients. The core challenge is no longer being the sole AI winner but defending its position as the essential enterprise software entry point amidst rapid technological commoditization and the shift towards always-on AI agents.

marsbit05/23 04:37

Has Microsoft Lost Its Way in the AI Race, and Can Copilot Bring It Back on Track?

marsbit05/23 04:37

GitHub Empire on the Brink of Collapse: Source Code Leak, 18-Year Veteran Leaves, Microsoft Loses 1.5 Billion Developers

GitHub is facing an unprecedented crisis, marked by a massive exodus of developers and severe operational failures. The tipping point came when Mitchell Hashimoto, creator of Ghostty and an 18-year GitHub user, publicly severed ties, citing persistent platform outages that made serious work impossible. This departure highlights a broader pattern of user frustration. The platform's instability has drawn complaints from major corporate clients like Citibank and Intel, forcing Microsoft to issue substantial service credits. A critical incident last month saw an accidentally triggered, unreleased feature cause widespread repository rollbacks, erasing recent code changes and pushing enterprises to migrate. Security has catastrophically breached. In May 2026, hackers infiltrated over 3,800 of GitHub's internal repositories via a poisoned VS Code extension installed by a developer, leading to the attempted sale of core source code for $50,000. This follows the discovery of a critical zero-day vulnerability in March that threatened access to millions of repositories. Internally, GitHub's autonomy has collapsed. After the resignation of CEO Thomas Dohmke in mid-2025, Microsoft eliminated the CEO role, folding GitHub into its CoreAI division under the unpopular leadership of Jay Parikh. This triggered a talent drain, with key executives and engineers leaving. A disruptive migration of GitHub's infrastructure to Azure servers, pushed by CTO Vladimir Fedorov, is blamed for the recurring outages. Competitively, GitHub Copilot is under "existential threat" from superior AI coding tools like Cursor (now owned by SpaceX) and Claude Code, which offer more advanced contextual coding and automation. Ironically, Microsoft's own engineers reportedly preferred Claude Code, forcing management to revoke licenses. Financially, GitHub is a loss leader. Despite Copilot surpassing 4.7 million paid users and $3 billion in annual revenue, the AI inference costs for free services massively outstrip subscription income, hurting Microsoft's cloud margins. The recent shift from a flat fee to a pay-as-you-go model for Copilot has further alienated developers. The core question for Microsoft is whether a centralized code repository remains essential in the AI agent era. The erosion of trust, developer culture, and platform reliability threatens the very ecosystem Microsoft spent decades building.

marsbit05/22 10:52

GitHub Empire on the Brink of Collapse: Source Code Leak, 18-Year Veteran Leaves, Microsoft Loses 1.5 Billion Developers

marsbit05/22 10:52

Cloud PC Gets a Second Chance, Google/Alibaba/Microsoft Battle for Cloud AI Dominance

Google unexpectedly announced "Android Computer," a new high-end productivity-focused PC series, positioning cloud AI as its core rather than an add-on. This move signals a potential revival for the "cloud computer" concept in the AI era. The article argues that current "AI PCs" are essentially traditional Windows machines with AI features grafted on, heavily reliant on cloud AI for complex tasks due to limited local consumer-grade hardware capabilities. This reliance raises questions about the value of premium local AI hardware. Cloud computers, which struggled with latency-sensitive applications like cloud gaming, are seen as a natural fit for AI PCs due to AI's higher tolerance for response time. Google's Android Computer deeply integrates AI (powered by its Gemini model) into the OS interface, making it contextually available. Its hardware-agnostic approach (supporting both x86 and ARM chips) further underscores the shift towards cloud-centric AI. Other players are adapting: Cloud service providers like Alibaba are enhancing their AI cloud computer offerings; chipmakers (Intel, AMD) are focusing on data center AI chips; traditional PC brands are adding AI software layers; and Apple is leveraging its ecosystem and affordable hardware. Microsoft is defining AI PC standards, embedding Copilot (powered by GPT and Bing) into Windows, and also relying on cloud AI. In conclusion, Android Computer challenges the traditional PC form factor by proposing a "light local, heavy cloud" model. This approach appears promising amid rising hardware costs and local compute bottlenecks. The future PC market will involve a multifaceted competition around cloud integration, OS-level AI, and cross-device ecosystems, potentially redefining the PC as a screen and network conduit to cloud-based AI productivity.

marsbit05/18 02:05

Cloud PC Gets a Second Chance, Google/Alibaba/Microsoft Battle for Cloud AI Dominance

marsbit05/18 02:05

Large Models Devour Word

On April 10, 2026, Anthropic integrated Claude into Microsoft Word, completing its integration into the core Office suite (Word, Excel, PowerPoint). This allows Claude to operate directly within Word’s interface, offering features like contract review with tracked changes—red for deletions, green for insertions—without altering user habits. Microsoft’s strategy is to prioritize interface ownership over model ownership, believing distribution channels matter more as AI models commoditize. By hosting Claude via Azure, Microsoft retains users rather than risk losing them to external platforms. The integration has disrupted legal tech firms; companies like Thomson Reuters and RELX saw significant stock drops, as Claude offers advanced legal review at a fraction of the cost. While some legal tech firms resist, others, like LexisNexis, have incorporated Claude into their offerings. Anthropic’s approach represents a new form of disruption: rather than building a competing platform, it embeds itself inside dominant software, offering superior functionality at lower cost. This forces incumbents into a dilemma—blocking better tools may drive users away, while allowing them cultivates internal competitors.

marsbit04/14 05:41

Large Models Devour Word

marsbit04/14 05:41

GitHub Announces Default Use of Copilot User Data for AI Model Training Starting April 24

GitHub has announced an update to its repository policy, effective April 24, 2026, allowing the use of user interaction data to train its AI models. The data collection will include users of Copilot Free, Pro, and Pro+, covering model inputs and outputs, code snippets, contextual information, repository structures, and chat logs. According to GitHub’s Chief Product Officer Mario Rodriguez, the move aims to enhance the accuracy and security of the model’suggestions, with internal Microsoft tests already showing improved acceptance rates. The policy follows an opt-out model, meaning affected users must manually disable data sharing in their privacy settings, sparking debate within the developer community over data ownership and the definition of private repositories. Copilot Business, Enterprise, and educational users are currently exempt due to contractual terms. GitHub defended the change as consistent with industry practices adopted by companies like Anthropic, JetBrains, and Microsoft. However, the inclusion of private repository code in training sets challenges conventional notions of privacy. This shift reflects a broader industry trend where leading AI providers are turning to user interaction data as high-quality public code resources diminish. It signals GitHub’s continued transition from an open-source platform to a closed-loop AI training ecosystem and highlights growing tensions between data compliance and AI model advancement.

marsbit03/26 01:39

GitHub Announces Default Use of Copilot User Data for AI Model Training Starting April 24

marsbit03/26 01:39

活动图片

Project Updatesright-arrow

Trading Strategiesright-arrow