Bloomberg: Биткоин закрывает один из лучших сентябрей в своей истории

investing.ruPubblicato 2024-09-27Pubblicato ultima volta 2024-09-27

По мнению опрошенных Bloomberg экспертов рынка, ценовая динамика биткоина в сентябре резко контрастирует с классическим для этого месяца поведением монеты — падением в среднем на 5,9%

Директор по торговле платформы ликвидности Arbelos Markets Шон МакНалти (Sean McNulty) заявил, что корреляция биткоина с денежно-кредитной политикой Федеральной резервной системы США (ФРС) продолжает оставаться самой высокой, и снижение процентных ставок оказывает дополнительную поддержку криптовалюте.

Однако замерший на отметке чуть выше $65 000 курс биткоина может оказаться неустойчивым из-за истечения большого количества опционных контрактов, отметила соучредитель компании-поставщика ликвидности для торговли деривативами цифровых активов Orbit Markets Кэролайн Маурон (Caroline Mayron).

Сейчас крипторынок ожидает финала президентской предвыборной гонки в США, и многие участники рынка ожидают подъема настроений инвесторов после того, как новая администрация Белого дома сформирует четкие правила регулирования криптовалют, заявили эксперты Bloomberg.

Ранее аналитики международной финансовой корпорации Standard Chartered (LON:STAN) указали на ряд факторов, способных поддержать рост биткоина в следующем месяце. По их словам, ожидания инвесторов после снижения ставки ФРС улучшились.

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Claude Accused of Becoming Dumber by the Entire Internet, Anthropic Steps In to Reveal: It’s Not the Model That’s Tricking You

When users complained that Claude was "getting dumber," the root cause wasn't the AI model itself. In an official blog post, Anthropic clarified the critical difference between two key settings in Claude Code: Model and Effort. Model refers to the core "brain"—the fixed, trained weights of a specific AI (like Sonnet, Opus, or Fable). Changing the Model addresses *capability* ("can it do this?"), but its knowledge is static post-training. Effort, however, controls the AI's *approach and thoroughness* for a specific task. A higher Effort level instructs Claude to read more files, run tests, perform verification, and complete multi-step reasoning before responding, significantly increasing its "work output" for that job. Conversely, low Effort leads to quicker, less thorough replies. This distinction explains the March 2024 uproar where users experienced a sudden drop in Claude's performance. The cause was not a model change but Anthropic quietly lowering the *default* Effort setting from "high" to "medium" to reduce latency, which was later reverted. The key insight is that a smaller, capable model (like Sonnet) on high Effort can often outperform a larger, more powerful model (like Opus) on low Effort for many tasks. The article provides a practical troubleshooting framework: if Claude makes an error, first check the context and instructions. If it seems to skip necessary steps or validations, increase Effort. If it diligently attempts the task but fails conceptually or makes consistent factual errors despite good context, then consider switching to a more capable Model. The takeaway is a shift in focus: effective AI programming is less about always choosing the "strongest" model and more about intelligently *orchestrating* models and effort levels—acting like a project manager to assign the right "brain" with the right level of diligence for each job, optimizing both results and cost.

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Claude Accused of Becoming Dumber by the Entire Internet, Anthropic Steps In to Reveal: It’s Not the Model That’s Tricking You

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Will the Ethereum Foundation Evolve into a 'Mascot'? Diversified Organizations Are Fragmenting Its Functions

The Ethereum Foundation (EF) is undergoing significant internal turmoil and functional erosion. Following its largest-ever layoff of 54 staff (20% of its workforce) and a major organizational restructuring announced in June, its Protocol Support Team has been officially dissolved. This comes alongside the high-profile resignation of key figures like co-executive director Xiaowei Wang, bringing senior departures this year to at least eight. Criticism of EF's rigid structure, opaque decision-making, and perceived lack of a clear value narrative for ETH has intensified within the community. The layoffs have catalyzed the emergence of independent, non-profit organizations like Ethlabs and Ethereum Institutional, founded by former EF researchers and members. These entities are now taking on core functions such as protocol research/development and institutional adoption, effectively fragmenting the EF's traditional leadership role. Concurrently, EF's security team is adapting to technological change, deploying specialized AI agents to audit Ethereum's codebase, which successfully discovered a critical vulnerability (CVE-2026-34219). While EF states AI complements rather than replaces researchers, it signals a potential future shift in its operational model. Faced with these challenges—internal restructuring, talent drain, the rise of competing organizations, and AI integration—the Ethereum Foundation appears to be stepping back from a central commanding role. Analysts and community observers speculate it may increasingly transition towards a symbolic "ecosystem mascot" function, while decentralized initiatives drive Ethereum's future growth and institutional adoption.

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Will the Ethereum Foundation Evolve into a 'Mascot'? Diversified Organizations Are Fragmenting Its Functions

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Nearly a Hundred Players Rush into Embodied Data: With 4.47 Billion Yuan in Financing in One Year, Who Can Really Make Money by 'Selling Data'?

The domestic embodied AI data industry has attracted nearly 100 players, with 70 focused on data collection and 27 on data infrastructure. In the past year, 15 independent embodied data service providers raised approximately 4.47 billion yuan. Despite this growth, the sector remains early-stage, fragmented, and faces significant challenges. Data collection methods are diverse, categorized into four main routes: teleoperation of real robots, human demonstration without a robot (using motion capture, exoskeletons, etc.), simulation synthesis, and distillation from internet videos. Most companies (43%) adopt hybrid approaches, combining multiple routes, as no single method can meet all training needs. Teleoperation alone is pursued by 31% of players, often by state-owned platforms and robot companies, while newer firms favor asset-light, no-hardware human demonstration. Independent data service providers now form the largest player group (40%), indicating the emergence of a distinct industry segment rather than just a subsidiary function for robot makers. Two-thirds of all players are "embodied-native" startups, while one-third are companies that pivoted from fields like AI data annotation, which are more prevalent in the data infrastructure layer. Current annual industry capacity is estimated at 1.6-1.8 million hours plus 70-80 million data points, with a short-term goal to increase this 15-20 fold within 1-3 years. Data collection factories are spread across 20 provinces in China, concentrated in the Yangtze River Delta, Beijing-Tianjin-Hebei, and Pearl River Delta regions. Financially, the 4.47 billion yuan raised in the past year pales compared to the 43.8 billion yuan raised by the broader embodied intelligence sector in just the first half of 2026, highlighting that data remains a less "sexy" bet for investors. The 15 funded independent providers show clear stratification: a top tier led by a unicorn (Lightwheel Intelligence, 3.1 billion yuan), a middle tier of 11 firms raising tens to hundreds of millions, and an early-stage tier of 3 companies. Sixty-nine investment institutions have participated, but none have made concentrated bets, reflecting uncertainty about viable business models. Over half of these funded companies are less than a year old, most are at pre-A or A rounds, and profitability remains largely unproven. In summary, the embodied data industry has become an independent track creating jobs and local economic activity. However, it is still nascent, with unformed consensus, unsolved problems, and unproven business models. The coming 1-2 years will be a critical validation window to see if companies can build sustainable, profitable businesses purely by "selling data."

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Nearly a Hundred Players Rush into Embodied Data: With 4.47 Billion Yuan in Financing in One Year, Who Can Really Make Money by 'Selling Data'?

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