2026-07-12 Domingo

Notícias de cripto - Página 2

Mantenha-se a par do mercado de cripto. Notícias em tempo real, análises, preços, histórias em alta e análise de especialistas — tudo num só lugar.

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.

marsbitHá 6h

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

marsbitHá 6h

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.

marsbitHá 6h

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."

marsbitHá 9h

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'?

marsbitHá 9h

Dialogue with Multicoin Partner: The Crypto Market Has Bottomed Out, Favoring Three Cryptocurrencies in This Cycle

In a recent interview, Multicoin Capital managing partner Tushar Jain shared his views on the crypto market. He believes the market has bottomed and is at an inflection point, citing that negative news no longer causes significant price declines and application adoption continues to grow. Jain remains highly bullish on Solana, viewing it as the correct architectural choice for internet capital markets, particularly for spot and tokenized security trading. He is also positive on Hyperliquid, noting its leadership in decentralized derivatives trading. His investment approach focuses on concentrating capital in top convictions rather than equal allocation. A distinct opportunity he highlights is Zcash (ZEC), which he sees as a return to the industry's cypherpunk ethos and a potential top-five asset by market cap. For assets like Zcash without cash flows, his valuation framework is based on relative market cap ranking. Regarding investment strategy, Jain employs a "three-part" entry method to avoid timing pitfalls and emphasizes long-term "active management" over "active trading." He outlines four sources of investment edge: informational, analytical, behavioral/psychological, and structural. On portfolio management, the fund uses Bitcoin as its "cash," selling assets into Bitcoin during market euphoria to reduce beta risk and using Bitcoin to buy dips. Sales occur only if a better opportunity arises, the investment thesis breaks, or valuations become excessively overheated. While respectful of Ethereum's resilience, he questions its unclear scaling roadmap. Finally, Jain reaffirms his commitment to the thesis that blockchains will form the foundational architecture for future capital markets.

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Dialogue with Multicoin Partner: The Crypto Market Has Bottomed Out, Favoring Three Cryptocurrencies in This Cycle

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Behind Robinhood's Chain Launch and Tokenized Stocks: No Equity Rights, How Far Can This Packaging Game Go?

Robinhood is launching its own Layer 2 blockchain (Robinhood Chain) and "tokenized stocks," but these are not actual equity shares. The tokens are legally structured as debt securities or derivatives, offering economic exposure to a reference stock without granting voting rights or direct ownership. This move represents Robinhood's strategy to expand from a traditional brokerage into a "financial super app," building a user-friendly, programmable financial interface on top of complex, legally compliant, and jurisdiction-specific backend structures. The company's existing business remains strong, driven by options, event contracts, and stock trading. The new blockchain and tokenization efforts are an ambitious layer of infrastructure built atop this core, aiming to make financial products more portable and globally accessible via crypto rails. Key components include the Robinhood Wallet, Bitstamp acquisition (for institutional reach), the Lighter perpetual contracts platform, and Robinhood Earn (DeFi yield). The central challenge is the "brokerage chain paradox": maintaining a simple, intuitive user experience while the underlying assets are highly structured, regulated, and legally distinct from direct ownership. The success of this strategy depends on users, developers, and regulators accepting this model. If the complexity is misunderstood or deemed misleading, it could create product liability issues and stall expansion. The initiative is a significant infrastructure play, but its long-term viability hinges on navigating this fundamental tension between simplicity and legal reality.

marsbitHá 11h

Behind Robinhood's Chain Launch and Tokenized Stocks: No Equity Rights, How Far Can This Packaging Game Go?

marsbitHá 11h

Zhipu, Afraid of Becoming the Next MiniMax

Title: Zhipu, Fearing to Become the Next MiniMax In July 2026, amid the success of its coding-focused AI, Zhipu's founder, Tang Jie, issued an internal letter titled "The Giant Wave Has Come." It notably avoided celebrating recent triumphs, such as Zhipu's trillion-HKD market cap and booming MaaS revenue driven by its GLM-5.2 model in coding applications. Instead, the letter pivoted the narrative to future-oriented concepts like Long Horizon Task, Autonomous Agents, Self-Evolving systems, and AGI. This strategic shift in messaging followed the sharp devaluation of its competitor, MiniMax. After its lock-up period expired, MiniMax's stock plummeted as the market began evaluating it with traditional SaaS metrics like ARR and user growth, rather than as a frontier AI pioneer. Seeing this, Tang Jie aimed to preempt a similar revaluation of Zhipu. He fears that if the market starts viewing Zhipu primarily as a profitable "AI coding company," its valuation would become anchored to conventional financial metrics, losing the premium associated with AGI potential. Therefore, the letter reframed Zhipu's mission. While acknowledging that coding was the current commercial driver, Tang positioned Zhipu on the "infrastructure path," akin to OpenAI and Anthropic. The new focus is on developing agents capable of complex, long-term planning and autonomous operation—moving from assisting individuals (OPC: One Person Company) to automating entire organizations (NPC: No People Company). This "Touch High" plan explicitly prioritizes long-term AGI research over short-term monetization. The article frames this as a critical divergence in China's AI landscape: the "commercialization path" (exemplified by MiniMax) versus the "infrastructure path" (chosen by Zhipu). The former risks being judged harshly by internet-era metrics once growth slows, while the latter risks failing if technological breakthroughs stall. Tang Jie's letter is thus a calculated move to secure Zhipu's identity as an AGI contender, buying time before the inevitable market demand for commercial proof. The core question remains: can Zhipu's "mo gao" (reach high) plan achieve genuine technological leaps fast enough to outpace the market's diminishing patience for stories over substance?

marsbitHá 11h

Zhipu, Afraid of Becoming the Next MiniMax

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