2026-08-03 Segunda

Notícias de cripto - Página 138

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.

The 'Great Divergence' of the Crypto Market in 2026: BTC Bear Market, but BlackRock, Franklin Templeton, and JPMorgan Are Simultaneously Doing One Thing

"2026 Crypto Market 'Great Divergence': BTC Bearish, But BlackRock, Franklin, JPMorgan Are Simultaneously Building Infrastructure." In July 2026, amidst BTC struggling at $62K, seven key events signal a profound shift: the 'Great Divergence' between price action and underlying infrastructure development. Franklin Templeton's CIO notes a "big disconnect" between price and fundamentals. Meanwhile, major institutions are advancing real-world blockchain adoption: BlackRock, Goldman Sachs, and JPMorgan join a UK government-backed tokenization taskforce targeting repo and gilts; Hyundai pilotes USDT for cross-border trade settlement; Bolivia considers integrating USDT into its national payment system; and Robinhood's new blockchain sees rapid adoption. This activity represents a quiet infrastructure bull market, driven by institutional strategy and long-term regulatory roadmaps, not short-term crypto price cycles. The core narrative is shifting from speculative price action to foundational utility. Infrastructure development—focused on upgrading traditional finance, enabling real-world payments, and tokenizing assets—is now decoupled from BTC's volatility. Historical parallels (e.g., dot-com bust/AWS birth, 2018 crypto winter/DeFi Summer) show that infrastructure built during downturns often becomes the next cycle's "toll booth." The critical question is no longer "Will BTC drop further?" but "Who will own the tolls when this new infrastructure is complete?" While BTC remains a key liquidity anchor, the valuation logic for crypto's real-world utility is increasingly separate from its most traded asset's price.

marsbit07/15 04:09

The 'Great Divergence' of the Crypto Market in 2026: BTC Bear Market, but BlackRock, Franklin Templeton, and JPMorgan Are Simultaneously Doing One Thing

marsbit07/15 04:09

Scaling Law a One-Size-Fits-All Solution? First Crystal Structure Manipulation Benchmark Shows Top Large Models Falling Short

Scaling Law Hits a Wall: New Benchmark Reveals AI's Struggles with Atomic-Level Material Manipulation A new benchmark called AtomWorld, developed by researchers, reveals a significant limitation in current large language models (LLMs). While powerful at understanding textual scientific knowledge, they perform poorly when tasked with physically manipulating atomic structures based on natural language instructions. The benchmark tests core atomic operations like replacing atoms, rotating structures, and expanding supercells. Results show that simply scaling up model size (Scaling Law) yields only modest and unstable improvements, particularly for tasks requiring strong 3D spatial reasoning and geometric planning. For instance, complex tasks like "rotating around a specific atom" see very low success rates even in top models like Claude Opus. This highlights a critical gap: textual knowledge does not automatically translate to reliable action in a physically constrained 3D space. The study argues that for AI in Science to progress, the focus must shift from just scaling language data (Language Scaling) to also scaling actionable capabilities (Action Scaling). This involves building training loops around "action-feedback-correction" cycles within simulated or real scientific environments. Ultimately, AtomWorld underscores that to become true lab assistants, AI models need to evolve beyond explaining knowledge to reliably executing precise, verifiable scientific actions.

marsbit07/15 03:56

Scaling Law a One-Size-Fits-All Solution? First Crystal Structure Manipulation Benchmark Shows Top Large Models Falling Short

marsbit07/15 03:56

Nobel Laureate Hassabis Shocks with Statement: AGI Impact Will Be 10 Times That of the Industrial Revolution

Nobel laureate and DeepMind CEO Demis Hassabis declares that Artificial General Intelligence (AGI), matching human cognitive abilities, is likely just a few years away. He states its impact could be ten times greater than the Industrial Revolution and unfold ten times faster, heralding an age of unprecedented abundance where resource scarcity may end. AGI promises transformative benefits, accelerating breakthroughs in medicine, clean energy, and advanced materials. However, its rapid development, driven by intense commercial and geopolitical competition, outpaces our understanding and increases risks in cybersecurity, bio-threats, and controlling autonomous, self-improving systems. To manage this, Hassabis proposes a U.S.-led framework: a new "Frontier AI Standards Body," modeled after organizations like FINRA. It would define "frontier-class" models through dynamic benchmarks. Labs creating such models would voluntarily submit them for a 30-day pre-release review, later formalized into mandatory assessments. This body would conduct rigorous evaluations on security and safety, update tests regularly, and, if necessary, coordinate a global slowdown in development. While technical challenges are surmountable, Hassabis emphasizes that profound economic and philosophical questions about post-scarcity societies, human values, and purpose remain. He concludes that responsibly navigating AGI's arrival is our defining task, offering a chance to shape a future of immense scientific progress and human flourishing.

marsbit07/15 03:33

Nobel Laureate Hassabis Shocks with Statement: AGI Impact Will Be 10 Times That of the Industrial Revolution

marsbit07/15 03:33

GPT-5.6 Sol Suddenly Gets Dumber Overnight? Thinking Budget Slashed from 960 to 128, No More Fixed-Intelligence Models?

The article discusses widespread user reports that OpenAI's GPT-5.6 Sol model, specifically its "Max" reasoning tier, has become less capable at complex, deep reasoning tasks. Users noted faster but shallower responses. Community investigation revealed an unpublicized internal parameter called "juice value," representing computational budget for reasoning. Observations indicated this value for the Max tier dropped dramatically from 960 to 128. In response, OpenAI's Thibault Sottiaux stated there was no intentional reduction in model capability ("nerf"). He explained the changes were part of an experiment to investigate unexpected high token usage following GPT-5.6's launch, which introduced features like longer reasoning and larger context windows. The experiment temporarily adjusted the "juice" parameter and rolled back the context window from 372k to 272k tokens to diagnose the usage spike. Sottiaux asserted these settings have been reverted and highlighted ongoing optimizations. The controversy highlights a tension between AI as a reliable, fixed-capability tool and its reality as a cloud service where providers can adjust performance parameters. The article argues that for AI to be trusted enterprise infrastructure, providers need clearer, transparent guarantees about the specific performance boundaries associated with service tiers.

marsbit07/15 03:28

GPT-5.6 Sol Suddenly Gets Dumber Overnight? Thinking Budget Slashed from 960 to 128, No More Fixed-Intelligence Models?

marsbit07/15 03:28

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