2026-06-21 Domingo

Notícias de cripto - Página 271

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.

Eight Years of Entrepreneurship Notes from a16z's AI Partner

An early generative AI entrepreneur reflects on his 8-year journey building Rosebud AI, founded in 2018—a time when the field was still called “synthetic media.” Initially experimenting with models like CycleGAN and StyleGAN, he believed AI could make creation as intuitive as playing a game. Over the years, his team launched multiple products, including the viral app TokkingHeads, which gained 2 million users, learning to design around imperfect model outputs to deliver “good enough” user experiences. The evolution from niche synthetic media to general-purpose AI infrastructure—especially after GPT-4’s release—reshaped product possibilities. Code generation matured enough by 2023 to enable text-to-game prototyping. The author emphasizes that the real differentiator now isn’t just model capability but product design, distribution, and business model innovation. Having stepped down as CEO of Rosebud AI, he joins a16z as a partner focused on investing in the frontier model stack—models, infrastructure, and tools. He remains optimistic about AI-driven progress in creative tools, coding, and scientific domains. The piece concludes with a forward-looking note: the next phase of AI will be less about what’s possible and more about how capabilities are productized and scaled in the real world.

marsbit04/26 12:03

Eight Years of Entrepreneurship Notes from a16z's AI Partner

marsbit04/26 12:03

How Many Tokens Away Is Yang Zhilin from the 'Moon Chasing the Light'?

The article explores the intense competition between two leading Chinese AI companies, DeepSeek and Kimi (Moon Dark Side), and the mounting pressure on Yang Zhilin, the founder of Kimi. While DeepSeek re-emerged after 15 months of silence with its powerful V4 model—boasting 1.6 trillion parameters and low-cost, long-context capabilities—Kimi has been focusing on long-context processing and multi-agent systems with its K2.6 model. Yang faces a threefold challenge: technological rivalry, commercialization pressure, and investor expectations. Despite Kimi’s high valuation (reaching $18 billion), its revenue heavily relies on a single product with low paid conversion rates, while DeepSeek’s strategic silence and open-source influence have strengthened its market position and valuation prospects, now targeting over $20 billion. Both companies reflect broader trends in China’s AI ecosystem: Kimi aims for global influence through open-source contributions and agent-based advancements, while DeepSeek prioritizes foundational innovation and hardware independence, notably shifting to Huawei’s chips. Their competition is seen as vital for China’s AI progress, with the gap between top Chinese and U.S. models narrowing to just 2.7% on the Elo rating scale. Ultimately, the article argues that this rivalry, though anxiety-inducing for leaders like Zhilin, is essential for driving innovation and solidifying China’s role in the global AI landscape.

marsbit04/26 11:25

How Many Tokens Away Is Yang Zhilin from the 'Moon Chasing the Light'?

marsbit04/26 11:25

TechFlow Intelligence Bureau: ChatGPT Helps Amateur Mathematician Crack 60-Year-Old Problem, CFTC Sues New York Regulator Over Coinbase and Gemini

An amateur mathematician, with the assistance of ChatGPT, has solved a combinatorial mathematics puzzle originally proposed by Hungarian mathematician Paul Erdős in the 1960s. This marks another milestone in AI-aided mathematical research, demonstrating the evolving capabilities of large language models in formal reasoning. In other AI developments, OpenAI introduced a new privacy filter tool for enterprise API usage, automatically screening sensitive data. Meanwhile, the Qwen3.6-27B model achieved 100 tokens per second on a single RTX 5090 GPU using quantization, significantly lowering the cost barrier for local AI deployment. In crypto and Web3, the U.S. CFTC sued New York’s financial regulator, challenging its oversight of Coinbase and Gemini—a first-of-its-kind federal-state regulatory clash. Following a vulnerability, KelpDAO and major DeFi protocols established a recovery fund. Tether froze $344 million in assets linked to Iran’s central bank upon U.S. Treasury request, highlighting the centralized control risks in stablecoins. Separately, Litecoin underwent a 3-hour chain reorganization to undo a privacy-layer exploit. In the U.S., former President Trump invoked the Defense Production Act to address power grid bottlenecks affecting AI data centers and dismissed the entire National Science Board, raising concerns over research independence. A retail trader gained 250% on a $600k Intel options bet amid AI-related speculation. Xiaomi announced its first performance electric vehicle, targeting rivals like Tesla. Meanwhile, iPhone users reported devices automatically reinstalling a hidden app daily, suspected to be MDM-related. A Chinese securities report noted that A-share institutional crowding has reached its second-longest streak since 2007, signaling high valuations and potential style rotation. The day’s developments reflect a dual narrative: AI is enabling unprecedented individual breakthroughs, while centralized power structures—whether governmental or corporate—are becoming more assertive, underscoring that decentralization is as much a political-economic challenge as a technical one.

marsbit04/26 11:02

TechFlow Intelligence Bureau: ChatGPT Helps Amateur Mathematician Crack 60-Year-Old Problem, CFTC Sues New York Regulator Over Coinbase and Gemini

marsbit04/26 11:02

Iran’s Crypto Lifeline Hit As US Freezes $344 Million In Funds

The US Treasury has frozen $344 million in cryptocurrency tied to Iranian military and political groups, specifically targeting two Tron blockchain wallets connected to the Islamic Revolutionary Guard Corps and Hizballah. This action follows reports that Iran was collecting Bitcoin payments from ships for safe passage through the Strait of Hormuz. The freeze occurred just one day after stablecoin issuer Tether locked the same amount of funds at the request of US law enforcement. The move is part of broader US efforts to disrupt Iran's ability to generate and move funds, highlighting the limitations of using cryptocurrency as a sanctions workaround when centralized issuers comply with enforcement actions.

bitcoinist04/26 10:01

Iran’s Crypto Lifeline Hit As US Freezes $344 Million In Funds

bitcoinist04/26 10:01

Amidst the Volatility in the Crypto Market, Who is Buying Against the Trend?

"In a volatile crypto market downturn of Q1 2026, institutional players demonstrated divergent strategies. While Bitcoin fell over 25% and Ethereum dropped 35%, key institutions accumulated: corporate treasuries like Strategy (MicroStrategy) added over $10 billion in BTC, sovereign wealth funds like Mubadala increased IBIT holdings by 46%, and major banks launched crypto services and ETFs. Notably, 26 new crypto ETFs were filed or launched under streamlined SEC rules, including bank-led products from Morgan Stanley and BlackRock’s staking ETH ETF. VC funding saw concentration: total investment reached ~$5 billion, but deal count plummeted 49%. Three mega-deals—BVNK ($1.8B), Kalshi ($1B), and Polymarket ($600M)—accounted for half the quarter’s funding. Investment shifted from DeFi/NFTs to payments, prediction markets, and regulated CeFi infrastructure. Selling pressure came from hedge funds (e.g., Brevan Howard cut IBIT by 85%) and Bitcoin miners liquidating holdings. The U.S. strategic Bitcoin reserve saw zero additions, highlighting that real buying came from corporations and sovereign funds, not governments. The institutional crypto landscape is bifurcating: long-term holders accumulate while short-term traders exit, signaling a foundation for the next cycle."

marsbit04/26 09:38

Amidst the Volatility in the Crypto Market, Who is Buying Against the Trend?

marsbit04/26 09:38

Hands-on with Hunyuan Hy3 Preview: Tencent's AI, Finally Competitive?

Tencent's Hunyuan AI team has released its latest language model, Hy3 preview, marking a significant step forward for the company's AI capabilities. With 295B total parameters and support for 256K context length, the model employs a mixture-of-experts architecture. It shows improvements in complex logic, instruction following, contextual learning, code generation, and agent task execution. In testing, Hy3 preview demonstrated strong performance in multi-step logical reasoning but showed occasional instability in identifying traps in trick questions. It performed well in extracting key information from disordered meeting transcripts and accurately followed new linguistic rules. As an AI agent, it successfully built functional applications like a Snake game and generated data analysis dashboards, though it sometimes fell short in fully completing complex open-ended tasks. In natural language use, it produced coherent and stylistically appropriate narratives with reduced “AI-like” tone. Priced competitively, Hy3 preview is already integrated into Tencent’s key products, including Tencent Cloud and WorkBuddy. While not leading in every benchmark, it represents a solid, practical model that signals Tencent’s renewed momentum in AI development.

marsbit04/26 07:17

Hands-on with Hunyuan Hy3 Preview: Tencent's AI, Finally Competitive?

marsbit04/26 07:17

From Theft to Re-entry: How Was $292 Million "Laundered"?

A sophisticated crypto laundering operation was executed following the $292 million hack of Kelp DAO on April 18. The attack, attributed to the North Korean Lazarus group, began with anonymous infrastructure preparation using Tornado Cash to fund wallets untraceably. The hacker exploited a vulnerability in Kelp’s cross-chain bridge, stealing 116,500 rsETH. To avoid crashing the market, the attacker used Aave and Compound as laundering tools—depositing the stolen rsETH as collateral to borrow $190 million in clean, liquid ETH. This move triggered a bank run on Aave, causing an $8 billion drop in TVL. After consolidating funds, the attacker fragmented them across hundreds of wallets to evade detection. A major breakpoint was THORChain, where over $460 million in volume—30 times its usual activity—was processed in 24 hours, converting ETH into Bitcoin. This shift to Bitcoin’s UTXO model exponentially increased tracing complexity by shattering funds into countless untraceable fragments. The final destination was Tron-based USDT, the primary channel for illicit crypto flows. From there, funds were cashed out via OTC brokers in China and Southeast Asia, using unlicensed underground banks and UnionPay networks outside Western sanctions scope. Ultimately, the laundered money supports North Korea’s weapons programs, which rely heavily on crypto hacking for foreign currency. The incident underscores structural challenges in DeFi: its openness, composability, and lack of central control make such laundering not just possible, but inherently difficult to prevent.

marsbit04/26 07:12

From Theft to Re-entry: How Was $292 Million "Laundered"?

marsbit04/26 07:12

Behind DeepSeek V4's Stunning Debut: Silicon Valley Is 'Building Walls,' China Is 'Paving Roads'

China's AI landscape is witnessing a strategic divergence from Silicon Valley’s closed-source competition to a collaborative open-source ecosystem. On April 24, DeepSeek released V4, a top-ranked open-source model on Hugging Face, featuring breakthroughs like million-token context length with minimal KV cache and native support for domestic chips like Huawei’s Ascend. Similarly, Kimi’s K2.6, released days earlier, also adopted open-source principles. Unlike U.S. giants such as OpenAI and Anthropic—locked in revenue disputes and tactical product clashes—Chinese firms embrace shared innovation. DeepSeek and Kimi openly build on each other’s advances, like the MLA architecture and Muon optimizer, avoiding redundant R&D and driving down costs. DeepSeek V4 focused on pushing base model capabilities, while Kimi specialized in Agent-based applications. Although U.S. firms lead in revenue and valuation, China’s open-source models achieve comparable performance at a fraction of the cost (e.g., DeepSeek V3 trained for $5.58M vs. GPT-5’s $500M+). With token usage growing exponentially, China’s collaborative model promises scalable, affordable AI built on domestic hardware, shaping a more accessible path to AGI.

marsbit04/26 07:03

Behind DeepSeek V4's Stunning Debut: Silicon Valley Is 'Building Walls,' China Is 'Paving Roads'

marsbit04/26 07:03

OpenClaw vs. Hermes: Which One Is Right for You?

Two leading AI agent projects, OpenClaw and Hermes, represent a fundamental divergence in design philosophy rather than just a feature competition. OpenClaw, a GitHub phenomenon, prioritizes user control and customization, offering a powerful, flexible platform for building and orchestrating agents. It boasts a massive ecosystem with over 44,000 skills, extensive model flexibility (supporting Anthropic, OpenAI, and others), and deep integrations across multiple messaging platforms. In contrast, Hermes Agent, developed by Nous Research, champions automation and efficiency. It is designed for lower cost and a lower barrier to entry, learning and improving automatically from user workflows. It runs tasks in isolated environments for security and offers significantly lower token costs out-of-the-box. The choice between them mirrors historical tech divides like Windows vs. Mac. OpenClaw is for users who want maximum control, customization, and don't mind a complex setup. Hermes is the smarter default for those seeking an affordable, easy-to-use agent that learns autonomously. The real competition is not about which is better, but which philosophy—a programmable personal OS or a self-evolving work proxy—better suits the user's needs for control, cost, and ease of use.

marsbit04/26 06:42

OpenClaw vs. Hermes: Which One Is Right for You?

marsbit04/26 06:42

Google and Amazon Simultaneously Invest Heavily in a Competitor: The Most Absurd Business Logic of the AI Era Is Becoming Reality

In a span of four days, Amazon announced an additional $25 billion investment, and Google pledged up to $40 billion—both direct competitors pouring over $65 billion into the same AI startup, Anthropic. Rather than a typical venture capital move, this signals the latest escalation in the cloud wars. The core of the deal is not equity but compute pre-orders: Anthropic must spend the majority of these funds on AWS and Google Cloud services and chips, effectively locking in massive future compute consumption. This reflects a shift in cloud market dynamics—enterprises now choose cloud providers based on which hosts the best AI models, not just price or stability. With OpenAI deeply tied to Microsoft, Anthropic’s Claude has become the only viable strategic asset for Google and Amazon to remain competitive. Anthropic’s annualized revenue has surged to $30 billion, and it is expanding into verticals like biotech, positioning itself as a cross-industry AI infrastructure layer. However, this funding comes with constraints: Anthropic’s independence is challenged as it balances two rival investors, its safety-first narrative faces pressure from regulatory scrutiny, and its path to IPO introduces new financial pressures. Globally, this accelerates a "tri-polar" closed-loop structure in AI infrastructure, with Microsoft-OpenAI, Google-Anthropic, and Amazon-Anthropic forming exclusive model-cloud alliances. In contrast, China’s landscape differs—investments like Alibaba and Tencent backing open-source model firm DeepSeek reflect a more decoupled approach, though closed-source models from major cloud providers still dominate. The $65 billion bet is ultimately about securing a seat at the table in an AI-defined future—where missing the model layer means losing the cloud war.

marsbit04/26 01:04