工信部:构建“以网管网”监管能力,加快探索大数据、区块链、人工智能等新技术在监管中的应用

币界网Pubblicato 2024-08-06Pubblicato ultima volta 2024-08-06

币界网报道:

工业和信息化部发布关于创新信息通信行业管理 优化营商环境的意见。其中提出,构建“以网管网”监管能力。强化技术赋能监管,推进现有技术监管能力迭代升级,建设互联网数据中心等重点电信业务大数据综合监管平台、面向移动互联网应用程序检测及认证公共服务平台,强化业务合规经营情况的线上监测分析、调查取证能力,健全技术监管体系。加快探索大数据、区块链、人工智能等新技术在监管中的应用,推行远程监管、线上监管方式,进一步提升监管效能。(工信部网站)

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Idle Macs Can Also Make Money? An Overview of Eigen Labs' Decentralized AI Inference Network Darkbloom

AI inference is becoming a crucial layer of internet infrastructure, yet it remains largely dependent on costly, capacity-limited centralized systems with potential security risks. Meanwhile, millions of powerful computers sit idle globally. Eigen Labs' Darkbloom network aims to utilize this idle capacity by enabling distributed AI inference on Mac computers, specifically those with Apple Silicon chips. Darkbloom's architecture consists of three components: users who send inference requests, a coordinator (operated by Eigen Labs) that routes these requests, and providers (Mac owners) whose machines run the models and return outputs without being able to see the request content. The system prioritizes privacy through a hardened provider process, software integrity checks, and hardware-supported attestation based on Apple's security architecture to ensure verifiable privacy. Economically, Darkbloom differs from traditional models. It leverages existing hardware, with marginal costs primarily driven by electricity, allowing it to offer pricing roughly 50% lower than major API aggregators. Providers keep 100% of the inference revenue, and the project does not rely on token subsidies; earnings come solely from real AI inference demand. However, early-stage earnings are modest, with top providers currently earning under $6 per day, influenced by factors like hardware specs, uptime, and network demand. The network currently supports models like Google's Gemma 4 and OpenAI's GPT-OSS via OpenRouter. To participate as a provider, users need an Apple Silicon Mac running macOS 14 or later, must install the Darkbloom provider software, and keep the machine online with a stable internet connection.

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Idle Macs Can Also Make Money? An Overview of Eigen Labs' Decentralized AI Inference Network Darkbloom

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Which Crypto Sectors Have Been "Eaten" by AI Agents?

The article examines which crypto sectors have been increasingly dominated by AI Agents and which remain human-centric. In certain high-speed, efficiency-driven areas, AI Agents have taken clear control. This includes derivatives/perpetuals trading, where bots outperform humans significantly (e.g., a contest showed 0% of AI Agents were liquidated vs. 43% of humans), arbitrage/MEV extraction, and yield optimization (with ~68% of new DeFi protocols in Q1 2026 featuring autonomous AI Agents). Spot trading and portfolio optimization are also seeing heavy Agent adoption. However, the shift is not universal. In "battleground" sectors, both Agents and humans coexist. In prediction markets, Agents dominate short-term arbitrage, but humans still outperform in long-term, nuanced judgment calls. In DeFi lending, while liquidation is automated, core deposit/borrow decisions remain largely human-driven. Sectors still firmly led by human activity include stablecoin payments and card-based spending (driven by real-world economic activity and remittances) and wallets, which serve as the crucial human-verification and approval layer. The rise of Agents increases the need for robust human-Agent verification layers. Projects like World/AgentKit, t54, Self Protocol, and Kite AI are building infrastructure to create trust, security, and accountability by binding Agents to verified human identities. In conclusion, while AI Agents have decisively "eaten" speed and optimization-focused crypto sectors, human judgment, trust, and real-world context remain dominant in areas that create broad economic value, such as payments and identity. The future likely involves a symbiotic relationship where Agents require human verification and oversight to operate effectively.

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Which Crypto Sectors Have Been "Eaten" by AI Agents?

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After Rising 11 Times in a Year, Micron's Earnings Report Becomes a Stress Test for the AI Memory Market

**Micron's Upcoming Earnings: A Crucial Test for the AI Memory Rally** Investors in AI memory stocks face a critical moment on June 24th, when Micron Technology reports quarterly earnings. The stock, having surged approximately 11-fold from $103 to $1,134 over the past year, carries immense market expectations. Wall Street consensus forecasts a staggering ~932% year-over-year jump in EPS to around $19.72 and ~270% revenue growth to ~$345 billion, largely driven by sold-out HBM (High Bandwidth Memory) capacity through 2026. Analysts have aggressively revised estimates upward over the last 90 days, with EPS expectations rising 68%. This creates a high bar: even strong results risk a sell-off if they fail to meet these elevated projections. Notably, price forecasts from institutions like Citi (predicting ~200% DRAM price increases in 2026) are already among the most bullish on Wall Street, not conservative. The key metric to watch is gross margin, guided to a record ~81%. Such peak profitability raises questions about sustainability in the historically cyclical memory sector. While management has signaled continued strength, the stock's direction post-earnings will likely hinge more on forward guidance for the next quarter and details on HBM capacity expansion for 2027, rather than the already-anticipated stellar past results. The report represents a major pressure test for the high-flying AI memory trade.

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After Rising 11 Times in a Year, Micron's Earnings Report Becomes a Stress Test for the AI Memory Market

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