0G Al Alliance Carnival正式启动，千万不要错过。 The 0G Al Alliance Carnival is officially live. Don’t miss it. 0G 亚太生态正在快速扩张。🌏 越来越多 AI Native 项目正在加入 0G，为社区带来更多早期参与机会。 The 0G ecosystem in APAC is growing rapidly, bringing early participation opportunities for creators and communities across AI + Web3. 本次 Carnival 将联合多个生态项目，通过线上任务、社区活动与线下曝光，共同推动 0G 生态增长，也让大家能抢先体验产品并获取早期 Alpha。 This campaign connects ecosystem projects through quests, community activations, and offline exposure to accelerate the growth of the 0G AI ecosystem. 👇 Participating Projects & Rewards | 参与项目及奖励 ━━━━━━━━━━━━━━ 🔹 @NeoSoulAI 基于 AI Agent Oracle 的原生 AI 预测市场。 AI-native prediction market powered by autonomous agentic oracles. 🎁 Rewards: • 1,000,000 NeoSoul Tokens • $OUL Points ━━━━━━━━━━━━━━ 🔹 @Ghast_AI 构建于 0G 之上的原生 AI Agent 基础设施，让 AI 记忆与交互成为可交易资产。 Native AI Agent infrastructure built on 0G — turning AI memory & interaction into tradable on-chain assets. 🎁 Rewards: • 50 Early Bird Codes ━━━━━━━━━━━━━━ 🔹 @moonfun_ai 将 Meme Token 演化为具备自主能力与社交智能的 AI Agent。 Transforming meme tokens into autonomous living AI agents with social intelligence. 🎁 Rewards: • 50,000 Moon Points ━━━━━━━━━━━━━━ 🔹 @primus_labs 面向链上链下数据与身份验证的隐私证明层。 Privacy-preserving verification layer for identity, data, and on-chain/off-chain activity. 🎁 Rewards: • Primus Reputation Score ━━━━━━━━━━━━━━ 🔹 @gmdottown 打造下一代 Agent Economy，实现 AI Agent 的自治协作与交易。 Building the next-generation Agent Economy for autonomous coordination and 24/7 AI workforce trading. 🎁 Rewards: • 50 OpenWhale Founding Member SBTs ━━━━━━━━━━━━━━ 更多 0G 生态项目即将加入。👀 More ecosystem projects are joining soon. 0G 亚太 AI 生态的增长才刚刚开始。 This is just the beginning of the 0G APAC AI expansion. 🌐 Online Quests 🌐 Offline Activations @ BEYOND Expo 🌐 Ecosystem-wide Collaboration 🌐 Early Community Rewards 更多任务与奖励即将公布，保持关注。 Stay tuned. 本次活动由 0G 生态项目 @lighthouse_2026 提供市场支持。 This event is supported by 0G ecosystem project @lighthouse_2026 for marketing.

#为什么是中国# #WhyChina# The Global Logic of China's Economic Growth in the First Half of 2026: A 4.7% GDP Increase 2026年上半年GDP增长4.7%：中国经济增长的全球逻辑 According to the latest semi-annual report, China's gross domestic product (GDP) reached 69.6 trillion yuan, representing a year-on-year increase of 4.7% at constant prices, in line with the annual growth target. Against a backdrop of intertwined international complexities and volatilities, China's economic performance is commendable. Yet, a closer look at the data reveals that the logic underpinning China's economic growth is being reshaped. (I) At the Industry Level, Notable Highlights Emerge: First, new quality productive forces are being cultivated and strengthened at an accelerated pace.** In the first half of the year, industrial production grew robustly. The value-added output of the equipment manufacturing sector increased by 9.3% year-on-year, and that of high-tech manufacturing grew by 13.3%, both outpacing the overall growth rate of industrial output above a designated scale. This underscores a clear trend toward a high-end, intelligent, green, and integrated industrial structure. Looking at specific products, the output of 3D printing equipment, lithium-ion batteries, industrial robots, and other products emblematic of new quality productive forces surged. The average daily token call volume has reached hundreds of trillions, showcasing the vitality and potential of the digital and intelligent economies. Second, new growth drivers are accelerating to take on a leading role. Preliminary estimates suggest that new growth drivers—encompassing high-end manufacturing, the digital economy, and modern services—contributed over 40% to economic growth in the first half of the year. The economy's distinct shift toward a higher quality and more optimized structure is evident, and this overall trend is accelerating. For instance, industries related to artificial intelligence, such as integrated circuit manufacturing and intelligent vehicle equipment manufacturing, have all maintained high growth rates exceeding 30%, vividly illustrating the pace of China's industrial upgrading. Third, confidence on the investment front is on the rise. In the first half of the year, investment in high-tech industries grew by 4.6% year-on-year. Notably, investment in the manufacturing of aircraft, spacecraft, and equipment, computer and office equipment manufacturing, and information services grew by 23.3%, 8.1%, and 15.5%, respectively. Investment structure best reflects market expectations. The increasing "new economy content" in investments signals an acceleration in the replacement of old growth drivers with new ones. Investment in intellectual property products grew by 9.4%, indicating that enterprises are placing greater emphasis on R&D and innovation. This suggests that technological advancement is not simply about capacity expansion but about qualitative change driven by innovation. (II) Observing a Major Economy Requires Looking Beyond the Immediate Figures to the Long-term Trajectory. Behind the "new economy content" of the semi-annual report lies China's ongoing transformation from a global manufacturing hub to a global center of innovation. At the 17th Annual Meeting of the New Champions (Summer Davos), observers noted a new phenomenon: a host of unicorn companies are heading to China. They are establishing R&D centers, regional headquarters, and deeply integrating into China's innovation and industrial chains—shifting from "produced in China" to "created in China." So, why China? Economist Justin Yifu Lin, in his book *Demystifying the Chinese Economy, touched upon the theory of the "speed of technological change." He argues that the essence of the industrial revolution is not just the application of new technologies, but more fundamentally, the ever-accelerating pace of technological change. Since the mid-18th century, starting with the steam engine reshaping the textile industry, the snowball of technological change has grown, rapidly sweeping through industries like chemicals and automobiles, ultimately redrawing the geographical map of great power competition. Looking at China today, the trajectory of accelerating technological change is equally clear. A leading enterprise can drive an entire industry, which in turn can boost a whole region. These burgeoning industrial clusters, growing from saplings to forests, not only enhance production efficiency and invigorate market vitality but also effectively improve development quality and resilience. For example, specialized and sophisticated "little giant" enterprises above a designated scale in Beijing, through deep cultivation of innovation chains, supply chain collaboration, and international expansion, have become "connecting points" and "accelerators" for the dual circulation strategy. More importantly, emerging industrial clusters possess powerful spillover effects. The rapid rise of new energy vehicles is not only reshaping the automotive industry but also driving transformations in chips, software, and energy networks, allowing more sectors to gain value from efficiency improvements. The swift advancements in AI and biomedicine are sparking a "gentle qualitative change" in people's livelihoods, significantly enhancing the sense of fulfillment and well-being through smarter, more affordable products and more livable environments. (III) Looking from the First Half to the Full Year, China's Development Momentum Remains Positive. Of course, during this critical period of transitioning between old and new growth drivers, China's economy still faces lingering issues and new challenges. Some core areas are still grappling with "bottleneck" technologies, certain high-tech industries face external risks of "decoupling" and supply chain disruptions, and "involution"-style competition affects the new energy market ecosystem. However, most of these are issues arising from development and transition, and they can be addressed with effort. The supporting conditions and fundamental trends for long-term economic improvement remain unchanged. China's economic journey toward a newer, higher-quality model is itself a process of encountering new problems and solving them along the way. By maintaining confidence, proceeding steadily, and balancing both qualitative improvements and quantitative growth, China's industries are poised to be brimming with dynamism, and the Chinese economy will continue to advance steadily and sustainably. #China# #Jiangxi# #JiangxiEconomy# #世界经济看中国# #赣出新精彩#

Anthropic 工程师 Barry Zhang 在 AI Engineer 工作坊上的一个分享 “如何构建有效的 Agent”，其中印象最深的一个观点：Don't build agents for everything，反过来理解就是别做什么都能干的 Agent，那是我们大模型要干的事情😆 构建有效 Agent 的三大要点： 1. 明智选择应用场景，并非所有任务都需要 Agent； 2. 找到合适的用例后，尽可能长时间地保持系统简单； 3. 在迭代过程中，尝试从 Agent 的视角思考，理解其局限并提供帮助； Barry 主要负责 Agentic System，演讲内容基于他和 Eric 合著的一篇博文，下面详细总结他们的核心观点，以及对 Agent 系统的演进和未来的思考。 Agent 系统的演进 - 简单功能： 起初是简单的任务，如摘要、分类、提取，这些在几年前看似神奇，现在已成为基础； - 工作流（Workflows）： 随着模型和产品成熟，开始编排多个模型调用，形成预定义的控制流，以牺牲成本和延迟换取更好性能。这被认为是 Agent 系统的前身； - Agent： 当前阶段，模型能力更强，领域特定的 Agent 开始出现。与工作流不同，Agent 可以根据环境反馈自主决定行动路径，几乎独立运作； - 未来（猜测）： 可能是更通用的单一 Agent，或多 Agent 协作。趋势是赋予系统更多自主权，使其更强大有用，但也伴随着更高的成本、延迟和错误后果。 核心观点一 并非所有场景都适合构建 Agent (Don't build agents for everything) - Agent 主要用于扩展复杂且有价值的任务，它们成本高、延迟高，不应作为所有用例的直接升级。对于可以清晰映射决策树的任务，显式构建工作流（Workflow）更具成本效益和可控性。 - 何时构建 Agent 的检查清单： 1. 任务复杂度 ： Agent 擅长处理模糊的问题空间。如果决策路径清晰，应优先选择工作流； 2. 任务价值： Agent 的探索性行为会消耗大量 token，任务的价值必须能证明其成本。对于预算有限（如每任务 10 美分）或高容量（如客服）场景，工作流可能更合适； 3. 关键能力的可行性 ： 需确保 Agent 在关键环节（如编码 Agent 的编写、调试、错误恢复能力）不存在严重瓶颈，否则会显著增加成本和延迟。如有瓶颈，应简化任务范围； 4. 错误成本与发现难度： 如果错误代价高昂且难以发现，就很难信任 Agent 自主行动。可以通过限制范围（如只读权限、增加人工干预）来缓解，但这也会限制其扩展性； - 编码（Coding）是一个很好的 Agent 用例，因为它任务复杂（从设计文档到 PR）、价值高、现有模型（如 Claude）在许多环节表现良好，且结果易于验证，例如单元测试、CI。 核心观点二 保持简单 (Keep it simple) - Agent 的核心结构： 模型（Model）+ 工具（Tools）+ 循环（Loop）在一个环境（Environment）中运作。 - 三个关键组成部分： 1. 环境：Agent 操作所在的系统； 2. 工具集： Agent 采取行动和获取反馈的接口； 3. 系统提示： 定义 Agent 的目标、约束和理想行为； - 迭代方法： 优先构建和迭代这三个基本组件，能获得最高的投资回报率。避免一开始就过度复杂化，这会扼杀迭代速度。优化（如缓存轨迹、并行化工具调用、改进用户界面以增强信任）应在基本行为确定后再进行。 - 一致性： 尽管不同 Agent 应用（编码、搜索、计算机使用）在产品层面、范围和能力上看起来不同，但它们共享几乎相同的简单后端架构。 核心观点三 像 Agent 一样思考 (Think like your agents) - 问题： 开发者常从自身角度出发，难以理解 Agent 为何会犯看似反常的错误； - 解决方法： 将自己置于 Agent 的“上下文窗口”中。Agent 在每一步的决策都基于有限的上下文信息（如 10k-20k token）； - 换位思考练习： 尝试从 Agent 的视角完成任务，体验其局限性（例如，只能看到静态截图，在推理和工具执行期间如同“闭眼”操作）。这有助于发现 Agent 真正需要哪些信息（如屏幕分辨率、推荐操作、限制条件）以避免不必要的探索； - 利用模型自身： 可以直接询问模型（如 Claude）：指令是否模糊？是否理解工具描述？为什么做出某个决策？如何帮助它做出更好的决策？这有助于弥合开发者与 Agent 之间的理解差距。 个人思考与未来展望 - 预算感知 Agent (Budget-aware Agents)： 需要更好地控制 Agent 的成本和延迟，定义和强制执行时间、金钱、token 预算，以便在生产环境中更广泛地部署。 - 自进化工具 (Self-evolving Tools)： Agent 或许能设计和改进自己的工具（元工具），使其更具通用性，能适应不同用例的需求。 - 多 Agent 协作 (Multi-agent Collaboration)： 预计今年年底将在生产中看到更多多 Agent 系统。其优势包括并行化、关注点分离、保护主 Agent 上下文窗口等。关键挑战在于 Agent 间的通信方式，如何实现异步通信，超越当前的用户-助手轮流模式。

第一次和日本巨根男優合作 竟期待又怕受傷害🥵🥵 First time collaborating with a Japanese well-endowed male performer. I’m excited, but also a little scared of what might happen. 🥵🥵