三套国产原创采样方案并行，悬停、触碰、附着适配复杂小行星地表环境#中国航天原创技术# Three domestically-originated sampling modes work together to adapt to rugged asteroid surface under extreme weak gravity. #OriginalAerospaceTech#

Agent Evaluation 是我觉得为数不多还需要人力去做判断、将来很难被AI 彻底取代的职业方向。我给大家找了一个非常好的学习资料入门：Hugging Face 的公开 Training Agent 课程 学完了，你应该会有更强的 sense，判断 AI 时代下哪些工作更适合人来做 这个资料是公开的，视频都在 Hugging Face 的 YouTube 频道，代码全部开源，下面有这六节： 1、Agentic Evaluations Workshop：怎么评价一个 Agent。 这一章节去看：为什么 benchmark 分数和真实使用体验对不上？目前 Agent Eval 做到了哪一步，还有哪些问题没解决。先把“怎样才算一个好 Agent”，等等都有 cover 2、RL for Agents Workshop：给 Agent 做强化学习，难在哪 这一章节去关注：从 LLM 的 RL 走到 Agent 的 RL，会多出哪些难点？环境怎么搭、rollout 怎么跑、reward 怎么设计，以及训练过程中推理为什么会成为 bottleneck 3、SFT on agent traces：用 Agent 的执行轨迹做微调 这一章节去关注：拿公开的 coding-agent traces（执行轨迹）整理成训练数据，用 TRL + LoRA 做 SFT，在 Hugging Face Jobs 上跑，用 Trackio 看指标。基本带着走了一遍小型的微调流程 4、Distillation：把大模型的能力蒸馏给小 Agent 这一章节去关注：怎么把 teacher model 的能力转移给更小的 coding agent。off-policy、on-policy、self-distillation 都会讲，看看这几种蒸馏方式分别怎么做 5、Reinforcement Learning：SFT 之后，怎么继续用 RL 训练 这一章节去关注：用 GRPO 接着训练，讲 group sampling、可验证的 reward，以及 reward、KL、输出长度这些曲线怎么看。看 Agent 怎么 reward hacking 6、From reward functions to environments：让 Agent 在真实环境里学习 这一章节去动手：从 reset()/step() 接口讲起，用 OpenEnv 搭环境、上传到 Hub，再接入 TRL 的 GRPOTrainer。最后通过 Harbor 和 Hugging Face sandboxes，用 AsyncGRPOTrainer 训练真正的 coding agent：OpenCode。让 Agent 在环境里采取动作、拿到反馈，再继续训练

#BuildInPublic# 开源了一个 AI 模拟引擎 SGO (语义梯度优化引擎） 在 AI 世界迭代产品或者功能，目前最最缺少的就是现实世界现实用户的反馈；这些反馈意见序列实际上构成了产品的演化路径。 然而用户（真人）反馈周期较长，且不能覆盖所有的情景。当下，我们常常让 LLM "假装"某一类用户得到一个近似的反馈，但这种反馈都是一个一个的数据点，完全取决于事先规划好了的角色。 SGO 采用的思路是： 用和人口普查对齐的合成数据来模拟真人用户。NVIDIA 开源了多个主权数据集，比如对于美国，Nemotron-Personas-USA 数据集里有一百万个基于美国人口普查数据生成的合成人物。不是那种 LLM 随便编的"有着三十年经验的工程师"，而是有完整背景的人——伊利诺伊郊区的建筑工人、德州农村的手工艺人、纽约的单亲妈妈等等。他们有各自的爱好、习惯、关注点。这些人的年龄、学历、职业、收入分布都跟真实人口一致。 SGO 的采样， 模拟和梯度计算框架可以让你直接从这些人里拿到反馈，周期大约 30 秒，LLM API 花费大约 $0.10。 使用方法也很简单：把要优化的东西贴进去，比如产品描述、融资 pitch、一则爆款文章等等（有一个用户甚至把他的约会 profile 放进去优化）。总之什么都行。 SGO 会很科学的帮你自动选择优化目标和目标受众。确定好以后，从这 100 万个有机数据人群中科学采样 (stratified sampling)、分类聚类、逐一询问反馈(contrafactual inquiry)、对照目标，逐一构建所谓的"语义梯度" （相当于目标对于各个变量的 Jacobian 矩阵）， 以及最终的汇总反馈和迭代方向。 代码开源，目前部署在 HuggingFace Spaces 上可以直接试用。 你可以把 SGO 作为 Skill 单独使用，也可以把它放在一个内循环里，和 auto-research 联合使用。 HF Space: 希望 SGO 和 auto-resesarch 结合，帮助大家优化那些跨越数字世界和现实世界的许多场景。 PS： 现有的跑通的场景 * 简历优化 * 商业计划 * App UX 设计 * 广告牌设计 * LOGO * 网页的版式和颜色 * 约会档案 * 一个甜点屋的名字

Grayscale 在报告中指出，Zcash（ZEC）本周迎来十周年，其持续创新能力部分源于将区块奖励的 20% 用于开发者资助。Zcash 从 Sprout 池演进至 Sapling、Orchard 及 2026 年推出的 Ironwood 池，后者增加了抗量子地址和形式化验证，以应对代码漏洞。Grayscale 希望并预期 Zcash 将继续扩展交易容量、为后量子时代及 AI 驱动的网络安全风险做好准备，这可能需要多方持续的资金支持和高效分配。

吴说获悉，Pirate Chain 发推披露，10 月 14 日发现其源代码存在交易验证漏洞，攻击者未经授权铸造约 3,980 万枚 ARRR，使总供应量增加约 20%，相关资金随后转入 Sapling 隐私池；NonKYC 已限制相关账户并阻止提现。该漏洞涉及两项验证缺陷，暂无证据表明公证密钥或隐私交易底层密码学遭到破坏。作为预防措施，dPoW 运营方已暂时暂停公证，交易所和矿池也被要求自愿暂停相关业务；Pirate Chain 网络仍在运行，但官方建议用户暂时不要转移资金。

Zcash 社区开发者 Dev Ojha（ValarDragon）提出 NU8 网络升级方案，目标于 2027 年 1 月下旬实施。该方案计划推出新一代 Tachyon 隐私交易池，将最大区块容量从 2 MB 提高至 8 MB，使隐私交易处理能力达到每秒约 200 笔。同时，升级拟引入基于 SPHINCS 的抗量子签名操作码，结合 Tachyon 的 ML-KEM 加密机制提升抗量子安全性，并确定旧版 Sapling 隐私池停止接受新存款的日期，推动用户迁移。方案还涉及调整未来代币发行分配机制及改进钱包兼容性。

$SAPLING | ZEC 计价发币盘的根票 就是把 式 meme 流量接到 最近大热是Zcash 隐私 BZFYNPeQAEW3HWQ4DNsTVahC1n4ZjTgn6jB2nnBbB96W 的币，报价资产是 Solana 上的 ZEC。每笔交易抽 creator fee，一部分沉淀成该币的 roots（ZEC 金库）。持有人烧掉代币，按比例领回 ZEC，并可在 Zcash 主网打一张 stamp 收据。 $SAPLING 是整盘的 rootstock 整体来讲第一个吃的是 9–10 月 ZEC 隐私 meta 外溢到 Solana meme 的流量。 介入价格：2.5-3M

吴说获悉，Zcash 开发者 Sean Bowe 表示，Zakura Common 是 Zcash 历史上规模最大的密码学优化版本之一，证明速度提升 14 倍、哈希速度提升 21 倍、试解密速度提升 1.5 倍、验证速度提升 4–8 倍，采用该组件的隐私钱包及 Zakura 全节点均可直接受益，且无需进行网络升级。Zcash 联合创始人 Zooko 则认为 Sapling 才是 Zcash 历史上更重要的版本，因为其实现了移动端支出，并将笔记本端生成证明的体验从“很差”改善至“尚可”。Sean Bowe 回应称，从优化数量和性能提升幅度来看，Zakura Common 的改进非常显著，但 Sapling 与其说是一次优化，不如说是一次重新设计。

#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#