<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>AI 科技观察</title><description>解释新技术进入工作与日常之后，真正改变了什么。</description><link>https://observe.bitanoworks.com/</link><language>zh-CN</language><item><title>买得到算力，买不到数据</title><link>https://observe.bitanoworks.com/stories/compute-you-can-buy-data-you-cannot</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/compute-you-can-buy-data-you-cannot</guid><description>Google 一个月处理的 token 已是全人类公开文本存量的十倍以上。这个比较说明「数据不够」这四个字被混用了。数据约束分为存量、许可、生产、搬运四层：可训练文本按中位估计 2028 年见顶，但许可层的天花板已经先到；合成数据只在有验证器的地方成立；而在物理层，HDD 产能按日历年售罄，存力开始需要像电力一样提前锁定。</description><pubDate>Wed, 19 Aug 2026 16:00:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:23:14 GMT</lastBuildDate></item><item><title>给 Agent 加了记忆，它反而变笨了</title><link>https://observe.bitanoworks.com/stories/agent-memory-hurts</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/agent-memory-hurts</guid><description>记忆几乎总是被当作纯增益功能加上去的，但 2026 年的多份公开测量显示：加了记忆的 Agent 在不少任务上比不加更差。本文拆解稀释、误差累积、陈旧状态三种退化机制，对比 compaction、结构化笔记、子 Agent 隔离三种手段的取舍，并给出写入侧的过滤与遗忘策略和三组可自测的对照实验。</description><pubDate>Wed, 19 Aug 2026 16:00:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:30:21 GMT</lastBuildDate></item><item><title>榜单帮不了你选模型：自建评测集的做法</title><link>https://observe.bitanoworks.com/stories/build-your-own-eval-set</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/build-your-own-eval-set</guid><description>公开榜单会受到题目污染、分数饱和和机制过拟合影响。本文解释这些失灵机制，并给出二十到五十道私有评测集的题目来源、分层、打分和样本量方法。</description><pubDate>Wed, 19 Aug 2026 08:34:31 GMT</pubDate><lastBuildDate>Wed, 19 Aug 2026 08:56:29 GMT</lastBuildDate></item><item><title>算力堆到一起之后，瓶颈变成了网络</title><link>https://observe.bitanoworks.com/stories/network-becomes-the-bottleneck</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/network-becomes-the-bottleneck</guid><description>机柜内每颗 GPU 有 1.8 TB/s 的互联带宽，跨出机柜的单端口速率是 800 Gb/s。从 scale-up、scale-out 到 scale-across，三层网络的落差决定了并行策略承受什么压力、以太网在 UEC 1.0 之后解决什么问题，以及跨数据中心训练能扩展到多远。</description><pubDate>Mon, 17 Aug 2026 08:30:32 GMT</pubDate><lastBuildDate>Wed, 19 Aug 2026 04:13:43 GMT</lastBuildDate></item><item><title>为什么 AI 总在长任务里跑偏：多步任务的失败点在哪</title><link>https://observe.bitanoworks.com/stories/why-agents-drift-in-long-tasks</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/why-agents-drift-in-long-tasks</guid><description>长任务失败往往出在重复执行的稳定性：模型需要把同一件简单事连续做几十次，错误会在过程中累积。本文用 ICLR 2026、NeurIPS 2025 的一手实验拆出三个具体失败点：错误的自我强化、多轮目标漂移、验收环节缺位，并给出按步数预算切分任务、回滚清除错误历史、用 pass^k 验收等六个可落地做法。</description><pubDate>Mon, 17 Aug 2026 04:06:50 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:37:27 GMT</lastBuildDate></item><item><title>上下文越长，答案越不准：资料该喂到什么程度</title><link>https://observe.bitanoworks.com/stories/how-much-context-to-feed</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/how-much-context-to-feed</guid><description>模型的可用上下文远短于标称上下文，退化也会在窗口用满之前逐步出现。五份独立测量揭示了长上下文失效的机制；成本、延迟和资料筛选共同决定一次调用该放多少内容。</description><pubDate>Mon, 17 Aug 2026 02:54:59 GMT</pubDate><lastBuildDate>Wed, 19 Aug 2026 02:51:44 GMT</lastBuildDate></item><item><title>AI 幻觉：哪些输出必须核验</title><link>https://observe.bitanoworks.com/stories/hallucination-what-to-verify</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/hallucination-what-to-verify</guid><description>幻觉会持续出现，背后有训练与评测规则的激励。文章结合一手研究，解释其成因和可检测程度，并按“错误能否被发现、能否撤回”整理核验清单。</description><pubDate>Mon, 17 Aug 2026 02:01:02 GMT</pubDate><lastBuildDate>Wed, 19 Aug 2026 04:16:27 GMT</lastBuildDate></item><item><title>同一个问题问两次 为什么模型会给出不同答案</title><link>https://observe.bitanoworks.com/stories/same-question-different-answers</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/same-question-different-answers</guid><description>同一个问题出现不同回答，未必只是“模型随机”。从逐词元生成、会话上下文到版本变动，拆解差异从哪里来，以及哪些差异必须核验。</description><pubDate>Fri, 14 Aug 2026 04:00:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:41:33 GMT</lastBuildDate></item><item><title>别把重要工作流押在一个 AI 工具上</title><link>https://observe.bitanoworks.com/stories/ai-workflow-portability</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/ai-workflow-portability</guid><description>从输入、规则、验收、适配层和人工接管出发，说明如何评估单一 AI 工具依赖，并用实测建立可维护的降级路径。</description><pubDate>Thu, 13 Aug 2026 03:10:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:41:43 GMT</lastBuildDate></item><item><title>把决定交给推荐之后 选择真的变轻松了吗</title><link>https://observe.bitanoworks.com/stories/recommendation-choice-ease</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/recommendation-choice-ease</guid><description>从选择过载、排序信号、探索范围和可干预性出发，拆解推荐到底替人省掉了什么，又没有替人做掉什么。</description><pubDate>Thu, 13 Aug 2026 03:00:00 GMT</pubDate><lastBuildDate>Wed, 19 Aug 2026 04:16:41 GMT</lastBuildDate></item><item><title>让 AI 替你办事之前，先决定哪些事不能交出去</title><link>https://observe.bitanoworks.com/stories/ai-delegation-boundaries</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/ai-delegation-boundaries</guid><description>从代理式系统的外部行动能力出发，提出以后果、权限、数据敏感性和可恢复性划分委托边界的方法，并说明审批、责任红线、最小权限、可追溯记录与演练恢复如何共同约束 AI 的行动。</description><pubDate>Wed, 12 Aug 2026 14:16:02 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:47:18 GMT</lastBuildDate></item><item><title>AI 到底怎样提高工作效率：它省下的是等待、切换和返工</title><link>https://observe.bitanoworks.com/stories/ai-work-efficiency-mechanisms</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/ai-work-efficiency-mechanisms</guid><description>从写作、客服、企业协作与软件开发研究出发，解释 AI 如何缩短直接任务、何时把时间转移到核验与返工，以及团队应怎样衡量净效率。</description><pubDate>Tue, 11 Aug 2026 16:12:43 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:42:21 GMT</lastBuildDate></item><item><title>当 AI 直接给出答案，谁还会点开原网站？</title><link>https://observe.bitanoworks.com/stories/ai-answers-original-websites</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/ai-answers-original-websites</guid><description>以 Pew 的浏览行为数据和生成式搜索研究为边界，区分答案满足、来源打开、核验和任务完成，并为内容网站提出可证伪的页面责任与测量框架。</description><pubDate>Tue, 11 Aug 2026 11:42:52 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:38:59 GMT</lastBuildDate></item><item><title>当 AI 开始替你工作，改变的是判断、权限与责任边界</title><link>https://observe.bitanoworks.com/stories/ai-starts-working-for-you</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/ai-starts-working-for-you</guid><description>从任务自动化与工作流委托的区别出发，分析 AI 连续执行任务后判断位置、行动权限、证据链、责任归属与能力形成机制如何变化，并提出一套可执行的五步治理方案。</description><pubDate>Mon, 10 Aug 2026 08:00:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:47:28 GMT</lastBuildDate></item><item><title>欧盟《AI 法案》进入实施阶段：企业合规指南</title><link>https://observe.bitanoworks.com/stories/eu-ai-act-enterprise-compliance-guide</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/eu-ai-act-enterprise-compliance-guide</guid><description>欧盟《AI 法案》进入实施阶段后，合规正在成为企业进入欧洲市场并持续经营 AI 的基础能力。本文从责任边界、风险控制、供应链证据和日常治理四个层面，说明企业如何把法规要求转化为贯穿产品生命周期的控制体系。</description><pubDate>Sun, 09 Aug 2026 17:00:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:39:38 GMT</lastBuildDate></item><item><title>从 128K 到 1M：长文本工作流省下了什么，又增加了什么</title><link>https://observe.bitanoworks.com/stories/long-context-reasoning</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/long-context-reasoning</guid><description>把长上下文放回合同审阅、跨版本对照和技术决策这些具体任务中，比较 128K 与 1M 各自解决什么问题，以及检索、引用和版本控制为何仍不可缺。</description><pubDate>Sun, 09 Aug 2026 08:00:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:39:25 GMT</lastBuildDate></item><item><title>开放模型会怎样影响用户的控制权、成本与选择</title><link>https://observe.bitanoworks.com/stories/openai-model-open-source</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/openai-model-open-source</guid><description>区分开放访问、开放权重与开放源码，比较托管 API、托管开放模型和自托管在数据控制、许可、成本、运维、安全更新与可迁移性方面的真实取舍，并给出八项决策检查。</description><pubDate>Fri, 07 Aug 2026 08:00:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:40:06 GMT</lastBuildDate></item><item><title>AI 到底贵在哪里</title><link>https://observe.bitanoworks.com/stories/infrastructure-war</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/infrastructure-war</guid><description>从正式训练运行一路拆到 HBM、先进封装、网络、折旧、利用率、推理调度和客户总拥有成本，解释 AI 的钱分别花在哪里，以及为什么单位价格下降并不保证总支出下降。</description><pubDate>Mon, 03 Aug 2026 08:00:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:40:25 GMT</lastBuildDate></item><item><title>算力扩张背后的电力秩序</title><link>https://observe.bitanoworks.com/stories/who-powers-models</link><guid isPermaLink="true">https://observe.bitanoworks.com/stories/who-powers-models</guid><description>从电量与容量的差别出发，追踪发电、输电、并网、冷却与算力调度如何共同决定模型能否持续运行。</description><pubDate>Sat, 18 Jul 2026 08:00:00 GMT</pubDate><lastBuildDate>Fri, 21 Aug 2026 15:42:11 GMT</lastBuildDate></item></channel></rss>