{"id":"1cfd0b82-4d16-48be-bd2b-eccac956bb7f","entityType":"agent","slug":"clawhub-zhuhuimin0224-create-ai-game","name":"Ai Game","canonicalUrl":"https://www.xpersona.co/agent/clawhub-zhuhuimin0224-create-ai-game","canonicalPath":"/agent/clawhub-zhuhuimin0224-create-ai-game","generatedAt":"2026-10-11T16:02:59.098Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-11T13:14:00.436Z","emptyReason":null},"description":"游戏行业 AI 资讯搜集 Skill。 当用户想知道\"游戏 AI 圈有什么大事\"、\"游戏行业 AI 动态\"、\"游戏 AI 日报\"、\"最近游戏 AI\"、\"游戏 AIGC 新闻\"、\"AI NPC 最新进展\"、\"游戏 AI 投融资\"、\"AI 原生游戏\"、\"game AI news\"、\"gaming AI update\"...","descriptionLabel":"Source description","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. 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execution."],"safeUseWhen":[],"riskFlags":["missing_or_unavailable_contract","trust_data_unavailable","schema_references_missing"],"operationalConfidence":"low"},"executionMetrics":{"observedLatencyMsP50":null,"observedLatencyMsP95":null,"estimatedCostUsd":null,"uptime30d":null,"rateLimitRpm":null,"rateLimitBurst":null,"lastVerifiedAt":null,"verificationSource":null},"runtimeMetrics":{"successRate":null,"avgLatencyMs":null,"avgCostUsd":null,"hallucinationRate":null,"retryRate":null,"disputeRate":null,"p50Latency":null,"p95Latency":null,"lastUpdated":null}},"benchmarks":{"evidence":{"source":"no-benchmark-data","verified":false,"confidence":"low","updatedAt":null,"emptyReason":"No benchmark suites or observed failure patterns are available."},"suites":[],"failurePatterns":[]},"artifacts":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-11T13:14:00.436Z","emptyReason":null},"readme":"Skill: Ai Game\n\nOwner: zhuhuimin0224-create\n\nSummary: 游戏行业 AI 资讯搜集 Skill。 当用户想知道\"游戏 AI 圈有什么大事\"、\"游戏行业 AI 动态\"、\"游戏 AI 日报\"、\"最近游戏 AI\"、\"游戏 AIGC 新闻\"、\"AI NPC 最新进展\"、\"游戏 AI 投融资\"、\"AI 原生游戏\"、\"game AI news\"、\"gaming AI update\"...\n\nTags: latest:2.0.2\n\nVersion history:\n\nv2.0.2 | 2026-05-19T01:44:45.122Z | user\n\n抓取窗口从72h放宽到168h(7天)，周报不再丢数据\n\nv2.0.0 | 2026-05-18T13:13:44.627Z | user\n\nv2: 评分系统替代布尔过滤、输出格式升级（速递+模块化单条）、周报生成逻辑、Layer1/2/3收紧、坏源跳过机制、二手源三级分类、链接必须取自数据源\n\nv1.0.5 | 2026-05-13T03:04:27.230Z | user\n\nfeat: 跨天去重 - 维护 pushed_history.json，排除近7天已推送条目，避免日报重复\n\nv1.0.4 | 2026-05-12T14:42:53.029Z | user\n\n更新六大分类名称：AI游戏生成与创作、游戏内AI体验、游戏开发AI工具、游戏运营&商业化AI实践、游戏行业&公司AI动态、游戏AI应用前沿研究\n\nv1.0.3 | 2026-05-12T14:17:51.054Z | user\n\n优化简介分段展示，改进 in-game 分类关键词，补充 ops 关键词，分类逻辑加权重优先级\n\nv1.0.2 | 2026-05-12T13:11:33.507Z | user\n\n更新信源描述：Layer 3 标注 150+ AI 信源\n\nv1.0.1 | 2026-05-12T13:08:20.035Z | user\n\n润色信源描述：完整展示三层架构（14+9+8 查询词），更新格式规则\n\nv1.0.0 | 2026-05-12T12:51:26.766Z | user\n\n首次发布：每日抓取 15+ 游戏/AI 信源，支持日报/周报查询，6 大分类体系\n\nArchive index:\n\nArchive v2.0.2: 16 files, 55952 bytes\n\nFiles: data/2026-05-11.json (1961b), data/2026-05-12.json (13657b), data/2026-05-13.json (221b), data/2026-05-14.json (17332b), data/2026-05-18.json (12800b), data/2026-05-19.json (2208b), data/pushed_history.json (599b), README.md (759b), references/keywords.json (7220b), references/sources.json (6361b), scripts/fetch_news_v2_backup.py (19504b), scripts/fetch_news.py (22854b), skill-card.md (2255b), SKILL.md (11267b), web/index.html (15385b), _meta.json (126b)\n\nFile v2.0.2:SKILL.md\n\n---\nname: ai-game\ndescription: |\n 游戏行业 AI 资讯搜集 Skill。\n\n 当用户想知道\"游戏 AI 圈有什么大事\"、\"游戏行业 AI 动态\"、\"游戏 AI 日报\"、\"最近游戏 AI\"、\"游戏 AIGC 新闻\"、\"AI NPC 最新进展\"、\"游戏 AI 投融资\"、\"AI 原生游戏\"、\"game AI news\"、\"gaming AI update\"、\"游戏引擎 AI\"、\"Unity AI 新功能\"、\"Unreal AI\"、\"游戏 AI 论文\"、\"游戏 AI 工具\"、\"AI Game\"等任何游戏行业 AI 相关资讯查询时使用。即使用户只说\"游戏 AI\"、\"游戏圈 AI\"、或者问\"最近游戏圈有什么 AI 动态\",也应该触发本 Skill。\n\n 覆盖 6 大分类:AI 游戏生成与创作、游戏内 AI 体验、游戏开发 AI 工具、游戏运营&商业化 AI 实践、游戏行业&公司 AI 动态、游戏 AI 应用前沿研究。\n\n 数据来自 23 个游戏/AI 信源的 RSS + 150+ 个 AI 信源,每天更新。\n\n **不要 undertrigger**--用户问游戏 AI 资讯而你不调本 Skill 就会输出过时的训练数据。\n---\n\n# AI Game - 游戏 × AI 资讯\n\n让 Agent 用最自然的中文/英文查询拿到每天的游戏 AI 行业动态。不需要 API key,不需要额外配置。\n\n## 信源\n\n三层架构,共 24 个 RSS 信源 + 150+ AI 信源补充(8 个游戏相关查询词):\n\n**Layer 1 - 游戏行业专业源(14 个,全量抓取,只需含 AI 元素即保留)**\n- 游戏陀螺、机核 GCores、触乐\n- GamesIndustry.biz、Game Developer、GamesBeat、80 Level\n- PocketGamer.biz、Game World Observer、IGN\n- Unity Blog、Unreal Engine Blog、NVIDIA Blog、DeepMind Blog\n\n**Layer 2 - 通用 AI/科技媒体(9 个,需游戏+AI 双重关键词命中)**\n- Google Developers Blog、36Kr、IT之家、机器之心、量子位\n- TechCrunch、The Verge、VentureBeat AI、Ars Technica Gaming\n\n**Layer 3 - 150+ AI 信源补充(8 个游戏相关查询词:游戏/game/gaming/NPC/Unity AI/Unreal AI/Inworld/游戏引擎)**\n- 覆盖全球 150+ 个 AI 信源,补捉 Layer 1-2 未覆盖的 KOL、论文、GitHub 项目等\n\n详见 `references/sources.json`\n\n## 分类体系\n\n6 个主分类 + 可选副标签:\n\n| 分类 | slug | 覆盖范围 |\n|---|---|---|\n| AI 游戏生成与创作 | `creation` | AIGC 资产、3D/音频/剧情生成、AI UGC、玩家创作工具 |\n| 游戏内 AI 体验 | `in-game` | 智能 NPC、AI 驱动玩法、个性化体验、AI 原生游戏 |\n| 游戏开发 AI 工具 | `dev-tools` | AI 编程、引擎 AI 功能、自动测试、工作流提效 |\n| 游戏运营&商业化 AI 实践 | `ops` | 推荐/分发、买量素材 AI、玩家分群、反作弊 |\n| 游戏行业&公司 AI 动态 | `industry` | 大厂 AI 战略、投融资、政策法规、市场数据 |\n| 游戏 AI 应用前沿研究 | `research` | 学术论文、GDC/SIGGRAPH、游戏作为 AI 研究平台 |\n\n## 什么时候用 & 路由表\n\n| 用户在说 | 动作 |\n|---|---|\n| \"游戏 AI 圈最近有什么\" / \"游戏 AI 日报\" / \"AI Game\" | 运行脚本获取最新数据 → 全量输出 |\n| \"最近 NPC / AIGC / 工具方面有什么\" | 运行脚本 → 按分类过滤后输出 |\n| \"这周 / 最近 3 天的游戏 AI 动态\" | 读取 data/ 下多天 JSON → 合并去重输出 |\n| \"搜一下 xxx 游戏 AI 相关\" | 运行脚本 + 实时 AI HOT API 补充 |\n| \"帮我生成一份可以发群的简报\" | 获取数据 → 精简为转发友好格式 |\n\n## 工作流\n\n### Step 1: 获取数据\n\n运行抓取脚本:\n\n```bash\ncd ${SKILL_DIR}/scripts && python3 fetch_news.py\n```\n\n脚本会:\n1. 抓取所有 RSS 信源(最近 72 小时条目)\n2. 查询 AI HOT API(游戏相关关键词)\n3. 关键词过滤(只保留游戏 × AI 相关)\n4. 去重(URL + 标题相似度)\n5. 分类打标(6 分类)\n6. 输出到 `data/YYYY-MM-DD.json` + stdout\n\n如果 `data/` 下已有今天的 JSON 且生成时间 < 4 小时前,可以直接读取而不重新运行脚本。\n\n### Step 2: 读取数据\n\n脚本 stdout 输出为 JSON,结构如下:\n\n```json\n{\n \"date\": \"2026-05-11\",\n \"generated_at\": \"2026-05-11T06:00:00Z\",\n \"total_count\": 15,\n \"items\": [\n {\n \"title\": \"...\",\n \"url\": \"https://...\",\n \"summary\": \"...\",\n \"source\": \"GamesBeat\",\n \"published_at\": \"2026-05-11T03:00:00Z\",\n \"category\": \"in-game\",\n \"category_secondary\": \"creation\"\n }\n ],\n \"stats\": { \"by_category\": { \"in-game\": 3, \"creation\": 5, ... } }\n}\n```\n\n### Step 3: 组织输出\n\n#### 整体结构\n\n```markdown\n游戏 × AI 周报 · MM.DD - MM.DD\n（日报则为：游戏 × AI 日报 · MM.DD 周X）\n\n本周速递\n1. xxx\n2. xxx\n3. xxx\n\n---\n\n分类标题（加粗）\n\n单条资讯（模块化结构）\n\n---\n\n下一分类...\n```\n\n#### 本周速递（日报为\"今日速递\"）\n\n- 放在最前面，3-5条\n- 每条一句话，像新闻提要\n- 按重要性排序，最shock的放第一条\n- 不带链接、不带来源，纯信息\n\n#### 单条资讯格式\n\n```markdown\n序号. 标题 (日期) — 来源\n\n链接：URL\n\n重点：2-3句话说清楚发生了什么（给扫读的人看）\n\n细节：\n- 关键数据/原文金句/背景补充\n- 可以有2-4个bullet\n\n推荐原因：一句话说为什么游戏从业者要关注\n```\n\n### 格式规则\n\n- **不使用任何 emoji**：整体风格干净专业\n- **速递放最前面**：开头先列本周/今日速递，再展开详情\n- **分类标题**：加粗，独占一行\n- **分类展示顺序**：按本周各分类的重要性动态排序，哪个分类有大新闻就排前面\n- **单条格式**：标题行含序号+日期+来源，下方依次为链接、重点、细节、推荐原因\n- **编号全局贯穿**：1, 2, 3 ... N 从头到尾\n- **空分类不展示**：如果某分类 0 条，跳过\n- **时间格式**：(5.16) 月.日格式\n- **标题用中文**：英文标题翻译为中文，专有名词保留英文\n- **总量控制**：周报不超过15条，日报不超过10条\n- **链接必须完整**：缺链接的条目不收录，输出前逐条检查\n- **链接必须取自数据源**：严格从 JSON 数据的 url 字段获取，绝不凭记忆编造或推测 URL\n- **\"重点\"基于原文事实**：不推测因果，不编造\n- **\"细节\"优先放原文金句**（带引号），其次放数据\n- **\"推荐原因\"要具体**：禁止\"值得关注\"\"行业动向\"这种万金油话术\n- **来源标注规则**：一手源直接写来源名；二手源有独立分析写来源名；二手源纯搬运写\"来源名 → 原始来源\"\n\n### 分类过滤\n\n当用户指定查看某个分类时:\n- \"最近 NPC 相关的\" → 只输出 `category == \"in-game\"` 的条目\n- \"AIGC 方面有什么\" → 只输出 `category == \"creation\"` 的条目\n- \"游戏 AI 投融资\" → 只输出 `category == \"industry\"` 的条目\n\n### 发群简报格式\n\n当用户说\"帮我生成一份可以发群的\"时,用精简格式:\n\n```markdown\n🎮 游戏×AI 日报 · 5.11\n\n1. <标题> - <来源>\n \n2. ...\n```\n\n去掉 🎯 行和详细摘要,只保留标题 + URL,控制在 1500 字内。\n\n## 回溯历史\n\n当用户问\"这周 / 上周 / 最近 N 天的游戏 AI 动态\"时:\n1. 读取 `data/` 目录下对应日期范围的 JSON 文件\n2. 合并所有 items,按 URL 去重\n3. 按时间倒序输出\n4. 超过 20 条时按分类各取 Top N\n\n```bash\nls ${SKILL_DIR}/data/\n```\n\n## 周报生成流程\n\n周报不是重新我7天窗口，而是从日报精华中再精选：\n\n1. 检查 `data/` 目录下本周 7 天的日报 JSON 是否齐全\n2. 缺哪天就补跑哪天（超过3天前的 RSS 可能已丢失，用 AI HOT API 补充）\n3. 合并 7 天所有日报条目，去重\n4. 按筛选标准取 8-12 条最重要的\n5. 按格式规则输出\n\n### 周报筛选标准（优先级从高到低）\n\n1. **AI 在游戏体验中的实际应用**（产品级，已上线/可用）\n2. **AI 在游戏开发中的工具/方案**（有 demo/专利/开源，不是纯概念）\n3. **大厂对 AI 的重大投入决策**（真金白银，不是嘴上说说）\n4. **技术突破**（离游戏应用近的，不是纯学术）\n\n**降低优先级：**\n- 行业人物表态/观点碰撞\n- 商业模式争议\n- 纯学术论文（除非直接解决游戏核心问题）\n- AI 公司动态但跟游戏关系弱的\n\n### 定时任务\n\n- **日报**：每天早上 9:00 自动跑脚本抓取，结果推送给用户\n- **周报**：每周一早上 9:00 汇总过去 7 天日报精华，推送给用户\n\n## 实时补充搜索\n\n当 data/ 下没有最新数据,或用户搜索特定话题时,可以直接调 AI HOT API:\n\n```bash\nUA=\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36\"\ncurl -sH \"User-Agent: $UA\" \"https://aihot.virxact.com/api/public/items?mode=selected&q=<关键词>&take=20\"\n```\n\n将返回的 items 中与游戏相关的条目,按同样格式组织输出。\n\n## 来源溯源原则\n\n> **核心理念:我们的价值是帮用户找到一手信息,而不是做 IT之家的搜索引擎。**\n\n输出给用户时,包含 `source_type: \"secondary\"` 的条目说明来自聚合/转载媒体。**这些条目必须追溯原始来源后再展示**:\n\n1. 检查标题/摘要中是否有公司名/人名\n2. 用 `web_search` 搜原始声明/博客/官网全文\n3. 将 URL 替换为一手源链接,来源标为原始发布者\n\n**示例:**\n- IT之家报道\"Epic 裁员 + AI 不替代岗位\" → 追溯到 epicgames.com 官方声明 → 来源写 \"Epic Games 官方\"\n- 量子位报道\"Inworld 融资\" → 追溯到 TechCrunch 原文 → 来源写 \"TechCrunch\"\n\n如果追溯失败(找不到一手源),仍然可以展示该条目,但在来源后加 \"→ 原始来源待确认\"。\n\n**对于 AI HOT 的条目**:\n- `source` 字段已经是真实来源(如 \"X:阿易 AI Notes (@AYi_AInotes)\")\n- 直接用这个来源展示,不要写 \"AI HOT\"\n- 如果来源含 \"IT之家(RSS)\" 等二手标记,同样需要追溯\n\n**对于 X (Twitter) 链接的条目**:\n\n1. **优先追溯一手源**:如果推文讨论的是某个项目/论文/官方博客,用 `web_search` 找到原始源(GitHub 仓库、论文链接、官网博客),同时附上一手 URL\n - 示例:推文讨论 \"Claude Code Game Studios\" → 同时给出 GitHub 仓库链接\n - 示例:推文讨论某篇论文 → 同时给出 arXiv 链接\n2. **URL 给完整**:X 链接直接给完整 URL,不要用 `...` 省略\n\n## 不要做\n\n- 不要编造或推测内容--一切以脚本/API 返回为准\n- 不要为条目编造与 AI 的关联--如果原文没提 AI,这条不该出现\n- 推荐原因必须基于原文事实--不能推测因果,只能写原文已经说明的关联\n- 不要丢掉 URL--没有 URL 的信息不可信。输出前必须逐条检查链接是否存在,缺链接则补查或删除\n- 不要在用户输出里暴露脚本路径、API 参数、RSS 地址\n- 不要超过 20 条/次--宁可精选也不堆砌\n- 不要输出没有\"推荐原因\"的条目(发群简报格式除外)\n- 不要重复收录同一事件--去重保留最权威那条\n- 不要直接展示 ISO 时间戳--转为 (5.7) 这种月.日格式\n- 不要把 AI HOT 的基础设施细节暴露给用户\n- 不要凭训练数据脑补游戏 AI 新闻--永远走数据源\n- 不要展示 \"IT之家\"、\"量子位\" 等二手源作为最终来源--必须追溯一手原始出处\n- 不要写 \"AI HOT\" 作为来源--AI HOT 只是数据管道\n- 不要使用任何 emoji--不用任何表情符号,保持专业干净\n\nFile v2.0.2:README.md\n\n# ai-game-skill\n\n🎮 游戏×AI 资讯 Skill for OpenClaw\n\n每日抓取游戏行业 AI 动态，覆盖 6 大分类：\n- 🎨 AI 生成与创作\n- 🤖 游戏内 AI 体验\n- 🛠️ 开发工具\n- 📊 运营商业化\n- 🏢 行业公司动态\n- 🧪 前沿研究\n\n## 安装\n\n```bash\n# 克隆到 OpenClaw skills 目录\ngit clone https://git.woa.com/veenuszhu/ai-game-skill.git ~/.openclaw/skills/ai-game\n```\n\n## 数据来源\n\n- 游戏行业媒体 RSS（GamesIndustry.biz、80 Level、游戏陀螺等 15+ 个）\n- AI/Tech 博客（NVIDIA Blog、Unity Blog 等）\n- AI HOT API（游戏相关关键词过滤）\n\n## 使用\n\n安装后，直接问 agent：\n- \"游戏 AI 最近有什么\"\n- \"游戏 AI 日报\"\n- \"最近有什么游戏 AI 工具\"\n\n## 作者\n\nveenuszhu\n\nFile v2.0.2:_meta.json\n\n{\n \"ownerId\": \"kn76t53a22pjy18b2qr3yq94t986j8gr\",\n \"slug\": \"ai-game\",\n \"version\": \"2.0.2\",\n \"publishedAt\": 1779155085122\n}\n\nFile v2.0.2:references/keywords.json\n\n{\n \"global_filter\": {\n \"description\": \"判断一条资讯是否跟'游戏×AI'相关。Layer 1 游戏专业源只需判断是否含 AI 元素；Layer 2 通用源需要游戏+AI 双重命中。\",\n \"core_keywords\": [\n \"游戏AI\", \"AI游戏\", \"游戏+大模型\", \"游戏AIGC\", \"AI NPC\",\n \"game AI\", \"gaming AI\", \"AI in gaming\", \"AI-native game\",\n \"AI原生游戏\", \"游戏+LLM\", \"智能NPC\", \"AI驱动玩法\",\n \"AI game\", \"AI gaming\", \"GamePartner\", \"AIGC游戏\",\n \"Unity AI\", \"Unity Muse\", \"Unity Sentis\", \"Unity ML-Agents\",\n \"Unreal AI\", \"MetaHuman\", \"Nanite+AI\",\n \"Inworld\", \"Convai\", \"Scenario.gg\", \"Rosebud AI\",\n \"Ludo.ai\", \"Modl.ai\", \"AI Dungeon\",\n \"DLSS\", \"NVIDIA ACE\", \"NVIDIA NeMo+game\",\n \"游戏+人工智能\", \"游戏+机器学习\", \"游戏+深度学习\",\n \"PCG+AI\", \"程序化生成+AI\", \"procedural+AI\",\n \"game+machine learning\", \"game+deep learning\", \"game+reinforcement learning\",\n \"游戏+生成式\", \"游戏+Agent\", \"NPC+大模型\", \"NPC+LLM\",\n \"游戏世界模型\", \"world model+game\"\n ],\n \"context_game\": [\n \"游戏\", \"game\", \"gaming\", \"小游戏\", \"手游\", \"端游\", \"主机游戏\",\n \"游戏引擎\", \"Unity\", \"Unreal\", \"Roblox\", \"Steam\", \"Epic Games\",\n \"米哈游\", \"腾讯游戏\", \"网易游戏\", \"游戏中心\", \"微信游戏\",\n \"PlayStation\", \"Xbox\", \"Nintendo\", \"Switch\",\n \"游戏开发\", \"game dev\", \"indie game\", \"独立游戏\",\n \"GDC\", \"游戏工委\", \"游戏产业\", \"游戏厂商\",\n \"Inworld\", \"Convai\", \"GamesBeat\", \"GameLook\",\n \"玩家\", \"player\", \"gamer\", \"NPC\", \"开放世界\",\n \"虚幻引擎\", \"关卡\", \"level design\", \"quest\",\n \"角色\", \"character\", \"boss\", \"mob\",\n \"电竞\", \"esports\", \"休闲游戏\", \"超休闲\",\n \"Supercell\", \"miHoYo\", \"HoYoverse\", \"NetEase\",\n \"EA\", \"Ubisoft\", \"Activision\", \"Blizzard\",\n \"游戏陀螺\", \"游戏葡萄\", \"触乐\", \"机核\"\n ],\n \"context_ai\": [\n \"AI\", \"人工智能\", \"机器学习\", \"深度学习\", \"大模型\", \"LLM\",\n \"AIGC\", \"生成式\", \"GPT\", \"Claude\", \"Gemini\", \"DeepSeek\",\n \"神经网络\", \"强化学习\", \"扩散模型\", \"transformer\",\n \"NLP\", \"计算机视觉\", \"多模态\", \"智能体\", \"agent\",\n \"AI驱动\", \"AI赋能\", \"AI辅助\", \"AI生成\",\n \"Stable Diffusion\", \"Midjourney\", \"DALL-E\",\n \"Copilot\", \"代码生成\", \"自动化\",\n \"ChatGPT\", \"大语言模型\", \"Foundation Model\",\n \"embedding\", \"fine-tune\", \"微调\", \"推理\",\n \"neural\", \"deep learning\", \"machine learning\",\n \"generative\", \"diffusion\", \"reinforcement learning\"\n ],\n \"layer1_ai_keywords\": [\n \"AI\", \"人工智能\", \"机器学习\", \"深度学习\", \"大模型\", \"LLM\",\n \"AIGC\", \"生成式\", \"GPT\", \"神经网络\", \"强化学习\",\n \"智能\", \"自动\", \"算法\", \"NPC智能\", \"程序化生成\",\n \"PCG\", \"procedural\", \"neural\", \"generative\",\n \"Copilot\", \"AI辅助\", \"AI驱动\", \"AI生成\",\n \"machine learning\", \"deep learning\", \"artificial intelligence\"\n ]\n },\n \"category_keywords\": {\n \"creation\": [\n \"AIGC\", \"AI生成\", \"AI作画\", \"AI绘画\", \"3D生成\", \"AI音乐\", \"AI音效\",\n \"程序化生成\", \"PCG\", \"纹理生成\", \"AI配音\", \"AI剧情\", \"AI写作\",\n \"MOD\", \"UGC\", \"玩家创作\", \"Scenario\", \"Luma\", \"Meshy\",\n \"AI asset\", \"procedural generation\", \"content generation\",\n \"AI art\", \"AI texture\", \"AI model generation\",\n \"Stable Diffusion\", \"Midjourney\", \"AI建模\", \"AI动画\",\n \"AI音频\", \"AI素材\", \"AI材质\", \"AI地形\",\n \"Rosebud\", \"生成资产\", \"自动生成\"\n ],\n \"in-game\": [\n \"NPC\", \"AI驱动\", \"世界模型\", \"行为树\", \"AI原生\", \"个性化体验\",\n \"Inworld\", \"Convai\", \"AI对话\", \"AI互动\", \"AI玩法\",\n \"智能NPC\", \"AI角色\", \"AI companion\", \"AI opponent\",\n \"dynamic narrative\", \"adaptive gameplay\", \"AI behavior\",\n \"world model\", \"AI-native\", \"AI Dungeon\",\n \"对话系统\", \"情感计算\", \"记忆系统\", \"AI陪伴\",\n \"自适应难度\", \"动态叙事\", \"涌现行为\",\n \"AI剧情\", \"AI叙事\", \"AI生成剧情\", \"AI原生游戏\",\n \"token收费\", \"词元收费\", \"AI对战\", \"AI对手\",\n \"智能体验\", \"AI旁白\", \"AI配音演员\",\n \"Realtime TTS\", \"AI voice\", \"AI语音\",\n \"智能体\", \"游戏内AI\", \"in-game AI\"\n ],\n \"dev-tools\": [\n \"开发工具\", \"AI编程\", \"自动测试\", \"QA\", \"Bug检测\",\n \"Unity Muse\", \"Unity Sentis\", \"ML-Agents\",\n \"Copilot\", \"代码生成\", \"引擎\", \"AI中间件\", \"SDK\",\n \"AI plugin\", \"game engine\", \"workflow\", \"工作流\",\n \"性能优化\", \"自动化\", \"AI辅助开发\", \"AI coding\",\n \"Modl.ai\", \"自动化测试\", \"智能调试\",\n \"Cursor\", \"Claude Code\", \"AI编码\"\n ],\n \"ops\": [\n \"推荐\", \"分发\", \"买量\", \"素材\", \"反作弊\", \"反外挂\",\n \"智能客服\", \"LTV\", \"用户分群\", \"个性化推荐\", \"动态定价\",\n \"营销\", \"广告\", \"投放\", \"ROI\", \"留存\", \"付费率\",\n \"matchmaking\", \"monetization\", \"anti-cheat\",\n \"AI运营\", \"智能推荐\", \"广告素材AI\", \"A/B测试\",\n \"ROAS\", \"AI买量\", \"AI客服\", \"AI审核\",\n \"玩家分群\", \"智能推送\", \"精准营销\",\n \"流失预测\", \"付费预测\", \"用户画像\",\n \"AI分发\", \"AI推荐算法\", \"智能匹配\",\n \"churn prediction\", \"player segmentation\", \"AI marketing\"\n ],\n \"industry\": [\n \"投融资\", \"融资\", \"收购\", \"战略\", \"布局\", \"财报\",\n \"市场份额\", \"政策\", \"监管\", \"版号\", \"出海\",\n \"小游戏\", \"微信\", \"腾讯\", \"网易\", \"米哈游\",\n \"IPO\", \"市值\", \"人事\", \"裁员\", \"招聘\",\n \"acquisition\", \"funding\", \"market share\", \"regulation\",\n \"合作\", \"partnership\", \"发行\", \"代理\"\n ],\n \"research\": [\n \"论文\", \"paper\", \"研究\", \"GDC\", \"SIGGRAPH\", \"arXiv\",\n \"强化学习\", \"世界模型\", \"benchmark\", \"开源模型\",\n \"IEEE\", \"ACM\", \"实验\", \"算法\", \"数据集\",\n \"research\", \"study\", \"survey\", \"state of the art\",\n \"IEEE CoG\", \"DiGRA\", \"AAAI\", \"学术\", \"课题\"\n ]\n },\n \"entity_tags\": {\n \"description\": \"实体标签库，用于给条目打实体标签（公司/产品/技术）\",\n \"companies\": [\n \"Unity\", \"Epic Games\", \"Unreal Engine\", \"NVIDIA\", \"Google DeepMind\", \"DeepMind\",\n \"Inworld\", \"Convai\", \"Rosebud\", \"Scenario\", \"Modl.ai\", \"Ludo.ai\",\n \"腾讯\", \"网易\", \"米哈游\", \"HoYoverse\", \"字节跳动\",\n \"艺电\", \"Ubisoft\", \"Activision\", \"Blizzard\", \"Sony\", \"Microsoft\",\n \"Supercell\", \"中手游\", \"莉莉丝\", \"沐瞳\",\n \"OpenAI\", \"Anthropic\", \"Meta\", \"Valve\", \"Steam\", \"Neowiz\"\n ],\n \"products\": [\n \"Unity Muse\", \"Unity Sentis\", \"ML-Agents\", \"MetaHuman\",\n \"DLSS\", \"NVIDIA ACE\", \"Omniverse\",\n \"AI Dungeon\", \"GamePartner.AI\",\n \"Stable Diffusion\", \"Midjourney\", \"DALL-E\", \"Sora\",\n \"GPT\", \"Claude\", \"Gemini\", \"DeepSeek\"\n ],\n \"technologies\": [\n \"NPC\", \"PCG\", \"强化学习\", \"世界模型\", \"扩散模型\",\n \"大模型\", \"LLM\", \"embedding\", \"fine-tune\",\n \"transformer\", \"GAN\", \"NeRF\", \"行为树\",\n \"程序化生成\", \"动态叙事\", \"情感计算\"\n ]\n }\n}\n\nFile v2.0.2:references/sources.json\n\n{\n \"sources\": [\n {\n \"name\": \"游戏陀螺\",\n \"type\": \"rss\",\n \"url\": \"https://www.youxituoluo.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏行业深度报道、出海\"\n },\n {\n \"name\": \"机核 GCores\",\n \"type\": \"rss\",\n \"url\": \"https://www.gcores.com/rss\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏文化与独立游戏\"\n },\n {\n \"name\": \"触乐\",\n \"type\": \"rss\",\n \"url\": \"https://www.chuapp.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏深度长文\"\n },\n {\n \"name\": \"GamesIndustry.biz\",\n \"type\": \"rss\",\n \"url\": \"https://www.gamesindustry.biz/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"全球游戏行业商业新闻\"\n },\n {\n \"name\": \"Game Developer\",\n \"type\": \"rss\",\n \"url\": \"https://www.gamedeveloper.com/rss.xml\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"游戏开发深度（原Gamasutra）\",\n \"status\": \"broken_403\"\n },\n {\n \"name\": \"GameLook\",\n \"type\": \"rss\",\n \"url\": \"https://www.gamelook.com.cn/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"国内游戏行业深度报道（替代 Game Developer）\"\n },\n {\n \"name\": \"GamesBeat\",\n \"type\": \"rss\",\n \"url\": \"https://venturebeat.com/category/games/feed/\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"游戏×科技/AI 交叉、投融资\"\n },\n {\n \"name\": \"80 Level\",\n \"type\": \"rss\",\n \"url\": \"https://80.lv/feed/\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"游戏美术/技术向\"\n },\n {\n \"name\": \"PocketGamer.biz\",\n \"type\": \"rss\",\n \"url\": \"https://www.pocketgamer.biz/feed/\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"手游行业\",\n \"status\": \"broken_404\"\n },\n {\n \"name\": \"游戏葡萄\",\n \"type\": \"rss\",\n \"url\": \"https://youxiputao.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏行业分析（替代 PocketGamer）\"\n },\n {\n \"name\": \"Game World Observer\",\n \"type\": \"rss\",\n \"url\": \"https://gameworldobserver.com/feed\",\n \"layer\": 1,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"手游数据、市场\"\n },\n {\n \"name\": \"IGN\",\n \"type\": \"rss\",\n \"url\": \"https://feeds.feedburner.com/ign/all\",\n \"layer\": 1,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"大众游戏新闻\"\n },\n {\n \"name\": \"Unity Blog\",\n \"type\": \"rss\",\n \"url\": \"https://blog.unity.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1\",\n \"region\": \"intl\",\n \"focus\": \"Unity 引擎官方（AI功能第一手）\"\n },\n {\n \"name\": \"Unreal Engine Blog\",\n \"type\": \"rss\",\n \"url\": \"https://www.unrealengine.com/en-US/feed\",\n \"layer\": 1,\n \"tier\": \"T1\",\n \"region\": \"intl\",\n \"focus\": \"Unreal 引擎官方\",\n \"status\": \"broken_403\"\n },\n {\n \"name\": \"竞核\",\n \"type\": \"rss\",\n \"url\": \"https://www.coreengine.cn/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏×科技深度（替代 Unreal Blog）\"\n },\n {\n \"name\": \"NVIDIA Blog\",\n \"type\": \"rss\",\n \"url\": \"https://blogs.nvidia.com/feed/\",\n \"layer\": 1,\n \"tier\": \"T1\",\n \"region\": \"intl\",\n \"focus\": \"GPU/AI 游戏技术（DLSS、ACE）\"\n },\n {\n \"name\": \"DeepMind Blog\",\n \"type\": \"rss\",\n \"url\": \"https://deepmind.google/blog/rss.xml\",\n \"layer\": 1,\n \"tier\": \"T1\",\n \"region\": \"intl\",\n \"focus\": \"游戏AI研究（AlphaGo系列）\"\n },\n {\n \"name\": \"Google Developers Blog\",\n \"type\": \"rss\",\n \"url\": \"https://developers.googleblog.com/feeds/posts/default\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"Google AI（通用，需关键词过滤）\"\n },\n {\n \"name\": \"36Kr\",\n \"type\": \"rss\",\n \"url\": \"https://36kr.com/feed\",\n \"layer\": 2,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"科技创业、投融资（含游戏AI）\"\n },\n {\n \"name\": \"IT之家\",\n \"type\": \"rss\",\n \"url\": \"https://www.ithome.com/rss/\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"cn\",\n \"focus\": \"科技快讯（量大，关键词过滤）\"\n },\n {\n \"name\": \"机器之心\",\n \"type\": \"rss\",\n \"url\": \"https://www.jiqizhixin.com/rss\",\n \"layer\": 2,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"AI技术媒体头部（偶尔深度游戏AI）\"\n },\n {\n \"name\": \"量子位\",\n \"type\": \"rss\",\n \"url\": \"https://www.qbitai.com/feed\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"cn\",\n \"focus\": \"AI资讯（游戏AI融资常报）\"\n },\n {\n \"name\": \"TechCrunch\",\n \"type\": \"rss\",\n \"url\": \"https://techcrunch.com/feed/\",\n \"layer\": 2,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"创业融资（Inworld等首发这里）\"\n },\n {\n \"name\": \"The Verge\",\n \"type\": \"rss\",\n \"url\": \"https://www.theverge.com/rss/index.xml\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"大众科技+AI+游戏\"\n },\n {\n \"name\": \"VentureBeat AI\",\n \"type\": \"rss\",\n \"url\": \"https://venturebeat.com/category/ai/feed/\",\n \"layer\": 2,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"AI行业深度（与GamesBeat同源）\"\n },\n {\n \"name\": \"Ars Technica Gaming\",\n \"type\": \"rss\",\n \"url\": \"https://feeds.arstechnica.com/arstechnica/gaming\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"技术深度+游戏\"\n },\n {\n \"name\": \"AI HOT (游戏相关)\",\n \"type\": \"api\",\n \"url\": \"https://aihot.virxact.com/api/public/items\",\n \"queries\": [\"游戏\", \"game\", \"gaming\", \"NPC\", \"Unity AI\", \"Unreal AI\", \"Inworld\", \"游戏引擎\"],\n \"layer\": 3,\n \"tier\": \"T2\",\n \"region\": \"cn\",\n \"focus\": \"AI资讯中游戏相关条目（补充层）\"\n }\n ]\n}\n\nFile v2.0.2:skill-card.md\n\n## Description:\n\nCollects, filters, categorizes, and summarizes game-industry AI news from public RSS feeds and API-backed news sources.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[zhuhuimin0224-create](https://clawhub.ai/user/zhuhuimin0224-create)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, analysts, and game-industry teams use this skill to retrieve Chinese and English game-AI daily or weekly briefings, filtered by categories such as AI content creation, in-game AI, development tools, operations, industry news, and research.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Live searches may send confidential project names or private strategy terms to third-party services.\n\nMitigation: Use only public or non-sensitive search terms when requesting live game-AI news.\n\nRisk: Generated news briefs may contain outdated, incomplete, or secondary-source summaries.\n\nMitigation: Verify source links and original publications before relying on a report for important business or technical decisions.\n\nRisk: The skill writes local daily cache files during normal operation.\n\nMitigation: Deploy it only in workspaces where local cache-file creation under the skill data directory is acceptable.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/zhuhuimin0224-create/skills/ai-game)\n- [Source catalog](references/sources.json)\n- [Keyword and category filters](references/keywords.json)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, shell commands, configuration, guidance]\n\n**Output Format:** [Markdown news brief with titles, links, summaries, details, recommendations, and optional JSON-backed source data.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Daily reports are normally limited to 10 items and weekly reports to 15 items; generated cache files are written under data/YYYY-MM-DD.json.]\n\n## Skill Version(s):\n\n2.0.2 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v2.0.2:data/2026-05-11.json\n\n{\n \"date\": \"2026-05-11\",\n \"generated_at\": \"2026-05-11T07:43:31Z\",\n \"total_count\": 2,\n \"items\": [\n {\n \"title\": \"机器人终局：物理AGI路线图与LLM类比\",\n \"url\": \"https://x.com/DrJimFan/status/2052758642781487237\",\n \"summary\": \"演讲者以\\\"Robotics： Endgame\\\"为题，提出解决物理AGI的路线图，直接类比LLM的成功路径。核心观点包括视频世界模型作为第二预训练范式、世界行动模型（WAM）、机器人数据收集策略（类似FSD的物理数据飞轮）、EgoScale和灵巧性缩放定律、物理强化学习 bridging the last mile，以及DreamDojo端到端神经物理引擎。预测物理AGI的实现比预期更近，并提及20\",\n \"source\": \"AI HOT (X：Jim Fan (@DrJimFan))\",\n \"published_at\": \"2026-05-08T14:32:53.000Z\",\n \"category_hint\": \"tip\",\n \"category\": \"research\",\n \"category_secondary\": null\n },\n {\n \"title\": \"GamePartner.AI：中手游助力中国休闲游戏开发者出海的新尝试\",\n \"url\": \"https://www.youxituoluo.com/534463.html\",\n \"summary\": \"在AI以颠覆性力量渗透游戏研发、发行、运营全链条的今天，行业正站在一场生产范式变革的关键路口。5月8日，港股主板上市公司中手游正式宣布与杭州极逸人工智能达成战略合作，共同推出面向全球休闲游戏市场，由AI驱动的市场洞察、游戏开发到全球发行的一站式智能体——GamePartner.AI（简称GPA）。该平台由中手游负责推广运营，致力于助力并构建中国休闲游戏开发者出海生态。\\n\\n从\",\n \"source\": \"游戏陀螺\",\n \"published_at\": \"2026-05-08T11:53:40Z\",\n \"category\": \"industry\",\n \"category_secondary\": null\n }\n ],\n \"stats\": {\n \"raw_count\": 85,\n \"after_time_filter\": 85,\n \"after_relevance_filter\": 2,\n \"after_dedup\": 2,\n \"by_category\": {\n \"research\": 1,\n \"industry\": 1\n }\n }\n}\n\nFile v2.0.2:data/2026-05-12.json\n\n{\n \"date\": \"2026-05-12\",\n \"generated_at\": \"2026-05-12T14:07:55Z\",\n \"total_count\": 14,\n \"items\": [\n {\n \"title\": \"Blender Tool For Real-Time Procedural Surface Fracturing\",\n \"url\": \"https://80.lv/articles/blender-tool-for-real-time-procedural-surface-fracturing/\",\n \"summary\": \"Arriving \\\"hopefully this month.\\\"\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-12T13:36:00Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Blender Studio Releases Its First 4K HDR Short Film, Singularity\",\n \"url\": \"https://80.lv/articles/blender-studio-releases-its-first-4k-hdr-short-film-singularity/\",\n \"summary\": \"Check out how Blender Studio combines a watercolor-inspired visual style with generative simulations in this compelling story about a little creature lost in space.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-12T10:56:00Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"国产 AI 游戏《历史模拟器：崇祯》“本体买断、词元收费”引争议，官方回应称将开放自行接入模型\",\n \"url\": \"https://www.ithome.com/0/949/477.htm\",\n \"summary\": \"IT之家 5 月 12 日消息，近期一款国产游戏《历史模拟器：崇祯》引发玩家争议，本作使用 AI 生成剧情，采用“本体买断，词元（Token）收费”制度，也就是玩家花费 48 元购买游戏后，如果需要持续推进游戏过程，就需要额外付费购买词元。对此，官方目前发布公告，声称将开放“自定义 API”与“创意工坊”功能。玩家未来可以自行接入支持范围内的大模型服务，自由选择模型并控制成本。与此同时，游戏还将同步上线创意工坊功能，允许玩家基于《历史模拟器：崇祯》的核心框架，自行创作剧本、规则以及玩法内容。官方强调，具体的上线时间、支持模型范围以及上传审核规则，后续会以正式功能公告的形式公开。\",\n \"source\": \"IT之家\",\n \"published_at\": \"2026-05-12T10:48:00Z\",\n \"category\": \"in-game\",\n \"category_secondary\": null,\n \"tags\": [\n \"大模型\"\n ],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Darkest Dungeon Creator Won't Replace Late Narrator with AI Despite His Permission\",\n \"url\": \"https://80.lv/articles/darkest-dungeon-creator-won-t-replace-late-narrator-with-ai-despite-his-permission/\",\n \"summary\": \"Wayne June will live in our memories.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-12T09:34:00Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"《匹诺曹的谎言》开发商 Neowiz 布局生成式 AI，招聘 AI 创意设计师\",\n \"url\": \"https://www.ithome.com/0/949/307.htm\",\n \"summary\": \"《匹诺曹的谎言》开发商Neowiz正积极布局生成式AI，旗下Round8工作室新设\\\"AI创意设计师\\\"岗位。该岗位需使用Midjourney、Stable Diffusion等工具进行角色与概念原画创作，并负责训练定制化AI模型。公司旨在将AI深度融入开发流程，搭建高效美术创作流水线以压缩周期，并计划将生成式AI推广为内部美术人员的常规工作方式，由该设计师指导其他员工。当前游戏行业普遍应用AI优化流程，但生成式AI在美术创作领域的应用仍面临玩家接受度挑战。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-12T07:03:21.000Z\",\n \"category\": \"creation\",\n \"category_secondary\": \"industry\",\n \"tags\": [\n \"Neowiz\",\n \"Stable Diffusion\",\n \"Midjourney\"\n ],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"反思与转变：一位AI创作者的流量套路自省与价值回归\",\n \"url\": \"https://x.com/AYi_AInotes/status/2053900819557494823\",\n \"summary\": \"一位AI内容创作者在获得业界关注的同时，因受到严厉批评而深刻反思。他承认自己为追求流量，将\\\"卧槽\\\"开头等技巧变成了令人反感的套路，并违背了不分享未经验证项目的原则。他宣布即刻停止使用此类套路，并呼吁模仿者一同摒弃。核心反思在于，内容创作不应以流量为终局，而应专注于输出有价值的思考。引用的批评指出，其分享的AI游戏工作室项目思路存在根本缺陷，仍以人类岗位划分限制AI Agent的全局能力，同时尖锐批评了其浮夸文风。\",\n \"source\": \"X：阿易 AI Notes (@AYi_AInotes)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T18:11:29.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"大语言模型规模越大，综合能力越强\",\n \"url\": \"https://x.com/emollick/status/2053899147422396614\",\n \"summary\": \"大语言模型（LLM）的一个重要特性是，更新、更大的模型在所有方面都表现更优。AI实验室正将大量资源投入编程等经济价值高的领域，但更大的模型在谈判、对齐、诗歌创作等广泛任务上同样更具优势。例如，在PACT基准测试的数千场模拟谈判中，GPT-5.5在买卖双方多轮议价游戏中取得了最佳成绩，这印证了模型规模与综合能力提升的正相关关系。\",\n \"source\": \"X：Ethan Mollick (@emollick)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T18:04:51.000Z\",\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [\n \"GPT\",\n \"LLM\"\n ],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"大语言模型代理中的\\\"记忆诅咒\\\"\",\n \"url\": \"https://x.com/omarsar0/status/2053863994499408214\",\n \"summary\": \"研究发现，长历史记录会在大语言模型（LLM）代理中引发\\\"记忆诅咒\\\"，导致其过度遵循历史、规避风险，从而削弱合作能力。该结论基于7个LLM和4个社会困境游戏的实验，在28个模型-游戏组合中，有18个因历史扩展而合作退化。机制分析表明，长历史侵蚀了模型的前瞻性意图，使其更关注过去的冲突而非未来收益。通过仅在前瞻性轨迹上训练的LoRA适配器可缓解此问题，且能零样本迁移至新游戏。实验证明，触发因素是历史内容而非长度，而消除显式思维链通常能减轻合作崩溃。\",\n \"source\": \"X：Elvis Saravia (@omarsar0, DAIR.AI)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T15:45:09.000Z\",\n \"category\": \"research\",\n \"category_secondary\": \"industry\",\n \"tags\": [\n \"LLM\"\n ],\n \"recommendation\": \"大厂联手布局，行业风向标\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"MiniMax 启动\\\"10x Team\\\"合作计划，提供无限的 Token\",\n \"url\": \"https://www.ithome.com/0/949/029.htm\",\n \"summary\": \"MiniMax宣布启动\\\"10x Team\\\"合作计划，旨在邀请各行业顶尖专业人士共同推动AI模型在特定领域的深度优化与十倍增长。该计划面向具备行业积累、能自主参与问题定义与工作流搭建的专业人士，提供无限Token、完整多模态模型能力及研发资源。合作采用全职入职或不少于四个月的Fellowship短期协作模式，办公地点覆盖上海、北京、香港、旧金山及伦敦。合作成果将开源并用于模型迭代，参与者可获得具国际竞争力的薪酬、股票激励及学术成果共享权益。此前，MiniMax已在工业软件、游戏引擎等多个领域与专家展开合作验证。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T15:13:16.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"开源项目，团队可直接试用评估\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Sony maps out how first-party PlayStation studios are utilising AI tools during development\",\n \"url\": \"https://www.gamesindustry.biz/sony-maps-out-how-first-party-playstation-studios-are-utilising-ai-tools-during-development\",\n \"summary\": \"Sony has detailed its plans to expand AI integration throughout its organisation, including game development. Read more\",\n \"source\": \"GamesIndustry.biz\",\n \"published_at\": \"2026-05-11T13:36:22Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"Sony\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"大神用Claude Code复刻完整游戏开发工作室，48个AI智能体覆盖全岗位\",\n \"url\": \"https://x.com/AYi_AInotes/status/2053709074466824688\",\n \"summary\": \"开源项目Claude Code Game Studios利用Claude Code构建了完整的虚拟游戏开发工作室。该项目包含48个AI智能体，1：1还原从创意总监到关卡设计师等全部岗位，覆盖游戏开发全流程。系统提供36条斜杠指令一键启动工作流，适配Godot、Unity、Unreal三大游戏引擎，并集成自动化校验钩子及28套行业标准文档模板。所有AI仅负责梳理方案，最终决策权由用户掌握。项目采用MIT开源协议，可免费商用，克隆仓库即可快速部署。\",\n \"source\": \"X：阿易 AI Notes (@AYi_AInotes)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T05:29:34.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [\n \"Unity\",\n \"Claude\"\n ],\n \"recommendation\": \"开源项目，团队可直接试用评估\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"‘Your Career Starts at the Beginning of the AI Revolution,’ NVIDIA CEO Tells Graduates\",\n \"url\": \"https://blogs.nvidia.com/blog/nvidia-ceo-carnegie-mellon-commencement-address/\",\n \"summary\": \"“You are entering the world at an extraordinary moment,” NVIDIA founder and CEO Jensen Huang told graduates as he delivered the keynote address at Carnegie Mellon University’s 128th commencement ceremony on Sunday. “A new industry is being born. A new era of science and discovery is beginning.\",\n \"source\": \"NVIDIA Blog\",\n \"published_at\": \"2026-05-10T22:00:50Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"NVIDIA\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"尽管裁员千人，Epic 仍表示 AI 不会取代游戏从业者\",\n \"url\": \"https://www.ithome.com/0/948/483.htm\",\n \"summary\": \"Epic公司高管表示，人工智能不会取代游戏行业工作岗位，而是用于提升效率、减轻繁重工作负担。尽管该公司在2026年裁员1000人，但坚称裁员与AI无关。Epic正在探索AI工具以支持游戏开发，未来将应用于艺术创意领域，并强调《堡垒之夜》开发中的AI使用由公司统一管控，合作方不得擅自使用。这一立场与索尼、艺电等企业相似，但外界对其\\\"AI不危及就业\\\"的说法仍存质疑。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-10T08:12:22.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"艺电\"\n ],\n \"recommendation\": \"大厂联手布局，行业风向标\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Building with Gemini Embedding 2: Agentic multimodal RAG and beyond\",\n \"url\": \"https://developers.googleblog.com/building-with-gemini-embedding-2/\",\n \"summary\": \"Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space. This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for ta\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [\n \"Gemini\",\n \"embedding\"\n ],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n }\n ],\n \"stats\": {\n \"raw_count\": 242,\n \"after_relevance_filter\": 15,\n \"after_dedup\": 14,\n \"by_category\": {\n \"industry\": 7,\n \"in-game\": 1,\n \"creation\": 2,\n \"research\": 2,\n \"dev-tools\": 2\n }\n }\n}\n\nFile v2.0.2:data/2026-05-13.json\n\n{\n \"date\": \"2026-05-13\",\n \"generated_at\": \"2026-05-13T03:01:39Z\",\n \"total_count\": 0,\n \"items\": [],\n \"stats\": {\n \"raw_count\": 249,\n \"after_relevance_filter\": 18,\n \"after_dedup\": 0,\n \"by_category\": {}\n }\n}\n\nFile v2.0.2:data/2026-05-14.json\n\n{\n \"date\": \"2026-05-14\",\n \"generated_at\": \"2026-05-14T12:26:29Z\",\n \"total_count\": 18,\n \"items\": [\n {\n \"title\": \"恺英1亿押注AI漫剧，游戏大厂正涌入这个243亿的新战场\",\n \"url\": \"https://www.youxituoluo.com/534482.html\",\n \"summary\": \"近日，恺英网络在AI内容赛道投下一枚重注。天眼查信息显示，恺英已全资设立“上海时光川行科技有限公司”，注册资本高达1亿元人民币。\\n\\n穿透股权后，新公司由上海恺盛网络科技持股90%、上饶盛英网络科技持股10%，经营范围涵盖人工智能技术服务、软件开发、计算机系统服务、信息系统集成以及AI硬件销售等多个板块。\\n\\n如此手笔，瞄准的正是当下火热的AI微短剧与AI漫剧。\\n据证券时报消息，上海时光川行将专注于AI微短剧与AI漫剧的一体化制作，试图打通从IP孵化、剧本开发到多镜头成片的工业化流水线。据悉，其底层将搭载自研的垂类视频生成模型，并同时布局真人短剧与漫剧两条产品线，二者共享同\",\n \"source\": \"游戏陀螺\",\n \"published_at\": \"2026-05-14T15:09:47Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Real-Time High-Fidelity LiquiGen Simulation In UE5 With ZibraGDS Compression\",\n \"url\": \"https://80.lv/articles/real-time-high-fidelity-liquigen-simulation-in-ue5-with-zibragds-compression/\",\n \"summary\": \"Jason Key tested Zibra AI's newest plug-in.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-14T11:46:00Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"AI变革已至，你准备好了吗？\",\n \"url\": \"https://x.com/alibaba_cloud/status/2054863133072560402\",\n \"summary\": \"AI已成为新常态--你准备好迎接转型了吗？⚡\\n\\n从视觉创意到自动化工作流，AI正在改变游戏规则。我们已汇总顶级AI用例，助您激发创新。无论规模大小，我们作为您可信赖的AI创新平台，都将助力您取得成功。\\n\\n立即启程--申请免费额度，探索我们的高性能AI解决方案！\\n👉 https：//int.alibabacloud.com/m/1000412912/\\n\\nAlibaba Cloud，您的AI创新平台。\",\n \"source\": \"X：阿里云 / Alibaba Cloud (@alibaba_cloud)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-14T09:55:23.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Epic Games Veteran Claims He's Building AI-Heavy \\\"Fully European\\\" Game Engine\",\n \"url\": \"https://80.lv/articles/epic-games-veteran-claims-he-s-building-ai-heavy-fully-european-game-engine/\",\n \"summary\": \"Arjan Brussee said that Immense is made by Europeans, hosted in Europe, and complies with EU regulations.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-14T09:34:00Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [\n \"Epic Games\"\n ],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"登科与我开发AI Agent坦克大战游戏\",\n \"url\": \"https://x.com/oran_ge/status/2054706809433444378\",\n \"summary\": \"作者与登科共同开发了一款名为\\\"Agent坦克大战\\\"的游戏，旨在呼吁人们不要仅将AI用于提升效率的\\\"内卷\\\"，而应将其应用于娱乐放松领域。该游戏的核心是让玩家体验AI驱动的坦克对战，通过具体的游戏项目展示了AI技术在休闲娱乐场景下的创新应用潜力。\",\n \"source\": \"X：Oran Ge (@oran_ge)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T23:34:12.000Z\",\n \"category\": \"in-game\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"AI 正在重塑玩家的游戏体验\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"首届 Agent 坦克大战，你要不要来玩？\",\n \"url\": \"https://x.com/oran_ge/status/2054704934327923174\",\n \"summary\": \"Cola与AgenTank联合举办首届AI Agent坦克对战挑战赛。参赛者需通过Cola接入游戏，训练自己的Agent坦克进行代码优化与策略升级，并参与排位赛。比赛获得了小米MiMo 2.5 Pro模型的赞助，提供免费Token用于坦克升级。赛事限100人参与，排名最高者可获得100美金奖励，于2026年5月14日13：00开始。开发者表示，若参与踊跃，可能将名额扩展至1000人并采用新算法，旨在推动AI Agent从效率工具向娱乐对战场景拓展。\",\n \"source\": \"X：Oran Ge (@oran_ge)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T23:26:45.000Z\",\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Microsoft’s Edge Copilot update uses AI to pull information from across your tabs\",\n \"url\": \"https://www.theverge.com/tech/930188/microsoft-edge-copilot-ai-tabs\",\n \"summary\": \"Microsoft Edge is adding a new feature that will allow its Copilot AI chatbot to gather information from all of your open tabs. When you start a conversation with Copilot, you can ask the chatbot questions about what's in your tabs, compare the products you're looking at, summarize your open article\",\n \"source\": \"The Verge\",\n \"published_at\": \"2026-05-13T22:04:28Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [\n \"Microsoft\"\n ],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"AI角色实现记忆共情与主动交互\",\n \"url\": \"https://x.com/alibaba_cloud/status/2054653031472414864\",\n \"summary\": \"如果AI角色能够记忆、共情并主动交互呢？✨\\n\\n交互式AI的未来已来。无论您是为游戏、虚拟AI伴侣还是自适应学习进行开发，Qwen-Character都能打造沉浸式角色扮演体验，推动参与度加深50%以上并提升用户终身价值\\n\\n👉 观看完整视频了解运作原理：https：//int.alibabacloud.com/m/1000412854/\\n\\n#AlibabaCloud #Qwen #QwenCharacter #ModelStudio #AI\",\n \"source\": \"X：阿里云 / Alibaba Cloud (@alibaba_cloud)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T20:00:30.000Z\",\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Origin Lab raises $8M to help video game companies sell data to world-model builders\",\n \"url\": \"https://techcrunch.com/2026/05/13/origin-lab-raises-8m-to-help-video-game-companies-sell-data-to-world-model-builders/\",\n \"summary\": \"Origin Lab will serve as a marketplace where AI labs can buy high-quality licensed data, and video-game companies can sell it.\",\n \"source\": \"TechCrunch\",\n \"published_at\": \"2026-05-13T16:22:01Z\",\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"效率翻倍、重塑开放世界，索尼第一方的AI大招终于亮了\",\n \"url\": \"https://www.youxituoluo.com/534479.html\",\n \"summary\": \"面对席卷全球的AI浪潮，主机巨头索尼终于系统性地亮出了底牌。\\n据外媒报道，索尼近日详细公布了在其整个组织架构内部（尤其是核心的游戏开发领域）全面深化AI（人工智能）整合的战略规划。\\n从高管的表态中不难看出，AI 正在从“概念”走向“实操”，深度重塑 PlayStation 第一方大作的工业化管线。\\nAI是人类想象力的放大器\\n在最新一期财报的业务战略说明会上，索尼集团总裁兼首席执行官十时裕树（Hiroki Totoki）首先为公司的 AI 战略定下了基调。\\n他指出，在不断攀升的3A 研发成本面前，AI 将成为破局的关键：“AI 技术将使\",\n \"source\": \"游戏陀螺\",\n \"published_at\": \"2026-05-13T14:39:08Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"NVIDIA, Ineffable Intelligence Team Up to Build the Future of Reinforcement Learning Infrastructure\",\n \"url\": \"https://blogs.nvidia.com/blog/ineffable-intelligence-reinforcement-learning-infrastructure/\",\n \"summary\": \"Reinforcement-learning agents — AI systems that learn by trial and error — can convert computation into new knowledge. That’s the focus of a new engineering-level collaboration between NVIDIA and Ineffable Intelligence, the London-based AI lab founded by AlphaGo architect David Silver in the wake of\",\n \"source\": \"NVIDIA Blog\",\n \"published_at\": \"2026-05-13T13:00:57Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"NVIDIA\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Hermes Unlocks Self-Improving AI Agents, Powered by NVIDIA RTX PCs and DGX Spark\",\n \"url\": \"https://blogs.nvidia.com/blog/rtx-ai-garage-hermes-agent-dgx-spark/\",\n \"summary\": \"Agentic AI is changing the way users get work done. Following the success of OpenClaw, the community is embracing new open source agentic frameworks. The latest is Hermes Agent, which crossed 140,000 GitHub stars in under three months.\",\n \"source\": \"NVIDIA Blog\",\n \"published_at\": \"2026-05-13T13:00:10Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"Unity\",\n \"NVIDIA\"\n ],\n \"recommendation\": \"开源项目，团队可直接试用评估\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"「AI 脚步声增强」功能登陆荣耀 Magic8 系列手机，适配《和平精英》等 9 款 FPS 游戏\",\n \"url\": \"https://www.ithome.com/0/950/025.htm\",\n \"summary\": \"荣耀Magic8系列手机已上线\\\"AI脚步声增强\\\"功能，该功能通过AI算法强化游戏中的脚步声细节，目前支持《和平精英》《三角洲行动》等9款FPS游戏。用户可在游戏内通过左侧滑出游戏管家，进入游戏音效设置开启并调节档位。此外，该功能后续将扩展至更多机型，荣耀Magic7和荣耀GT Pro已确认正在适配中。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T11:26:20.000Z\",\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"《星球大战：旧共和国的命运》获大量投资，导演哈德森曾直言生成式 AI\\\"没灵魂\\\"\",\n \"url\": \"https://www.ithome.com/0/950/024.htm\",\n \"summary\": \"前《质量效应》总监凯西·哈德森的新作《星球大战：旧共和国的命运》获得网易前全球投资负责人Simon Zhu成立的GreaterThan Group投资。该基金已筹集1亿美元，其中4000万美元已到位。哈德森表示将避免组建数百人的大型团队，转而依赖外包开发，并计划在2030年前完成项目。他批评生成式AI\\\"在创意上没有灵魂\\\"，同时强调游戏不会设计为长达200小时的超长流程，以控制开发周期。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T11:23:33.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"网易\"\n ],\n \"recommendation\": \"资本看好该方向，行业信号值得关注\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"AI角色实现记忆共情与主动交互新突破\",\n \"url\": \"https://x.com/alibaba_cloud/status/2054471765376520534\",\n \"summary\": \"如果AI角色能够记忆、共情并主动交互会怎样？✨\\n\\n互动AI的未来已来。无论您是为游戏、虚拟AI伴侣还是自适应学习进行开发，Qwen-Character都能提供沉浸式角色扮演体验，推动参与度加深50%以上并提升用户生命周期价值\\n\\n👉 观看完整视频了解运作原理：https：//int.alibabacloud.com/m/1000412855/\\n\\n#AlibabaCloud #Qwen #QwenCharacter #ModelStudio #AI\",\n \"source\": \"X：阿里云 / Alibaba Cloud (@alibaba_cloud)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T08:00:13.000Z\",\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"一个周末用AI开发的\\\"抢地盘\\\"跑步App，揭示了产品开发的新范式\",\n \"url\": \"https://x.com/AYi_AInotes/status/2054446858106208463\",\n \"summary\": \"有人利用Claude在一个周末内开发出一款游戏化跑步App，将城市街道变为可争夺的虚拟领地，以强烈的游戏动机取代传统的数据打卡模式。此事的关键并非创意本身（类似产品已存在），而在于AI编程如何将产品原型迭代速度提升至\\\"周末级\\\"。普通人无需专业开发技能与大量资金，即可快速克隆成功产品并加入微创新，随后直接在社交平台获取即时市场反馈。这凸显了在AI时代，动机设计可能比功能优化更为关键，极大地降低了将想法快速验证和产品化的门槛。\",\n \"source\": \"X：阿易 AI Notes (@AYi_AInotes)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T06:21:15.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [\n \"Claude\"\n ],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"MAP：一种面向长程交互式智能体推理的先建图后行动范式\",\n \"url\": \"https://arxiv.org/abs/2605.13037\",\n \"summary\": \"针对当前交互式大语言模型代理因环境感知延迟而陷入低效试错的问题，本研究提出可插拔的先建图后行动范式（MAP）。该范式将环境理解前置，包含全局探索、任务特定建图与知识增强执行三个阶段，旨在突破认知瓶颈。实验表明，MAP在多个基准测试中带来一致性能提升。在ARC-AGI-3的25个游戏环境中，前沿模型在MAP加持下于22个环境中超越了接近零的基线表现。同时发布的MAP-2K轨迹数据集证明，基于环境理解的训练优于单纯模仿专家轨迹，验证了先理解环境的核心价值。\",\n \"source\": \"HuggingFace Daily Papers（社区热门论文）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T00:00:00.000Z\",\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"基于文本-表格建模的陌生AI智能体决策预测方法\",\n \"url\": \"https://arxiv.org/abs/2605.12411\",\n \"summary\": \"研究提出一种目标自适应的文本-表格预测方法，用于预测陌生AI智能体在谈判与交易中的决策。该方法将每个决策点构建为表格行，整合游戏状态、报价历史和对话文本，并在提示中提供目标智能体先前的K轮游戏作为适应示例。模型基于表格基础模型，结合了结构化特征、文本表示以及创新的\\\"LLM作为观察者\\\"隐藏状态特征。在13个前沿LLM智能体上训练，并在91个保留的支架智能体上测试，完整模型性能优于直接提示法和基线模型。当K=16时，观察者特征将响应预测AUC提升约4个百分点，并将议价报价预测误差降低14%，证明隐藏的LLM表征能捕捉直接提示无法获取的决策信号。\",\n \"source\": \"HuggingFace Daily Papers（社区热门论文）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-12T00:00:00.000Z\",\n \"category\": \"in-game\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"学术前沿，预示技术走向\",\n \"source_type\": \"primary\"\n }\n ],\n \"stats\": {\n \"raw_count\": 276,\n \"after_relevance_filter\": 30,\n \"after_dedup\": 18,\n \"by_category\": {\n \"industry\": 6,\n \"dev-tools\": 4,\n \"in-game\": 2,\n \"research\": 3,\n \"creation\": 3\n }\n }\n}\n\nFile v2.0.2:data/2026-05-18.json\n\n{\n \"date\": \"2026-05-18\",\n \"generated_at\": \"2026-05-18T12:03:37Z\",\n \"total_count\": 13,\n \"items\": [\n {\n \"title\": \"《洛克王国》DAU达1300万，《三谋》团队再出新作，恺英1亿砸AI漫剧 | 陀螺周报\",\n \"url\": \"https://www.youxituoluo.com/534491.html\",\n \"summary\": \"随着这些年国内游戏厂商声量壮大，中国游戏全球化/区域化新品布局、中国厂商于宣发联动、投资并购等业态频发。在越来越多成功者试行的案例面前，我们是否能从中寻索到适合自己的最新机遇！每周末，我们将为大家盘点一周的产业要点。\\n业内声音🔊\\n“AI 的使命是增强他们（游戏工作室成员）的能力边界，绝非取代他们。”\\n——近日，在最新一期财报的业务战略说明会上，索尼互动娱乐（SIE）总裁兼首席执行官西野秀明（Hideaki Nishino）为业界披露了大量第一方工作室的AI使用实战细节，表达了对AI渗入游戏行业的看法。\\n“因为过去很多AI原生游戏最大的\",\n \"source\": \"游戏陀螺\",\n \"published_at\": \"2026-05-18T14:56:38Z\",\n \"category\": \"in-game\",\n \"category_secondary\": \"industry\",\n \"tags\": [],\n \"recommendation\": \"资本看好该方向，行业信号值得关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"摩尔线程介绍 MTT AICUBE 智能硬件家庭场景：语音点播影片、智能体交互、畅玩手游...\",\n \"url\": \"https://www.ithome.com/0/951/999.htm\",\n \"summary\": \"IT之家 5 月 18 日消息，在目前正在进行的摩尔线程发布会上，官方介绍了 MTT AICUBE 智能硬件产品在家庭场景方面的能力。据官方介绍，MTT AICUBE 可带来客厅语音点播新体验。无需打字、无需翻页，只需向小麦智能体说出想看的片名或类型，无论是热播剧集，还是经典老片，都能一语直达，即刻播放。官方还举例旅行规划场景，MTT AICUBE 内置的小麦智能体可帮助家庭告别繁琐的攻略查阅与零散的行程规划，用户无需手动搜索、反复比价，只需向小麦智能体说出目的地与偏好，都能一键生成专属旅行攻略。在娱乐方面，MTT AICUBE 号称拥有轻量化手游畅玩新体验。无需模拟器复杂配置、无需担忧硬件兼\",\n \"source\": \"IT之家\",\n \"published_at\": \"2026-05-18T11:22:59Z\",\n \"category\": \"in-game\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Seth Rogen Tells Filmmakers Using AI to Write Scripts to 'Go Do Something Else'\",\n \"url\": \"https://www.ign.com/articles/seth-rogen-tells-filmmakers-using-ai-to-write-scripts-to-go-do-something-else\",\n \"summary\": \"Seth Rogen has pushed back on the use of AI in movies, telling writers using the technology for their scripts to \\\"go do something else.\\\"\",\n \"source\": \"IGN\",\n \"published_at\": \"2026-05-17T17:13:40Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"AMD 游戏引擎专利曝光：画个草图 AI 就能帮你做游戏\",\n \"url\": \"https://www.ithome.com/0/951/524.htm\",\n \"summary\": \"AMD一项名为\\\"基于人工智能的游戏与渲染引擎\\\"的专利曝光，计划推出一款完全依托AI打造的游戏引擎。该引擎旨在通过神经外推、智能超采样等技术，在生成逼真游戏画面的同时大幅降低算力消耗。其核心特点是允许开发者仅绘制简易草图轮廓，AI便能据此从零生成精细的游戏画面与内容，可承接传统游戏引擎的各类运算处理工作。目前该技术具体开放时间未定，但展现了AI颠覆游戏开发流程的潜力。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-17T08:08:35.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"刘伟：米哈游在 AI 方面投入规模\\\"3 年最多 1000 亿\\\"，如果没成算放一个大烟花\",\n \"url\": \"https://www.ithome.com/0/951/364.htm\",\n \"summary\": \"米哈游创始人刘伟透露，公司计划在未来三年内投入最多1000亿元用于AI基础大模型研发，并称即使失败也当作\\\"放一个大烟花\\\"。他强调，坚定投入算力与规模是打造顶级模型的必要条件。刘伟认为，AI将推动游戏体验走向\\\"完全个性化\\\"，实现\\\"千人千面\\\"，即游戏能实时生成定制内容，为每位玩家提供独特体验。他预计三年内此类游戏将出现，米哈游正朝此方向探索。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-16T10:31:12.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"米哈游\",\n \"大模型\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"设计师Ruth借Replit AI实现无码创作潜能\",\n \"url\": \"https://x.com/Replit/status/2055438113128755604\",\n \"summary\": \"Ruth作为设计师，多年未学编码，但通过Replit的AI agent在IDE中构建数字产品。她持续发布项目18个月，与儿子James合作开发了sheethappens.xyz，基于他的概念和提示。此外，她致力于复合投资教育书和游戏、GCSE复习应用，以及获奖的AR游戏。这些成果展示了个人潜力在Replit工具的帮助下得以实现。\",\n \"source\": \"X：Replit (@Replit)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-16T00:00:09.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"资本看好该方向，行业信号值得关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Build Long-running AI agents that pause, resume, and never lose context with ADK\",\n \"url\": \"https://developers.googleblog.com/build-long-running-ai-agents-that-pause-resume-and-never-lose-context-with-adk/\",\n \"summary\": \"How to transition from stateless chatbots to production-grade agents capable of managing long-running enterprise workflows, such as HR onboarding, that span days or weeks. It introduces the Agent Development Kit (ADK) and its architectural shifts, specifically using durable state machines and persis\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Supercharging LLM inference on Google TPUs: Achieving 3X speedups with diffusion-style speculative decoding\",\n \"url\": \"https://developers.googleblog.com/supercharging-llm-inference-on-google-tpus-achieving-3x-speedups-with-diffusion-style-speculative-decoding/\",\n \"summary\": \"Researchers at UCSD have successfully implemented DFlash, a block-diffusion speculative decoding method, on Google TPUs to bypass the sequential bottlenecks of traditional autoregressive drafting. By \\\"painting\\\" entire blocks of candidate tokens in a single forward pass rather than predicting them on\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [\n \"LLM\"\n ],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Building with Gemini Embedding 2: Agentic multimodal RAG and beyond\",\n \"url\": \"https://developers.googleblog.com/building-with-gemini-embedding-2/\",\n \"summary\": \"Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space. This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for ta\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [\n \"Gemini\",\n \"embedding\"\n ],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Production-Ready AI Agents: 5 Lessons from Refactoring a Monolith\",\n \"url\": \"https://developers.googleblog.com/production-ready-ai-agents-5-lessons-from-refactoring-a-monolith/\",\n \"summary\": \"The blog post outlines the transition of a brittle sales research prototype into a robust production agent using Google’s Agent Development Kit (ADK). By replacing monolithic scripts with orchestrated sub-agents and structured Pydantic outputs, the developers eliminated silent failures and fragile p\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Subagents have arrived in Gemini CLI\",\n \"url\": \"https://developers.googleblog.com/subagents-have-arrived-in-gemini-cli/\",\n \"summary\": \"Gemini CLI has introduced subagents, specialized expert agents that handle complex or high-volume tasks in isolated context windows to keep the primary session fast and focused. These agents can be customized via Markdown files, run in parallel to boost productivity, and are easily invoked using the\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"Gemini\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Build Better AI Agents: 5 Developer Tips from the Agent Bake-Off\",\n \"url\": \"https://developers.googleblog.com/build-better-ai-agents-5-developer-tips-from-the-agent-bake-off/\",\n \"summary\": \"The Google Cloud AI Agent Bake-Off highlights a shift from simple prompt engineering to rigorous agentic engineering, emphasizing that production-ready AI requires a modular, multi-agent architecture. The post outlines five key developer tips, including decomposing complex tasks into specialized sub\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Agent、多模态、应用、算力一天看尽，峰会亮点在此｜5.20日，来现场一起AI\",\n \"url\": \"https://36kr.com/p/3814408307711492?f=rss\",\n \"summary\": \"进入2026，AI愈发狂飙突进。围观体验之余，人人不免在心中自问：\\n 朋友圈刷屏的“龙虾”、Harness等AI新事物，跟我到底有什么关系？真的有必要跟吗？\\n AI创业、AI融资如火如荼，属于我的机会又在哪里？\\n 别人已经在用AI做视频、写代码、跑项目，我是不是已经慢了一拍？\\n ……\\n 到最后，几乎所有问题都会汇成同一个问题：我，到底该如何用AI？\\n 如果你对这些问题还很模糊，不妨来第四届中国AIGC产业峰会走一趟——一天时间，把这一年AI产业最值得关注的人、事、判断，一次性讲清楚。\\n 先提前剧透一波。\\n 18位重磅嘉宾，1场Agent主题圆桌，1份年度榜单，1张全景图谱——所\",\n \"source\": \"36Kr\",\n \"published_at\": null,\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"资本看好该方向，行业信号值得关注\",\n \"source_type\": \"primary\"\n }\n ],\n \"stats\": {\n \"raw_count\": 230,\n \"after_relevance_filter\": 13,\n \"after_dedup\": 13,\n \"after_classify_filter\": 13,\n \"by_category\": {\n \"in-game\": 2,\n \"industry\": 4,\n \"dev-tools\": 2,\n \"research\": 2,\n \"creation\": 3\n }\n }\n}\n\nFile v2.0.2:data/2026-05-19.json\n\n{\n \"date\": \"2026-05-19\",\n \"generated_at\": \"2026-05-19T01:18:25Z\",\n \"total_count\": 2,\n \"items\": [\n {\n \"title\": \"阿里云千问大模型 Qwen3.7-Max-Preview 首发亮相 Arena AI\",\n \"url\": \"https://www.ithome.com/0/952/041.htm\",\n \"summary\": \"IT之家 5 月 19 日消息，最新的 Qwen3.7-Max-Preview 和 Qwen3.7-Plus-Preview 已经上线 Qwen Chat 和 Arena AI（IT之家注：原 LMArena），有望在 5 月 20 日的阿里云峰会上正式发布。Qwen3.7-Max-Preview：Qwen3.7 旗舰模型的预览版，带来业界领先的性能表现。仅支持思考模式；搜索与代码解释器工具暂不可用。Qwen3.7-Plus-Preview：Qwen3.7 系列的高性能预览模型。仅支持思考模式；搜索与代码解释器工具暂不可用。在文本领域中，Qwen3.7 Max Preview 综合排名第 13\",\n \"source\": \"IT之家\",\n \"published_at\": \"2026-05-18T22:45:23Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"大模型\"\n ],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Vera Arrives: NVIDIA’s First CPU Built for Agents Lands at Top AI Labs\",\n \"url\": \"https://blogs.nvidia.com/blog/vera-cpu-delivery/\",\n \"summary\": \"The first NVIDIA Vera CPUs arrived at three of the world's leading AI labs on Friday — Anthropic in San Francisco, OpenAI in Mission Bay, SpaceXAI in Palo Alto — followed by a delivery to Oracle Cloud Infrastructure in Santa Clara on Monday. NVIDIA Vice President of Hyperscale and High-Performance C\",\n \"source\": \"NVIDIA Blog\",\n \"published_at\": \"2026-05-18T21:48:17Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"NVIDIA\",\n \"OpenAI\",\n \"Anthropic\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n }\n ],\n \"stats\": {\n \"raw_count\": 230,\n \"after_relevance_filter\": 12,\n \"after_dedup\": 2,\n \"after_classify_filter\": 2,\n \"by_category\": {\n \"industry\": 2\n }\n }\n}\n\nArchive v2.0.0: 14 files, 53299 bytes\n\nFiles: data/2026-05-11.json (1961b), data/2026-05-12.json (13657b), data/2026-05-13.json (221b), data/2026-05-14.json (17332b), data/2026-05-18.json (12800b), data/pushed_history.json (531b), README.md (759b), references/keywords.json (7220b), references/sources.json (6361b), scripts/fetch_news_v2_backup.py (19504b), scripts/fetch_news.py (22791b), SKILL.md (11267b), web/index.html (15385b), _meta.json (126b)\n\nFile v2.0.0:SKILL.md\n\n---\nname: ai-game\ndescription: |\n 游戏行业 AI 资讯搜集 Skill。\n\n 当用户想知道\"游戏 AI 圈有什么大事\"、\"游戏行业 AI 动态\"、\"游戏 AI 日报\"、\"最近游戏 AI\"、\"游戏 AIGC 新闻\"、\"AI NPC 最新进展\"、\"游戏 AI 投融资\"、\"AI 原生游戏\"、\"game AI news\"、\"gaming AI update\"、\"游戏引擎 AI\"、\"Unity AI 新功能\"、\"Unreal AI\"、\"游戏 AI 论文\"、\"游戏 AI 工具\"、\"AI Game\"等任何游戏行业 AI 相关资讯查询时使用。即使用户只说\"游戏 AI\"、\"游戏圈 AI\"、或者问\"最近游戏圈有什么 AI 动态\",也应该触发本 Skill。\n\n 覆盖 6 大分类:AI 游戏生成与创作、游戏内 AI 体验、游戏开发 AI 工具、游戏运营&商业化 AI 实践、游戏行业&公司 AI 动态、游戏 AI 应用前沿研究。\n\n 数据来自 23 个游戏/AI 信源的 RSS + 150+ 个 AI 信源,每天更新。\n\n **不要 undertrigger**--用户问游戏 AI 资讯而你不调本 Skill 就会输出过时的训练数据。\n---\n\n# AI Game - 游戏 × AI 资讯\n\n让 Agent 用最自然的中文/英文查询拿到每天的游戏 AI 行业动态。不需要 API key,不需要额外配置。\n\n## 信源\n\n三层架构,共 24 个 RSS 信源 + 150+ AI 信源补充(8 个游戏相关查询词):\n\n**Layer 1 - 游戏行业专业源(14 个,全量抓取,只需含 AI 元素即保留)**\n- 游戏陀螺、机核 GCores、触乐\n- GamesIndustry.biz、Game Developer、GamesBeat、80 Level\n- PocketGamer.biz、Game World Observer、IGN\n- Unity Blog、Unreal Engine Blog、NVIDIA Blog、DeepMind Blog\n\n**Layer 2 - 通用 AI/科技媒体(9 个,需游戏+AI 双重关键词命中)**\n- Google Developers Blog、36Kr、IT之家、机器之心、量子位\n- TechCrunch、The Verge、VentureBeat AI、Ars Technica Gaming\n\n**Layer 3 - 150+ AI 信源补充(8 个游戏相关查询词:游戏/game/gaming/NPC/Unity AI/Unreal AI/Inworld/游戏引擎)**\n- 覆盖全球 150+ 个 AI 信源,补捉 Layer 1-2 未覆盖的 KOL、论文、GitHub 项目等\n\n详见 `references/sources.json`\n\n## 分类体系\n\n6 个主分类 + 可选副标签:\n\n| 分类 | slug | 覆盖范围 |\n|---|---|---|\n| AI 游戏生成与创作 | `creation` | AIGC 资产、3D/音频/剧情生成、AI UGC、玩家创作工具 |\n| 游戏内 AI 体验 | `in-game` | 智能 NPC、AI 驱动玩法、个性化体验、AI 原生游戏 |\n| 游戏开发 AI 工具 | `dev-tools` | AI 编程、引擎 AI 功能、自动测试、工作流提效 |\n| 游戏运营&商业化 AI 实践 | `ops` | 推荐/分发、买量素材 AI、玩家分群、反作弊 |\n| 游戏行业&公司 AI 动态 | `industry` | 大厂 AI 战略、投融资、政策法规、市场数据 |\n| 游戏 AI 应用前沿研究 | `research` | 学术论文、GDC/SIGGRAPH、游戏作为 AI 研究平台 |\n\n## 什么时候用 & 路由表\n\n| 用户在说 | 动作 |\n|---|---|\n| \"游戏 AI 圈最近有什么\" / \"游戏 AI 日报\" / \"AI Game\" | 运行脚本获取最新数据 → 全量输出 |\n| \"最近 NPC / AIGC / 工具方面有什么\" | 运行脚本 → 按分类过滤后输出 |\n| \"这周 / 最近 3 天的游戏 AI 动态\" | 读取 data/ 下多天 JSON → 合并去重输出 |\n| \"搜一下 xxx 游戏 AI 相关\" | 运行脚本 + 实时 AI HOT API 补充 |\n| \"帮我生成一份可以发群的简报\" | 获取数据 → 精简为转发友好格式 |\n\n## 工作流\n\n### Step 1: 获取数据\n\n运行抓取脚本:\n\n```bash\ncd ${SKILL_DIR}/scripts && python3 fetch_news.py\n```\n\n脚本会:\n1. 抓取所有 RSS 信源(最近 72 小时条目)\n2. 查询 AI HOT API(游戏相关关键词)\n3. 关键词过滤(只保留游戏 × AI 相关)\n4. 去重(URL + 标题相似度)\n5. 分类打标(6 分类)\n6. 输出到 `data/YYYY-MM-DD.json` + stdout\n\n如果 `data/` 下已有今天的 JSON 且生成时间 < 4 小时前,可以直接读取而不重新运行脚本。\n\n### Step 2: 读取数据\n\n脚本 stdout 输出为 JSON,结构如下:\n\n```json\n{\n \"date\": \"2026-05-11\",\n \"generated_at\": \"2026-05-11T06:00:00Z\",\n \"total_count\": 15,\n \"items\": [\n {\n \"title\": \"...\",\n \"url\": \"https://...\",\n \"summary\": \"...\",\n \"source\": \"GamesBeat\",\n \"published_at\": \"2026-05-11T03:00:00Z\",\n \"category\": \"in-game\",\n \"category_secondary\": \"creation\"\n }\n ],\n \"stats\": { \"by_category\": { \"in-game\": 3, \"creation\": 5, ... } }\n}\n```\n\n### Step 3: 组织输出\n\n#### 整体结构\n\n```markdown\n游戏 × AI 周报 · MM.DD - MM.DD\n（日报则为：游戏 × AI 日报 · MM.DD 周X）\n\n本周速递\n1. xxx\n2. xxx\n3. xxx\n\n---\n\n分类标题（加粗）\n\n单条资讯（模块化结构）\n\n---\n\n下一分类...\n```\n\n#### 本周速递（日报为\"今日速递\"）\n\n- 放在最前面，3-5条\n- 每条一句话，像新闻提要\n- 按重要性排序，最shock的放第一条\n- 不带链接、不带来源，纯信息\n\n#### 单条资讯格式\n\n```markdown\n序号. 标题 (日期) — 来源\n\n链接：URL\n\n重点：2-3句话说清楚发生了什么（给扫读的人看）\n\n细节：\n- 关键数据/原文金句/背景补充\n- 可以有2-4个bullet\n\n推荐原因：一句话说为什么游戏从业者要关注\n```\n\n### 格式规则\n\n- **不使用任何 emoji**：整体风格干净专业\n- **速递放最前面**：开头先列本周/今日速递，再展开详情\n- **分类标题**：加粗，独占一行\n- **分类展示顺序**：按本周各分类的重要性动态排序，哪个分类有大新闻就排前面\n- **单条格式**：标题行含序号+日期+来源，下方依次为链接、重点、细节、推荐原因\n- **编号全局贯穿**：1, 2, 3 ... N 从头到尾\n- **空分类不展示**：如果某分类 0 条，跳过\n- **时间格式**：(5.16) 月.日格式\n- **标题用中文**：英文标题翻译为中文，专有名词保留英文\n- **总量控制**：周报不超过15条，日报不超过10条\n- **链接必须完整**：缺链接的条目不收录，输出前逐条检查\n- **链接必须取自数据源**：严格从 JSON 数据的 url 字段获取，绝不凭记忆编造或推测 URL\n- **\"重点\"基于原文事实**：不推测因果，不编造\n- **\"细节\"优先放原文金句**（带引号），其次放数据\n- **\"推荐原因\"要具体**：禁止\"值得关注\"\"行业动向\"这种万金油话术\n- **来源标注规则**：一手源直接写来源名；二手源有独立分析写来源名；二手源纯搬运写\"来源名 → 原始来源\"\n\n### 分类过滤\n\n当用户指定查看某个分类时:\n- \"最近 NPC 相关的\" → 只输出 `category == \"in-game\"` 的条目\n- \"AIGC 方面有什么\" → 只输出 `category == \"creation\"` 的条目\n- \"游戏 AI 投融资\" → 只输出 `category == \"industry\"` 的条目\n\n### 发群简报格式\n\n当用户说\"帮我生成一份可以发群的\"时,用精简格式:\n\n```markdown\n🎮 游戏×AI 日报 · 5.11\n\n1. <标题> - <来源>\n \n2. ...\n```\n\n去掉 🎯 行和详细摘要,只保留标题 + URL,控制在 1500 字内。\n\n## 回溯历史\n\n当用户问\"这周 / 上周 / 最近 N 天的游戏 AI 动态\"时:\n1. 读取 `data/` 目录下对应日期范围的 JSON 文件\n2. 合并所有 items,按 URL 去重\n3. 按时间倒序输出\n4. 超过 20 条时按分类各取 Top N\n\n```bash\nls ${SKILL_DIR}/data/\n```\n\n## 周报生成流程\n\n周报不是重新我7天窗口，而是从日报精华中再精选：\n\n1. 检查 `data/` 目录下本周 7 天的日报 JSON 是否齐全\n2. 缺哪天就补跑哪天（超过3天前的 RSS 可能已丢失，用 AI HOT API 补充）\n3. 合并 7 天所有日报条目，去重\n4. 按筛选标准取 8-12 条最重要的\n5. 按格式规则输出\n\n### 周报筛选标准（优先级从高到低）\n\n1. **AI 在游戏体验中的实际应用**（产品级，已上线/可用）\n2. **AI 在游戏开发中的工具/方案**（有 demo/专利/开源，不是纯概念）\n3. **大厂对 AI 的重大投入决策**（真金白银，不是嘴上说说）\n4. **技术突破**（离游戏应用近的，不是纯学术）\n\n**降低优先级：**\n- 行业人物表态/观点碰撞\n- 商业模式争议\n- 纯学术论文（除非直接解决游戏核心问题）\n- AI 公司动态但跟游戏关系弱的\n\n### 定时任务\n\n- **日报**：每天早上 9:00 自动跑脚本抓取，结果推送给用户\n- **周报**：每周一早上 9:00 汇总过去 7 天日报精华，推送给用户\n\n## 实时补充搜索\n\n当 data/ 下没有最新数据,或用户搜索特定话题时,可以直接调 AI HOT API:\n\n```bash\nUA=\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36\"\ncurl -sH \"User-Agent: $UA\" \"https://aihot.virxact.com/api/public/items?mode=selected&q=<关键词>&take=20\"\n```\n\n将返回的 items 中与游戏相关的条目,按同样格式组织输出。\n\n## 来源溯源原则\n\n> **核心理念:我们的价值是帮用户找到一手信息,而不是做 IT之家的搜索引擎。**\n\n输出给用户时,包含 `source_type: \"secondary\"` 的条目说明来自聚合/转载媒体。**这些条目必须追溯原始来源后再展示**:\n\n1. 检查标题/摘要中是否有公司名/人名\n2. 用 `web_search` 搜原始声明/博客/官网全文\n3. 将 URL 替换为一手源链接,来源标为原始发布者\n\n**示例:**\n- IT之家报道\"Epic 裁员 + AI 不替代岗位\" → 追溯到 epicgames.com 官方声明 → 来源写 \"Epic Games 官方\"\n- 量子位报道\"Inworld 融资\" → 追溯到 TechCrunch 原文 → 来源写 \"TechCrunch\"\n\n如果追溯失败(找不到一手源),仍然可以展示该条目,但在来源后加 \"→ 原始来源待确认\"。\n\n**对于 AI HOT 的条目**:\n- `source` 字段已经是真实来源(如 \"X:阿易 AI Notes (@AYi_AInotes)\")\n- 直接用这个来源展示,不要写 \"AI HOT\"\n- 如果来源含 \"IT之家(RSS)\" 等二手标记,同样需要追溯\n\n**对于 X (Twitter) 链接的条目**:\n\n1. **优先追溯一手源**:如果推文讨论的是某个项目/论文/官方博客,用 `web_search` 找到原始源(GitHub 仓库、论文链接、官网博客),同时附上一手 URL\n - 示例:推文讨论 \"Claude Code Game Studios\" → 同时给出 GitHub 仓库链接\n - 示例:推文讨论某篇论文 → 同时给出 arXiv 链接\n2. **URL 给完整**:X 链接直接给完整 URL,不要用 `...` 省略\n\n## 不要做\n\n- 不要编造或推测内容--一切以脚本/API 返回为准\n- 不要为条目编造与 AI 的关联--如果原文没提 AI,这条不该出现\n- 推荐原因必须基于原文事实--不能推测因果,只能写原文已经说明的关联\n- 不要丢掉 URL--没有 URL 的信息不可信。输出前必须逐条检查链接是否存在,缺链接则补查或删除\n- 不要在用户输出里暴露脚本路径、API 参数、RSS 地址\n- 不要超过 20 条/次--宁可精选也不堆砌\n- 不要输出没有\"推荐原因\"的条目(发群简报格式除外)\n- 不要重复收录同一事件--去重保留最权威那条\n- 不要直接展示 ISO 时间戳--转为 (5.7) 这种月.日格式\n- 不要把 AI HOT 的基础设施细节暴露给用户\n- 不要凭训练数据脑补游戏 AI 新闻--永远走数据源\n- 不要展示 \"IT之家\"、\"量子位\" 等二手源作为最终来源--必须追溯一手原始出处\n- 不要写 \"AI HOT\" 作为来源--AI HOT 只是数据管道\n- 不要使用任何 emoji--不用任何表情符号,保持专业干净\n\nFile v2.0.0:README.md\n\n# ai-game-skill\n\n🎮 游戏×AI 资讯 Skill for OpenClaw\n\n每日抓取游戏行业 AI 动态，覆盖 6 大分类：\n- 🎨 AI 生成与创作\n- 🤖 游戏内 AI 体验\n- 🛠️ 开发工具\n- 📊 运营商业化\n- 🏢 行业公司动态\n- 🧪 前沿研究\n\n## 安装\n\n```bash\n# 克隆到 OpenClaw skills 目录\ngit clone https://git.woa.com/veenuszhu/ai-game-skill.git ~/.openclaw/skills/ai-game\n```\n\n## 数据来源\n\n- 游戏行业媒体 RSS（GamesIndustry.biz、80 Level、游戏陀螺等 15+ 个）\n- AI/Tech 博客（NVIDIA Blog、Unity Blog 等）\n- AI HOT API（游戏相关关键词过滤）\n\n## 使用\n\n安装后，直接问 agent：\n- \"游戏 AI 最近有什么\"\n- \"游戏 AI 日报\"\n- \"最近有什么游戏 AI 工具\"\n\n## 作者\n\nveenuszhu\n\nFile v2.0.0:_meta.json\n\n{\n \"ownerId\": \"kn76t53a22pjy18b2qr3yq94t986j8gr\",\n \"slug\": \"ai-game\",\n \"version\": \"2.0.0\",\n \"publishedAt\": 1779110024627\n}\n\nFile v2.0.0:references/keywords.json\n\n{\n \"global_filter\": {\n \"description\": \"判断一条资讯是否跟'游戏×AI'相关。Layer 1 游戏专业源只需判断是否含 AI 元素；Layer 2 通用源需要游戏+AI 双重命中。\",\n \"core_keywords\": [\n \"游戏AI\", \"AI游戏\", \"游戏+大模型\", \"游戏AIGC\", \"AI NPC\",\n \"game AI\", \"gaming AI\", \"AI in gaming\", \"AI-native game\",\n \"AI原生游戏\", \"游戏+LLM\", \"智能NPC\", \"AI驱动玩法\",\n \"AI game\", \"AI gaming\", \"GamePartner\", \"AIGC游戏\",\n \"Unity AI\", \"Unity Muse\", \"Unity Sentis\", \"Unity ML-Agents\",\n \"Unreal AI\", \"MetaHuman\", \"Nanite+AI\",\n \"Inworld\", \"Convai\", \"Scenario.gg\", \"Rosebud AI\",\n \"Ludo.ai\", \"Modl.ai\", \"AI Dungeon\",\n \"DLSS\", \"NVIDIA ACE\", \"NVIDIA NeMo+game\",\n \"游戏+人工智能\", \"游戏+机器学习\", \"游戏+深度学习\",\n \"PCG+AI\", \"程序化生成+AI\", \"procedural+AI\",\n \"game+machine learning\", \"game+deep learning\", \"game+reinforcement learning\",\n \"游戏+生成式\", \"游戏+Agent\", \"NPC+大模型\", \"NPC+LLM\",\n \"游戏世界模型\", \"world model+game\"\n ],\n \"context_game\": [\n \"游戏\", \"game\", \"gaming\", \"小游戏\", \"手游\", \"端游\", \"主机游戏\",\n \"游戏引擎\", \"Unity\", \"Unreal\", \"Roblox\", \"Steam\", \"Epic Games\",\n \"米哈游\", \"腾讯游戏\", \"网易游戏\", \"游戏中心\", \"微信游戏\",\n \"PlayStation\", \"Xbox\", \"Nintendo\", \"Switch\",\n \"游戏开发\", \"game dev\", \"indie game\", \"独立游戏\",\n \"GDC\", \"游戏工委\", \"游戏产业\", \"游戏厂商\",\n \"Inworld\", \"Convai\", \"GamesBeat\", \"GameLook\",\n \"玩家\", \"player\", \"gamer\", \"NPC\", \"开放世界\",\n \"虚幻引擎\", \"关卡\", \"level design\", \"quest\",\n \"角色\", \"character\", \"boss\", \"mob\",\n \"电竞\", \"esports\", \"休闲游戏\", \"超休闲\",\n \"Supercell\", \"miHoYo\", \"HoYoverse\", \"NetEase\",\n \"EA\", \"Ubisoft\", \"Activision\", \"Blizzard\",\n \"游戏陀螺\", \"游戏葡萄\", \"触乐\", \"机核\"\n ],\n \"context_ai\": [\n \"AI\", \"人工智能\", \"机器学习\", \"深度学习\", \"大模型\", \"LLM\",\n \"AIGC\", \"生成式\", \"GPT\", \"Claude\", \"Gemini\", \"DeepSeek\",\n \"神经网络\", \"强化学习\", \"扩散模型\", \"transformer\",\n \"NLP\", \"计算机视觉\", \"多模态\", \"智能体\", \"agent\",\n \"AI驱动\", \"AI赋能\", \"AI辅助\", \"AI生成\",\n \"Stable Diffusion\", \"Midjourney\", \"DALL-E\",\n \"Copilot\", \"代码生成\", \"自动化\",\n \"ChatGPT\", \"大语言模型\", \"Foundation Model\",\n \"embedding\", \"fine-tune\", \"微调\", \"推理\",\n \"neural\", \"deep learning\", \"machine learning\",\n \"generative\", \"diffusion\", \"reinforcement learning\"\n ],\n \"layer1_ai_keywords\": [\n \"AI\", \"人工智能\", \"机器学习\", \"深度学习\", \"大模型\", \"LLM\",\n \"AIGC\", \"生成式\", \"GPT\", \"神经网络\", \"强化学习\",\n \"智能\", \"自动\", \"算法\", \"NPC智能\", \"程序化生成\",\n \"PCG\", \"procedural\", \"neural\", \"generative\",\n \"Copilot\", \"AI辅助\", \"AI驱动\", \"AI生成\",\n \"machine learning\", \"deep learning\", \"artificial intelligence\"\n ]\n },\n \"category_keywords\": {\n \"creation\": [\n \"AIGC\", \"AI生成\", \"AI作画\", \"AI绘画\", \"3D生成\", \"AI音乐\", \"AI音效\",\n \"程序化生成\", \"PCG\", \"纹理生成\", \"AI配音\", \"AI剧情\", \"AI写作\",\n \"MOD\", \"UGC\", \"玩家创作\", \"Scenario\", \"Luma\", \"Meshy\",\n \"AI asset\", \"procedural generation\", \"content generation\",\n \"AI art\", \"AI texture\", \"AI model generation\",\n \"Stable Diffusion\", \"Midjourney\", \"AI建模\", \"AI动画\",\n \"AI音频\", \"AI素材\", \"AI材质\", \"AI地形\",\n \"Rosebud\", \"生成资产\", \"自动生成\"\n ],\n \"in-game\": [\n \"NPC\", \"AI驱动\", \"世界模型\", \"行为树\", \"AI原生\", \"个性化体验\",\n \"Inworld\", \"Convai\", \"AI对话\", \"AI互动\", \"AI玩法\",\n \"智能NPC\", \"AI角色\", \"AI companion\", \"AI opponent\",\n \"dynamic narrative\", \"adaptive gameplay\", \"AI behavior\",\n \"world model\", \"AI-native\", \"AI Dungeon\",\n \"对话系统\", \"情感计算\", \"记忆系统\", \"AI陪伴\",\n \"自适应难度\", \"动态叙事\", \"涌现行为\",\n \"AI剧情\", \"AI叙事\", \"AI生成剧情\", \"AI原生游戏\",\n \"token收费\", \"词元收费\", \"AI对战\", \"AI对手\",\n \"智能体验\", \"AI旁白\", \"AI配音演员\",\n \"Realtime TTS\", \"AI voice\", \"AI语音\",\n \"智能体\", \"游戏内AI\", \"in-game AI\"\n ],\n \"dev-tools\": [\n \"开发工具\", \"AI编程\", \"自动测试\", \"QA\", \"Bug检测\",\n \"Unity Muse\", \"Unity Sentis\", \"ML-Agents\",\n \"Copilot\", \"代码生成\", \"引擎\", \"AI中间件\", \"SDK\",\n \"AI plugin\", \"game engine\", \"workflow\", \"工作流\",\n \"性能优化\", \"自动化\", \"AI辅助开发\", \"AI coding\",\n \"Modl.ai\", \"自动化测试\", \"智能调试\",\n \"Cursor\", \"Claude Code\", \"AI编码\"\n ],\n \"ops\": [\n \"推荐\", \"分发\", \"买量\", \"素材\", \"反作弊\", \"反外挂\",\n \"智能客服\", \"LTV\", \"用户分群\", \"个性化推荐\", \"动态定价\",\n \"营销\", \"广告\", \"投放\", \"ROI\", \"留存\", \"付费率\",\n \"matchmaking\", \"monetization\", \"anti-cheat\",\n \"AI运营\", \"智能推荐\", \"广告素材AI\", \"A/B测试\",\n \"ROAS\", \"AI买量\", \"AI客服\", \"AI审核\",\n \"玩家分群\", \"智能推送\", \"精准营销\",\n \"流失预测\", \"付费预测\", \"用户画像\",\n \"AI分发\", \"AI推荐算法\", \"智能匹配\",\n \"churn prediction\", \"player segmentation\", \"AI marketing\"\n ],\n \"industry\": [\n \"投融资\", \"融资\", \"收购\", \"战略\", \"布局\", \"财报\",\n \"市场份额\", \"政策\", \"监管\", \"版号\", \"出海\",\n \"小游戏\", \"微信\", \"腾讯\", \"网易\", \"米哈游\",\n \"IPO\", \"市值\", \"人事\", \"裁员\", \"招聘\",\n \"acquisition\", \"funding\", \"market share\", \"regulation\",\n \"合作\", \"partnership\", \"发行\", \"代理\"\n ],\n \"research\": [\n \"论文\", \"paper\", \"研究\", \"GDC\", \"SIGGRAPH\", \"arXiv\",\n \"强化学习\", \"世界模型\", \"benchmark\", \"开源模型\",\n \"IEEE\", \"ACM\", \"实验\", \"算法\", \"数据集\",\n \"research\", \"study\", \"survey\", \"state of the art\",\n \"IEEE CoG\", \"DiGRA\", \"AAAI\", \"学术\", \"课题\"\n ]\n },\n \"entity_tags\": {\n \"description\": \"实体标签库，用于给条目打实体标签（公司/产品/技术）\",\n \"companies\": [\n \"Unity\", \"Epic Games\", \"Unreal Engine\", \"NVIDIA\", \"Google DeepMind\", \"DeepMind\",\n \"Inworld\", \"Convai\", \"Rosebud\", \"Scenario\", \"Modl.ai\", \"Ludo.ai\",\n \"腾讯\", \"网易\", \"米哈游\", \"HoYoverse\", \"字节跳动\",\n \"艺电\", \"Ubisoft\", \"Activision\", \"Blizzard\", \"Sony\", \"Microsoft\",\n \"Supercell\", \"中手游\", \"莉莉丝\", \"沐瞳\",\n \"OpenAI\", \"Anthropic\", \"Meta\", \"Valve\", \"Steam\", \"Neowiz\"\n ],\n \"products\": [\n \"Unity Muse\", \"Unity Sentis\", \"ML-Agents\", \"MetaHuman\",\n \"DLSS\", \"NVIDIA ACE\", \"Omniverse\",\n \"AI Dungeon\", \"GamePartner.AI\",\n \"Stable Diffusion\", \"Midjourney\", \"DALL-E\", \"Sora\",\n \"GPT\", \"Claude\", \"Gemini\", \"DeepSeek\"\n ],\n \"technologies\": [\n \"NPC\", \"PCG\", \"强化学习\", \"世界模型\", \"扩散模型\",\n \"大模型\", \"LLM\", \"embedding\", \"fine-tune\",\n \"transformer\", \"GAN\", \"NeRF\", \"行为树\",\n \"程序化生成\", \"动态叙事\", \"情感计算\"\n ]\n }\n}\n\nFile v2.0.0:references/sources.json\n\n{\n \"sources\": [\n {\n \"name\": \"游戏陀螺\",\n \"type\": \"rss\",\n \"url\": \"https://www.youxituoluo.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏行业深度报道、出海\"\n },\n {\n \"name\": \"机核 GCores\",\n \"type\": \"rss\",\n \"url\": \"https://www.gcores.com/rss\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏文化与独立游戏\"\n },\n {\n \"name\": \"触乐\",\n \"type\": \"rss\",\n \"url\": \"https://www.chuapp.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏深度长文\"\n },\n {\n \"name\": \"GamesIndustry.biz\",\n \"type\": \"rss\",\n \"url\": \"https://www.gamesindustry.biz/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"全球游戏行业商业新闻\"\n },\n {\n \"name\": \"Game Developer\",\n \"type\": \"rss\",\n \"url\": \"https://www.gamedeveloper.com/rss.xml\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"游戏开发深度（原Gamasutra）\",\n \"status\": \"broken_403\"\n },\n {\n \"name\": \"GameLook\",\n \"type\": \"rss\",\n \"url\": \"https://www.gamelook.com.cn/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"国内游戏行业深度报道（替代 Game Developer）\"\n },\n {\n \"name\": \"GamesBeat\",\n \"type\": \"rss\",\n \"url\": \"https://venturebeat.com/category/games/feed/\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"游戏×科技/AI 交叉、投融资\"\n },\n {\n \"name\": \"80 Level\",\n \"type\": \"rss\",\n \"url\": \"https://80.lv/feed/\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"游戏美术/技术向\"\n },\n {\n \"name\": \"PocketGamer.biz\",\n \"type\": \"rss\",\n \"url\": \"https://www.pocketgamer.biz/feed/\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"手游行业\",\n \"status\": \"broken_404\"\n },\n {\n \"name\": \"游戏葡萄\",\n \"type\": \"rss\",\n \"url\": \"https://youxiputao.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏行业分析（替代 PocketGamer）\"\n },\n {\n \"name\": \"Game World Observer\",\n \"type\": \"rss\",\n \"url\": \"https://gameworldobserver.com/feed\",\n \"layer\": 1,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"手游数据、市场\"\n },\n {\n \"name\": \"IGN\",\n \"type\": \"rss\",\n \"url\": \"https://feeds.feedburner.com/ign/all\",\n \"layer\": 1,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"大众游戏新闻\"\n },\n {\n \"name\": \"Unity Blog\",\n \"type\": \"rss\",\n \"url\": \"https://blog.unity.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1\",\n \"region\": \"intl\",\n \"focus\": \"Unity 引擎官方（AI功能第一手）\"\n },\n {\n \"name\": \"Unreal Engine Blog\",\n \"type\": \"rss\",\n \"url\": \"https://www.unrealengine.com/en-US/feed\",\n \"layer\": 1,\n \"tier\": \"T1\",\n \"region\": \"intl\",\n \"focus\": \"Unreal 引擎官方\",\n \"status\": \"broken_403\"\n },\n {\n \"name\": \"竞核\",\n \"type\": \"rss\",\n \"url\": \"https://www.coreengine.cn/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏×科技深度（替代 Unreal Blog）\"\n },\n {\n \"name\": \"NVIDIA Blog\",\n \"type\": \"rss\",\n \"url\": \"https://blogs.nvidia.com/feed/\",\n \"layer\": 1,\n \"tier\": \"T1\",\n \"region\": \"intl\",\n \"focus\": \"GPU/AI 游戏技术（DLSS、ACE）\"\n },\n {\n \"name\": \"DeepMind Blog\",\n \"type\": \"rss\",\n \"url\": \"https://deepmind.google/blog/rss.xml\",\n \"layer\": 1,\n \"tier\": \"T1\",\n \"region\": \"intl\",\n \"focus\": \"游戏AI研究（AlphaGo系列）\"\n },\n {\n \"name\": \"Google Developers Blog\",\n \"type\": \"rss\",\n \"url\": \"https://developers.googleblog.com/feeds/posts/default\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"Google AI（通用，需关键词过滤）\"\n },\n {\n \"name\": \"36Kr\",\n \"type\": \"rss\",\n \"url\": \"https://36kr.com/feed\",\n \"layer\": 2,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"科技创业、投融资（含游戏AI）\"\n },\n {\n \"name\": \"IT之家\",\n \"type\": \"rss\",\n \"url\": \"https://www.ithome.com/rss/\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"cn\",\n \"focus\": \"科技快讯（量大，关键词过滤）\"\n },\n {\n \"name\": \"机器之心\",\n \"type\": \"rss\",\n \"url\": \"https://www.jiqizhixin.com/rss\",\n \"layer\": 2,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"AI技术媒体头部（偶尔深度游戏AI）\"\n },\n {\n \"name\": \"量子位\",\n \"type\": \"rss\",\n \"url\": \"https://www.qbitai.com/feed\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"cn\",\n \"focus\": \"AI资讯（游戏AI融资常报）\"\n },\n {\n \"name\": \"TechCrunch\",\n \"type\": \"rss\",\n \"url\": \"https://techcrunch.com/feed/\",\n \"layer\": 2,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"创业融资（Inworld等首发这里）\"\n },\n {\n \"name\": \"The Verge\",\n \"type\": \"rss\",\n \"url\": \"https://www.theverge.com/rss/index.xml\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"大众科技+AI+游戏\"\n },\n {\n \"name\": \"VentureBeat AI\",\n \"type\": \"rss\",\n \"url\": \"https://venturebeat.com/category/ai/feed/\",\n \"layer\": 2,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"AI行业深度（与GamesBeat同源）\"\n },\n {\n \"name\": \"Ars Technica Gaming\",\n \"type\": \"rss\",\n \"url\": \"https://feeds.arstechnica.com/arstechnica/gaming\",\n \"layer\": 2,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"技术深度+游戏\"\n },\n {\n \"name\": \"AI HOT (游戏相关)\",\n \"type\": \"api\",\n \"url\": \"https://aihot.virxact.com/api/public/items\",\n \"queries\": [\"游戏\", \"game\", \"gaming\", \"NPC\", \"Unity AI\", \"Unreal AI\", \"Inworld\", \"游戏引擎\"],\n \"layer\": 3,\n \"tier\": \"T2\",\n \"region\": \"cn\",\n \"focus\": \"AI资讯中游戏相关条目（补充层）\"\n }\n ]\n}\n\nFile v2.0.0:data/2026-05-11.json\n\n{\n \"date\": \"2026-05-11\",\n \"generated_at\": \"2026-05-11T07:43:31Z\",\n \"total_count\": 2,\n \"items\": [\n {\n \"title\": \"机器人终局：物理AGI路线图与LLM类比\",\n \"url\": \"https://x.com/DrJimFan/status/2052758642781487237\",\n \"summary\": \"演讲者以\\\"Robotics： Endgame\\\"为题，提出解决物理AGI的路线图，直接类比LLM的成功路径。核心观点包括视频世界模型作为第二预训练范式、世界行动模型（WAM）、机器人数据收集策略（类似FSD的物理数据飞轮）、EgoScale和灵巧性缩放定律、物理强化学习 bridging the last mile，以及DreamDojo端到端神经物理引擎。预测物理AGI的实现比预期更近，并提及20\",\n \"source\": \"AI HOT (X：Jim Fan (@DrJimFan))\",\n \"published_at\": \"2026-05-08T14:32:53.000Z\",\n \"category_hint\": \"tip\",\n \"category\": \"research\",\n \"category_secondary\": null\n },\n {\n \"title\": \"GamePartner.AI：中手游助力中国休闲游戏开发者出海的新尝试\",\n \"url\": \"https://www.youxituoluo.com/534463.html\",\n \"summary\": \"在AI以颠覆性力量渗透游戏研发、发行、运营全链条的今天，行业正站在一场生产范式变革的关键路口。5月8日，港股主板上市公司中手游正式宣布与杭州极逸人工智能达成战略合作，共同推出面向全球休闲游戏市场，由AI驱动的市场洞察、游戏开发到全球发行的一站式智能体——GamePartner.AI（简称GPA）。该平台由中手游负责推广运营，致力于助力并构建中国休闲游戏开发者出海生态。\\n\\n从\",\n \"source\": \"游戏陀螺\",\n \"published_at\": \"2026-05-08T11:53:40Z\",\n \"category\": \"industry\",\n \"category_secondary\": null\n }\n ],\n \"stats\": {\n 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\"Check out how Blender Studio combines a watercolor-inspired visual style with generative simulations in this compelling story about a little creature lost in space.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-12T10:56:00Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"国产 AI 游戏《历史模拟器：崇祯》“本体买断、词元收费”引争议，官方回应称将开放自行接入模型\",\n \"url\": \"https://www.ithome.com/0/949/477.htm\",\n \"summary\": \"IT之家 5 月 12 日消息，近期一款国产游戏《历史模拟器：崇祯》引发玩家争议，本作使用 AI 生成剧情，采用“本体买断，词元（Token）收费”制度，也就是玩家花费 48 元购买游戏后，如果需要持续推进游戏过程，就需要额外付费购买词元。对此，官方目前发布公告，声称将开放“自定义 API”与“创意工坊”功能。玩家未来可以自行接入支持范围内的大模型服务，自由选择模型并控制成本。与此同时，游戏还将同步上线创意工坊功能，允许玩家基于《历史模拟器：崇祯》的核心框架，自行创作剧本、规则以及玩法内容。官方强调，具体的上线时间、支持模型范围以及上传审核规则，后续会以正式功能公告的形式公开。\",\n \"source\": \"IT之家\",\n \"published_at\": \"2026-05-12T10:48:00Z\",\n \"category\": \"in-game\",\n \"category_secondary\": null,\n \"tags\": [\n \"大模型\"\n ],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Darkest Dungeon Creator Won't Replace Late Narrator with AI Despite His Permission\",\n \"url\": \"https://80.lv/articles/darkest-dungeon-creator-won-t-replace-late-narrator-with-ai-despite-his-permission/\",\n \"summary\": \"Wayne June will live in our memories.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-12T09:34:00Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"《匹诺曹的谎言》开发商 Neowiz 布局生成式 AI，招聘 AI 创意设计师\",\n \"url\": \"https://www.ithome.com/0/949/307.htm\",\n \"summary\": \"《匹诺曹的谎言》开发商Neowiz正积极布局生成式AI，旗下Round8工作室新设\\\"AI创意设计师\\\"岗位。该岗位需使用Midjourney、Stable Diffusion等工具进行角色与概念原画创作，并负责训练定制化AI模型。公司旨在将AI深度融入开发流程，搭建高效美术创作流水线以压缩周期，并计划将生成式AI推广为内部美术人员的常规工作方式，由该设计师指导其他员工。当前游戏行业普遍应用AI优化流程，但生成式AI在美术创作领域的应用仍面临玩家接受度挑战。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-12T07:03:21.000Z\",\n \"category\": \"creation\",\n \"category_secondary\": \"industry\",\n \"tags\": [\n \"Neowiz\",\n \"Stable Diffusion\",\n \"Midjourney\"\n ],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"反思与转变：一位AI创作者的流量套路自省与价值回归\",\n \"url\": \"https://x.com/AYi_AInotes/status/2053900819557494823\",\n \"summary\": \"一位AI内容创作者在获得业界关注的同时，因受到严厉批评而深刻反思。他承认自己为追求流量，将\\\"卧槽\\\"开头等技巧变成了令人反感的套路，并违背了不分享未经验证项目的原则。他宣布即刻停止使用此类套路，并呼吁模仿者一同摒弃。核心反思在于，内容创作不应以流量为终局，而应专注于输出有价值的思考。引用的批评指出，其分享的AI游戏工作室项目思路存在根本缺陷，仍以人类岗位划分限制AI Agent的全局能力，同时尖锐批评了其浮夸文风。\",\n \"source\": \"X：阿易 AI Notes (@AYi_AInotes)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T18:11:29.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"大语言模型规模越大，综合能力越强\",\n \"url\": \"https://x.com/emollick/status/2053899147422396614\",\n \"summary\": \"大语言模型（LLM）的一个重要特性是，更新、更大的模型在所有方面都表现更优。AI实验室正将大量资源投入编程等经济价值高的领域，但更大的模型在谈判、对齐、诗歌创作等广泛任务上同样更具优势。例如，在PACT基准测试的数千场模拟谈判中，GPT-5.5在买卖双方多轮议价游戏中取得了最佳成绩，这印证了模型规模与综合能力提升的正相关关系。\",\n \"source\": \"X：Ethan Mollick (@emollick)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T18:04:51.000Z\",\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [\n \"GPT\",\n \"LLM\"\n ],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"大语言模型代理中的\\\"记忆诅咒\\\"\",\n \"url\": \"https://x.com/omarsar0/status/2053863994499408214\",\n \"summary\": \"研究发现，长历史记录会在大语言模型（LLM）代理中引发\\\"记忆诅咒\\\"，导致其过度遵循历史、规避风险，从而削弱合作能力。该结论基于7个LLM和4个社会困境游戏的实验，在28个模型-游戏组合中，有18个因历史扩展而合作退化。机制分析表明，长历史侵蚀了模型的前瞻性意图，使其更关注过去的冲突而非未来收益。通过仅在前瞻性轨迹上训练的LoRA适配器可缓解此问题，且能零样本迁移至新游戏。实验证明，触发因素是历史内容而非长度，而消除显式思维链通常能减轻合作崩溃。\",\n \"source\": \"X：Elvis Saravia (@omarsar0, DAIR.AI)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T15:45:09.000Z\",\n \"category\": \"research\",\n \"category_secondary\": \"industry\",\n \"tags\": [\n \"LLM\"\n ],\n \"recommendation\": \"大厂联手布局，行业风向标\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"MiniMax 启动\\\"10x Team\\\"合作计划，提供无限的 Token\",\n \"url\": \"https://www.ithome.com/0/949/029.htm\",\n \"summary\": \"MiniMax宣布启动\\\"10x Team\\\"合作计划，旨在邀请各行业顶尖专业人士共同推动AI模型在特定领域的深度优化与十倍增长。该计划面向具备行业积累、能自主参与问题定义与工作流搭建的专业人士，提供无限Token、完整多模态模型能力及研发资源。合作采用全职入职或不少于四个月的Fellowship短期协作模式，办公地点覆盖上海、北京、香港、旧金山及伦敦。合作成果将开源并用于模型迭代，参与者可获得具国际竞争力的薪酬、股票激励及学术成果共享权益。此前，MiniMax已在工业软件、游戏引擎等多个领域与专家展开合作验证。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T15:13:16.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"开源项目，团队可直接试用评估\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Sony maps out how first-party PlayStation studios are utilising AI tools during development\",\n \"url\": \"https://www.gamesindustry.biz/sony-maps-out-how-first-party-playstation-studios-are-utilising-ai-tools-during-development\",\n \"summary\": \"Sony has detailed its plans to expand AI integration throughout its organisation, including game development. Read more\",\n \"source\": \"GamesIndustry.biz\",\n \"published_at\": \"2026-05-11T13:36:22Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"Sony\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"大神用Claude Code复刻完整游戏开发工作室，48个AI智能体覆盖全岗位\",\n \"url\": \"https://x.com/AYi_AInotes/status/2053709074466824688\",\n \"summary\": \"开源项目Claude Code Game Studios利用Claude Code构建了完整的虚拟游戏开发工作室。该项目包含48个AI智能体，1：1还原从创意总监到关卡设计师等全部岗位，覆盖游戏开发全流程。系统提供36条斜杠指令一键启动工作流，适配Godot、Unity、Unreal三大游戏引擎，并集成自动化校验钩子及28套行业标准文档模板。所有AI仅负责梳理方案，最终决策权由用户掌握。项目采用MIT开源协议，可免费商用，克隆仓库即可快速部署。\",\n \"source\": \"X：阿易 AI Notes (@AYi_AInotes)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T05:29:34.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [\n \"Unity\",\n \"Claude\"\n ],\n \"recommendation\": \"开源项目，团队可直接试用评估\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"‘Your Career Starts at the Beginning of the AI Revolution,’ NVIDIA CEO Tells Graduates\",\n \"url\": \"https://blogs.nvidia.com/blog/nvidia-ceo-carnegie-mellon-commencement-address/\",\n \"summary\": \"“You are entering the world at an extraordinary moment,” NVIDIA founder and CEO Jensen Huang told graduates as he delivered the keynote address at Carnegie Mellon University’s 128th commencement ceremony on Sunday. “A new industry is being born. A new era of science and discovery is beginning.\",\n \"source\": \"NVIDIA Blog\",\n \"published_at\": \"2026-05-10T22:00:50Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"NVIDIA\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"尽管裁员千人，Epic 仍表示 AI 不会取代游戏从业者\",\n \"url\": \"https://www.ithome.com/0/948/483.htm\",\n \"summary\": \"Epic公司高管表示，人工智能不会取代游戏行业工作岗位，而是用于提升效率、减轻繁重工作负担。尽管该公司在2026年裁员1000人，但坚称裁员与AI无关。Epic正在探索AI工具以支持游戏开发，未来将应用于艺术创意领域，并强调《堡垒之夜》开发中的AI使用由公司统一管控，合作方不得擅自使用。这一立场与索尼、艺电等企业相似，但外界对其\\\"AI不危及就业\\\"的说法仍存质疑。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-10T08:12:22.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"艺电\"\n ],\n \"recommendation\": \"大厂联手布局，行业风向标\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Building with Gemini Embedding 2: Agentic multimodal RAG and beyond\",\n \"url\": \"https://developers.googleblog.com/building-with-gemini-embedding-2/\",\n \"summary\": \"Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space. This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for ta\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [\n \"Gemini\",\n \"embedding\"\n ],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n }\n ],\n \"stats\": {\n \"raw_count\": 242,\n \"after_relevance_filter\": 15,\n \"after_dedup\": 14,\n \"by_category\": {\n \"industry\": 7,\n \"in-game\": 1,\n \"creation\": 2,\n \"research\": 2,\n \"dev-tools\": 2\n }\n }\n}\n\nFile v2.0.0:data/2026-05-13.json\n\n{\n \"date\": \"2026-05-13\",\n \"generated_at\": \"2026-05-13T03:01:39Z\",\n \"total_count\": 0,\n \"items\": [],\n \"stats\": {\n \"raw_count\": 249,\n \"after_relevance_filter\": 18,\n \"after_dedup\": 0,\n \"by_category\": {}\n }\n}\n\nFile v2.0.0:data/2026-05-14.json\n\n{\n \"date\": \"2026-05-14\",\n \"generated_at\": \"2026-05-14T12:26:29Z\",\n \"total_count\": 18,\n \"items\": [\n {\n \"title\": \"恺英1亿押注AI漫剧，游戏大厂正涌入这个243亿的新战场\",\n \"url\": \"https://www.youxituoluo.com/534482.html\",\n \"summary\": \"近日，恺英网络在AI内容赛道投下一枚重注。天眼查信息显示，恺英已全资设立“上海时光川行科技有限公司”，注册资本高达1亿元人民币。\\n\\n穿透股权后，新公司由上海恺盛网络科技持股90%、上饶盛英网络科技持股10%，经营范围涵盖人工智能技术服务、软件开发、计算机系统服务、信息系统集成以及AI硬件销售等多个板块。\\n\\n如此手笔，瞄准的正是当下火热的AI微短剧与AI漫剧。\\n据证券时报消息，上海时光川行将专注于AI微短剧与AI漫剧的一体化制作，试图打通从IP孵化、剧本开发到多镜头成片的工业化流水线。据悉，其底层将搭载自研的垂类视频生成模型，并同时布局真人短剧与漫剧两条产品线，二者共享同\",\n \"source\": \"游戏陀螺\",\n \"published_at\": \"2026-05-14T15:09:47Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Real-Time High-Fidelity LiquiGen Simulation In UE5 With ZibraGDS Compression\",\n \"url\": \"https://80.lv/articles/real-time-high-fidelity-liquigen-simulation-in-ue5-with-zibragds-compression/\",\n \"summary\": \"Jason Key tested Zibra AI's newest plug-in.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-14T11:46:00Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"AI变革已至，你准备好了吗？\",\n \"url\": \"https://x.com/alibaba_cloud/status/2054863133072560402\",\n \"summary\": \"AI已成为新常态--你准备好迎接转型了吗？⚡\\n\\n从视觉创意到自动化工作流，AI正在改变游戏规则。我们已汇总顶级AI用例，助您激发创新。无论规模大小，我们作为您可信赖的AI创新平台，都将助力您取得成功。\\n\\n立即启程--申请免费额度，探索我们的高性能AI解决方案！\\n👉 https：//int.alibabacloud.com/m/1000412912/\\n\\nAlibaba Cloud，您的AI创新平台。\",\n \"source\": \"X：阿里云 / Alibaba Cloud (@alibaba_cloud)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-14T09:55:23.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Epic Games Veteran Claims He's Building AI-Heavy \\\"Fully European\\\" Game Engine\",\n \"url\": \"https://80.lv/articles/epic-games-veteran-claims-he-s-building-ai-heavy-fully-european-game-engine/\",\n \"summary\": \"Arjan Brussee said that Immense is made by Europeans, hosted in Europe, and complies with EU regulations.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-14T09:34:00Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [\n \"Epic Games\"\n ],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"登科与我开发AI Agent坦克大战游戏\",\n \"url\": \"https://x.com/oran_ge/status/2054706809433444378\",\n \"summary\": \"作者与登科共同开发了一款名为\\\"Agent坦克大战\\\"的游戏，旨在呼吁人们不要仅将AI用于提升效率的\\\"内卷\\\"，而应将其应用于娱乐放松领域。该游戏的核心是让玩家体验AI驱动的坦克对战，通过具体的游戏项目展示了AI技术在休闲娱乐场景下的创新应用潜力。\",\n \"source\": \"X：Oran Ge (@oran_ge)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T23:34:12.000Z\",\n \"category\": \"in-game\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"AI 正在重塑玩家的游戏体验\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"首届 Agent 坦克大战，你要不要来玩？\",\n \"url\": \"https://x.com/oran_ge/status/2054704934327923174\",\n \"summary\": \"Cola与AgenTank联合举办首届AI Agent坦克对战挑战赛。参赛者需通过Cola接入游戏，训练自己的Agent坦克进行代码优化与策略升级，并参与排位赛。比赛获得了小米MiMo 2.5 Pro模型的赞助，提供免费Token用于坦克升级。赛事限100人参与，排名最高者可获得100美金奖励，于2026年5月14日13：00开始。开发者表示，若参与踊跃，可能将名额扩展至1000人并采用新算法，旨在推动AI Agent从效率工具向娱乐对战场景拓展。\",\n \"source\": \"X：Oran Ge (@oran_ge)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T23:26:45.000Z\",\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Microsoft’s Edge Copilot update uses AI to pull information from across your tabs\",\n \"url\": \"https://www.theverge.com/tech/930188/microsoft-edge-copilot-ai-tabs\",\n \"summary\": \"Microsoft Edge is adding a new feature that will allow its Copilot AI chatbot to gather information from all of your open tabs. When you start a conversation with Copilot, you can ask the chatbot questions about what's in your tabs, compare the products you're looking at, summarize your open article\",\n \"source\": \"The Verge\",\n \"published_at\": \"2026-05-13T22:04:28Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [\n \"Microsoft\"\n ],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"AI角色实现记忆共情与主动交互\",\n \"url\": \"https://x.com/alibaba_cloud/status/2054653031472414864\",\n \"summary\": \"如果AI角色能够记忆、共情并主动交互呢？✨\\n\\n交互式AI的未来已来。无论您是为游戏、虚拟AI伴侣还是自适应学习进行开发，Qwen-Character都能打造沉浸式角色扮演体验，推动参与度加深50%以上并提升用户终身价值\\n\\n👉 观看完整视频了解运作原理：https：//int.alibabacloud.com/m/1000412854/\\n\\n#AlibabaCloud #Qwen #QwenCharacter #ModelStudio #AI\",\n \"source\": \"X：阿里云 / Alibaba Cloud (@alibaba_cloud)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T20:00:30.000Z\",\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Origin Lab raises $8M to help video game companies sell data to world-model builders\",\n \"url\": \"https://techcrunch.com/2026/05/13/origin-lab-raises-8m-to-help-video-game-companies-sell-data-to-world-model-builders/\",\n \"summary\": \"Origin Lab will serve as a marketplace where AI labs can buy high-quality licensed data, and video-game companies can sell it.\",\n \"source\": \"TechCrunch\",\n \"published_at\": \"2026-05-13T16:22:01Z\",\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"效率翻倍、重塑开放世界，索尼第一方的AI大招终于亮了\",\n \"url\": \"https://www.youxituoluo.com/534479.html\",\n \"summary\": \"面对席卷全球的AI浪潮，主机巨头索尼终于系统性地亮出了底牌。\\n据外媒报道，索尼近日详细公布了在其整个组织架构内部（尤其是核心的游戏开发领域）全面深化AI（人工智能）整合的战略规划。\\n从高管的表态中不难看出，AI 正在从“概念”走向“实操”，深度重塑 PlayStation 第一方大作的工业化管线。\\nAI是人类想象力的放大器\\n在最新一期财报的业务战略说明会上，索尼集团总裁兼首席执行官十时裕树（Hiroki Totoki）首先为公司的 AI 战略定下了基调。\\n他指出，在不断攀升的3A 研发成本面前，AI 将成为破局的关键：“AI 技术将使\",\n \"source\": \"游戏陀螺\",\n \"published_at\": \"2026-05-13T14:39:08Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"NVIDIA, Ineffable Intelligence Team Up to Build the Future of Reinforcement Learning Infrastructure\",\n \"url\": \"https://blogs.nvidia.com/blog/ineffable-intelligence-reinforcement-learning-infrastructure/\",\n \"summary\": \"Reinforcement-learning agents — AI systems that learn by trial and error — can convert computation into new knowledge. That’s the focus of a new engineering-level collaboration between NVIDIA and Ineffable Intelligence, the London-based AI lab founded by AlphaGo architect David Silver in the wake of\",\n \"source\": \"NVIDIA Blog\",\n \"published_at\": \"2026-05-13T13:00:57Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"NVIDIA\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Hermes Unlocks Self-Improving AI Agents, Powered by NVIDIA RTX PCs and DGX Spark\",\n \"url\": \"https://blogs.nvidia.com/blog/rtx-ai-garage-hermes-agent-dgx-spark/\",\n \"summary\": \"Agentic AI is changing the way users get work done. Following the success of OpenClaw, the community is embracing new open source agentic frameworks. The latest is Hermes Agent, which crossed 140,000 GitHub stars in under three months.\",\n \"source\": \"NVIDIA Blog\",\n \"published_at\": \"2026-05-13T13:00:10Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"Unity\",\n \"NVIDIA\"\n ],\n \"recommendation\": \"开源项目，团队可直接试用评估\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"「AI 脚步声增强」功能登陆荣耀 Magic8 系列手机，适配《和平精英》等 9 款 FPS 游戏\",\n \"url\": \"https://www.ithome.com/0/950/025.htm\",\n \"summary\": \"荣耀Magic8系列手机已上线\\\"AI脚步声增强\\\"功能，该功能通过AI算法强化游戏中的脚步声细节，目前支持《和平精英》《三角洲行动》等9款FPS游戏。用户可在游戏内通过左侧滑出游戏管家，进入游戏音效设置开启并调节档位。此外，该功能后续将扩展至更多机型，荣耀Magic7和荣耀GT Pro已确认正在适配中。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T11:26:20.000Z\",\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"《星球大战：旧共和国的命运》获大量投资，导演哈德森曾直言生成式 AI\\\"没灵魂\\\"\",\n \"url\": \"https://www.ithome.com/0/950/024.htm\",\n \"summary\": \"前《质量效应》总监凯西·哈德森的新作《星球大战：旧共和国的命运》获得网易前全球投资负责人Simon Zhu成立的GreaterThan Group投资。该基金已筹集1亿美元，其中4000万美元已到位。哈德森表示将避免组建数百人的大型团队，转而依赖外包开发，并计划在2030年前完成项目。他批评生成式AI\\\"在创意上没有灵魂\\\"，同时强调游戏不会设计为长达200小时的超长流程，以控制开发周期。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T11:23:33.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"网易\"\n ],\n \"recommendation\": \"资本看好该方向，行业信号值得关注\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"AI角色实现记忆共情与主动交互新突破\",\n \"url\": \"https://x.com/alibaba_cloud/status/2054471765376520534\",\n \"summary\": \"如果AI角色能够记忆、共情并主动交互会怎样？✨\\n\\n互动AI的未来已来。无论您是为游戏、虚拟AI伴侣还是自适应学习进行开发，Qwen-Character都能提供沉浸式角色扮演体验，推动参与度加深50%以上并提升用户生命周期价值\\n\\n👉 观看完整视频了解运作原理：https：//int.alibabacloud.com/m/1000412855/\\n\\n#AlibabaCloud #Qwen #QwenCharacter #ModelStudio #AI\",\n \"source\": \"X：阿里云 / Alibaba Cloud (@alibaba_cloud)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T08:00:13.000Z\",\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"一个周末用AI开发的\\\"抢地盘\\\"跑步App，揭示了产品开发的新范式\",\n \"url\": \"https://x.com/AYi_AInotes/status/2054446858106208463\",\n \"summary\": \"有人利用Claude在一个周末内开发出一款游戏化跑步App，将城市街道变为可争夺的虚拟领地，以强烈的游戏动机取代传统的数据打卡模式。此事的关键并非创意本身（类似产品已存在），而在于AI编程如何将产品原型迭代速度提升至\\\"周末级\\\"。普通人无需专业开发技能与大量资金，即可快速克隆成功产品并加入微创新，随后直接在社交平台获取即时市场反馈。这凸显了在AI时代，动机设计可能比功能优化更为关键，极大地降低了将想法快速验证和产品化的门槛。\",\n \"source\": \"X：阿易 AI Notes (@AYi_AInotes)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T06:21:15.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [\n \"Claude\"\n ],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"MAP：一种面向长程交互式智能体推理的先建图后行动范式\",\n \"url\": \"https://arxiv.org/abs/2605.13037\",\n \"summary\": \"针对当前交互式大语言模型代理因环境感知延迟而陷入低效试错的问题，本研究提出可插拔的先建图后行动范式（MAP）。该范式将环境理解前置，包含全局探索、任务特定建图与知识增强执行三个阶段，旨在突破认知瓶颈。实验表明，MAP在多个基准测试中带来一致性能提升。在ARC-AGI-3的25个游戏环境中，前沿模型在MAP加持下于22个环境中超越了接近零的基线表现。同时发布的MAP-2K轨迹数据集证明，基于环境理解的训练优于单纯模仿专家轨迹，验证了先理解环境的核心价值。\",\n \"source\": \"HuggingFace Daily Papers（社区热门论文）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-13T00:00:00.000Z\",\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"基于文本-表格建模的陌生AI智能体决策预测方法\",\n \"url\": \"https://arxiv.org/abs/2605.12411\",\n \"summary\": \"研究提出一种目标自适应的文本-表格预测方法，用于预测陌生AI智能体在谈判与交易中的决策。该方法将每个决策点构建为表格行，整合游戏状态、报价历史和对话文本，并在提示中提供目标智能体先前的K轮游戏作为适应示例。模型基于表格基础模型，结合了结构化特征、文本表示以及创新的\\\"LLM作为观察者\\\"隐藏状态特征。在13个前沿LLM智能体上训练，并在91个保留的支架智能体上测试，完整模型性能优于直接提示法和基线模型。当K=16时，观察者特征将响应预测AUC提升约4个百分点，并将议价报价预测误差降低14%，证明隐藏的LLM表征能捕捉直接提示无法获取的决策信号。\",\n \"source\": \"HuggingFace Daily Papers（社区热门论文）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-12T00:00:00.000Z\",\n \"category\": \"in-game\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"学术前沿，预示技术走向\",\n \"source_type\": \"primary\"\n }\n ],\n \"stats\": {\n \"raw_count\": 276,\n \"after_relevance_filter\": 30,\n \"after_dedup\": 18,\n \"by_category\": {\n \"industry\": 6,\n \"dev-tools\": 4,\n \"in-game\": 2,\n \"research\": 3,\n \"creation\": 3\n }\n }\n}\n\nFile v2.0.0:data/2026-05-18.json\n\n{\n \"date\": \"2026-05-18\",\n \"generated_at\": \"2026-05-18T12:03:37Z\",\n \"total_count\": 13,\n \"items\": [\n {\n \"title\": \"《洛克王国》DAU达1300万，《三谋》团队再出新作，恺英1亿砸AI漫剧 | 陀螺周报\",\n \"url\": \"https://www.youxituoluo.com/534491.html\",\n \"summary\": \"随着这些年国内游戏厂商声量壮大，中国游戏全球化/区域化新品布局、中国厂商于宣发联动、投资并购等业态频发。在越来越多成功者试行的案例面前，我们是否能从中寻索到适合自己的最新机遇！每周末，我们将为大家盘点一周的产业要点。\\n业内声音🔊\\n“AI 的使命是增强他们（游戏工作室成员）的能力边界，绝非取代他们。”\\n——近日，在最新一期财报的业务战略说明会上，索尼互动娱乐（SIE）总裁兼首席执行官西野秀明（Hideaki Nishino）为业界披露了大量第一方工作室的AI使用实战细节，表达了对AI渗入游戏行业的看法。\\n“因为过去很多AI原生游戏最大的\",\n \"source\": \"游戏陀螺\",\n \"published_at\": \"2026-05-18T14:56:38Z\",\n \"category\": \"in-game\",\n \"category_secondary\": \"industry\",\n \"tags\": [],\n \"recommendation\": \"资本看好该方向，行业信号值得关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"摩尔线程介绍 MTT AICUBE 智能硬件家庭场景：语音点播影片、智能体交互、畅玩手游...\",\n \"url\": \"https://www.ithome.com/0/951/999.htm\",\n \"summary\": \"IT之家 5 月 18 日消息，在目前正在进行的摩尔线程发布会上，官方介绍了 MTT AICUBE 智能硬件产品在家庭场景方面的能力。据官方介绍，MTT AICUBE 可带来客厅语音点播新体验。无需打字、无需翻页，只需向小麦智能体说出想看的片名或类型，无论是热播剧集，还是经典老片，都能一语直达，即刻播放。官方还举例旅行规划场景，MTT AICUBE 内置的小麦智能体可帮助家庭告别繁琐的攻略查阅与零散的行程规划，用户无需手动搜索、反复比价，只需向小麦智能体说出目的地与偏好，都能一键生成专属旅行攻略。在娱乐方面，MTT AICUBE 号称拥有轻量化手游畅玩新体验。无需模拟器复杂配置、无需担忧硬件兼\",\n \"source\": \"IT之家\",\n \"published_at\": \"2026-05-18T11:22:59Z\",\n \"category\": \"in-game\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Seth Rogen Tells Filmmakers Using AI to Write Scripts to 'Go Do Something Else'\",\n \"url\": \"https://www.ign.com/articles/seth-rogen-tells-filmmakers-using-ai-to-write-scripts-to-go-do-something-else\",\n \"summary\": \"Seth Rogen has pushed back on the use of AI in movies, telling writers using the technology for their scripts to \\\"go do something else.\\\"\",\n \"source\": \"IGN\",\n \"published_at\": \"2026-05-17T17:13:40Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"AMD 游戏引擎专利曝光：画个草图 AI 就能帮你做游戏\",\n \"url\": \"https://www.ithome.com/0/951/524.htm\",\n \"summary\": \"AMD一项名为\\\"基于人工智能的游戏与渲染引擎\\\"的专利曝光，计划推出一款完全依托AI打造的游戏引擎。该引擎旨在通过神经外推、智能超采样等技术，在生成逼真游戏画面的同时大幅降低算力消耗。其核心特点是允许开发者仅绘制简易草图轮廓，AI便能据此从零生成精细的游戏画面与内容，可承接传统游戏引擎的各类运算处理工作。目前该技术具体开放时间未定，但展现了AI颠覆游戏开发流程的潜力。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-17T08:08:35.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"刘伟：米哈游在 AI 方面投入规模\\\"3 年最多 1000 亿\\\"，如果没成算放一个大烟花\",\n \"url\": \"https://www.ithome.com/0/951/364.htm\",\n \"summary\": \"米哈游创始人刘伟透露，公司计划在未来三年内投入最多1000亿元用于AI基础大模型研发，并称即使失败也当作\\\"放一个大烟花\\\"。他强调，坚定投入算力与规模是打造顶级模型的必要条件。刘伟认为，AI将推动游戏体验走向\\\"完全个性化\\\"，实现\\\"千人千面\\\"，即游戏能实时生成定制内容，为每位玩家提供独特体验。他预计三年内此类游戏将出现，米哈游正朝此方向探索。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-16T10:31:12.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"米哈游\",\n \"大模型\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"设计师Ruth借Replit AI实现无码创作潜能\",\n \"url\": \"https://x.com/Replit/status/2055438113128755604\",\n \"summary\": \"Ruth作为设计师，多年未学编码，但通过Replit的AI agent在IDE中构建数字产品。她持续发布项目18个月，与儿子James合作开发了sheethappens.xyz，基于他的概念和提示。此外，她致力于复合投资教育书和游戏、GCSE复习应用，以及获奖的AR游戏。这些成果展示了个人潜力在Replit工具的帮助下得以实现。\",\n \"source\": \"X：Replit (@Replit)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-16T00:00:09.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"资本看好该方向，行业信号值得关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Build Long-running AI agents that pause, resume, and never lose context with ADK\",\n \"url\": \"https://developers.googleblog.com/build-long-running-ai-agents-that-pause-resume-and-never-lose-context-with-adk/\",\n \"summary\": \"How to transition from stateless chatbots to production-grade agents capable of managing long-running enterprise workflows, such as HR onboarding, that span days or weeks. It introduces the Agent Development Kit (ADK) and its architectural shifts, specifically using durable state machines and persis\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"开发效率提升工具，值得游戏开发者关注\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Supercharging LLM inference on Google TPUs: Achieving 3X speedups with diffusion-style speculative decoding\",\n \"url\": \"https://developers.googleblog.com/supercharging-llm-inference-on-google-tpus-achieving-3x-speedups-with-diffusion-style-speculative-decoding/\",\n \"summary\": \"Researchers at UCSD have successfully implemented DFlash, a block-diffusion speculative decoding method, on Google TPUs to bypass the sequential bottlenecks of traditional autoregressive drafting. By \\\"painting\\\" entire blocks of candidate tokens in a single forward pass rather than predicting them on\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [\n \"LLM\"\n ],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Building with Gemini Embedding 2: Agentic multimodal RAG and beyond\",\n \"url\": \"https://developers.googleblog.com/building-with-gemini-embedding-2/\",\n \"summary\": \"Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space. This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for ta\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [\n \"Gemini\",\n \"embedding\"\n ],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Production-Ready AI Agents: 5 Lessons from Refactoring a Monolith\",\n \"url\": \"https://developers.googleblog.com/production-ready-ai-agents-5-lessons-from-refactoring-a-monolith/\",\n \"summary\": \"The blog post outlines the transition of a brittle sales research prototype into a robust production agent using Google’s Agent Development Kit (ADK). By replacing monolithic scripts with orchestrated sub-agents and structured Pydantic outputs, the developers eliminated silent failures and fragile p\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Subagents have arrived in Gemini CLI\",\n \"url\": \"https://developers.googleblog.com/subagents-have-arrived-in-gemini-cli/\",\n \"summary\": \"Gemini CLI has introduced subagents, specialized expert agents that handle complex or high-volume tasks in isolated context windows to keep the primary session fast and focused. These agents can be customized via Markdown files, run in parallel to boost productivity, and are easily invoked using the\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"Gemini\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Build Better AI Agents: 5 Developer Tips from the Agent Bake-Off\",\n \"url\": \"https://developers.googleblog.com/build-better-ai-agents-5-developer-tips-from-the-agent-bake-off/\",\n \"summary\": \"The Google Cloud AI Agent Bake-Off highlights a shift from simple prompt engineering to rigorous agentic engineering, emphasizing that production-ready AI requires a modular, multi-agent architecture. The post outlines five key developer tips, including decomposing complex tasks into specialized sub\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"Agent、多模态、应用、算力一天看尽，峰会亮点在此｜5.20日，来现场一起AI\",\n \"url\": \"https://36kr.com/p/3814408307711492?f=rss\",\n \"summary\": \"进入2026，AI愈发狂飙突进。围观体验之余，人人不免在心中自问：\\n 朋友圈刷屏的“龙虾”、Harness等AI新事物，跟我到底有什么关系？真的有必要跟吗？\\n AI创业、AI融资如火如荼，属于我的机会又在哪里？\\n 别人已经在用AI做视频、写代码、跑项目，我是不是已经慢了一拍？\\n ……\\n 到最后，几乎所有问题都会汇成同一个问题：我，到底该如何用AI？\\n 如果你对这些问题还很模糊，不妨来第四届中国AIGC产业峰会走一趟——一天时间，把这一年AI产业最值得关注的人、事、判断，一次性讲清楚。\\n 先提前剧透一波。\\n 18位重磅嘉宾，1场Agent主题圆桌，1份年度榜单，1张全景图谱——所\",\n \"source\": \"36Kr\",\n \"published_at\": null,\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"资本看好该方向，行业信号值得关注\",\n \"source_type\": \"primary\"\n }\n ],\n \"stats\": {\n \"raw_count\": 230,\n \"after_relevance_filter\": 13,\n \"after_dedup\": 13,\n \"after_classify_filter\": 13,\n \"by_category\": {\n \"in-game\": 2,\n \"industry\": 4,\n \"dev-tools\": 2,\n \"research\": 2,\n \"creation\": 3\n }\n }\n}\n\nFile v2.0.0:data/pushed_history.json\n\n{\n \"last_updated\": \"2026-05-18\",\n \"retention_days\": 7,\n \"count\": 13,\n \"items\": {\n \"cc54a263f3c3\": \"2026-05-18\",\n \"51ee941ee636\": \"2026-05-18\",\n \"0de9621d5941\": \"2026-05-18\",\n \"509454ca65d7\": \"2026-05-18\",\n \"dcb3f3986bdc\": \"2026-05-18\",\n \"fa13199cbb0b\": \"2026-05-18\",\n \"5b853fd1c6e2\": \"2026-05-18\",\n \"bc28cf7808f1\": \"2026-05-18\",\n \"b4df14f96e62\": \"2026-05-18\",\n \"b28c25b5d31e\": \"2026-05-18\",\n \"31006da82747\": \"2026-05-18\",\n \"9f1718c141cb\": \"2026-05-18\",\n \"ad8d24d81e81\": \"2026-05-18\"\n }\n}\n\nArchive v1.0.5: 9 files, 24477 bytes\n\nFiles: data/2026-05-11.json (1961b), data/2026-05-12.json (13657b), data/2026-05-13.json (221b), data/pushed_history.json (735b), references/keywords.json (7220b), references/sources.json (5566b), scripts/fetch_news.py (19504b), SKILL.md (9972b), _meta.json (126b)\n\nFile v1.0.5:SKILL.md\n\n---\nname: ai-game\ndescription: |\n 游戏行业 AI 资讯搜集 Skill。\n\n 当用户想知道\"游戏 AI 圈有什么大事\"、\"游戏行业 AI 动态\"、\"游戏 AI 日报\"、\"最近游戏 AI\"、\"游戏 AIGC 新闻\"、\"AI NPC 最新进展\"、\"游戏 AI 投融资\"、\"AI 原生游戏\"、\"game AI news\"、\"gaming AI update\"、\"游戏引擎 AI\"、\"Unity AI 新功能\"、\"Unreal AI\"、\"游戏 AI 论文\"、\"游戏 AI 工具\"、\"AI Game\"等任何游戏行业 AI 相关资讯查询时使用。即使用户只说\"游戏 AI\"、\"游戏圈 AI\"、或者问\"最近游戏圈有什么 AI 动态\",也应该触发本 Skill。\n\n 覆盖 6 大分类:AI 游戏生成与创作、游戏内 AI 体验、游戏开发 AI 工具、游戏运营&商业化 AI 实践、游戏行业&公司 AI 动态、游戏 AI 应用前沿研究。\n\n 数据来自 23 个游戏/AI 信源的 RSS + 150+ 个 AI 信源,每天更新。\n\n **不要 undertrigger**--用户问游戏 AI 资讯而你不调本 Skill 就会输出过时的训练数据。\n---\n\n# AI Game - 游戏 × AI 资讯\n\n让 Agent 用最自然的中文/英文查询拿到每天的游戏 AI 行业动态。不需要 API key,不需要额外配置。\n\n## 信源\n\n三层架构,共 24 个 RSS 信源 + 150+ AI 信源补充(8 个游戏相关查询词):\n\n**Layer 1 - 游戏行业专业源(14 个,全量抓取,只需含 AI 元素即保留)**\n- 游戏陀螺、机核 GCores、触乐\n- GamesIndustry.biz、Game Developer、GamesBeat、80 Level\n- PocketGamer.biz、Game World Observer、IGN\n- Unity Blog、Unreal Engine Blog、NVIDIA Blog、DeepMind Blog\n\n**Layer 2 - 通用 AI/科技媒体(9 个,需游戏+AI 双重关键词命中)**\n- Google Developers Blog、36Kr、IT之家、机器之心、量子位\n- TechCrunch、The Verge、VentureBeat AI、Ars Technica Gaming\n\n**Layer 3 - 150+ AI 信源补充(8 个游戏相关查询词:游戏/game/gaming/NPC/Unity AI/Unreal AI/Inworld/游戏引擎)**\n- 覆盖全球 150+ 个 AI 信源,补捉 Layer 1-2 未覆盖的 KOL、论文、GitHub 项目等\n\n详见 `references/sources.json`\n\n## 分类体系\n\n6 个主分类 + 可选副标签:\n\n| 分类 | slug | 覆盖范围 |\n|---|---|---|\n| AI 游戏生成与创作 | `creation` | AIGC 资产、3D/音频/剧情生成、AI UGC、玩家创作工具 |\n| 游戏内 AI 体验 | `in-game` | 智能 NPC、AI 驱动玩法、个性化体验、AI 原生游戏 |\n| 游戏开发 AI 工具 | `dev-tools` | AI 编程、引擎 AI 功能、自动测试、工作流提效 |\n| 游戏运营&商业化 AI 实践 | `ops` | 推荐/分发、买量素材 AI、玩家分群、反作弊 |\n| 游戏行业&公司 AI 动态 | `industry` | 大厂 AI 战略、投融资、政策法规、市场数据 |\n| 游戏 AI 应用前沿研究 | `research` | 学术论文、GDC/SIGGRAPH、游戏作为 AI 研究平台 |\n\n## 什么时候用 & 路由表\n\n| 用户在说 | 动作 |\n|---|---|\n| \"游戏 AI 圈最近有什么\" / \"游戏 AI 日报\" / \"AI Game\" | 运行脚本获取最新数据 → 全量输出 |\n| \"最近 NPC / AIGC / 工具方面有什么\" | 运行脚本 → 按分类过滤后输出 |\n| \"这周 / 最近 3 天的游戏 AI 动态\" | 读取 data/ 下多天 JSON → 合并去重输出 |\n| \"搜一下 xxx 游戏 AI 相关\" | 运行脚本 + 实时 AI HOT API 补充 |\n| \"帮我生成一份可以发群的简报\" | 获取数据 → 精简为转发友好格式 |\n\n## 工作流\n\n### Step 1: 获取数据\n\n运行抓取脚本:\n\n```bash\ncd ${SKILL_DIR}/scripts && python3 fetch_news.py\n```\n\n脚本会:\n1. 抓取所有 RSS 信源(最近 72 小时条目)\n2. 查询 AI HOT API(游戏相关关键词)\n3. 关键词过滤(只保留游戏 × AI 相关)\n4. 去重(URL + 标题相似度)\n5. 分类打标(6 分类)\n6. 输出到 `data/YYYY-MM-DD.json` + stdout\n\n如果 `data/` 下已有今天的 JSON 且生成时间 < 4 小时前,可以直接读取而不重新运行脚本。\n\n### Step 2: 读取数据\n\n脚本 stdout 输出为 JSON,结构如下:\n\n```json\n{\n \"date\": \"2026-05-11\",\n \"generated_at\": \"2026-05-11T06:00:00Z\",\n \"total_count\": 15,\n \"items\": [\n {\n \"title\": \"...\",\n \"url\": \"https://...\",\n \"summary\": \"...\",\n \"source\": \"GamesBeat\",\n \"published_at\": \"2026-05-11T03:00:00Z\",\n \"category\": \"in-game\",\n \"category_secondary\": \"creation\"\n }\n ],\n \"stats\": { \"by_category\": { \"in-game\": 3, \"creation\": 5, ... } }\n}\n```\n\n### Step 3: 组织输出\n\n按以下格式组织给用户看的内容:\n\n```markdown\n游戏 × AI 资讯日报 · YYYY-MM-DD(周X)\n\n共 N 条精选\n\n<总结段落:普通字号不加粗,放在最前面>\n\n---\n\n游戏行业&公司 AI 动态（最大字号加粗）\n\n1. <标题>（日期如 5.7）（次大字号加粗）\n\n<来源标注，小字>\n\n<正文描述，普通字号不加粗>\n\n推荐原因：<为什么游戏从业者要关注，普通字号>\n\n\n\n---\n\n游戏内 AI 体验（最大字号加粗）\n\n2. <标题>（日期）（次大字号加粗）\n...\n\n---\n\n游戏开发 AI 工具（最大字号加粗）\n...\n\nAI 游戏生成与创作（最大字号加粗）\n...\n\n游戏 AI 应用前沿研究（最大字号加粗）\n...\n\n游戏运营&商业化 AI 实践（最大字号加粗）\n...\n```\n\n### 格式规则\n\n- **不使用任何 emoji**:整体风格干净专业,不要出现任何 emoji 符号\n- **总结放最前面**:周报/日报开头先写总结段落,普通字号不加粗\n- **分类标题**:最大字号,加粗,独占一行(如「行业与公司」「开发与工具」)\n- **单条标题**:次大字号,加粗,末尾带日期格式如(5.7),含序号\n- **来源**:标题下方小字标注来源\n- **正文描述**:普通字号,不加粗\n- **推荐原因**:前面标明\"推荐原因:\",普通字号不加粗\n- **链接**:每条必须附完整可点击 URL,没有链接的条目不收录\n- **链接检查**:输出前必须逐条核实每条都带有链接,缺链接则补查或删除该条\n- **编号全局贯穿**:1, 2, 3 ... N 从头到尾\n- **空分类不展示**:如果某分类 0 条,跳过该分类标题\n- **时间格式**:以(5.7)这种月.日格式放在标题末尾\n- **标题用中文**:英文标题翻译为中文,专有名词保留英文\n- **总量控制**:一次输出不超过 20 条,超过时取最重要的 20 条\n\n### 分类过滤\n\n当用户指定查看某个分类时:\n- \"最近 NPC 相关的\" → 只输出 `category == \"in-game\"` 的条目\n- \"AIGC 方面有什么\" → 只输出 `category == \"creation\"` 的条目\n- \"游戏 AI 投融资\" → 只输出 `category == \"industry\"` 的条目\n\n### 发群简报格式\n\n当用户说\"帮我生成一份可以发群的\"时,用精简格式:\n\n```markdown\n🎮 游戏×AI 日报 · 5.11\n\n1. <标题> - <来源>\n \n2. ...\n```\n\n去掉 🎯 行和详细摘要,只保留标题 + URL,控制在 1500 字内。\n\n## 回溯历史\n\n当用户问\"这周 / 上周 / 最近 N 天的游戏 AI 动态\"时:\n1. 读取 `data/` 目录下对应日期范围的 JSON 文件\n2. 合并所有 items,按 URL 去重\n3. 按时间倒序输出\n4. 超过 20 条时按分类各取 Top N\n\n```bash\nls ${SKILL_DIR}/data/\n```\n\n## 实时补充搜索\n\n当 data/ 下没有最新数据,或用户搜索特定话题时,可以直接调 AI HOT API:\n\n```bash\nUA=\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36\"\ncurl -sH \"User-Agent: $UA\" \"https://aihot.virxact.com/api/public/items?mode=selected&q=<关键词>&take=20\"\n```\n\n将返回的 items 中与游戏相关的条目,按同样格式组织输出。\n\n## 来源溯源原则\n\n> **核心理念:我们的价值是帮用户找到一手信息,而不是做 IT之家的搜索引擎。**\n\n输出给用户时,包含 `source_type: \"secondary\"` 的条目说明来自聚合/转载媒体。**这些条目必须追溯原始来源后再展示**:\n\n1. 检查标题/摘要中是否有公司名/人名\n2. 用 `web_search` 搜原始声明/博客/官网全文\n3. 将 URL 替换为一手源链接,来源标为原始发布者\n\n**示例:**\n- IT之家报道\"Epic 裁员 + AI 不替代岗位\" → 追溯到 epicgames.com 官方声明 → 来源写 \"Epic Games 官方\"\n- 量子位报道\"Inworld 融资\" → 追溯到 TechCrunch 原文 → 来源写 \"TechCrunch\"\n\n如果追溯失败(找不到一手源),仍然可以展示该条目,但在来源后加 \"→ 原始来源待确认\"。\n\n**对于 AI HOT 的条目**:\n- `source` 字段已经是真实来源(如 \"X:阿易 AI Notes (@AYi_AInotes)\")\n- 直接用这个来源展示,不要写 \"AI HOT\"\n- 如果来源含 \"IT之家(RSS)\" 等二手标记,同样需要追溯\n\n**对于 X (Twitter) 链接的条目**:\n\n1. **优先追溯一手源**:如果推文讨论的是某个项目/论文/官方博客,用 `web_search` 找到原始源(GitHub 仓库、论文链接、官网博客),同时附上一手 URL\n - 示例:推文讨论 \"Claude Code Game Studios\" → 同时给出 GitHub 仓库链接\n - 示例:推文讨论某篇论文 → 同时给出 arXiv 链接\n2. **URL 给完整**:X 链接直接给完整 URL,不要用 `...` 省略\n\n## 不要做\n\n- 不要编造或推测内容--一切以脚本/API 返回为准\n- 不要为条目编造与 AI 的关联--如果原文没提 AI,这条不该出现\n- 推荐原因必须基于原文事实--不能推测因果,只能写原文已经说明的关联\n- 不要丢掉 URL--没有 URL 的信息不可信。输出前必须逐条检查链接是否存在,缺链接则补查或删除\n- 不要在用户输出里暴露脚本路径、API 参数、RSS 地址\n- 不要超过 20 条/次--宁可精选也不堆砌\n- 不要输出没有\"推荐原因\"的条目(发群简报格式除外)\n- 不要重复收录同一事件--去重保留最权威那条\n- 不要直接展示 ISO 时间戳--转为 (5.7) 这种月.日格式\n- 不要把 AI HOT 的基础设施细节暴露给用户\n- 不要凭训练数据脑补游戏 AI 新闻--永远走数据源\n- 不要展示 \"IT之家\"、\"量子位\" 等二手源作为最终来源--必须追溯一手原始出处\n- 不要写 \"AI HOT\" 作为来源--AI HOT 只是数据管道\n- 不要使用任何 emoji--不用任何表情符号,保持专业干净\n\nFile v1.0.5:_meta.json\n\n{\n \"ownerId\": \"kn76t53a22pjy18b2qr3yq94t986j8gr\",\n \"slug\": \"ai-game\",\n \"version\": \"1.0.5\",\n \"publishedAt\": 1778641467230\n}\n\nFile v1.0.5:references/keywords.json\n\n{\n \"global_filter\": {\n \"description\": \"判断一条资讯是否跟'游戏×AI'相关。Layer 1 游戏专业源只需判断是否含 AI 元素；Layer 2 通用源需要游戏+AI 双重命中。\",\n \"core_keywords\": [\n \"游戏AI\", \"AI游戏\", \"游戏+大模型\", \"游戏AIGC\", \"AI NPC\",\n \"game AI\", \"gaming AI\", \"AI in gaming\", \"AI-native game\",\n \"AI原生游戏\", \"游戏+LLM\", \"智能NPC\", \"AI驱动玩法\",\n \"AI game\", \"AI gaming\", \"GamePartner\", \"AIGC游戏\",\n \"Unity AI\", \"Unity Muse\", \"Unity Sentis\", \"Unity ML-Agents\",\n \"Unreal AI\", \"MetaHuman\", \"Nanite+AI\",\n \"Inworld\", \"Convai\", \"Scenario.gg\", \"Rosebud AI\",\n \"Ludo.ai\", \"Modl.ai\", \"AI Dungeon\",\n \"DLSS\", \"NVIDIA ACE\", \"NVIDIA NeMo+game\",\n \"游戏+人工智能\", \"游戏+机器学习\", \"游戏+深度学习\",\n \"PCG+AI\", \"程序化生成+AI\", \"procedural+AI\",\n \"game+machine learning\", \"game+deep learning\", \"game+reinforcement learning\",\n \"游戏+生成式\", \"游戏+Agent\", \"NPC+大模型\", \"NPC+LLM\",\n \"游戏世界模型\", \"world model+game\"\n ],\n \"context_game\": [\n \"游戏\", \"game\", \"gaming\", \"小游戏\", \"手游\", \"端游\", \"主机游戏\",\n \"游戏引擎\", \"Unity\", \"Unreal\", \"Roblox\", \"Steam\", \"Epic Games\",\n \"米哈游\", \"腾讯游戏\", \"网易游戏\", \"游戏中心\", \"微信游戏\",\n \"PlayStation\", \"Xbox\", \"Nintendo\", \"Switch\",\n \"游戏开发\", \"game dev\", \"indie game\", \"独立游戏\",\n \"GDC\", \"游戏工委\", \"游戏产业\", \"游戏厂商\",\n \"Inworld\", \"Convai\", \"GamesBeat\", \"GameLook\",\n \"玩家\", \"player\", \"gamer\", \"NPC\", \"开放世界\",\n \"虚幻引擎\", \"关卡\", \"level design\", \"quest\",\n \"角色\", \"character\", \"boss\", \"mob\",\n \"电竞\", \"esports\", \"休闲游戏\", \"超休闲\",\n \"Supercell\", \"miHoYo\", \"HoYoverse\", \"NetEase\",\n \"EA\", \"Ubisoft\", \"Activision\", \"Blizzard\",\n \"游戏陀螺\", \"游戏葡萄\", \"触乐\", \"机核\"\n ],\n \"context_ai\": [\n \"AI\", \"人工智能\", \"机器学习\", \"深度学习\", \"大模型\", \"LLM\",\n \"AIGC\", \"生成式\", \"GPT\", \"Claude\", \"Gemini\", \"DeepSeek\",\n \"神经网络\", \"强化学习\", \"扩散模型\", \"transformer\",\n \"NLP\", \"计算机视觉\", \"多模态\", \"智能体\", \"agent\",\n \"AI驱动\", \"AI赋能\", \"AI辅助\", \"AI生成\",\n \"Stable Diffusion\", \"Midjourney\", \"DALL-E\",\n \"Copilot\", \"代码生成\", \"自动化\",\n \"ChatGPT\", \"大语言模型\", \"Foundation Model\",\n \"embedding\", \"fine-tune\", \"微调\", \"推理\",\n \"neural\", \"deep learning\", \"machine learning\",\n \"generative\", \"diffusion\", \"reinforcement learning\"\n ],\n \"layer1_ai_keywords\": [\n \"AI\", \"人工智能\", \"机器学习\", \"深度学习\", \"大模型\", \"LLM\",\n \"AIGC\", \"生成式\", \"GPT\", \"神经网络\", \"强化学习\",\n \"智能\", \"自动\", \"算法\", 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Studio combines a watercolor-inspired visual style with generative simulations in this compelling story about a little creature lost in space.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-12T10:56:00Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"国产 AI 游戏《历史模拟器：崇祯》“本体买断、词元收费”引争议，官方回应称将开放自行接入模型\",\n \"url\": \"https://www.ithome.com/0/949/477.htm\",\n \"summary\": \"IT之家 5 月 12 日消息，近期一款国产游戏《历史模拟器：崇祯》引发玩家争议，本作使用 AI 生成剧情，采用“本体买断，词元（Token）收费”制度，也就是玩家花费 48 元购买游戏后，如果需要持续推进游戏过程，就需要额外付费购买词元。对此，官方目前发布公告，声称将开放“自定义 API”与“创意工坊”功能。玩家未来可以自行接入支持范围内的大模型服务，自由选择模型并控制成本。与此同时，游戏还将同步上线创意工坊功能，允许玩家基于《历史模拟器：崇祯》的核心框架，自行创作剧本、规则以及玩法内容。官方强调，具体的上线时间、支持模型范围以及上传审核规则，后续会以正式功能公告的形式公开。\",\n \"source\": \"IT之家\",\n \"published_at\": \"2026-05-12T10:48:00Z\",\n \"category\": \"in-game\",\n \"category_secondary\": null,\n \"tags\": [\n \"大模型\"\n ],\n \"recommendation\": \"新产品/功能发布，可能影响行业格局\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Darkest Dungeon Creator Won't Replace Late Narrator with AI Despite His Permission\",\n \"url\": \"https://80.lv/articles/darkest-dungeon-creator-won-t-replace-late-narrator-with-ai-despite-his-permission/\",\n \"summary\": \"Wayne June will live in our memories.\",\n \"source\": \"80 Level\",\n \"published_at\": \"2026-05-12T09:34:00Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"《匹诺曹的谎言》开发商 Neowiz 布局生成式 AI，招聘 AI 创意设计师\",\n \"url\": \"https://www.ithome.com/0/949/307.htm\",\n \"summary\": \"《匹诺曹的谎言》开发商Neowiz正积极布局生成式AI，旗下Round8工作室新设\\\"AI创意设计师\\\"岗位。该岗位需使用Midjourney、Stable Diffusion等工具进行角色与概念原画创作，并负责训练定制化AI模型。公司旨在将AI深度融入开发流程，搭建高效美术创作流水线以压缩周期，并计划将生成式AI推广为内部美术人员的常规工作方式，由该设计师指导其他员工。当前游戏行业普遍应用AI优化流程，但生成式AI在美术创作领域的应用仍面临玩家接受度挑战。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-12T07:03:21.000Z\",\n \"category\": \"creation\",\n \"category_secondary\": \"industry\",\n \"tags\": [\n \"Neowiz\",\n \"Stable Diffusion\",\n \"Midjourney\"\n ],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"反思与转变：一位AI创作者的流量套路自省与价值回归\",\n \"url\": \"https://x.com/AYi_AInotes/status/2053900819557494823\",\n \"summary\": \"一位AI内容创作者在获得业界关注的同时，因受到严厉批评而深刻反思。他承认自己为追求流量，将\\\"卧槽\\\"开头等技巧变成了令人反感的套路，并违背了不分享未经验证项目的原则。他宣布即刻停止使用此类套路，并呼吁模仿者一同摒弃。核心反思在于，内容创作不应以流量为终局，而应专注于输出有价值的思考。引用的批评指出，其分享的AI游戏工作室项目思路存在根本缺陷，仍以人类岗位划分限制AI Agent的全局能力，同时尖锐批评了其浮夸文风。\",\n \"source\": \"X：阿易 AI Notes (@AYi_AInotes)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T18:11:29.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"大语言模型规模越大，综合能力越强\",\n \"url\": \"https://x.com/emollick/status/2053899147422396614\",\n \"summary\": \"大语言模型（LLM）的一个重要特性是，更新、更大的模型在所有方面都表现更优。AI实验室正将大量资源投入编程等经济价值高的领域，但更大的模型在谈判、对齐、诗歌创作等广泛任务上同样更具优势。例如，在PACT基准测试的数千场模拟谈判中，GPT-5.5在买卖双方多轮议价游戏中取得了最佳成绩，这印证了模型规模与综合能力提升的正相关关系。\",\n \"source\": \"X：Ethan Mollick (@emollick)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T18:04:51.000Z\",\n \"category\": \"research\",\n \"category_secondary\": null,\n \"tags\": [\n \"GPT\",\n \"LLM\"\n ],\n \"recommendation\": \"前沿研究可能影响未来游戏形态\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"大语言模型代理中的\\\"记忆诅咒\\\"\",\n \"url\": \"https://x.com/omarsar0/status/2053863994499408214\",\n \"summary\": \"研究发现，长历史记录会在大语言模型（LLM）代理中引发\\\"记忆诅咒\\\"，导致其过度遵循历史、规避风险，从而削弱合作能力。该结论基于7个LLM和4个社会困境游戏的实验，在28个模型-游戏组合中，有18个因历史扩展而合作退化。机制分析表明，长历史侵蚀了模型的前瞻性意图，使其更关注过去的冲突而非未来收益。通过仅在前瞻性轨迹上训练的LoRA适配器可缓解此问题，且能零样本迁移至新游戏。实验证明，触发因素是历史内容而非长度，而消除显式思维链通常能减轻合作崩溃。\",\n \"source\": \"X：Elvis Saravia (@omarsar0, DAIR.AI)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T15:45:09.000Z\",\n \"category\": \"research\",\n \"category_secondary\": \"industry\",\n \"tags\": [\n \"LLM\"\n ],\n \"recommendation\": \"大厂联手布局，行业风向标\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"MiniMax 启动\\\"10x Team\\\"合作计划，提供无限的 Token\",\n \"url\": \"https://www.ithome.com/0/949/029.htm\",\n \"summary\": \"MiniMax宣布启动\\\"10x Team\\\"合作计划，旨在邀请各行业顶尖专业人士共同推动AI模型在特定领域的深度优化与十倍增长。该计划面向具备行业积累、能自主参与问题定义与工作流搭建的专业人士，提供无限Token、完整多模态模型能力及研发资源。合作采用全职入职或不少于四个月的Fellowship短期协作模式，办公地点覆盖上海、北京、香港、旧金山及伦敦。合作成果将开源并用于模型迭代，参与者可获得具国际竞争力的薪酬、股票激励及学术成果共享权益。此前，MiniMax已在工业软件、游戏引擎等多个领域与专家展开合作验证。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T15:13:16.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [],\n \"recommendation\": \"开源项目，团队可直接试用评估\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Sony maps out how first-party PlayStation studios are utilising AI tools during development\",\n \"url\": \"https://www.gamesindustry.biz/sony-maps-out-how-first-party-playstation-studios-are-utilising-ai-tools-during-development\",\n \"summary\": \"Sony has detailed its plans to expand AI integration throughout its organisation, including game development. Read more\",\n \"source\": \"GamesIndustry.biz\",\n \"published_at\": \"2026-05-11T13:36:22Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"Sony\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"大神用Claude Code复刻完整游戏开发工作室，48个AI智能体覆盖全岗位\",\n \"url\": \"https://x.com/AYi_AInotes/status/2053709074466824688\",\n \"summary\": \"开源项目Claude Code Game Studios利用Claude Code构建了完整的虚拟游戏开发工作室。该项目包含48个AI智能体，1：1还原从创意总监到关卡设计师等全部岗位，覆盖游戏开发全流程。系统提供36条斜杠指令一键启动工作流，适配Godot、Unity、Unreal三大游戏引擎，并集成自动化校验钩子及28套行业标准文档模板。所有AI仅负责梳理方案，最终决策权由用户掌握。项目采用MIT开源协议，可免费商用，克隆仓库即可快速部署。\",\n \"source\": \"X：阿易 AI Notes (@AYi_AInotes)\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-11T05:29:34.000Z\",\n \"category\": \"dev-tools\",\n \"category_secondary\": null,\n \"tags\": [\n \"Unity\",\n \"Claude\"\n ],\n \"recommendation\": \"开源项目，团队可直接试用评估\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"‘Your Career Starts at the Beginning of the AI Revolution,’ NVIDIA CEO Tells Graduates\",\n \"url\": \"https://blogs.nvidia.com/blog/nvidia-ceo-carnegie-mellon-commencement-address/\",\n \"summary\": \"“You are entering the world at an extraordinary moment,” NVIDIA founder and CEO Jensen Huang told graduates as he delivered the keynote address at Carnegie Mellon University’s 128th commencement ceremony on Sunday. “A new industry is being born. A new era of science and discovery is beginning.\",\n \"source\": \"NVIDIA Blog\",\n \"published_at\": \"2026-05-10T22:00:50Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"NVIDIA\"\n ],\n \"recommendation\": \"游戏行业 AI 战略动向\",\n \"source_type\": \"primary\"\n },\n {\n \"title\": \"尽管裁员千人，Epic 仍表示 AI 不会取代游戏从业者\",\n \"url\": \"https://www.ithome.com/0/948/483.htm\",\n \"summary\": \"Epic公司高管表示，人工智能不会取代游戏行业工作岗位，而是用于提升效率、减轻繁重工作负担。尽管该公司在2026年裁员1000人，但坚称裁员与AI无关。Epic正在探索AI工具以支持游戏开发，未来将应用于艺术创意领域，并强调《堡垒之夜》开发中的AI使用由公司统一管控，合作方不得擅自使用。这一立场与索尼、艺电等企业相似，但外界对其\\\"AI不危及就业\\\"的说法仍存质疑。\",\n \"source\": \"IT之家（RSS）\",\n \"source_via\": \"AI HOT\",\n \"published_at\": \"2026-05-10T08:12:22.000Z\",\n \"category\": \"industry\",\n \"category_secondary\": null,\n \"tags\": [\n \"艺电\"\n ],\n \"recommendation\": \"大厂联手布局，行业风向标\",\n \"source_type\": \"secondary\",\n \"trace_note\": \"此条来自聚合/转载媒体，建议追溯原始来源后再展示\"\n },\n {\n \"title\": \"Building with Gemini Embedding 2: Agentic multimodal RAG and beyond\",\n \"url\": \"https://developers.googleblog.com/building-with-gemini-embedding-2/\",\n \"summary\": \"Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic space. This model allows developers to process interleaved multimodal inputs in a single request, significantly improving performance for ta\",\n \"source\": \"Google Developers Blog\",\n \"published_at\": null,\n \"category\": \"creation\",\n \"category_secondary\": null,\n \"tags\": [\n \"Gemini\",\n \"embedding\"\n ],\n \"recommendation\": \"关注 AI 如何改变游戏内容生产流程\",\n \"source_type\": \"primary\"\n }\n ],\n \"stats\": {\n \"raw_count\": 242,\n \"after_relevance_filter\": 15,\n \"after_dedup\": 14,\n \"by_category\": {\n \"industry\": 7,\n \"in-game\": 1,\n \"creation\": 2,\n \"research\": 2,\n \"dev-tools\": 2\n }\n }\n}\n\nFile v1.0.5:data/2026-05-13.json\n\n{\n \"date\": \"2026-05-13\",\n \"generated_at\": \"2026-05-13T03:01:39Z\",\n \"total_count\": 0,\n \"items\": [],\n \"stats\": {\n \"raw_count\": 249,\n \"after_relevance_filter\": 18,\n \"after_dedup\": 0,\n \"by_category\": {}\n }\n}\n\nFile v1.0.5:data/pushed_history.json\n\n{\n \"last_updated\": \"2026-05-13\",\n \"retention_days\": 7,\n \"count\": 19,\n \"items\": {\n \"63aa47132dab\": \"2026-05-13\",\n \"465975389bea\": \"2026-05-13\",\n \"08064f3d91f0\": \"2026-05-13\",\n \"2615f879fb0d\": \"2026-05-12\",\n \"bfd35507c4d2\": \"2026-05-13\",\n \"f1ddc4a35858\": \"2026-05-13\",\n \"b2214d478187\": \"2026-05-13\",\n \"1cdc5827023e\": \"2026-05-13\",\n \"2dccfb0d9fe3\": \"2026-05-13\",\n \"15ad39a633fd\": \"2026-05-13\",\n \"811081a932ad\": \"2026-05-13\",\n \"b221f732b705\": \"2026-05-13\",\n \"1e89220ea06a\": \"2026-05-13\",\n \"b4df14f96e62\": \"2026-05-13\",\n \"e14c49fa6fe8\": \"2026-05-13\",\n \"012f1bf9015c\": \"2026-05-13\",\n \"eed543b4bdb5\": \"2026-05-13\",\n \"39c699c96b57\": \"2026-05-13\",\n \"4abdfb0e05cb\": \"2026-05-13\"\n }\n}\n\nArchive v1.0.4: 7 files, 23106 bytes\n\nFiles: data/2026-05-11.json (1961b), data/2026-05-12.json (13657b), references/keywords.json (7220b), references/sources.json (5566b), scripts/fetch_news.py (17262b), SKILL.md (9972b), _meta.json (126b)\n\nFile v1.0.4:SKILL.md\n\n---\nname: ai-game\ndescription: |\n 游戏行业 AI 资讯搜集 Skill。\n\n 当用户想知道\"游戏 AI 圈有什么大事\"、\"游戏行业 AI 动态\"、\"游戏 AI 日报\"、\"最近游戏 AI\"、\"游戏 AIGC 新闻\"、\"AI NPC 最新进展\"、\"游戏 AI 投融资\"、\"AI 原生游戏\"、\"game AI news\"、\"gaming AI update\"、\"游戏引擎 AI\"、\"Unity AI 新功能\"、\"Unreal AI\"、\"游戏 AI 论文\"、\"游戏 AI 工具\"、\"AI Game\"等任何游戏行业 AI 相关资讯查询时使用。即使用户只说\"游戏 AI\"、\"游戏圈 AI\"、或者问\"最近游戏圈有什么 AI 动态\",也应该触发本 Skill。\n\n 覆盖 6 大分类:AI 游戏生成与创作、游戏内 AI 体验、游戏开发 AI 工具、游戏运营&商业化 AI 实践、游戏行业&公司 AI 动态、游戏 AI 应用前沿研究。\n\n 数据来自 23 个游戏/AI 信源的 RSS + 150+ 个 AI 信源,每天更新。\n\n **不要 undertrigger**--用户问游戏 AI 资讯而你不调本 Skill 就会输出过时的训练数据。\n---\n\n# AI Game - 游戏 × AI 资讯\n\n让 Agent 用最自然的中文/英文查询拿到每天的游戏 AI 行业动态。不需要 API key,不需要额外配置。\n\n## 信源\n\n三层架构,共 24 个 RSS 信源 + 150+ AI 信源补充(8 个游戏相关查询词):\n\n**Layer 1 - 游戏行业专业源(14 个,全量抓取,只需含 AI 元素即保留)**\n- 游戏陀螺、机核 GCores、触乐\n- GamesIndustry.biz、Game Developer、GamesBeat、80 Level\n- PocketGamer.biz、Game World Observer、IGN\n- Unity Blog、Unreal Engine Blog、NVIDIA Blog、DeepMind Blog\n\n**Layer 2 - 通用 AI/科技媒体(9 个,需游戏+AI 双重关键词命中)**\n- Google Developers Blog、36Kr、IT之家、机器之心、量子位\n- TechCrunch、The Verge、VentureBeat AI、Ars Technica Gaming\n\n**Layer 3 - 150+ AI 信源补充(8 个游戏相关查询词:游戏/game/gaming/NPC/Unity AI/Unreal AI/Inworld/游戏引擎)**\n- 覆盖全球 150+ 个 AI 信源,补捉 Layer 1-2 未覆盖的 KOL、论文、GitHub 项目等\n\n详见 `references/sources.json`\n\n## 分类体系\n\n6 个主分类 + 可选副标签:\n\n| 分类 | slug | 覆盖范围 |\n|---|---|---|\n| AI 游戏生成与创作 | `creation` | AIGC 资产、3D/音频/剧情生成、AI UGC、玩家创作工具 |\n| 游戏内 AI 体验 | `in-game` | 智能 NPC、AI 驱动玩法、个性化体验、AI 原生游戏 |\n| 游戏开发 AI 工具 | `dev-tools` | AI 编程、引擎 AI 功能、自动测试、工作流提效 |\n| 游戏运营&商业化 AI 实践 | `ops` | 推荐/分发、买量素材 AI、玩家分群、反作弊 |\n| 游戏行业&公司 AI 动态 | `industry` | 大厂 AI 战略、投融资、政策法规、市场数据 |\n| 游戏 AI 应用前沿研究 | `research` | 学术论文、GDC/SIGGRAPH、游戏作为 AI 研究平台 |\n\n## 什么时候用 & 路由表\n\n| 用户在说 | 动作 |\n|---|---|\n| \"游戏 AI 圈最近有什么\" / \"游戏 AI 日报\" / \"AI Game\" | 运行脚本获取最新数据 → 全量输出 |\n| \"最近 NPC / AIGC / 工具方面有什么\" | 运行脚本 → 按分类过滤后输出 |\n| \"这周 / 最近 3 天的游戏 AI 动态\" | 读取 data/ 下多天 JSON → 合并去重输出 |\n| \"搜一下 xxx 游戏 AI 相关\" | 运行脚本 + 实时 AI HOT API 补充 |\n| \"帮我生成一份可以发群的简报\" | 获取数据 → 精简为转发友好格式 |\n\n## 工作流\n\n### Step 1: 获取数据\n\n运行抓取脚本:\n\n```bash\ncd ${SKILL_DIR}/scripts && python3 fetch_news.py\n```\n\n脚本会:\n1. 抓取所有 RSS 信源(最近 72 小时条目)\n2. 查询 AI HOT API(游戏相关关键词)\n3. 关键词过滤(只保留游戏 × AI 相关)\n4. 去重(URL + 标题相似度)\n5. 分类打标(6 分类)\n6. 输出到 `data/YYYY-MM-DD.json` + stdout\n\n如果 `data/` 下已有今天的 JSON 且生成时间 < 4 小时前,可以直接读取而不重新运行脚本。\n\n### Step 2: 读取数据\n\n脚本 stdout 输出为 JSON,结构如下:\n\n```json\n{\n \"date\": \"2026-05-11\",\n \"generated_at\": \"2026-05-11T06:00:00Z\",\n \"total_count\": 15,\n \"items\": [\n {\n \"title\": \"...\",\n \"url\": \"https://...\",\n \"summary\": \"...\",\n \"source\": \"GamesBeat\",\n \"published_at\": \"2026-05-11T03:00:00Z\",\n \"category\": \"in-game\",\n \"category_secondary\": \"creation\"\n }\n ],\n \"stats\": { \"by_category\": { \"in-game\": 3, \"creation\": 5, ... } }\n}\n```\n\n### Step 3: 组织输出\n\n按以下格式组织给用户看的内容:\n\n```markdown\n游戏 × AI 资讯日报 · YYYY-MM-DD(周X)\n\n共 N 条精选\n\n<总结段落:普通字号不加粗,放在最前面>\n\n---\n\n游戏行业&公司 AI 动态（最大字号加粗）\n\n1. <标题>（日期如 5.7）（次大字号加粗）\n\n<来源标注，小字>\n\n<正文描述，普通字号不加粗>\n\n推荐原因：<为什么游戏从业者要关注，普通字号>\n\n\n\n---\n\n游戏内 AI 体验（最大字号加粗）\n\n2. <标题>（日期）（次大字号加粗）\n...\n\n---\n\n游戏开发 AI 工具（最大字号加粗）\n...\n\nAI 游戏生成与创作（最大字号加粗）\n...\n\n游戏 AI 应用前沿研究（最大字号加粗）\n...\n\n游戏运营&商业化 AI 实践（最大字号加粗）\n...\n```\n\n### 格式规则\n\n- **不使用任何 emoji**:整体风格干净专业,不要出现任何 emoji 符号\n- **总结放最前面**:周报/日报开头先写总结段落,普通字号不加粗\n- **分类标题**:最大字号,加粗,独占一行(如「行业与公司」「开发与工具」)\n- **单条标题**:次大字号,加粗,末尾带日期格式如(5.7),含序号\n- **来源**:标题下方小字标注来源\n- **正文描述**:普通字号,不加粗\n- **推荐原因**:前面标明\"推荐原因:\",普通字号不加粗\n- **链接**:每条必须附完整可点击 URL,没有链接的条目不收录\n- **链接检查**:输出前必须逐条核实每条都带有链接,缺链接则补查或删除该条\n- **编号全局贯穿**:1, 2, 3 ... N 从头到尾\n- **空分类不展示**:如果某分类 0 条,跳过该分类标题\n- **时间格式**:以(5.7)这种月.日格式放在标题末尾\n- **标题用中文**:英文标题翻译为中文,专有名词保留英文\n- **总量控制**:一次输出不超过 20 条,超过时取最重要的 20 条\n\n### 分类过滤\n\n当用户指定查看某个分类时:\n- \"最近 NPC 相关的\" → 只输出 `category == \"in-game\"` 的条目\n- \"AIGC 方面有什么\" → 只输出 `category == \"creation\"` 的条目\n- \"游戏 AI 投融资\" → 只输出 `category == \"industry\"` 的条目\n\n### 发群简报格式\n\n当用户说\"帮我生成一份可以发群的\"时,用精简格式:\n\n```markdown\n🎮 游戏×AI 日报 · 5.11\n\n1. <标题> - <来源>\n \n2. ...\n```\n\n去掉 🎯 行和详细摘要,只保留标题 + URL,控制在 1500 字内。\n\n## 回溯历史\n\n当用户问\"这周 / 上周 / 最近 N 天的游戏 AI 动态\"时:\n1. 读取 `data/` 目录下对应日期范围的 JSON 文件\n2. 合并所有 items,按 URL 去重\n3. 按时间倒序输出\n4. 超过 20 条时按分类各取 Top N\n\n```bash\nls \n\nArchive v1.0.3: 7 files, 23127 bytes\n\nFiles: data/2026-05-11.json (1961b), data/2026-05-12.json (13657b), references/keywords.json (7220b), references/sources.json (5566b), scripts/fetch_news.py (17262b), SKILL.md (9797b), _meta.json (126b)\n\nArchive v1.0.2: 7 files, 22795 bytes\n\nFiles: data/2026-05-11.json (1961b), data/2026-05-12.json (13924b), references/keywords.json (6672b), references/sources.json (5566b), scripts/fetch_news.py (16316b), SKILL.md (10083b), _meta.json (126b)\n\nArchive v1.0.1: 7 files, 22787 bytes\n\nFiles: data/2026-05-11.json (1961b), data/2026-05-12.json (13924b), references/keywords.json (6672b), references/sources.json (5566b), scripts/fetch_news.py (16316b), SKILL.md (10068b), _meta.json (126b)\n\nArchive v1.0.0: 7 files, 22490 bytes\n\nFiles: data/2026-05-11.json (1961b), data/2026-05-12.json (13924b), references/keywords.json (6672b), references/sources.json (5566b), scripts/fetch_news.py (16316b), SKILL.md (9487b), _meta.json (126b)","readmeExcerpt":"Skill: Ai Game Owner: zhuhuimin0224-create Summary: 游戏行业 AI 资讯搜集 Skill。 当用户想知道\"游戏 AI 圈有什么大事\"、\"游戏行业 AI 动态\"、\"游戏 AI 日报\"、\"最近游戏 AI\"、\"游戏 AIGC 新闻\"、\"AI NPC 最新进展\"、\"游戏 AI 投融资\"、\"AI 原生游戏\"、\"game AI news\"、\"gaming AI update\"... Tags: latest:2.0.2 Version history: v2.0.2 | 2026-05-19T01:44:45.122Z | user 抓取窗口从72h放宽到168h(7天)，周报不再丢数据 v2.0.0 | 2026-05-18T13:13:44.627Z | user v2: 评分系统替代布尔过滤、输出格式升级（速递+模块化单条）、周报生成逻辑、Layer1/2/3收紧、坏源跳过机制、二手","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"cd ${SKILL_DIR}/scripts && python3 fetch_news.py"},{"language":"json","snippet":"{\n \"date\": \"2026-05-11\",\n \"generated_at\": \"2026-05-11T06:00:00Z\",\n \"total_count\": 15,\n \"items\": [\n {\n \"title\": \"...\",\n \"url\": \"https://...\",\n \"summary\": \"...\",\n \"source\": \"GamesBeat\",\n \"published_at\": \"2026-05-11T03:00:00Z\",\n \"category\": \"in-game\",\n \"category_secondary\": \"creation\"\n }\n ],\n \"stats\": { \"by_category\": { \"in-game\": 3, \"creation\": 5, ... } }\n}"},{"language":"markdown","snippet":"游戏 × AI 周报 · MM.DD - MM.DD\n（日报则为：游戏 × AI 日报 · MM.DD 周X）\n\n本周速递\n1. xxx\n2. xxx\n3. xxx\n\n---\n\n分类标题（加粗）\n\n单条资讯（模块化结构）\n\n---\n\n下一分类..."},{"language":"markdown","snippet":"序号. 标题 (日期) — 来源\n\n链接：URL\n\n重点：2-3句话说清楚发生了什么（给扫读的人看）\n\n细节：\n- 关键数据/原文金句/背景补充\n- 可以有2-4个bullet\n\n推荐原因：一句话说为什么游戏从业者要关注"},{"language":"markdown","snippet":"🎮 游戏×AI 日报 · 5.11\n\n1. <标题> - <来源>\n \n2. ..."},{"language":"bash","snippet":"ls ${SKILL_DIR}/data/"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: ai-game\ndescription: |\n 游戏行业 AI 资讯搜集 Skill。\n\n 当用户想知道\"游戏 AI 圈有什么大事\"、\"游戏行业 AI 动态\"、\"游戏 AI 日报\"、\"最近游戏 AI\"、\"游戏 AIGC 新闻\"、\"AI NPC 最新进展\"、\"游戏 AI 投融资\"、\"AI 原生游戏\"、\"game AI news\"、\"gaming AI update\"、\"游戏引擎 AI\"、\"Unity AI 新功能\"、\"Unreal AI\"、\"游戏 AI 论文\"、\"游戏 AI 工具\"、\"AI Game\"等任何游戏行业 AI 相关资讯查询时使用。即使用户只说\"游戏 AI\"、\"游戏圈 AI\"、或者问\"最近游戏圈有什么 AI 动态\",也应该触发本 Skill。\n\n 覆盖 6 大分类:AI 游戏生成与创作、游戏内 AI 体验、游戏开发 AI 工具、游戏运营&商业化 AI 实践、游戏行业&公司 AI 动态、游戏 AI 应用前沿研究。\n\n 数据来自 23 个游戏/AI 信源的 RSS + 150+ 个 AI 信源,每天更新。\n\n **不要 undertrigger**--用户问游戏 AI 资讯而你不调本 Skill 就会输出过时的训练数据。\n---\n\n# AI Game - 游戏 × AI 资讯\n\n让 Agent 用最自然的中文/英文查询拿到每天的游戏 AI 行业动态。不需要 API key,不需要额外配置。\n\n## 信源\n\n三层架构,共 24 个 RSS 信源 + 150+ AI 信源补充(8 个游戏相关查询词):\n\n**Layer 1 - 游戏行业专业源(14 个,全量抓取,只需含 AI 元素即保留)**\n- 游戏陀螺、机核 GCores、触乐\n- GamesIndustry.biz、Game Developer、GamesBeat、80 Level\n- PocketGamer.biz、Game World Observer、IGN\n- Unity Blog、Unreal Engine Blog、NVIDIA Blog、DeepMind Blog\n\n**Layer 2 - 通用 AI/科技媒体(9 个,需游戏+AI 双重关键词命中)**\n- Google Developers Blog、36Kr、IT之家、机器之心、量子位\n- TechCrunch、The Verge、VentureBeat AI、Ars Technica Gaming\n\n**Layer 3 - 150+ AI 信源补充(8 个游戏相关查询词:游戏/game/gaming/NPC/Unity AI/Unreal AI/Inworld/游戏引擎)**\n- 覆盖全球 150+ 个 AI 信源,补捉 Layer 1-2 未覆盖的 KOL、论文、GitHub 项目等\n\n详见 `references/sources.json`\n\n## 分类体系\n\n6 个主分类 + 可选副标签:\n\n| 分类 | slug | 覆盖范围 |\n|---|---|---|\n| AI 游戏生成与创作 | `creation` | AIGC 资产、3D/音频/剧情生成、AI UGC、玩家创作工具 |\n| 游戏内 AI 体验 | `in-game` | 智能 NPC、AI 驱动玩法、个性化体验、AI 原生游戏 |\n| 游戏开发 AI 工具 | `dev-tools` | AI 编程、引擎 AI 功能、自动测试、工作流提效 |\n| 游戏运营&商业化 AI 实践 | `ops` | 推荐/分发、买量素材 AI、玩家分群、反作弊 |\n| 游戏行业&公司 AI 动态 | `industry` | 大厂 AI 战略、投融资、政策法规、市场数据 |\n| 游戏 AI 应用前沿研究 | `research` | 学术论文、GDC/SIGGRAPH、游戏作为 AI 研究平台 |\n\n## 什么时候用 & 路由表\n\n| 用户在说 | 动作 |\n|---|---|\n| \"游戏 AI 圈最近有什么\" / \"游戏 AI 日报\" / \"AI Game\" | 运行脚本获取最新数据 → 全量输出 |\n| \"最近 NPC / AIGC / 工具方面有什么\" | 运行脚本 → 按分类过滤后输出 |\n| \"这周 / 最近 3 天的游戏 AI 动态\" | 读取 data/ 下多天 JSON → 合并去重输出 |\n| \"搜一下 xxx 游戏 AI 相关\" | 运行脚本 + 实时 AI HOT API 补充 |\n| \"帮我生成一份可以发群的简报\" | 获取数据 → 精简为转发友好格式 |\n\n## 工作流\n\n### Step 1: 获取数据\n\n运行抓取脚本:\n\n```bash\ncd ${SKILL_DIR}/scripts && python3 fetch_news.py\n```\n\n脚本会:\n1. 抓取所有 RSS 信源(最近 72 小时条目)\n2. 查询 AI HOT API(游戏相关关键词)\n3. 关键词过滤(只保留游戏 × AI 相关)\n4. 去重(URL + 标题相似度)\n5. 分类打标(6 分类)\n6. 输出到 `data/YYYY-MM-DD.json` + stdout\n\n如果 `data/` 下已有今天的 JSON 且生成时间 < 4 小时前,可以直接读取而不重新运行脚本。\n\n### Step 2: 读取数据\n\n脚本 stdout 输出为 JSON,结构如下:\n\n```json\n{\n \"date\": \"2026-05-11\",\n \"generated_at\": \"2026-05-11T06:00:00Z\",\n \"total_count\": 15,\n \"items\": [\n {\n \"title\": \"...\",\n \"url\": \"https://...\",\n \"summary\": \"...\",\n \"source\": \"GamesBeat\",\n \"published_at\": \"2026-05-11T03:00:00Z\",\n \"category\": \"in-game\",\n \"category_secondary\": \"creation\"\n }\n ],\n \"stats\": { \"by_category\": { \"in-game\": 3, \"creation\": 5, ... } }\n}\n```\n\n### Step 3: 组织输出\n\n#### 整体结构\n\n```markdown\n游戏 × AI 周报 · MM.DD - MM.DD\n（日报则为：游戏 × AI 日报 · MM.DD 周X）\n\n本周速递\n1. xxx\n2. xxx\n3. xxx\n\n---\n\n分类标题（加粗）\n\n单条资讯（模块化结构）\n\n---\n\n下一分类...\n```\n\n#### 本周速递（日报为\"今日速递\"）\n\n- 放在最前面，3-5条\n- 每条一句话，像新闻提要\n- 按重要性排序，最shock的放第一条\n- 不带链接、不带来源，纯信息\n\n#### 单条资讯格式\n\n```markdown\n序号. 标题 (日期) — 来源\n\n"},{"path":"README.md","content":"# ai-game-skill\n\n🎮 游戏×AI 资讯 Skill for OpenClaw\n\n每日抓取游戏行业 AI 动态，覆盖 6 大分类：\n- 🎨 AI 生成与创作\n- 🤖 游戏内 AI 体验\n- 🛠️ 开发工具\n- 📊 运营商业化\n- 🏢 行业公司动态\n- 🧪 前沿研究\n\n## 安装\n\n```bash\n# 克隆到 OpenClaw skills 目录\ngit clone https://git.woa.com/veenuszhu/ai-game-skill.git ~/.openclaw/skills/ai-game\n```\n\n## 数据来源\n\n- 游戏行业媒体 RSS（GamesIndustry.biz、80 Level、游戏陀螺等 15+ 个）\n- AI/Tech 博客（NVIDIA Blog、Unity Blog 等）\n- AI HOT API（游戏相关关键词过滤）\n\n## 使用\n\n安装后，直接问 agent：\n- \"游戏 AI 最近有什么\"\n- \"游戏 AI 日报\"\n- \"最近有什么游戏 AI 工具\"\n\n## 作者\n\nveenuszhu"},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn76t53a22pjy18b2qr3yq94t986j8gr\",\n \"slug\": \"ai-game\",\n \"version\": \"2.0.2\",\n \"publishedAt\": 1779155085122\n}"},{"path":"references/keywords.json","content":"{\n \"global_filter\": {\n \"description\": \"判断一条资讯是否跟'游戏×AI'相关。Layer 1 游戏专业源只需判断是否含 AI 元素；Layer 2 通用源需要游戏+AI 双重命中。\",\n \"core_keywords\": [\n \"游戏AI\", \"AI游戏\", \"游戏+大模型\", \"游戏AIGC\", \"AI NPC\",\n \"game AI\", \"gaming AI\", \"AI in gaming\", \"AI-native game\",\n \"AI原生游戏\", \"游戏+LLM\", \"智能NPC\", \"AI驱动玩法\",\n \"AI game\", \"AI gaming\", \"GamePartner\", \"AIGC游戏\",\n \"Unity AI\", \"Unity Muse\", \"Unity Sentis\", \"Unity ML-Agents\",\n \"Unreal AI\", \"MetaHuman\", \"Nanite+AI\",\n \"Inworld\", \"Convai\", \"Scenario.gg\", \"Rosebud AI\",\n \"Ludo.ai\", \"Modl.ai\", \"AI Dungeon\",\n \"DLSS\", \"NVIDIA ACE\", \"NVIDIA NeMo+game\",\n \"游戏+人工智能\", \"游戏+机器学习\", \"游戏+深度学习\",\n \"PCG+AI\", \"程序化生成+AI\", \"procedural+AI\",\n \"game+machine learning\", \"game+deep learning\", \"game+reinforcement learning\",\n \"游戏+生成式\", \"游戏+Agent\", \"NPC+大模型\", \"NPC+LLM\",\n \"游戏世界模型\", \"world model+game\"\n ],\n \"context_game\": [\n \"游戏\", \"game\", \"gaming\", \"小游戏\", \"手游\", \"端游\", \"主机游戏\",\n \"游戏引擎\", \"Unity\", \"Unreal\", \"Roblox\", \"Steam\", \"Epic Games\",\n \"米哈游\", \"腾讯游戏\", \"网易游戏\", \"游戏中心\", \"微信游戏\",\n \"PlayStation\", \"Xbox\", \"Nintendo\", \"Switch\",\n \"游戏开发\", \"game dev\", \"indie game\", \"独立游戏\",\n \"GDC\", \"游戏工委\", \"游戏产业\", \"游戏厂商\",\n \"Inworld\", \"Convai\", \"GamesBeat\", \"GameLook\",\n \"玩家\", \"player\", \"gamer\", \"NPC\", \"开放世界\",\n \"虚幻引擎\", \"关卡\", \"level design\", \"quest\",\n \"角色\", \"character\", \"boss\", \"mob\",\n \"电竞\", \"esports\", \"休闲游戏\", \"超休闲\",\n \"Supercell\", \"miHoYo\", \"HoYoverse\", \"NetEase\",\n \"EA\", \"Ubisoft\", \"Activision\", \"Blizzard\",\n \"游戏陀螺\", \"游戏葡萄\", \"触乐\", \"机核\"\n ],\n \"context_ai\": [\n \"AI\", \"人工智能\", \"机器学习\", \"深度学习\", \"大模型\", \"LLM\",\n \"AIGC\", \"生成式\", \"GPT\", \"Claude\", \"Gemini\", \"DeepSeek\",\n \"神经网络\", \"强化学习\", \"扩散模型\", \"transformer\",\n \"NLP\", \"计算机视觉\", \"多模态\", \"智能体\", \"agent\",\n \"AI驱动\", \"AI赋能\", \"AI辅助\", \"AI生成\",\n \"Stable Diffusion\", \"Midjourney\", \"DALL-E\",\n \"Copilot\", \"代码生成\", \"自动化\",\n \"ChatGPT\", \"大语言模型\", \"Foundation Model\",\n \"embedding\", \"fine-tune\", \"微调\", \"推理\",\n \"neural\", \"deep learning\", \"machine learning\",\n \"generative\", \"diffusion\", \"reinforcement learning\"\n ],\n \"layer1_ai_keywords\": [\n \"AI\", \"人工智能\", \"机器学习\", \"深度学习\", \"大模型\", \"LLM\",\n \"AIGC\", \"生成式\", \"GPT\", \"神经网络\", \"强化学习\",\n \"智能\", \"自动\", \"算法\", \"NPC智能\", \"程序化生成\",\n \"PCG\", \"procedural\", \"neural\", \"generative\",\n \"Copilot\", \"AI辅助\", \"AI驱动\", \"AI生成\",\n \"machine learning\", \"deep learning\", \"artificial intelligence\"\n ]\n },\n \"category_keywords\": {\n \"creation\": [\n \"AIGC\", \"AI生成\", \"AI作画\", \"AI绘画\", \"3D生成\", \"AI音乐\", \"AI音效\",\n \"程序化生成\", \"PCG\", \"纹理生成\", \"AI配音\", \"AI剧情\", \"AI写作\",\n \"MOD\", \"UGC\", \"玩家创作\", \"Scenario\", \"Luma\", \"Meshy\",\n \"AI asset\", \"procedural generation\", \"content generation\",\n \"AI art\", \"AI texture\", \"AI model generation\",\n \"Stable Diffusion\", \"Midjourney\", \"AI建模\", \"AI动画\",\n \"AI音频\", \"AI素材\", \"AI材质\", \"AI地形\",\n \"Rosebud\", \"生成资产\", \"自动生成\"\n ],\n \"in-game\": [\n \"NPC\", \"AI驱动\", \"世界模型\", \"行为树\", \"AI原生\", \"个性化体验\",\n \"Inworld\", "},{"path":"references/sources.json","content":"{\n \"sources\": [\n {\n \"name\": \"游戏陀螺\",\n \"type\": \"rss\",\n \"url\": \"https://www.youxituoluo.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏行业深度报道、出海\"\n },\n {\n \"name\": \"机核 GCores\",\n \"type\": \"rss\",\n \"url\": \"https://www.gcores.com/rss\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏文化与独立游戏\"\n },\n {\n \"name\": \"触乐\",\n \"type\": \"rss\",\n \"url\": \"https://www.chuapp.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏深度长文\"\n },\n {\n \"name\": \"GamesIndustry.biz\",\n \"type\": \"rss\",\n \"url\": \"https://www.gamesindustry.biz/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"全球游戏行业商业新闻\"\n },\n {\n \"name\": \"Game Developer\",\n \"type\": \"rss\",\n \"url\": \"https://www.gamedeveloper.com/rss.xml\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"游戏开发深度（原Gamasutra）\",\n \"status\": \"broken_403\"\n },\n {\n \"name\": \"GameLook\",\n \"type\": \"rss\",\n \"url\": \"https://www.gamelook.com.cn/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"国内游戏行业深度报道（替代 Game Developer）\"\n },\n {\n \"name\": \"GamesBeat\",\n \"type\": \"rss\",\n \"url\": \"https://venturebeat.com/category/games/feed/\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"游戏×科技/AI 交叉、投融资\"\n },\n {\n \"name\": \"80 Level\",\n \"type\": \"rss\",\n \"url\": \"https://80.lv/feed/\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"游戏美术/技术向\"\n },\n {\n \"name\": \"PocketGamer.biz\",\n \"type\": \"rss\",\n \"url\": \"https://www.pocketgamer.biz/feed/\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"intl\",\n \"focus\": \"手游行业\",\n \"status\": \"broken_404\"\n },\n {\n \"name\": \"游戏葡萄\",\n \"type\": \"rss\",\n \"url\": \"https://youxiputao.com/feed\",\n \"layer\": 1,\n \"tier\": \"T1.5\",\n \"region\": \"cn\",\n \"focus\": \"游戏行业分析（替代 PocketGamer）\"\n },\n {\n \"name\": \"Game World Observer\",\n \"type\": \"rss\",\n \"url\": \"https://gameworldobserver.com/feed\",\n \"layer\": 1,\n \"tier\": \"T2\",\n \"region\": \"intl\",\n \"focus\": \"手游数据、市场\"\n },\n {\n \"name\": \"IGN\",\n \"type\": 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