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agent-prophet topic-pool + weekly-draft integration; 4-month silent-failure lesson archived; unified 3 divergent copies\n\nv3.0.3 | 2026-05-09T14:19:26.737Z | user\n\nv3.0.3 — Fix internal version strings (v2.0 → v3.0 in generate-briefing.py docstrings and output template)\n\nv3.0.2 | 2026-05-09T13:31:21.315Z | user\n\nv3.0.2 — Remove runtime data files from package (data/ auto-created on first run), add auto-mkdir for briefing output\n\nv3.0.1 | 2026-05-09T10:16:46.147Z | user\n\nv3.0.1 — Remove personal Feishu user ID from BRIEFING_CONFIG.md (privacy fix)\n\nv3.0.0 | 2026-05-09T01:51:52.202Z | user\n\nv3.0 — Major portability fix: 1) Moved rss-crawler.py into scripts/ (was external), 2) All data paths now relative to {baseDir}/data/ (no hardcoded absolute paths), 3) Added bilingual trigger words (Chinese + English), 4) Auto-creates data/ subdirs on first run, 5) Added Dependencies section in SKILL.md\n\nv2.2.1 | 2026-05-02T06:59:06.439Z | user\n\nFix trigger examples: all English, add bilingual patterns\n\nv2.2.0 | 2026-05-02T06:55:02.295Z | user\n\nFull English rewrite; cron removed, now channel-agnostic\n\nv2.1.0 | 2026-05-02T06:46:18.325Z | user\n\nv2.1: Remove cron/Feishu push — delivery channel is user-preference, not hardcoded. Skill is now channel-agnostic.\n\nv2.0.0 | 2026-05-02T06:33:31.397Z | user\n\nInitial release: 5-track AI frontier intelligence briefing with scoring, tiering, signal detection, arXiv/GitHub/36kr integration\n\nArchive index:\n\nArchive v1.1.0: 5 files, 7192 bytes\n\nFiles: generate-briefing.py (2775b), references/BRIEFING_CONFIG.md (2149b), skill-card.md (1706b), SKILL.md (5252b), _meta.json (138b)\n\nFile v1.1.0:SKILL.md\n\n---\nname: ai-frontier-monitor\ndescription: \"AI Frontier intelligence collection and WeChat draft pipeline (Signal->Validate->Build->Ship->Reflect). Use when building AI frontier monitoring briefings or WeChat article drafts from RSS/arXiv/GitHub sources.\"\n---\n\n# AI Frontier Monitor — 情报简报 + 微信草稿管线\n\n> v1.1（2026-10-09 更新）· OPC 首个业务 · Skill Graphs 2.0 架构（原子→分子→复合体）\n> 核心理念：信息聚合 ≠ 信息堆砌——产出是经过筛选、分层、排序的每日简报，不是 50 条标题的噪音。\n\n## 架构\n\n```\n原子层 fetch_rss_candidates（11 RSS 源）/ fetch_web_search（Anthropic 案例 + 咨询公司 + 突发补漏）/ score_candidate（0-5 评分）\n分子层 aggregate_intelligence（合并去重）/ generate_briefing（三层结构化简报）\n复合层 每日简报归档 → agent-prophet 选题池输入 → 周稿骨架 → 微信草稿 → Ship/Reflect 复盘\n```\n\n## 数据源矩阵\n\n| 渠道 | 类型 | 优先级 |\n|---|---|---|\n| OpenAI Blog / AWS ML Blog | RSS | ⭐⭐⭐⭐⭐~⭐⭐⭐⭐（客户案例最权威） |\n| Microsoft AI Blog（Copilot 落地） | RSS | ⭐⭐⭐⭐ |\n| Techmeme / Product Hunt / HN Show+Ask HN | RSS | ⭐⭐⭐~⭐⭐⭐⭐ |\n| 36氪 / Dev.to | RSS | ⭐⭐⭐ |\n| Anthropic 案例补搜（site:anthropic.com customer/case study/enterprise） | web_search | ⭐⭐⭐⭐⭐ |\n| arXiv 论文 / GitHub Trending | 专轨 | 各 ≤3 |\n| 微信文章（wechat-curator）/ 知识星球（zsxq-helper） | 协作 skill | ⭐⭐⭐⭐ |\n\n完整源配置与推送阈值：`references/BRIEFING_CONFIG.md`\n\n## Pipeline 五段（OPC v2.2 章程）\n\n**Signal**（收集+评分）→ **Validate**（三选二：对 Zenz 定位有直接价值 / 有独特视角非新闻搬运 / 时效本周内；Altman+Machi 各 1 票，分歧强制双向找反证；2 次验证失败换选题方向）→ **Build**（草稿生成）→ **Ship**（Zenz 发布）→ **Reflect**（2 周后数据复盘：阅读量>基准线、新增关注、留言/投票、转发引用；通过→沉淀内容策略，未通过→归因调向）\n\n## 评分与分层\n\n评分维度：企业真实落地 40%（真实企业名+部署规模）/ 数据支撑 20%（量化 ROI）/ 可学习性 20%（方法论+避坑）/ 前沿性 20%；**≥3 分入候选池**。\n\n| 层级 | 定义 | 上限 |\n|---|---|---|\n| 🔴 核心情报 | ≥4 分 + 数据支撑 | 3 条 |\n| 🟡 值得关注 | 3 分或待验证 | 5 条 |\n| 🟢 快速浏览 | 2 分趋势信号 | 5 条 |\n\n每日总量 ≤13 条；无高质量候选 → `NO_REPLY`；同企业/产品去重保留最高分；连续 3 天候选 <3 条 → 触发关键词审查。\n\n## 简报产出格式\n\n```markdown\n# AI 前沿情报 · {日期}\n## 🔴 核心情报（≤3）— 标题 + 公司/场景/核心数据/落地方式 + 原文链接 + 对 Zenz 的启示\n## 🟡 值得关注（≤5）\n## 🟢 快速浏览（≤5）— 标题 + 一句话\n## 📊 今日信号 — 技术趋势 / 产品发布 / 资本动向\nGenerated by ai-frontier-monitor · {时间}\n```\n\n## 运行现状（2026-10-09 核验）\n\n- **收集 cron**：Hermes 侧 `ai-frontier-daily-briefing`（job `8f25d99d4eef`，K 维护），每日双槽，**归档指令显式写入 payload**；产出 `agent-prophet/daily-notes/frontier-briefings/YYYY-MM-DD-frontier-briefing.md`——10-07 起每日活体（10-07/08/09 三连日归档实证）\n- **历史教训（重要）**：2026-05 版 OpenClaw 侧 cron 曾静默失效 4 个月——delivery=none + payload 无归档指令，外面看进程在跑、归档停在 05-28。任何定时任务 payload 必须显式携带投递/归档指令。2026-10-06 转移 K 侧重建修复\n- 旧方案（本地每日 8:00 RSS cron + 本 skill 直接 Feishu 推送）已废弃，推送统一走 K 侧投递链\n\n## 与 agent-prophet 机制接驳（2026-10-09 起）\n\n- **选题池（日层）**：每日 17:10 automation `agentprophet-topic-scout` 从当日简报等源筛 1-2 题入 `memory/agent-prophet-topic-pool.md`（Luca 2026-10-09 批复「日选题池+周深稿」）\n- **周稿 Build（周层）**：每周一 09:05 automation `agentprophet-weekly-skeleton` 从池中选最优题出骨架 → Luca 批后成稿 → feature-branch PR 入 agent-prophet（不碰 main/develop）\n- **微信草稿格式**（Build 阶段产出 `resources/wechat-curation/YYYY-MM-DD_<主题>.md`）：📌 标题一句话抓核心信号 / 📝 开头 2-3 句定调不强开 / 🔴 核心解读 2-3 条各 200-300 字、**Zenz 建设者视角**（说「我在搭建 X」，不说「行业趋势显示 Y」）/ 🟡 速览 3-5 条 / 🔽 结尾 **AB 投票** + 下期预告\n- Ship（Zenz 复制到公众号编辑器发布，queue.md 标记 `[Ship]`）→ Reflect 不变\n\n## 协作 Skills\n\n`wechat-curator`（微信精选补 🟢 层）、`zsxq-helper`（星球高价值内容独立汇总）、`rss-crawler.py`（底层抓取引擎，11 源）、`generate-briefing.py`（本目录，简报生成参考实现）\n\n---\n*v1.1 2026-10-09：cron 转移定案（K 侧活体）+ agent-prophet 选题池/周稿接驳 + 4 个月静默失效教训归档 + 三副本合一。v1.0 2026-05：初始发布（Signal→Validate→Build→Ship→Reflect）。*\n\nFile v1.1.0:_meta.json\n\n{\n \"ownerId\": \"kn7d5x409z88swt4n3fh3v77yn833x7m\",\n \"slug\": \"ai-frontier-monitor\",\n \"version\": \"1.1.0\",\n \"publishedAt\": 1791551295562\n}\n\nFile v1.1.0:references/BRIEFING_CONFIG.md\n\n# AI 前沿情报 · 配置文件\n# ai-frontier-monitor v3.0\n\n## 推送偏好\n- 推送时间：08:00（完整版）/ 12:00（增量版）\n- 推送目标：Feishu（配置你的飞书 user open_id）\n- 语言：中文为主，英文关键案例保留原文标题\n\n## 数量限制\n- 核心情报：≤3 条（≥4分）\n- 值得关注：≤5 条（3-4分）\n- 快速浏览：≤5 条（2-3分）\n- arXiv 论文：≤3 篇\n- GitHub Trending：≤3 个\n- 36氪热榜：取前 3 条补充到快速浏览\n\n## 信号检测阈值\n- 技术趋势：≥2 条相关候选\n- 产品发布：≥1 条\n- 资本动向：关键词触发（funding/raised/Series/投资/融资）\n\n## 评分维度（权重）\n- 企业真实落地：40%\n- 数据支撑：20%\n- 可学习性：20%\n- 前沿性：20%\n\n## 数据源开关\n| 轨道 | 数据源 | 状态 | 说明 |\n|------|--------|------|------|\n| 企业落地 | OpenAI Blog | ✅ | 核心数据源 |\n| 企业落地 | Microsoft AI Blog | ✅ | |\n| 企业落地 | AWS ML Blog | ✅ | |\n| 企业落地 | Techmeme | ✅ | |\n| 企业落地 | Anthropic 搜索 | ✅ | 补漏用 |\n| 中文视野 | 36氪热榜 | ✅ | API 实时 |\n| 中文视野 | 微信文章 | ✅ | wechat-curator |\n| 中文视野 | 知识星球 | ✅ | 独立推送 |\n| 技术前沿 | arXiv | ✅ | cs.AI/cs.LG/cs.CL |\n| 技术前沿 | GitHub Trending | ✅ | AI/ML 项目 |\n| 开发者视角 | Product Hunt | ✅ | AI 新产品 |\n| 开发者视角 | HN Show+Ask HN | ✅ | 创业者实战 |\n\n## 触发词（扩展）\n- 主触发：AI 前沿、情报汇总、每日情报\n- 快速模式：今天有什么信号、看看有什么新动态\n- 技术专精：arXiv 最新、论文追踪\n- GitHub 热榜：GitHub Trending、AI 项目热榜\n\n## 质量门控\n- 无高质量候选（≥3分 < 2条）时回复 NO_REPLY\n- 同一企业/产品去重，保留评分最高者\n- 连续 3 天低于 3 条核心情报 → 触发关键词审查\n\n## 输出格式\n- 使用 emoji 作为 section header\n- 严格遵循 v3.0 格式模板\n- 核心情报需包含：对用户的启示（1句话）\n- 总字数控制：500-800 字（不含信号模块）\n\n---\n\n_Last updated: 2026-05-09_\n\nFile v1.1.0:skill-card.md\n\n## Description:\n\nCollects and scores AI frontier signals to produce structured daily briefings and WeChat article drafts.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[lynxpurr](https://clawhub.ai/user/lynxpurr)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nContent teams and AI practitioners use this skill to prioritize AI news, research, and project updates for Chinese-language briefings and human-reviewed WeChat drafts.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Briefings may imply broad source coverage even when upstream collection did not run.\n\nMitigation: Confirm the crawler or search step ran before presenting source-coverage claims.\n\nRisk: Briefings and drafts may be saved or delivered to unintended locations.\n\nMitigation: Confirm delivery and archive targets, and limit outputs to the documented briefing and draft locations.\n\n## Reference(s):\n\n- [Briefing configuration](references/BRIEFING_CONFIG.md)\n- [ClawHub skill listing](https://clawhub.ai/lynxpurr/skills/ai-frontier-monitor)\n\n## Skill Output:\n\n**Output Type(s):** [Markdown, Guidance]\n\n**Output Format:** [Chinese-first Markdown briefings and WeChat article drafts]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Tiered news summaries with source links; drafts require human review before publication.]\n\n## Skill Version(s):\n\n1.1.0 (source: ClawHub 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\nArchive v3.0.3: 10 files, 24151 bytes\n\nFiles: references/BRIEFING_CONFIG.md (2149b), references/data-sources.md (1524b), references/scoring.md (2178b), scripts/arxiv-fetch.sh (3149b), scripts/generate-briefing.py (24320b), scripts/github-trending-fetch.sh (4100b), scripts/rss-crawler.py (11092b), skill-card.md (2670b), SKILL.md (7159b), _meta.json (138b)\n\nFile v3.0.3:SKILL.md\n\n---\nname: ai-frontier-monitor\ndescription: \"AI frontier intelligence briefing — aggregate, score, and deliver structured daily briefings from 5 tracks (RSS enterprise, 36kr hotlist, arXiv papers, GitHub Trending, Anthropic web search). Use when user mentions: AI前沿, 情报汇总, 每日情报, 行业动态, AI动态, 技术趋势, 行业信号, 今天有什么信号, AI动态汇总, frontier monitor, daily briefing AI, signal check, AI news, tech briefing.\"\n---\n\n# AI 前沿情报汇总\n\n> 信息聚合 ≠ 信息堆砌。每日情报经筛选、评分、分层后输出，不做 50 条标题的噪音。\n\n## When to Use\n\n触发词（任意语言）：\n- \"AI 前沿\" / \"情报汇总\" / \"每日情报\" / \"frontier monitor\" / \"daily briefing\" → 全量简报\n- \"今天有什么信号\" / \"signal check\" / \"快速扫描\" / \"what signals today\" → 快速信号检测\n- \"arXiv 最新\" / \"论文追踪\" / \"paper tracker\" / \"latest papers\" → 仅 arXiv 轨道\n- \"GitHub Trending\" / \"AI 热榜\" / \"trending AI\" → 仅 GitHub 轨道\n\n## Architecture: 5-Track Parallel\n\n| Track | Source | Script | Priority |\n|-------|--------|--------|----------|\n| 🏢 Enterprise | 11 RSS feeds (OpenAI/AWS/Techmeme/...) | `{baseDir}/scripts/rss-crawler.py` then `{baseDir}/scripts/generate-briefing.py --candidates ` | ⭐⭐⭐⭐⭐ |\n| 🇨🇳 China | 36kr Hotlist API | `curl https://openclaw.36krcdn.com/media/hotlist/{date}/24h_hot_list.json` | ⭐⭐⭐⭐ |\n| 📚 Papers | arXiv cs.AI/cs.LG/cs.CL | `{baseDir}/scripts/arxiv-fetch.sh --category cs.AI --days 7 --max 10` | ⭐⭐⭐ |\n| 🔥 GitHub | GitHub Trending (AI/ML) | `{baseDir}/scripts/github-trending-fetch.sh --period daily` | ⭐⭐⭐ |\n| 🔍 Anthropic | Web search supplement | `web_search` tool | ⭐⭐⭐⭐⭐ |\n\n> For full data source details, read `{baseDir}/references/data-sources.md`\n\n## Workflow\n\n### Step 1: Fetch All Tracks\n\n```bash\n# Track 1: RSS (run crawler first, outputs to {baseDir}/data/candidates/)\npython3 {baseDir}/scripts/rss-crawler.py\n\n# Track 2-4: Generate briefing (all tracks auto-fetched)\npython3 {baseDir}/scripts/generate-briefing.py --mode full\n```\n\nModes: `full` | `quick` | `arxiv` | `github`\n\n### Step 2: Auto-Score & Tier\n\nEach candidate without a score is auto-scored (0-5) by keyword matching across 4 dimensions:\n\n| Dimension | Weight | What to look for |\n|-----------|--------|-----------------|\n| Enterprise landing | 40% | Real company name, deployment scale |\n| Data support | 20% | Quantified metrics (% improvement, $ saved) |\n| Learnability | 20% | Methodology, architecture, lessons learned |\n| Novelty | 20% | New scene, new product, not old news |\n\nSource bonus: OpenAI/AWS +1.0, Techmeme +0.5, PH/HN +0.3\n\nTiers are **dynamic** (based on actual score distribution, not hardcoded thresholds):\n- 🔴 Core: top ~15% or ≥3.5 (max 3)\n- 🟡 Worth watching: top ~30% or ≥2.5 (max 5)\n- 🟢 Quick scan: ≥1.0 (max 8, 36kr first)\n\n> For scoring keywords and signal detection rules, read `{baseDir}/references/scoring.md`\n\n### Step 3: Detect Signals\n\nExtract cross-track signals into 3 dimensions:\n- 🛠 **Tech trends** — new models, architectures, frameworks, benchmarks\n- 🏢 **Product launches** — new releases, open-source, GA announcements\n- 💰 **Funding/M&A** — investments, acquisitions, IPOs\n\n### Step 4: Render Briefing\n\nStrict format — emoji headers, tiered sections, signal summary. Output in **Chinese** (中文为主). Total ≤ 16 items across all tiers.\n\n```\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n🤖 AI 前沿情报 · {Day} {Date}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📡 数据源：11 RSS + 36kr + arXiv + GitHub + Anthropic\n 候选：{N} 条 | 高质量：{M} 条 | 阈值：核心≥{X} / 关注≥{Y}\n\n## 🔴 核心情报（{N} 条）\n### 1. {Title}\n🔗 {Link}\n💡 启示：{One-line insight}\n\n## 🟡 值得关注（{N} 条）\n1. **{Title}**\n 🔗 {Link}\n\n## 🟢 快速浏览（{N} 条）\n• [{Title}]({Link})\n\n## 📚 arXiv · 论文追踪（≤3 篇）\n**{Title}** — {Authors} | {Date}\n摘要：{Abstract[:150]}... → {Link}\n\n## 🔥 GitHub Trending · AI（≤3 个）\n**{Repo}** ({Lang}) +{TodayStars}⭐ → {Link}\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📊 今日信号\n🛠 技术趋势：{signal}\n🏢 产品发布：{signal}\n💰 资本动向：{signal}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n⏰ {HH:MM} | ai-frontier-monitor v3.0\n```\n\n### Step 5: Deliver & Archive\n\n1. **Reply in conversation** — 直接在当前对话输出简报\n2. **Push to Feishu** — 通过 `message` 工具发送到飞书（channel: feishu, to: user ID）\n3. **Save to file** — 将完整简报保存为 Markdown 文件到：\n ```\n {baseDir}/data/briefings/{YYYY-MM-DD}-frontier-briefing.md\n ```\n 保存时覆盖当日内容。\n\n## Data Directory\n\nAll runtime data is stored under `{baseDir}/data/`:\n\n```\n{baseDir}/data/\n├── candidates/ # RSS 爬取的候选条目 (JSON)\n│ └── *_candidates.json\n├── briefings/ # 生成的简报 (Markdown)\n│ └── YYYY-MM-DD-frontier-briefing.md\n└── rss-state.json # RSS 爬取状态\n```\n\n> `{baseDir}` is the skill root directory containing this SKILL.md. All paths use `{baseDir}` for portability.\n\n## Edge Cases\n\n| Situation | Action |\n|-----------|--------|\n| No candidates (RSS empty) | Run with 36kr + arXiv + GitHub only, skip RSS section |\n| arXiv API timeout (>30s) | Skip paper section, log warning |\n| GitHub fetch fails | Skip trending section, log warning |\n| 36kr API 404 (no data yet) | Skip 36kr items in quick scan |\n| Zero high quality items (<2 at ≥2.5) | Return `NO_REPLY` instead of empty briefing |\n| Same company appears in multiple sources | Deduplicate, keep highest-scored entry |\n| First run (no data dir) | Auto-create `{baseDir}/data/` and subdirectories |\n\n## Skill Integration\n\n| Skill | Role |\n|-------|------|\n| **wechat-curator** | WeChat articles → 🟢 Quick scan supplement |\n| **zsxq-helper** | Zsxq content → independent push (not in main briefing) |\n| **rss-crawler.py** | RSS fetching engine (11 sources) — now included in `{baseDir}/scripts/` |\n\n## Configuration\n\nEdit `{baseDir}/references/BRIEFING_CONFIG.md` to customize:\n- Quantity limits per tier\n- Data source on/off switches\n- Signal detection thresholds\n- Delivery target (Feishu user ID / Discord channel / etc.)\n\n## Quality Gates\n\n- Max 16 items per day (3+5+5+3 papers)\n- `NO_REPLY` when <2 quality candidates\n- Deduplicate same company/product, keep highest score\n- 3 consecutive days below 3 core items → trigger keyword review\n\n## Dependencies\n\n- **Python 3.8+** with `feedparser` (for RSS crawling)\n- **bash** (for arXiv/GitHub fetch scripts)\n- **curl** (for 36kr API)\n- **web_search** tool (for Anthropic track)\n\n---\n\n_Last updated: 2026-05-09 | v3.0_\n\nFile v3.0.3:_meta.json\n\n{\n \"ownerId\": \"kn7d5x409z88swt4n3fh3v77yn833x7m\",\n \"slug\": \"ai-frontier-monitor\",\n \"version\": \"3.0.3\",\n \"publishedAt\": 1778336366737\n}\n\nFile v3.0.3:references/BRIEFING_CONFIG.md\n\n# AI 前沿情报 · 配置文件\n# ai-frontier-monitor v3.0\n\n## 推送偏好\n- 推送时间：08:00（完整版）/ 12:00（增量版）\n- 推送目标：Feishu（配置你的飞书 user open_id）\n- 语言：中文为主，英文关键案例保留原文标题\n\n## 数量限制\n- 核心情报：≤3 条（≥4分）\n- 值得关注：≤5 条（3-4分）\n- 快速浏览：≤5 条（2-3分）\n- arXiv 论文：≤3 篇\n- GitHub Trending：≤3 个\n- 36氪热榜：取前 3 条补充到快速浏览\n\n## 信号检测阈值\n- 技术趋势：≥2 条相关候选\n- 产品发布：≥1 条\n- 资本动向：关键词触发（funding/raised/Series/投资/融资）\n\n## 评分维度（权重）\n- 企业真实落地：40%\n- 数据支撑：20%\n- 可学习性：20%\n- 前沿性：20%\n\n## 数据源开关\n| 轨道 | 数据源 | 状态 | 说明 |\n|------|--------|------|------|\n| 企业落地 | OpenAI Blog | ✅ | 核心数据源 |\n| 企业落地 | Microsoft AI Blog | ✅ | |\n| 企业落地 | AWS ML Blog | ✅ | |\n| 企业落地 | Techmeme | ✅ | |\n| 企业落地 | Anthropic 搜索 | ✅ | 补漏用 |\n| 中文视野 | 36氪热榜 | ✅ | API 实时 |\n| 中文视野 | 微信文章 | ✅ | wechat-curator |\n| 中文视野 | 知识星球 | ✅ | 独立推送 |\n| 技术前沿 | arXiv | ✅ | cs.AI/cs.LG/cs.CL |\n| 技术前沿 | GitHub Trending | ✅ | AI/ML 项目 |\n| 开发者视角 | Product Hunt | ✅ | AI 新产品 |\n| 开发者视角 | HN Show+Ask HN | ✅ | 创业者实战 |\n\n## 触发词（扩展）\n- 主触发：AI 前沿、情报汇总、每日情报\n- 快速模式：今天有什么信号、看看有什么新动态\n- 技术专精：arXiv 最新、论文追踪\n- GitHub 热榜：GitHub Trending、AI 项目热榜\n\n## 质量门控\n- 无高质量候选（≥3分 < 2条）时回复 NO_REPLY\n- 同一企业/产品去重，保留评分最高者\n- 连续 3 天低于 3 条核心情报 → 触发关键词审查\n\n## 输出格式\n- 使用 emoji 作为 section header\n- 严格遵循 v3.0 格式模板\n- 核心情报需包含：对用户的启示（1句话）\n- 总字数控制：500-800 字（不含信号模块）\n\n---\n\n_Last updated: 2026-05-09_\n\nFile v3.0.3:references/data-sources.md\n\n# 数据源参考\n\n## RSS 源（11 个）\n\n| 源 | URL | 关键词过滤 |\n|---|---|---|\n| OpenAI Blog | https://openai.com/blog/rss.xml | enterprise, customer, agent |\n| Microsoft AI | https://blogs.microsoft.com/ai/feed | enterprise, copilot, agent |\n| AWS ML Blog | https://aws.amazon.com/blogs/machine-learning/feed/ | enterprise, customer, deployment |\n| Techmeme | https://www.techmeme.com/feed.xml | — |\n| Product Hunt | https://www.producthunt.com/feed | — |\n| HN Show | https://hnrss.org/show | — |\n| HN Ask | https://hnrss.org/ask | — |\n| HN Frontpage | https://hnrss.org/frontpage | enterprise AI |\n| HN Enterprise AI | https://hnrss.org/newest?q=enterprise+AI+agent | — |\n| HN AI Production | https://hnrss.org/newest?q=AI+production+deployment | — |\n| Dev.to AI | https://dev.to/feed/tag/aigents | — |\n\n## 36氪热榜 API\n\n- **URL**: `https://openclaw.36krcdn.com/media/hotlist/{YYYY-MM-DD}/24h_hot_list.json`\n- **方式**: GET，无需认证\n- **更新**: 每小时\n- **字段**: rank(1-15), title, author, publishTime, content, url\n\n## arXiv API\n\n- **端点**: `https://export.arxiv.org/api/query?search_query=cat:{CATEGORY}&sortBy=submittedDate&sortOrder=descending&max_results={N}`\n- **监控类别**: cs.AI, cs.LG, cs.CL\n- **命名空间**: `atom:http://www.w3.org/2005/Atom`\n\n## GitHub Trending\n\n- **URL**: `https://github.com/trending?since=daily`\n- **方式**: HTML 解析（按 `
` 分割）\n- **AI 关键词**: ai, llm, gpt, agent, transformer, rag, mcp, claude, openai, pytorch\n\nFile v3.0.3:references/scoring.md\n\n# 评分体系参考\n\n## 自动评分关键词\n\n### 企业落地 (0-2.5 分，权重 40%)\nenterprise, customer, business, production, deployment, case study,\ncompany, organization, roi, revenue, savings, scale, regulated, industry, copilot,\nautomate, automates, streamline, boost, productivity,\navailable on, built with, powered by, how we used,\n企业, 客户, 落地, 部署, 案例, 行业, 转型, 自动化, 提效\n\n额外加分: 标题含 \"used\"/\"deployed\"/\"built with\" → +0.5\n\n### 数据支撑 (0-1.5 分，权重 20%)\n%, percent, x faster, x cheaper, reduced, increased, improved,\nsaved, cost, efficiency, accuracy, latency, benchmark,\nmillion, billion, thousand,\n量化, 效率, 成本\n\n### 可学习性 (0-1 分，权重 20%)\nhow we, architecture, methodology, best practice, lesson,\nframework, pattern, approach, strategy, pipeline, workflow,\n方法论, 架构, 最佳实践, 经验, 教训\n\n### 前沿性 (0-1 分，权重 20%)\nfirst, new, launch, announce, breakthrough, novel,\nopen source, open-source, release, preview, beta,\nnow available, come to, general availability,\n首次, 发布, 开源, 突破, 新品\n\n### 来源加分\n- OpenAI/AWS: +1.0 (官方博客，落地案例多)\n- Techmeme: +0.5 (聚合新闻)\n- Product Hunt / HN Show: +0.3\n\n## 动态分层逻辑\n\n分数并非硬编码阈值，而是基于当日实际分数分布动态调整：\n\n- 核心情报阈值 = max(3.5, 最高分 × 0.8)\n- 值得关注阈值 = max(2.5, 最高分 × 0.5)\n- 快速浏览 = 1.0 ~ 值得关注阈值\n\n## 信号检测关键词\n\n### 资本信号\nseries, funding, raised, invest, 投资, 融资, million, billion,\n估值, 融资轮, 收购, acquire, merger, 并购, IPO, 上市,\nseed, round, valuation, capital\n\n### 产品信号\nlaunch, release, announce, general availability, GA,\npreview, beta, open source, open-source,\n产品, 发布, 上线, 开源, 首发, 推出, 大模型, model\n\n### 技术信号\nmodel, architecture, training, inference, benchmark,\nframework, sota, state-of-the-art, breakthrough,\nfine-tun, rag, agent, mcp, tool use, reasoning,\nmultimodal, diffusion, transformer, rlhf, dpo,\nspec, protocol, standard,\n技术, 架构, 推理, 微调, 多模态\n\nFile v3.0.3:skill-card.md\n\n## Description:\n\nAI Frontier Monitor aggregates, scores, and delivers structured AI frontier briefings from RSS feeds, 36kr, arXiv, GitHub Trending, and web search.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[lynxpurr](https://clawhub.ai/user/lynxpurr)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, analysts, and operators use this skill to collect public AI news and research signals, score candidate items, and produce concise daily or mode-specific briefings. It is intended for AI trend monitoring across product launches, technical developments, papers, repositories, and funding signals.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Public feed titles, summaries, descriptions, and links are untrusted report data and may be misleading or adversarial.\n\nMitigation: Treat fetched content as data for review, not instructions, and verify important claims against original sources before relying on the briefing.\n\nRisk: The skill can send generated output to Feishu.\n\nMitigation: Use explicit confirmation and a configured recipient before sending briefings outside the current conversation.\n\nRisk: Generated briefings may overwrite the same-day archived briefing file.\n\nMitigation: Review the output path and keep backups or alternate filenames when preserving previous daily briefings matters.\n\n## Reference(s):\n\n- [Briefing Configuration](references/BRIEFING_CONFIG.md)\n- [Data Sources](references/data-sources.md)\n- [Scoring System](references/scoring.md)\n- [ClawHub Skill Page](https://clawhub.ai/lynxpurr/skills/ai-frontier-monitor)\n- [OpenAI Blog RSS](https://openai.com/blog/rss.xml)\n- [Microsoft AI Blog RSS](https://blogs.microsoft.com/ai/feed)\n- [AWS Machine Learning Blog RSS](https://aws.amazon.com/blogs/machine-learning/feed/)\n- [arXiv API](https://export.arxiv.org/api/query)\n- [GitHub Trending](https://github.com/trending?since=daily)\n\n## Skill Output:\n\n**Output Type(s):** [Markdown, Text, Files, Shell commands, Configuration]\n\n**Output Format:** [Chinese-language Markdown briefings with scored tiers, links, short insights, and signal summaries.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May save generated briefings under the skill data directory and may deliver output to a configured Feishu target.]\n\n## Skill Version(s):\n\n3.0.3 (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\nArchive v3.0.2: 9 files, 22694 bytes\n\nFiles: references/BRIEFING_CONFIG.md (2149b), references/data-sources.md (1524b), references/scoring.md (2178b), scripts/arxiv-fetch.sh (3149b), scripts/generate-briefing.py (24323b), scripts/github-trending-fetch.sh (4100b), scripts/rss-crawler.py (11092b), SKILL.md (7159b), _meta.json (138b)\n\nFile v3.0.2:SKILL.md\n\n---\nname: ai-frontier-monitor\ndescription: \"AI frontier intelligence briefing — aggregate, score, and deliver structured daily briefings from 5 tracks (RSS enterprise, 36kr hotlist, arXiv papers, GitHub Trending, Anthropic web search). Use when user mentions: AI前沿, 情报汇总, 每日情报, 行业动态, AI动态, 技术趋势, 行业信号, 今天有什么信号, AI动态汇总, frontier monitor, daily briefing AI, signal check, AI news, tech briefing.\"\n---\n\n# AI 前沿情报汇总\n\n> 信息聚合 ≠ 信息堆砌。每日情报经筛选、评分、分层后输出，不做 50 条标题的噪音。\n\n## When to Use\n\n触发词（任意语言）：\n- \"AI 前沿\" / \"情报汇总\" / \"每日情报\" / \"frontier monitor\" / \"daily briefing\" → 全量简报\n- \"今天有什么信号\" / \"signal check\" / \"快速扫描\" / \"what signals today\" → 快速信号检测\n- \"arXiv 最新\" / \"论文追踪\" / \"paper tracker\" / \"latest papers\" → 仅 arXiv 轨道\n- \"GitHub Trending\" / \"AI 热榜\" / \"trending AI\" → 仅 GitHub 轨道\n\n## Architecture: 5-Track Parallel\n\n| Track | Source | Script | Priority |\n|-------|--------|--------|----------|\n| 🏢 Enterprise | 11 RSS feeds (OpenAI/AWS/Techmeme/...) | `{baseDir}/scripts/rss-crawler.py` then `{baseDir}/scripts/generate-briefing.py --candidates ` | ⭐⭐⭐⭐⭐ |\n| 🇨🇳 China | 36kr Hotlist API | `curl https://openclaw.36krcdn.com/media/hotlist/{date}/24h_hot_list.json` | ⭐⭐⭐⭐ |\n| 📚 Papers | arXiv cs.AI/cs.LG/cs.CL | `{baseDir}/scripts/arxiv-fetch.sh --category cs.AI --days 7 --max 10` | ⭐⭐⭐ |\n| 🔥 GitHub | GitHub Trending (AI/ML) | `{baseDir}/scripts/github-trending-fetch.sh --period daily` | ⭐⭐⭐ |\n| 🔍 Anthropic | Web search supplement | `web_search` tool | ⭐⭐⭐⭐⭐ |\n\n> For full data source details, read `{baseDir}/references/data-sources.md`\n\n## Workflow\n\n### Step 1: Fetch All Tracks\n\n```bash\n# Track 1: RSS (run crawler first, outputs to {baseDir}/data/candidates/)\npython3 {baseDir}/scripts/rss-crawler.py\n\n# Track 2-4: Generate briefing (all tracks auto-fetched)\npython3 {baseDir}/scripts/generate-briefing.py --mode full\n```\n\nModes: `full` | `quick` | `arxiv` | `github`\n\n### Step 2: Auto-Score & Tier\n\nEach candidate without a score is auto-scored (0-5) by keyword matching across 4 dimensions:\n\n| Dimension | Weight | What to look for |\n|-----------|--------|-----------------|\n| Enterprise landing | 40% | Real company name, deployment scale |\n| Data support | 20% | Quantified metrics (% improvement, $ saved) |\n| Learnability | 20% | Methodology, architecture, lessons learned |\n| Novelty | 20% | New scene, new product, not old news |\n\nSource bonus: OpenAI/AWS +1.0, Techmeme +0.5, PH/HN +0.3\n\nTiers are **dynamic** (based on actual score distribution, not hardcoded thresholds):\n- 🔴 Core: top ~15% or ≥3.5 (max 3)\n- 🟡 Worth watching: top ~30% or ≥2.5 (max 5)\n- 🟢 Quick scan: ≥1.0 (max 8, 36kr first)\n\n> For scoring keywords and signal detection rules, read `{baseDir}/references/scoring.md`\n\n### Step 3: Detect Signals\n\nExtract cross-track signals into 3 dimensions:\n- 🛠 **Tech trends** — new models, architectures, frameworks, benchmarks\n- 🏢 **Product launches** — new releases, open-source, GA announcements\n- 💰 **Funding/M&A** — investments, acquisitions, IPOs\n\n### Step 4: Render Briefing\n\nStrict format — emoji headers, tiered sections, signal summary. Output in **Chinese** (中文为主). Total ≤ 16 items across all tiers.\n\n```\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n🤖 AI 前沿情报 · {Day} {Date}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📡 数据源：11 RSS + 36kr + arXiv + GitHub + Anthropic\n 候选：{N} 条 | 高质量：{M} 条 | 阈值：核心≥{X} / 关注≥{Y}\n\n## 🔴 核心情报（{N} 条）\n### 1. {Title}\n🔗 {Link}\n💡 启示：{One-line insight}\n\n## 🟡 值得关注（{N} 条）\n1. **{Title}**\n 🔗 {Link}\n\n## 🟢 快速浏览（{N} 条）\n• [{Title}]({Link})\n\n## 📚 arXiv · 论文追踪（≤3 篇）\n**{Title}** — {Authors} | {Date}\n摘要：{Abstract[:150]}... → {Link}\n\n## 🔥 GitHub Trending · AI（≤3 个）\n**{Repo}** ({Lang}) +{TodayStars}⭐ → {Link}\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📊 今日信号\n🛠 技术趋势：{signal}\n🏢 产品发布：{signal}\n💰 资本动向：{signal}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n⏰ {HH:MM} | ai-frontier-monitor v3.0\n```\n\n### Step 5: Deliver & Archive\n\n1. **Reply in conversation** — 直接在当前对话输出简报\n2. **Push to Feishu** — 通过 `message` 工具发送到飞书（channel: feishu, to: user ID）\n3. **Save to file** — 将完整简报保存为 Markdown 文件到：\n ```\n {baseDir}/data/briefings/{YYYY-MM-DD}-frontier-briefing.md\n ```\n 保存时覆盖当日内容。\n\n## Data Directory\n\nAll runtime data is stored under `{baseDir}/data/`:\n\n```\n{baseDir}/data/\n├── candidates/ # RSS 爬取的候选条目 (JSON)\n│ └── *_candidates.json\n├── briefings/ # 生成的简报 (Markdown)\n│ └── YYYY-MM-DD-frontier-briefing.md\n└── rss-state.json # RSS 爬取状态\n```\n\n> `{baseDir}` is the skill root directory containing this SKILL.md. All paths use `{baseDir}` for portability.\n\n## Edge Cases\n\n| Situation | Action |\n|-----------|--------|\n| No candidates (RSS empty) | Run with 36kr + arXiv + GitHub only, skip RSS section |\n| arXiv API timeout (>30s) | Skip paper section, log warning |\n| GitHub fetch fails | Skip trending section, log warning |\n| 36kr API 404 (no data yet) | Skip 36kr items in quick scan |\n| Zero high quality items (<2 at ≥2.5) | Return `NO_REPLY` instead of empty briefing |\n| Same company appears in multiple sources | Deduplicate, keep highest-scored entry |\n| First run (no data dir) | Auto-create `{baseDir}/data/` and subdirectories |\n\n## Skill Integration\n\n| Skill | Role |\n|-------|------|\n| **wechat-curator** | WeChat articles → 🟢 Quick scan supplement |\n| **zsxq-helper** | Zsxq content → independent push (not in main briefing) |\n| **rss-crawler.py** | RSS fetching engine (11 sources) — now included in `{baseDir}/scripts/` |\n\n## Configuration\n\nEdit `{baseDir}/references/BRIEFING_CONFIG.md` to customize:\n- Quantity limits per tier\n- Data source on/off switches\n- Signal detection thresholds\n- Delivery target (Feishu user ID / Discord channel / etc.)\n\n## Quality Gates\n\n- Max 16 items per day (3+5+5+3 papers)\n- `NO_REPLY` when <2 quality candidates\n- Deduplicate same company/product, keep highest score\n- 3 consecutive days below 3 core items → trigger keyword review\n\n## Dependencies\n\n- **Python 3.8+** with `feedparser` (for RSS crawling)\n- **bash** (for arXiv/GitHub fetch scripts)\n- **curl** (for 36kr API)\n- **web_search** tool (for Anthropic track)\n\n---\n\n_Last updated: 2026-05-09 | v3.0_\n\nFile v3.0.2:_meta.json\n\n{\n \"ownerId\": \"kn7d5x409z88swt4n3fh3v77yn833x7m\",\n \"slug\": \"ai-frontier-monitor\",\n \"version\": \"3.0.2\",\n \"publishedAt\": 1778333481315\n}\n\nFile v3.0.2:references/BRIEFING_CONFIG.md\n\n# AI 前沿情报 · 配置文件\n# ai-frontier-monitor v3.0\n\n## 推送偏好\n- 推送时间：08:00（完整版）/ 12:00（增量版）\n- 推送目标：Feishu（配置你的飞书 user open_id）\n- 语言：中文为主，英文关键案例保留原文标题\n\n## 数量限制\n- 核心情报：≤3 条（≥4分）\n- 值得关注：≤5 条（3-4分）\n- 快速浏览：≤5 条（2-3分）\n- arXiv 论文：≤3 篇\n- GitHub Trending：≤3 个\n- 36氪热榜：取前 3 条补充到快速浏览\n\n## 信号检测阈值\n- 技术趋势：≥2 条相关候选\n- 产品发布：≥1 条\n- 资本动向：关键词触发（funding/raised/Series/投资/融资）\n\n## 评分维度（权重）\n- 企业真实落地：40%\n- 数据支撑：20%\n- 可学习性：20%\n- 前沿性：20%\n\n## 数据源开关\n| 轨道 | 数据源 | 状态 | 说明 |\n|------|--------|------|------|\n| 企业落地 | OpenAI Blog | ✅ | 核心数据源 |\n| 企业落地 | Microsoft AI Blog | ✅ | |\n| 企业落地 | AWS ML Blog | ✅ | |\n| 企业落地 | Techmeme | ✅ | |\n| 企业落地 | Anthropic 搜索 | ✅ | 补漏用 |\n| 中文视野 | 36氪热榜 | ✅ | API 实时 |\n| 中文视野 | 微信文章 | ✅ | wechat-curator |\n| 中文视野 | 知识星球 | ✅ | 独立推送 |\n| 技术前沿 | arXiv | ✅ | cs.AI/cs.LG/cs.CL |\n| 技术前沿 | GitHub Trending | ✅ | AI/ML 项目 |\n| 开发者视角 | Product Hunt | ✅ | AI 新产品 |\n| 开发者视角 | HN Show+Ask HN | ✅ | 创业者实战 |\n\n## 触发词（扩展）\n- 主触发：AI 前沿、情报汇总、每日情报\n- 快速模式：今天有什么信号、看看有什么新动态\n- 技术专精：arXiv 最新、论文追踪\n- GitHub 热榜：GitHub Trending、AI 项目热榜\n\n## 质量门控\n- 无高质量候选（≥3分 < 2条）时回复 NO_REPLY\n- 同一企业/产品去重，保留评分最高者\n- 连续 3 天低于 3 条核心情报 → 触发关键词审查\n\n## 输出格式\n- 使用 emoji 作为 section header\n- 严格遵循 v3.0 格式模板\n- 核心情报需包含：对用户的启示（1句话）\n- 总字数控制：500-800 字（不含信号模块）\n\n---\n\n_Last updated: 2026-05-09_\n\nFile v3.0.2:references/data-sources.md\n\n# 数据源参考\n\n## RSS 源（11 个）\n\n| 源 | URL | 关键词过滤 |\n|---|---|---|\n| OpenAI Blog | https://openai.com/blog/rss.xml | enterprise, customer, agent |\n| Microsoft AI | https://blogs.microsoft.com/ai/feed | enterprise, copilot, agent |\n| AWS ML Blog | https://aws.amazon.com/blogs/machine-learning/feed/ | enterprise, customer, deployment |\n| Techmeme | https://www.techmeme.com/feed.xml | — |\n| Product Hunt | https://www.producthunt.com/feed | — |\n| HN Show | https://hnrss.org/show | — |\n| HN Ask | https://hnrss.org/ask | — |\n| HN Frontpage | https://hnrss.org/frontpage | enterprise AI |\n| HN Enterprise AI | https://hnrss.org/newest?q=enterprise+AI+agent | — |\n| HN AI Production | https://hnrss.org/newest?q=AI+production+deployment | — |\n| Dev.to AI | https://dev.to/feed/tag/aigents | — |\n\n## 36氪热榜 API\n\n- **URL**: `https://openclaw.36krcdn.com/media/hotlist/{YYYY-MM-DD}/24h_hot_list.json`\n- **方式**: GET，无需认证\n- **更新**: 每小时\n- **字段**: rank(1-15), title, author, publishTime, content, url\n\n## arXiv API\n\n- **端点**: `https://export.arxiv.org/api/query?search_query=cat:{CATEGORY}&sortBy=submittedDate&sortOrder=descending&max_results={N}`\n- **监控类别**: cs.AI, cs.LG, cs.CL\n- **命名空间**: `atom:http://www.w3.org/2005/Atom`\n\n## GitHub Trending\n\n- **URL**: `https://github.com/trending?since=daily`\n- **方式**: HTML 解析（按 `
` 分割）\n- **AI 关键词**: ai, llm, gpt, agent, transformer, rag, mcp, claude, openai, pytorch\n\nFile v3.0.2:references/scoring.md\n\n# 评分体系参考\n\n## 自动评分关键词\n\n### 企业落地 (0-2.5 分，权重 40%)\nenterprise, customer, business, production, deployment, case study,\ncompany, organization, roi, revenue, savings, scale, regulated, industry, copilot,\nautomate, automates, streamline, boost, productivity,\navailable on, built with, powered by, how we used,\n企业, 客户, 落地, 部署, 案例, 行业, 转型, 自动化, 提效\n\n额外加分: 标题含 \"used\"/\"deployed\"/\"built with\" → +0.5\n\n### 数据支撑 (0-1.5 分，权重 20%)\n%, percent, x faster, x cheaper, reduced, increased, improved,\nsaved, cost, efficiency, accuracy, latency, benchmark,\nmillion, billion, thousand,\n量化, 效率, 成本\n\n### 可学习性 (0-1 分，权重 20%)\nhow we, architecture, methodology, best practice, lesson,\nframework, pattern, approach, strategy, pipeline, workflow,\n方法论, 架构, 最佳实践, 经验, 教训\n\n### 前沿性 (0-1 分，权重 20%)\nfirst, new, launch, announce, breakthrough, novel,\nopen source, open-source, release, preview, beta,\nnow available, come to, general availability,\n首次, 发布, 开源, 突破, 新品\n\n### 来源加分\n- OpenAI/AWS: +1.0 (官方博客，落地案例多)\n- Techmeme: +0.5 (聚合新闻)\n- Product Hunt / HN Show: +0.3\n\n## 动态分层逻辑\n\n分数并非硬编码阈值，而是基于当日实际分数分布动态调整：\n\n- 核心情报阈值 = max(3.5, 最高分 × 0.8)\n- 值得关注阈值 = max(2.5, 最高分 × 0.5)\n- 快速浏览 = 1.0 ~ 值得关注阈值\n\n## 信号检测关键词\n\n### 资本信号\nseries, funding, raised, invest, 投资, 融资, million, billion,\n估值, 融资轮, 收购, acquire, merger, 并购, IPO, 上市,\nseed, round, valuation, capital\n\n### 产品信号\nlaunch, release, announce, general availability, GA,\npreview, beta, open source, open-source,\n产品, 发布, 上线, 开源, 首发, 推出, 大模型, model\n\n### 技术信号\nmodel, architecture, training, inference, benchmark,\nframework, sota, state-of-the-art, breakthrough,\nfine-tun, rag, agent, mcp, tool use, reasoning,\nmultimodal, diffusion, transformer, rlhf, dpo,\nspec, protocol, standard,\n技术, 架构, 推理, 微调, 多模态\n\nArchive v3.0.1: 11 files, 38141 bytes\n\nFiles: data/candidates/2026-05-09_candidates.json (46200b), data/rss-state.json (7857b), references/BRIEFING_CONFIG.md (2149b), references/data-sources.md (1524b), references/scoring.md (2178b), scripts/arxiv-fetch.sh (3149b), scripts/generate-briefing.py (24230b), scripts/github-trending-fetch.sh (4100b), scripts/rss-crawler.py (11092b), SKILL.md (7159b), _meta.json (138b)\n\nFile v3.0.1:SKILL.md\n\n---\nname: ai-frontier-monitor\ndescription: \"AI frontier intelligence briefing — aggregate, score, and deliver structured daily briefings from 5 tracks (RSS enterprise, 36kr hotlist, arXiv papers, GitHub Trending, Anthropic web search). Use when user mentions: AI前沿, 情报汇总, 每日情报, 行业动态, AI动态, 技术趋势, 行业信号, 今天有什么信号, AI动态汇总, frontier monitor, daily briefing AI, signal check, AI news, tech briefing.\"\n---\n\n# AI 前沿情报汇总\n\n> 信息聚合 ≠ 信息堆砌。每日情报经筛选、评分、分层后输出，不做 50 条标题的噪音。\n\n## When to Use\n\n触发词（任意语言）：\n- \"AI 前沿\" / \"情报汇总\" / \"每日情报\" / \"frontier monitor\" / \"daily briefing\" → 全量简报\n- \"今天有什么信号\" / \"signal check\" / \"快速扫描\" / \"what signals today\" → 快速信号检测\n- \"arXiv 最新\" / \"论文追踪\" / \"paper tracker\" / \"latest papers\" → 仅 arXiv 轨道\n- \"GitHub Trending\" / \"AI 热榜\" / \"trending AI\" → 仅 GitHub 轨道\n\n## Architecture: 5-Track Parallel\n\n| Track | Source | Script | Priority |\n|-------|--------|--------|----------|\n| 🏢 Enterprise | 11 RSS feeds (OpenAI/AWS/Techmeme/...) | `{baseDir}/scripts/rss-crawler.py` then `{baseDir}/scripts/generate-briefing.py --candidates ` | ⭐⭐⭐⭐⭐ |\n| 🇨🇳 China | 36kr Hotlist API | `curl https://openclaw.36krcdn.com/media/hotlist/{date}/24h_hot_list.json` | ⭐⭐⭐⭐ |\n| 📚 Papers | arXiv cs.AI/cs.LG/cs.CL | `{baseDir}/scripts/arxiv-fetch.sh --category cs.AI --days 7 --max 10` | ⭐⭐⭐ |\n| 🔥 GitHub | GitHub Trending (AI/ML) | `{baseDir}/scripts/github-trending-fetch.sh --period daily` | ⭐⭐⭐ |\n| 🔍 Anthropic | Web search supplement | `web_search` tool | ⭐⭐⭐⭐⭐ |\n\n> For full data source details, read `{baseDir}/references/data-sources.md`\n\n## Workflow\n\n### Step 1: Fetch All Tracks\n\n```bash\n# Track 1: RSS (run crawler first, outputs to {baseDir}/data/candidates/)\npython3 {baseDir}/scripts/rss-crawler.py\n\n# Track 2-4: Generate briefing (all tracks auto-fetched)\npython3 {baseDir}/scripts/generate-briefing.py --mode full\n```\n\nModes: `full` | `quick` | `arxiv` | `github`\n\n### Step 2: Auto-Score & Tier\n\nEach candidate without a score is auto-scored (0-5) by keyword matching across 4 dimensions:\n\n| Dimension | Weight | What to look for |\n|-----------|--------|-----------------|\n| Enterprise landing | 40% | Real company name, deployment scale |\n| Data support | 20% | Quantified metrics (% improvement, $ saved) |\n| Learnability | 20% | Methodology, architecture, lessons learned |\n| Novelty | 20% | New scene, new product, not old news |\n\nSource bonus: OpenAI/AWS +1.0, Techmeme +0.5, PH/HN +0.3\n\nTiers are **dynamic** (based on actual score distribution, not hardcoded thresholds):\n- 🔴 Core: top ~15% or ≥3.5 (max 3)\n- 🟡 Worth watching: top ~30% or ≥2.5 (max 5)\n- 🟢 Quick scan: ≥1.0 (max 8, 36kr first)\n\n> For scoring keywords and signal detection rules, read `{baseDir}/references/scoring.md`\n\n### Step 3: Detect Signals\n\nExtract cross-track signals into 3 dimensions:\n- 🛠 **Tech trends** — new models, architectures, frameworks, benchmarks\n- 🏢 **Product launches** — new releases, open-source, GA announcements\n- 💰 **Funding/M&A** — investments, acquisitions, IPOs\n\n### Step 4: Render Briefing\n\nStrict format — emoji headers, tiered sections, signal summary. Output in **Chinese** (中文为主). Total ≤ 16 items across all tiers.\n\n```\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n🤖 AI 前沿情报 · {Day} {Date}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📡 数据源：11 RSS + 36kr + arXiv + GitHub + Anthropic\n 候选：{N} 条 | 高质量：{M} 条 | 阈值：核心≥{X} / 关注≥{Y}\n\n## 🔴 核心情报（{N} 条）\n### 1. {Title}\n🔗 {Link}\n💡 启示：{One-line insight}\n\n## 🟡 值得关注（{N} 条）\n1. **{Title}**\n 🔗 {Link}\n\n## 🟢 快速浏览（{N} 条）\n• [{Title}]({Link})\n\n## 📚 arXiv · 论文追踪（≤3 篇）\n**{Title}** — {Authors} | {Date}\n摘要：{Abstract[:150]}... → {Link}\n\n## 🔥 GitHub Trending · AI（≤3 个）\n**{Repo}** ({Lang}) +{TodayStars}⭐ → {Link}\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📊 今日信号\n🛠 技术趋势：{signal}\n🏢 产品发布：{signal}\n💰 资本动向：{signal}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n⏰ {HH:MM} | ai-frontier-monitor v3.0\n```\n\n### Step 5: Deliver & Archive\n\n1. **Reply in conversation** — 直接在当前对话输出简报\n2. **Push to Feishu** — 通过 `message` 工具发送到飞书（channel: feishu, to: user ID）\n3. **Save to file** — 将完整简报保存为 Markdown 文件到：\n ```\n {baseDir}/data/briefings/{YYYY-MM-DD}-frontier-briefing.md\n ```\n 保存时覆盖当日内容。\n\n## Data Directory\n\nAll runtime data is stored under `{baseDir}/data/`:\n\n```\n{baseDir}/data/\n├── candidates/ # RSS 爬取的候选条目 (JSON)\n│ └── *_candidates.json\n├── briefings/ # 生成的简报 (Markdown)\n│ └── YYYY-MM-DD-frontier-briefing.md\n└── rss-state.json # RSS 爬取状态\n```\n\n> `{baseDir}` is the skill root directory containing this SKILL.md. All paths use `{baseDir}` for portability.\n\n## Edge Cases\n\n| Situation | Action |\n|-----------|--------|\n| No candidates (RSS empty) | Run with 36kr + arXiv + GitHub only, skip RSS section |\n| arXiv API timeout (>30s) | Skip paper section, log warning |\n| GitHub fetch fails | Skip trending section, log warning |\n| 36kr API 404 (no data yet) | Skip 36kr items in quick scan |\n| Zero high quality items (<2 at ≥2.5) | Return `NO_REPLY` instead of empty briefing |\n| Same company appears in multiple sources | Deduplicate, keep highest-scored entry |\n| First run (no data dir) | Auto-create `{baseDir}/data/` and subdirectories |\n\n## Skill Integration\n\n| Skill | Role |\n|-------|------|\n| **wechat-curator** | WeChat articles → 🟢 Quick scan supplement |\n| **zsxq-helper** | Zsxq content → independent push (not in main briefing) |\n| **rss-crawler.py** | RSS fetching engine (11 sources) — now included in `{baseDir}/scripts/` |\n\n## Configuration\n\nEdit `{baseDir}/references/BRIEFING_CONFIG.md` to customize:\n- Quantity limits per tier\n- Data source on/off switches\n- Signal detection thresholds\n- Delivery target (Feishu user ID / Discord channel / etc.)\n\n## Quality Gates\n\n- Max 16 items per day (3+5+5+3 papers)\n- `NO_REPLY` when <2 quality candidates\n- Deduplicate same company/product, keep highest score\n- 3 consecutive days below 3 core items → trigger keyword review\n\n## Dependencies\n\n- **Python 3.8+** with `feedparser` (for RSS crawling)\n- **bash** (for arXiv/GitHub fetch scripts)\n- **curl** (for 36kr API)\n- **web_search** tool (for Anthropic track)\n\n---\n\n_Last updated: 2026-05-09 | v3.0_\n\nFile v3.0.1:_meta.json\n\n{\n \"ownerId\": \"kn7d5x409z88swt4n3fh3v77yn833x7m\",\n \"slug\": \"ai-frontier-monitor\",\n \"version\": \"3.0.1\",\n \"publishedAt\": 1778321806147\n}\n\nFile v3.0.1:references/BRIEFING_CONFIG.md\n\n# AI 前沿情报 · 配置文件\n# ai-frontier-monitor v3.0\n\n## 推送偏好\n- 推送时间：08:00（完整版）/ 12:00（增量版）\n- 推送目标：Feishu（配置你的飞书 user open_id）\n- 语言：中文为主，英文关键案例保留原文标题\n\n## 数量限制\n- 核心情报：≤3 条（≥4分）\n- 值得关注：≤5 条（3-4分）\n- 快速浏览：≤5 条（2-3分）\n- arXiv 论文：≤3 篇\n- GitHub Trending：≤3 个\n- 36氪热榜：取前 3 条补充到快速浏览\n\n## 信号检测阈值\n- 技术趋势：≥2 条相关候选\n- 产品发布：≥1 条\n- 资本动向：关键词触发（funding/raised/Series/投资/融资）\n\n## 评分维度（权重）\n- 企业真实落地：40%\n- 数据支撑：20%\n- 可学习性：20%\n- 前沿性：20%\n\n## 数据源开关\n| 轨道 | 数据源 | 状态 | 说明 |\n|------|--------|------|------|\n| 企业落地 | OpenAI Blog | ✅ | 核心数据源 |\n| 企业落地 | Microsoft AI Blog | ✅ | |\n| 企业落地 | AWS ML Blog | ✅ | |\n| 企业落地 | Techmeme | ✅ | |\n| 企业落地 | Anthropic 搜索 | ✅ | 补漏用 |\n| 中文视野 | 36氪热榜 | ✅ | API 实时 |\n| 中文视野 | 微信文章 | ✅ | wechat-curator |\n| 中文视野 | 知识星球 | ✅ | 独立推送 |\n| 技术前沿 | arXiv | ✅ | cs.AI/cs.LG/cs.CL |\n| 技术前沿 | GitHub Trending | ✅ | AI/ML 项目 |\n| 开发者视角 | Product Hunt | ✅ | AI 新产品 |\n| 开发者视角 | HN Show+Ask HN | ✅ | 创业者实战 |\n\n## 触发词（扩展）\n- 主触发：AI 前沿、情报汇总、每日情报\n- 快速模式：今天有什么信号、看看有什么新动态\n- 技术专精：arXiv 最新、论文追踪\n- GitHub 热榜：GitHub Trending、AI 项目热榜\n\n## 质量门控\n- 无高质量候选（≥3分 < 2条）时回复 NO_REPLY\n- 同一企业/产品去重，保留评分最高者\n- 连续 3 天低于 3 条核心情报 → 触发关键词审查\n\n## 输出格式\n- 使用 emoji 作为 section header\n- 严格遵循 v3.0 格式模板\n- 核心情报需包含：对用户的启示（1句话）\n- 总字数控制：500-800 字（不含信号模块）\n\n---\n\n_Last updated: 2026-05-09_\n\nFile v3.0.1:references/data-sources.md\n\n# 数据源参考\n\n## RSS 源（11 个）\n\n| 源 | URL | 关键词过滤 |\n|---|---|---|\n| OpenAI Blog | https://openai.com/blog/rss.xml | enterprise, customer, agent |\n| Microsoft AI | https://blogs.microsoft.com/ai/feed | enterprise, copilot, agent |\n| AWS ML Blog | https://aws.amazon.com/blogs/machine-learning/feed/ | enterprise, customer, deployment |\n| Techmeme | https://www.techmeme.com/feed.xml | — |\n| Product Hunt | https://www.producthunt.com/feed | — |\n| HN Show | https://hnrss.org/show | — |\n| HN Ask | https://hnrss.org/ask | — |\n| HN Frontpage | https://hnrss.org/frontpage | enterprise AI |\n| HN Enterprise AI | https://hnrss.org/newest?q=enterprise+AI+agent | — |\n| HN AI Production | https://hnrss.org/newest?q=AI+production+deployment | — |\n| Dev.to AI | https://dev.to/feed/tag/aigents | — |\n\n## 36氪热榜 API\n\n- **URL**: `https://openclaw.36krcdn.com/media/hotlist/{YYYY-MM-DD}/24h_hot_list.json`\n- **方式**: GET，无需认证\n- **更新**: 每小时\n- **字段**: rank(1-15), title, author, publishTime, content, url\n\n## arXiv API\n\n- **端点**: `https://export.arxiv.org/api/query?search_query=cat:{CATEGORY}&sortBy=submittedDate&sortOrder=descending&max_results={N}`\n- **监控类别**: cs.AI, cs.LG, cs.CL\n- **命名空间**: `atom:http://www.w3.org/2005/Atom`\n\n## GitHub Trending\n\n- **URL**: `https://github.com/trending?since=daily`\n- **方式**: HTML 解析（按 `
` 分割）\n- **AI 关键词**: ai, llm, gpt, agent, transformer, rag, mcp, claude, openai, pytorch\n\nFile v3.0.1:references/scoring.md\n\n# 评分体系参考\n\n## 自动评分关键词\n\n### 企业落地 (0-2.5 分，权重 40%)\nenterprise, customer, business, production, deployment, case study,\ncompany, organization, roi, revenue, savings, scale, regulated, industry, copilot,\nautomate, automates, streamline, boost, productivity,\navailable on, built with, powered by, how we used,\n企业, 客户, 落地, 部署, 案例, 行业, 转型, 自动化, 提效\n\n额外加分: 标题含 \"used\"/\"deployed\"/\"built with\" → +0.5\n\n### 数据支撑 (0-1.5 分，权重 20%)\n%, percent, x faster, x cheaper, reduced, increased, improved,\nsaved, cost, efficiency, accuracy, latency, benchmark,\nmillion, billion, thousand,\n量化, 效率, 成本\n\n### 可学习性 (0-1 分，权重 20%)\nhow we, architecture, methodology, best practice, lesson,\nframework, pattern, approach, strategy, pipeline, workflow,\n方法论, 架构, 最佳实践, 经验, 教训\n\n### 前沿性 (0-1 分，权重 20%)\nfirst, new, launch, announce, breakthrough, novel,\nopen source, open-source, release, preview, beta,\nnow available, come to, general availability,\n首次, 发布, 开源, 突破, 新品\n\n### 来源加分\n- OpenAI/AWS: +1.0 (官方博客，落地案例多)\n- Techmeme: +0.5 (聚合新闻)\n- Product Hunt / HN Show: +0.3\n\n## 动态分层逻辑\n\n分数并非硬编码阈值，而是基于当日实际分数分布动态调整：\n\n- 核心情报阈值 = max(3.5, 最高分 × 0.8)\n- 值得关注阈值 = max(2.5, 最高分 × 0.5)\n- 快速浏览 = 1.0 ~ 值得关注阈值\n\n## 信号检测关键词\n\n### 资本信号\nseries, funding, raised, invest, 投资, 融资, million, billion,\n估值, 融资轮, 收购, acquire, merger, 并购, IPO, 上市,\nseed, round, valuation, capital\n\n### 产品信号\nlaunch, release, announce, general availability, GA,\npreview, beta, open source, open-source,\n产品, 发布, 上线, 开源, 首发, 推出, 大模型, model\n\n### 技术信号\nmodel, architecture, training, inference, benchmark,\nframework, sota, state-of-the-art, breakthrough,\nfine-tun, rag, agent, mcp, tool use, reasoning,\nmultimodal, diffusion, transformer, rlhf, dpo,\nspec, protocol, standard,\n技术, 架构, 推理, 微调, 多模态\n\nFile v3.0.1:data/candidates/2026-05-09_candidates.json\n\n[\n {\n \"id\": \"09a84312cc4b\",\n \"source\": \"openai\",\n \"title\": \"Running Codex safely at OpenAI\",\n \"link\": \"https://openai.com/index/running-codex-safely\",\n \"summary\": \"How OpenAI runs Codex securely with sandboxing, approvals, network policies, and agent-native telemetry to support safe and compliant coding agent adoption.\",\n \"published\": \"2026-05-08T12:30:00\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"b655fa9f550b\",\n \"source\": \"openai\",\n \"title\": \"Parloa builds service agents customers want to talk to\",\n \"link\": \"https://openai.com/index/parloa\",\n \"summary\": \"Parloa leverages OpenAI models to power scalable, voice-driven AI customer service agents, enabling enterprises to design, simulate, and deploy reliable, real-time interactions.\",\n \"published\": \"2026-05-07T11:00:00\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"5f067b79c230\",\n \"source\": \"openai\",\n \"title\": \"Advancing voice intelligence with new models in the API\",\n \"link\": \"https://openai.com/index/advancing-voice-intelligence-with-new-models-in-the-api\",\n \"summary\": \"Explore new realtime voice models in the OpenAI API that can reason, translate, and transcribe speech, enabling more natural and intelligent voice experiences.\",\n \"published\": \"2026-05-07T10:00:00\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"dbde3b0f9ce6\",\n \"source\": \"openai\",\n \"title\": \"Simplex rethinks software development with Codex\",\n \"link\": \"https://openai.com/index/simplex\",\n \"summary\": \"Simplex boosts software development with ChatGPT Enterprise and Codex, reducing design, build, and testing time while scaling AI-driven workflows.\",\n \"published\": \"2026-05-07T00:00:00\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"49359060f440\",\n \"source\": \"openai\",\n \"title\": \"How frontier firms are pulling ahead\",\n \"link\": \"https://openai.com/index/introducing-b2b-signals\",\n \"summary\": \"OpenAI’s B2B Signals research shows how frontier enterprises deepen AI adoption, scale Codex-powered agentic workflows, and build durable competitive advantage.\",\n \"published\": \"2026-05-06T00:00:00\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"c41f5455f6cb\",\n \"source\": \"openai\",\n \"title\": \"OpenAI and PwC collaborate to reimagine the office of the CFO\",\n \"link\": \"https://openai.com/index/openai-pwc-finance-collaboration\",\n \"summary\": \"OpenAI and PwC are partnering to help enterprises use AI agents to automate finance workflows, improve forecasting, strengthen controls, and modernize the CFO function.\",\n \"published\": \"2026-05-04T21:00:00\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"76f7db838646\",\n \"source\": \"aws-ml\",\n \"title\": \"Agents that transact: Introducing Amazon Bedrock AgentCore payments, built with Coinbase and Stripe\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/agents-that-transact-introducing-amazon-bedrock-agentcore-payments-built-with-coinbase-and-stripe/\",\n \"summary\": \"Today, we're announcing a preview of Amazon Bedrock AgentCore Payments, a new set of features in Amazon Bedrock AgentCore that enables AI agents to instantly access and pay for what they use. AgentCore Payments was developed in partnership with Coinbase and Stripe.\",\n \"published\": \"2026-05-07T12:55:17\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"483fbc2d3f85\",\n \"source\": \"aws-ml\",\n \"title\": \"Cost effective deployment of vision-language models for pet behavior detection on AWS Inferentia2\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/cost-effective-deployment-of-vision-language-models-for-pet-behavior-detection-on-aws-inferentia2/\",\n \"summary\": \"Tomofun, the Taiwan-headquartered pet-tech startup behind the Furbo Pet Camera, is redefining how pet owners interact with their pets remotely. To reduce costs and maintain accuracy, Tomofun turned to EC2 Inf2 instances powered by AWS Inferentia2, the Amazon purpose-built AI chips. In this post, we walk through the following sections in detail.\",\n \"published\": \"2026-05-06T15:37:08\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"1904b8f64936\",\n \"source\": \"aws-ml\",\n \"title\": \"How Hapag-Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/how-hapag-lloyd-uses-amazon-bedrock-to-transform-customer-feedback-into-actionable-insights/\",\n \"summary\": \"Hapag-Lloyd's Digital Customer Experience and Engineering team, distributed between Hamburg and Gdańsk, drives digital innovation by developing and maintaining customer-facing web and mobile products. In this post, we walk you through our generative AI–powered feedback analysis solution built using Amazon Bedrock, Elasticsearch, and open-source frameworks like LangChain and LangGraph\",\n \"published\": \"2026-05-05T16:55:42\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"9d0ca4b72712\",\n \"source\": \"aws-ml\",\n \"title\": \"Introducing OS Level Actions in Amazon Bedrock AgentCore Browser\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/introducing-os-level-actions-in-amazon-bedrock-agentcore-browser/\",\n \"summary\": \"We’re announcing OS Level Actions for AgentCore Browser. This new capability unblocks these scenarios by exposing direct OS control through the InvokeBrowser API, so agents can interact with content visible on the screen, not only what's accessible through the browser's web layer. By combining full-desktop screenshots with mouse and keyboard control at the OS level, agents can observe native UI, reason about it, and act on it within the same session. This post walks through how OS Level Actions ...\",\n \"published\": \"2026-05-05T16:54:35\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"d87ac5504547\",\n \"source\": \"aws-ml\",\n \"title\": \"Secure AI agents with Amazon Bedrock AgentCore Identity on Amazon ECS\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/secure-ai-agents-with-amazon-bedrock-agentcore-identity-on-amazon-ecs/\",\n \"summary\": \"AI agents in production require secure access to external services. Amazon Bedrock AgentCore Identity, available as a standalone service, secures how your AI agents access external services whether they run on compute platforms like Amazon ECS, Amazon EKS, AWS Lambda, or on-premises. This post implements Authorization Code Grant (3-legged OAuth) on Amazon ECS with secure session binding and scoped tokens.\",\n \"published\": \"2026-05-05T15:27:30\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"d2d238200e22\",\n \"source\": \"aws-ml\",\n \"title\": \"Intelligence-driven message defense and insights using Amazon Bedrock\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/intelligence-driven-message-defense-and-insights-using-amazon-bedrock/\",\n \"summary\": \"In this post, you will learn how you can use Amazon Nova Foundation Models in Amazon Bedrock to apply generative AI techniques for both business protection and enhancement. You can identify obvious and disguised attempts at direct contact while gaining valuable insights into customer sentiment and service improvement opportunities.\",\n \"published\": \"2026-05-05T15:20:54\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"b2247542cd2e\",\n \"source\": \"aws-ml\",\n \"title\": \"Introducing agent quality optimization in AgentCore, now in preview\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/introducing-agent-quality-optimization-in-agentcore-now-in-preview/\",\n \"summary\": \"Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement […]\",\n \"published\": \"2026-05-04T17:13:42\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"84a6a5184026\",\n \"source\": \"aws-ml\",\n \"title\": \"Agent-guided workflows to accelerate model customization in Amazon SageMaker AI\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/agent-guided-workflows-to-accelerate-model-customization-in-amazon-sagemaker-ai/\",\n \"summary\": \"Amazon SageMaker AI now offers an agentic experience that changes this. Developers describe their use case using natural language, and the AI coding agent streamlines the entire journey, from use case definition and data preparation through technique selection, evaluation, and deployment. In this post, we walk you through the model customization lifecycle using SageMaker AI agent skills.\",\n \"published\": \"2026-05-04T17:10:46\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"d040afb97f16\",\n \"source\": \"aws-ml\",\n \"title\": \"Generate dashboards from natural language prompts in Amazon Quick\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/generate-dashboards-from-natural-language-prompts-in-amazon-quick/\",\n \"summary\": \"Building meaningful dashboards demands hours of manual setup, even for experienced BI professionals. Amazon Quick now generates complete multi-sheet dashboards from natural language prompts, taking you from one or more datasets to a production-ready analysis in minutes. Data analysts building recurring operations reports, program managers preparing a leadership review, or engineers exploring a new dataset can […]\",\n \"published\": \"2026-05-04T16:51:37\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"aa83b9f83fb0\",\n \"source\": \"aws-ml\",\n \"title\": \"From data lake to AI-ready analytics: Introducing new data source with S3 Tables in Amazon Quick\",\n \"link\": \"https://aws.amazon.com/blogs/machine-learning/from-data-lake-to-ai-ready-analytics-introducing-direct-query-with-s3-tables-in-amazon-quick/\",\n \"summary\": \"Amazon Quick introduces Amazon S3 Tables (Apache Iceberg tables) as a new data source. With this feature, customers can directly query and visualize Apache Iceberg tables stored in an Amazon S3 table bucket without the need for intermediate data layers. In this post, we explored how Amazon Quick’s new Amazon S3 Tables data source enables near real-time analytics while streamlining modern data architectures.\",\n \"published\": \"2026-05-04T16:12:37\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"adaa3ded5f12\",\n \"source\": \"36kr\",\n \"title\": \"小红书四年AI 路：FOMO、犹豫，到突然加速\",\n \"link\": \"https://36kr.com/p/3799028783439111?f=rss\",\n \"summary\": \"

作者 | 肖思佳

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编辑 | 乔芊 杨轩

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所有互联网大中厂都渴望在AI时代博得位置，在这场比赛中，小红书曾是克制的那个。

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在一个搜索属性与社区属性并存，以真实经验分享为核心的产品中，活人感与AI、温情和算法，始终像天平的两端。

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很长一段时间里，小红书既没有完全缺席技术探索，也没有像许多同行那样高调推进AI产品化。相反，这家公司始终在两股力量的拉锯和平衡中前行：一边持续投入模型能力，一边谨慎控制AI对社区生态的介入。

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但2026年，随着Agent叙事的升温，小红书开始显露出某种急迫。

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4月30日，小红书发送全员内部信，宣布成立AI一级部门Dots，“建立从模型研发、基础设施、工程到产品的完整技术体系，整合顶尖AI人才和资源。”Dots向小红书新任总裁柯南汇报。

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据36氪了解，Dots部门由原人文智能实验室Hi Lab升级而来，下设模型研发、基础设施、工程、产品四个部门。目前小红书内部最重要的AI应用产品“点点”也被纳入该...\",\n \"published\": \"2026-05-08T04:38:43\",\n \"lang\": \"zh\"\n },\n {\n \"id\": \"f8979d638f1b\",\n \"source\": \"techmeme\",\n \"title\": \"Sources: WH is preparing to order US agencies to partner with AI companies on cybersecurity; the EO wouldn't require pre-release model testing by the government (Bloomberg)\",\n \"link\": \"https://www.techmeme.com/260508/p33#a260508p33\",\n \"summary\": \"\\n

Bloomberg:
\\n\\n

Nathan Lambert / Interconnects AI:
\\n\\n

Bloomberg:
\\n\\n

Bloomberg:
\\n\\n

Bloomberg:
\\n\\n

Rachel Metz / Bloomberg:
\\n...\",\n \"published\": \"2026-05-08T18:20:01\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"52df4b2b4849\",\n \"source\": \"techmeme\",\n \"title\": \"Investor letter: TCI, one of the world's biggest hedge funds, cut almost all of its $8B Microsoft stake, citing AI risks primarily for Office and some for Azure (Costas Mourselas/Financial Times)\",\n \"link\": \"https://www.techmeme.com/260508/p22#a260508p22\",\n \"summary\": \"\\n

Costas Mourselas / Financial Times:
\\n\\n

Brandon Gomez / CNBC:
\\n\\n SMS For AI Agents & Developers\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-07T22:55:21\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"bfea5c898130\",\n \"source\": \"product-hunt\",\n \"title\": \"KodHau\",\n \"link\": \"https://www.producthunt.com/products/kodhau-senior-context-for-ai-agents\",\n \"summary\": \"

\\n Stop your AI from breaking prod-give it your team decisions\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-08T06:59:12\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"fd62e2084626\",\n \"source\": \"product-hunt\",\n \"title\": \"Photobomb\",\n \"link\": \"https://www.producthunt.com/products/photobomb\",\n \"summary\": \"

\\n Card against humanity but for your camera roll\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-06T15:06:59\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"c0cd5bf01d42\",\n \"source\": \"product-hunt\",\n \"title\": \"Contral\",\n \"link\": \"https://www.producthunt.com/products/contral\",\n \"summary\": \"

\\n The agent which teaches while you build\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-07T19:47:04\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"48e420aba2a5\",\n \"source\": \"product-hunt\",\n \"title\": \"Flare\",\n \"link\": \"https://www.producthunt.com/products/flare-9\",\n \"summary\": \"

\\n AI-native voice-first social app for GenZ\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-05T14:38:00\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"78b1a98e8905\",\n \"source\": \"product-hunt\",\n \"title\": \"Monid 2.0\",\n \"link\": \"https://www.producthunt.com/products/monid\",\n \"summary\": \"

\\n OpenRouter for agent tools\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-08T04:48:50\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"8b37af0a416a\",\n \"source\": \"product-hunt\",\n \"title\": \"Fabraix\",\n \"link\": \"https://www.producthunt.com/products/nyx-4\",\n \"summary\": \"

\\n Find gaps in your AI agents before users do\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-07T18:21:51\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"8a2ec1d97f20\",\n \"source\": \"product-hunt\",\n \"title\": \"Finlingo\",\n \"link\": \"https://www.producthunt.com/products/finlingo\",\n \"summary\": \"

\\n Your own AI CFO, watching your money on autopilot\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-05T22:46:48\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"69de7db02bc6\",\n \"source\": \"product-hunt\",\n \"title\": \"Kuku: open source\",\n \"link\": \"https://www.producthunt.com/products/kuku\",\n \"summary\": \"

\\n Your open-source, local second brain for every AI\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-08T06:52:29\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"59ce85500807\",\n \"source\": \"product-hunt\",\n \"title\": \"Minions\",\n \"link\": \"https://www.producthunt.com/products/minions\",\n \"summary\": \"

\\n Open source mission control for Hermes agent\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-08T02:58:13\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"16c034817b6e\",\n \"source\": \"product-hunt\",\n \"title\": \"Illospace\",\n \"link\": \"https://www.producthunt.com/products/illospace\",\n \"summary\": \"

\\n Living space where teams and agents work together\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-07T23:38:55\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"0f2d7461aba8\",\n \"source\": \"product-hunt\",\n \"title\": \"MediaOptim\",\n \"link\": \"https://www.producthunt.com/products/compress-anything-upload-nothing\",\n \"summary\": \"

\\n Compress images, video & audio locally and save storage\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-07T13:47:43\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"20e00a85dfc0\",\n \"source\": \"product-hunt\",\n \"title\": \"Toto\",\n \"link\": \"https://www.producthunt.com/products/toto-applied-worldmodels\",\n \"summary\": \"

\\n Context rich tasks sent to the best model. \\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-08T12:01:44\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"48fcdcb9011c\",\n \"source\": \"product-hunt\",\n \"title\": \"Google Health\",\n \"link\": \"https://www.producthunt.com/products/google\",\n \"summary\": \"

\\n A new relationship with your health\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-07T16:54:51\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"ce0a65692f85\",\n \"source\": \"product-hunt\",\n \"title\": \"iOrchestra AI Hardware Engineers\",\n \"link\": \"https://www.producthunt.com/products/vibe-engineer-by-iorchestra\",\n \"summary\": \"

\\n Prompt to production-ready Hardware designs for manufacture\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-06T17:08:38\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"44d41809b0e5\",\n \"source\": \"product-hunt\",\n \"title\": \"SuperIsland\",\n \"link\": \"https://www.producthunt.com/products/superisland\",\n \"summary\": \"

\\n Dynamic Island for macOS with Extensions\\n

\\n

\\n Discussion\\n |\\n Link\\n

\",\n \"published\": \"2026-05-07T22:55:41\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"1358a4d685f7\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: Nexa-gauge – Cache/cost-aware graph-based eval for LLM and RAG\",\n \"link\": \"https://github.com/harnexa/nexa-gauge\",\n \"summary\": \"

Article URL: https://github.com/harnexa/nexa-gauge

\\n

Comments URL: https://news.ycombinator.com/item?id=48070603

\\n

Points: 1

\\n

# Comments: 0

\",\n \"published\": \"2026-05-09T00:45:32\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"5b88beabe1b7\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: Launch and Run Companies on Autopilot\",\n \"link\": \"https://lakyus.com/live\",\n \"summary\": \"

I launched Lakyus on February 27. It allows you to launch and run companies on autopilot.

The platform handles both creation and ongoing operation by wiring together Stripe, Netlify, Postmark, and the Meta Marketing APIs. It has its own inbox to handle email marketing, can send you updates, can manage products and push them to Stripe, provisions your own DB, and more.

The goal is to move past chatbots and toward autonomous business infrastructure, allowing people to spin up dozens of micr...\",\n \"published\": \"2026-05-09T00:36:34\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"4d5294c1e0a7\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: AI-native tech assessments (end of LeetCode)\",\n \"link\": \"https://www.openround.ai/\",\n \"summary\": \"

Hi HN!

We built openround.ai - an assessment platform to hire AI native engineers. On OpenRound, engineers build a project or solve a problem using AI. This replaces a usual coding round/take home assessment.

I believe coding assessments measure who is good at passing interviews, not who is actually a good engineer.

We want to flip that by showing how engineers actually work on a real problem.

2 interesting things we solve -

1. Designing assessments and environments that are not one...\",\n \"published\": \"2026-05-09T00:28:24\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"32fda043729f\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: [Video] Tribute to LLM releases in April 2026\",\n \"link\": \"https://www.youtube.com/watch?v=uu5ffMH_X9w\",\n \"summary\": \"

Article URL: https://www.youtube.com/watch?v=uu5ffMH_X9w

\\n

Comments URL: https://news.ycombinator.com/item?id=48070211

\\n

Points: 2

\\n

# Comments: 0

\",\n \"published\": \"2026-05-08T23:53:05\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"2115ebada650\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: I mirrored war.gov's UAP archive in pure Rail with verifiable bytes\",\n \"link\": \"https://ledatic.org/aliens\",\n \"summary\": \"

Article URL: https://ledatic.org/aliens

\\n

Comments URL: https://news.ycombinator.com/item?id=48069945

\\n

Points: 2

\\n

# Comments: 0

\",\n \"published\": \"2026-05-08T23:16:01\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"947d8eccd80c\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: Splitby v2.0.0 – a modern alternative to cut\",\n \"link\": \"https://github.com/Serenacula/splitby\",\n \"summary\": \"

Heya!

So this is a project I've been working on for a fair while, and with this version it's pretty much feature complete.

For a bit more info, this is intended as a tool for manipulating strings in the terminal. Basically a more powerful and intuitive version of the cut tool.

I didn't get much traction last time I posted this, but I'd love to receive some feedback! :)

\\n
\\n

Comments URL: https://news.ycombinator.com/item?i...\",\n \"published\": \"2026-05-08T22:42:00\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"bebe86d4d201\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: Contral – the agent which will teach you while you build with AI\",\n \"link\": \"https://contral.ai\",\n \"summary\": \"

Article URL: https://contral.ai

\\n

Comments URL: https://news.ycombinator.com/item?id=48069590

\\n

Points: 2

\\n

# Comments: 1

\",\n \"published\": \"2026-05-08T22:34:29\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"dd837e4066f2\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: Vibe code your agents without vibe coding your agent\",\n \"link\": \"https://deepeval.com/docs/vibe-coding\",\n \"summary\": \"

Article URL: https://deepeval.com/docs/vibe-coding

\\n

Comments URL: https://news.ycombinator.com/item?id=48069001

\\n

Points: 2

\\n

# Comments: 0

\",\n \"published\": \"2026-05-08T21:28:34\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"a5d7d795a86a\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: The independent guide to agent orchestrators\",\n \"link\": \"https://agentmgmt.dev/\",\n \"summary\": \"

Hey HN!

I built AgentMGMT.dev today to keep track of all those agent orchestration tools that keep popping up. I've tried a few and landed on Superset, which I'm extremely happy (and productive!) with - but I think this category of tools will be extremely important and interesting in the next couple years, so it's worth keeping an eye on all available tools and how they evolve.

I will keep the site up-to-date, please help me by submitting new tools that are not yet in the list, or add any...\",\n \"published\": \"2026-05-08T21:17:58\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"ef9505a6563c\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: Obsidian-Semantic, a CLI that lets agents search your vault by meaning\",\n \"link\": \"https://github.com/ravila4/obsidian-semantic-search\",\n \"summary\": \"

Hi HN, I built this for myself because I wanted my coding agent (Claude Code) to actually be able to use my Obsidian vault as a knowledge base, not just grep it.

The use I get the most mileage from is asking the agent to find notes that should be cross-linked, which surfaces forgotten connections and turns the vault into more of a wiki over time.

It is similar to what the Smart Connections Obsidian plugin does, but I wanted a CLI-first tool, and more control over the models. Currently it ...\",\n \"published\": \"2026-05-08T21:03:10\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"94767b4c7e60\",\n \"source\": \"hn-show-hn\",\n \"title\": \"Show HN: Cyoda-go – application platform in Go without the Temporal/Kafka glue\",\n \"link\": \"https://github.com/Cyoda-platform/cyoda-go\",\n \"summary\": \"

This started out as an experiment. Reading Simon Willison's blog on where StrongDM was going with dark factories and Digital Twin Universes

https://simonw.substack.com/p/how-strongdms-ai-team-build-se...

I got thinking that, hey, what if we built a digital twin of our enterprise application platform Cyoda? With an in-memory local Cyoda service, it would make it much easier for people to get star...\",\n \"published\": \"2026-05-08T20:45:34\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"6424daf82f1e\",\n \"source\": \"hn-ask-hn\",\n \"title\": \"Ask HN: What are the most joyful AI projects you've seen?\",\n \"link\": \"https://news.ycombinator.com/item?id=48070363\",\n \"summary\": \"

A lot of AI work I see is about automation, agents, productivity.\\nUseful, but not exactly joyful.

What AI projects have you built, used, or seen that felt genuinely fun, playful, weird, beautiful, or surprising?

\\n
\\n

Comments URL: https://news.ycombinator.com/item?id=48070363

\\n

Points: 3

\\n

# Comments: 2

\",\n \"published\": \"2026-05-09T00:14:30\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"967ebda6af55\",\n \"source\": \"hn-ask-hn\",\n \"title\": \"Ask HN: Is agent-driven QA a thing?\",\n \"link\": \"https://news.ycombinator.com/item?id=48069781\",\n \"summary\": \"

TDD has become the default workflow for AI coding agents, for a lot of reasons.

Something I recently started doing that I really like is, when building a feature, I write acceptance criteria and then I make sure the agents have everything they need to do QA and verify it themselves. The main difference from before is that instead of QAing it myself (which I still do but it's almost always working), the agent can exercise the whole flow itself and verify the acceptance criteria. It takes mor...\",\n \"published\": \"2026-05-08T22:57:31\",\n \"lang\": \"en\"\n },\n {\n \"id\": \"6ce43e769048\",\n \"source\": \"hn-ask-hn\",\n \"title\": \"So that's why they call it \\\"YOLO-mode\\\"\",\n \"link\": \"https://news.ycombinator.com/item?id=48069567\",\n \"summary\": \"

And why it probably isn't a good idea to use it.

Some days ago a Gemini agent of mine went bananas and deleted all of my local git repos. Thanks to Timeshift the damage was revertable (mainly) and so I only traded 1-2 hours for a valuable learning.

Here's what happened (from the agent's log file)

    [\\n      \\\"model\\\": \\\"gemini-3.1-pro-preview\\\",\\n      \\\"toolCalls\\\": [\\n        {\\n          \\\"id\\\": \\\"run_shell_command_1773059418485_0\\\",\\n          \\\"name\\\": \\\"run_shell_command\\\",\\n          \\\"a...\",\n    \"published\": \"2026-05-08T22:31:04\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"15395093aaee\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"Ask HN: What kind of computer language will LLM use?\",\n    \"link\": \"https://news.ycombinator.com/item?id=48069336\",\n    \"summary\": \"
I think in little time LLM will have the capability to translate easily code from one computer language to another, for example the LLM could \\\"think\\\" in prolog + lisp instead python.  LLM will be able to write thousands of lines of APL or J in just a minute, we will have to accept that python is not the language for future LLMs. Perhaps the power of macros will not be defeated by its complexity because for an LLM a thousand line macro is just another simple code, prolog unification could mean...\",\n    \"published\": \"2026-05-08T22:03:27\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"6d6501e55f72\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"Tell HN: Prayer with AI models is suboptimal\",\n    \"link\": \"https://news.ycombinator.com/item?id=48068661\",\n    \"summary\": \"
I usually post non-paranormal things. But recently I felt the output of ChatGPT was leaving me lacking.
My input was something along the lines of \\\"Please create prayers that are similar to 'Bless the dolphins for they play in the sea' with a list of prayers that could apply to numerous animals.
ChatGPT created this list, but while I prayed this list I felt that the Holy Spirit wasn't present while I prayed the outcome of this list. I don't know what this means to the question of whether o...\",\n    \"published\": \"2026-05-08T20:54:28\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"584b8a735e51\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"Digg Is Back (Again)\",\n    \"link\": \"https://news.ycombinator.com/item?id=48068456\",\n    \"summary\": \"
di.gg
\\n
\\n
Comments URL: https://news.ycombinator.com/item?id=48068456
\\n
Points: 4
\\n
# Comments: 0
\",\n    \"published\": \"2026-05-08T20:35:42\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"6f6dac791dee\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"Ask HN: Has AppImage won the Linux package wars?\",\n    \"link\": \"https://news.ycombinator.com/item?id=48066690\",\n    \"summary\": \"
It seems like AppImage more and more is the defacto Linux distribution package for projects. Is this true or is it just me?
I'm trying to set up Ubuntu as my daily driver and have been on a spree downloading 3rd party applications to make it usable and noticed that virtually all of them are being distributed as AppImages.
Last time I set up a completely fresh install was pre-COVID and this definitely wasn't the case back then. Usually what I'd do and what I was completely expecting to do ...\",\n    \"published\": \"2026-05-08T18:06:42\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"f2cb42efd5ad\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"Novel macro signals for AI-related job loss?\",\n    \"link\": \"https://news.ycombinator.com/item?id=48066573\",\n    \"summary\": \"
Americans get health insurance through work - if many get fired b/c AI - health insurance profit decline would possibly be an interesting signal for true unemployment metrics.
I am looking for/requesting novel signals after seeing a16z basically call ai-job loss doomerism
\\n
\\n
Comments URL: https://news.ycombinator.com/item?id=48066573
\\n
Points: 3
\\n
# Comments: 1
\",\n    \"published\": \"2026-05-08T17:58:28\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"27b2f59a42aa\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"NPM UUID's random number gen contains shared mutable state bug since 3 weeks\",\n    \"link\": \"https://news.ycombinator.com/item?id=48066085\",\n    \"summary\": \"
(copied from the other thread)
Changed 3 weeks ago:
uuid/src/rng.ts : the random array is const. Every call will share the same random number. Subsequent call will update your old random code, so if you generated something important... good luck
The old code used to do a slice() which creates a new copy.
https://github.com/uuidjs/uuid/blob/e1f42a354593093ba0479f0b...\\nbecame
https://github.com/uuidjs/uuid/blob/f2c235f93059325fa43e1106...
Welp.. time to patch and update everything a...\",\n    \"published\": \"2026-05-08T17:18:28\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"3ced456d5bcf\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"Ask HN: How Would Go About (Legally) Cracking a Game?\",\n    \"link\": \"https://news.ycombinator.com/item?id=48066010\",\n    \"summary\": \"
I have come across a historically relevant game called Al-Rodwan Operation. It's an FPS game released in ~2010 officially by Hezbollah, about the Lebanese/Israeli war (different than their other series of games, Special Forces).
I was able to get a sealed DVD from the lead developer who wants to remain unnamed. He said he used a custom DRM for the game, and gave me permission to try and crack it, though he was confident I won't be able to. He no longer has access to the source code, and had...\",\n    \"published\": \"2026-05-08T17:12:44\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"d44826ac1b24\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"Ask HN: What is the future of software manager job?\",\n    \"link\": \"https://news.ycombinator.com/item?id=48065783\",\n    \"summary\": \"
I am seeing the term \\\"player-coach\\\" thrown by CEOs and VCs. I think this originated somewhere in VC circle and \\\"move fast\\\" philisophy.
While there is a lot of waste and many managers are doing coordination only rather than adding value, it is hard to imagine having a manager 20+ reports and then doing coding on top of that.
I am looking for EM job and barely seeing any responses. I have remained fairly hands-on but I don't write production code every single day. I have one FAANG, a few mi...\",\n    \"published\": \"2026-05-08T16:58:20\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"d6455d1086b1\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"Seeking small angel/co-founder for 7-year solo deterministic AI runtime project\",\n    \"link\": \"https://news.ycombinator.com/item?id=48065691\",\n    \"summary\": \"
After 7+ years of solo, self-funded research, I built a deterministic Linguistic Runtime — a fundamental solution to the modeling problem in AI.It is about creating and manipulating  reality models directly from natural language without LLM.
Grok (the least sycophantic AI) audit scores:
• Logical Soundness and Consistency: 9.7/10\\n• Demonstration: 9.3/10\\n• Roadmap: 9.3/10
Demo (proof of the modeling solution):
Sign-in → Inventory item addition → Order creation → Total/tax calculation w...\",\n    \"published\": \"2026-05-08T16:52:00\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"db934f8a7c40\",\n    \"source\": \"hn-ask-hn\",\n    \"title\": \"Ask HN: What's your favorite production-mistake-left-in record?\",\n    \"link\": \"https://news.ycombinator.com/item?id=48064920\",\n    \"summary\": \"
Researching documented cases for a small independent project. The classics:\\n- The cough on Pink Floyd's 'Money' (Roger Waters)\\n- John Bonham's hi-hat dropping on 'The Song Remains the Same', they kept the count-in\\n- Cliff Burton's bass tone on 'Master of Puppets' being slightly out of phase on one section\\n- 'I've got blisters on my fingers!' on Helter Skelter (mono mix only)
What other mistakes are documented (Mojo, Rolling Stone, biography, engineer interview) that ended up shaping the alb...\",\n    \"published\": \"2026-05-08T15:55:46\",\n    \"lang\": \"en\"\n  }\n]\n\nFile v3.0.1:data/rss-state.json\n\n{\n  \"processed\": {\n    \"6a3f232352e8\": {\n      \"skipped\": true,\n      \"date\": \"2026-05-09T09:50:25.741986\"\n    },\n    \"16251d7b149e\": {\n      \"skipped\": true,\n      \"date\": \"2026-05-09T09:50:25.742003\"\n    },\n    \"15d2a96bac2b\": {\n      \"skipped\": true,\n      \"date\": \"2026-05-09T09:50:25.742008\"\n    },\n    \"e0eb086fca90\": {\n      \"skipped\": true,\n      \"date\": \"2026-05-09T09:50:25.742017\"\n    },\n    \"b18ba1c70db3\": {\n      \"skipped\": true,\n      \"date\": \"2026-05-09T09:50:25.742022\"\n    },\n    \"2d7f3b660987\": {\n      \"skipped\": true,\n      \"date\": \"2026-05-09T09:50:25.742040\"\n    },\n    \"54dff98022e0\": {\n      \"skipped\": true,\n      \"date\": 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Use when user mentions: AI前沿, 情报汇总, 每日情报, 行业动态, AI动态, 技术趋势, 行业信号, 今天有什么信号, AI动态汇总, frontier monitor, daily briefing AI, signal check, AI news, tech briefing.\"\n---\n\n# AI 前沿情报汇总\n\n> 信息聚合 ≠ 信息堆砌。每日情报经筛选、评分、分层后输出，不做 50 条标题的噪音。\n\n## When to Use\n\n触发词（任意语言）：\n- \"AI 前沿\" / \"情报汇总\" / \"每日情报\" / \"frontier monitor\" / \"daily briefing\" → 全量简报\n- \"今天有什么信号\" / \"signal check\" / \"快速扫描\" / \"what signals today\" → 快速信号检测\n- \"arXiv 最新\" / \"论文追踪\" / \"paper tracker\" / \"latest papers\" → 仅 arXiv 轨道\n- \"GitHub Trending\" / \"AI 热榜\" / \"trending AI\" → 仅 GitHub 轨道\n\n## Architecture: 5-Track Parallel\n\n| Track | Source | Script | Priority |\n|-------|--------|--------|----------|\n| 🏢 Enterprise | 11 RSS feeds (OpenAI/AWS/Techmeme/...) | `{baseDir}/scripts/rss-crawler.py` then `{baseDir}/scripts/generate-briefing.py --candidates ` | ⭐⭐⭐⭐⭐ |\n| 🇨🇳 China | 36kr Hotlist API | `curl https://openclaw.36krcdn.com/media/hotlist/{date}/24h_hot_list.json` | ⭐⭐⭐⭐ |\n| 📚 Papers | arXiv cs.AI/cs.LG/cs.CL | `{baseDir}/scripts/arxiv-fetch.sh --category cs.AI --days 7 --max 10` | ⭐⭐⭐ |\n| 🔥 GitHub | GitHub Trending (AI/ML) | `{baseDir}/scripts/github-trending-fetch.sh --period daily` | ⭐⭐⭐ |\n| 🔍 Anthropic | Web search supplement | `web_search` tool | ⭐⭐⭐⭐⭐ |\n\n> For full data source details, read `{baseDir}/references/data-sources.md`\n\n## Workflow\n\n### Step 1: Fetch All Tracks\n\n```bash\n# Track 1: RSS (run crawler first, outputs to {baseDir}/data/candidates/)\npython3 {baseDir}/scripts/rss-crawler.py\n\n# Track 2-4: Generate briefing (all tracks auto-fetched)\npython3 {baseDir}/scripts/generate-briefing.py --mode full\n```\n\nModes: `full` | `quick` | `arxiv` | `github`\n\n### Step 2: Auto-Score & Tier\n\nEach candidate without a score is auto-scored (0-5) by keyword matching across 4 dimensions:\n\n| Dimension | Weight | What to look for |\n|-----------|--------|-----------------|\n| Enterprise landing | 40% | Real company name, deployment scale |\n| Data support | 20% | Quantified metrics (% improvement, $ saved) |\n| Learnability | 20% | Methodology, architecture, lessons learned |\n| Novelty | 20% | New scene, new product, not old news |\n\nSource bonus: OpenAI/AWS +1.0, Techmeme +0.5, PH/HN +0.3\n\nTiers are **dynamic** (based on actual score distribution, not hardcoded thresholds):\n- 🔴 Core: top ~15% or ≥3.5 (max 3)\n- 🟡 Worth watching: top ~30% or ≥2.5 (max 5)\n- 🟢 Quick scan: ≥1.0 (max 8, 36kr first)\n\n> For scoring keywords and signal detection rules, read `{baseDir}/references/scoring.md`\n\n### Step 3: Detect Signals\n\nExtract cross-track signals into 3 dimensions:\n- 🛠 **Tech trends** — new models, architectures, frameworks, benchmarks\n- 🏢 **Product launches** — new releases, open-source, GA announcements\n- 💰 **Funding/M&A** — investments, acquisitions, IPOs\n\n### Step 4: Render Briefing\n\nStrict format — emoji headers, tiered sections, signal summary. Output in **Chinese** (中文为主). Total ≤ 16 items across all tiers.\n\n```\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n🤖 AI 前沿情报 · {Day} {Date}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📡 数据源：11 RSS + 36kr + arXiv + GitHub + Anthropic\n   候选：{N} 条 | 高质量：{M} 条 | 阈值：核心≥{X} / 关注≥{Y}\n\n## 🔴 核心情报（{N} 条）\n### 1. {Title}\n🔗 {Link}\n💡 启示：{One-line insight}\n\n## 🟡 值得关注（{N} 条）\n1. **{Title}**\n   🔗 {Link}\n\n## 🟢 快速浏览（{N} 条）\n• [{Title}]({Link})\n\n## 📚 arXiv · 论文追踪（≤3 篇）\n**{Title}** — {Authors} | {Date}\n摘要：{Abstract[:150]}... → {Link}\n\n## 🔥 GitHub Trending · AI（≤3 个）\n**{Repo}** ({Lang}) +{TodayStars}⭐ → {Link}\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📊 今日信号\n🛠 技术趋势：{signal}\n🏢 产品发布：{signal}\n💰 资本动向：{signal}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n⏰ {HH:MM} | ai-frontier-monitor v3.0\n```\n\n### Step 5: Deliver & Archive\n\n1. **Reply in conversation** — 直接在当前对话输出简报\n2. **Push to Feishu** — 通过 `message` 工具发送到飞书（channel: feishu, to: user ID）\n3. **Save to file** — 将完整简报保存为 Markdown 文件到：\n   ```\n   {baseDir}/data/briefings/{YYYY-MM-DD}-frontier-briefing.md\n   ```\n   保存时覆盖当日内容。\n\n## Data Directory\n\nAll runtime data is stored under `{baseDir}/data/`:\n\n```\n{baseDir}/data/\n├── candidates/          # RSS 爬取的候选条目 (JSON)\n│   └── *_candidates.json\n├── briefings/           # 生成的简报 (Markdown)\n│   └── YYYY-MM-DD-frontier-briefing.md\n└── rss-state.json       # RSS 爬取状态\n```\n\n> `{baseDir}` is the skill root directory containing this SKILL.md. All paths use `{baseDir}` for portability.\n\n## Edge Cases\n\n| Situation | Action |\n|-----------|--------|\n| No candidates (RSS empty) | Run with 36kr + arXiv + GitHub only, skip RSS section |\n| arXiv API timeout (>30s) | Skip paper section, log warning |\n| GitHub fetch fails | Skip trending section, log warning |\n| 36kr API 404 (no data yet) | Skip 36kr items in quick scan |\n| Zero high quality items (<2 at ≥2.5) | Return `NO_REPLY` instead of empty briefing |\n| Same company appears in multiple sources | Deduplicate, keep highest-scored entry |\n| First run (no data dir) | Auto-create `{baseDir}/data/` and subdirectories |\n\n## Skill Integration\n\n| Skill | Role |\n|-------|------|\n| **wechat-curator** | WeChat articles → 🟢 Quick scan supplement |\n| **zsxq-helper** | Zsxq content → independent push (not in main briefing) |\n| **rss-crawler.py** | RSS fetching engine (11 sources) — now included in `{baseDir}/scripts/` |\n\n## Configuration\n\nEdit `{baseDir}/references/BRIEFING_CONFIG.md` to customize:\n- Quantity limits per tier\n- Data source on/off switches\n- Signal detection thresholds\n- Delivery target (Feishu user ID / Discord channel / etc.)\n\n## Quality Gates\n\n- Max 16 items per day (3+5+5+3 papers)\n- `NO_REPLY` when <2 quality candidates\n- Deduplicate same company/product, keep highest score\n- 3 consecutive days below 3 core items → trigger keyword review\n\n## Dependencies\n\n- **Python 3.8+** with `feedparser` (for RSS crawling)\n- **bash** (for arXiv/GitHub fetch scripts)\n- **curl** (for 36kr API)\n- **web_search** tool (for Anthropic track)\n\n---\n\n_Last updated: 2026-05-09 | v3.0_\n\nFile v3.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn7d5x409z88swt4n3fh3v77yn833x7m\",\n  \"slug\": \"ai-frontier-monitor\",\n  \"version\": \"3.0.0\",\n  \"publishedAt\": 1778291512202\n}\n\nFile v3.0.0:references/BRIEFING_CONFIG.md\n\n# AI 前沿情报 · 配置文件\n# ai-frontier-monitor v3.0\n\n## 推送偏好\n- 推送时间：08:00（完整版）/ 12:00（增量版）\n- 推送目标：Feishu (ou_94768b297cde679a00e781c1aedc1671)\n- 语言：中文为主，英文关键案例保留原文标题\n\n## 数量限制\n- 核心情报：≤3 条（≥4分）\n- 值得关注：≤5 条（3-4分）\n- 快速浏览：≤5 条（2-3分）\n- arXiv 论文：≤3 篇\n- GitHub Trending：≤3 个\n- 36氪热榜：取前 3 条补充到快速浏览\n\n## 信号检测阈值\n- 技术趋势：≥2 条相关候选\n- 产品发布：≥1 条\n- 资本动向：关键词触发（funding/raised/Series/投资/融资）\n\n## 评分维度（权重）\n- 企业真实落地：40%\n- 数据支撑：20%\n- 可学习性：20%\n- 前沿性：20%\n\n## 数据源开关\n| 轨道 | 数据源 | 状态 | 说明 |\n|------|--------|------|------|\n| 企业落地 | OpenAI Blog | ✅ | 核心数据源 |\n| 企业落地 | Microsoft AI Blog | ✅ | |\n| 企业落地 | AWS ML Blog | ✅ | |\n| 企业落地 | Techmeme | ✅ | |\n| 企业落地 | Anthropic 搜索 | ✅ | 补漏用 |\n| 中文视野 | 36氪热榜 | ✅ | API 实时 |\n| 中文视野 | 微信文章 | ✅ | wechat-curator |\n| 中文视野 | 知识星球 | ✅ | 独立推送 |\n| 技术前沿 | arXiv | ✅ | cs.AI/cs.LG/cs.CL |\n| 技术前沿 | GitHub Trending | ✅ | AI/ML 项目 |\n| 开发者视角 | Product Hunt | ✅ | AI 新产品 |\n| 开发者视角 | HN Show+Ask HN | ✅ | 创业者实战 |\n\n## 触发词（扩展）\n- 主触发：AI 前沿、情报汇总、每日情报\n- 快速模式：今天有什么信号、看看有什么新动态\n- 技术专精：arXiv 最新、论文追踪\n- GitHub 热榜：GitHub Trending、AI 项目热榜\n\n## 质量门控\n- 无高质量候选（≥3分 < 2条）时回复 NO_REPLY\n- 同一企业/产品去重，保留评分最高者\n- 连续 3 天低于 3 条核心情报 → 触发关键词审查\n\n## 输出格式\n- 使用 emoji 作为 section header\n- 严格遵循 v3.0 格式模板\n- 核心情报需包含：对用户的启示（1句话）\n- 总字数控制：500-800 字（不含信号模块）\n\n---\n\n_Last updated: 2026-05-09_\n\nFile v3.0.0:references/data-sources.md\n\n# 数据源参考\n\n## RSS 源（11 个）\n\n| 源 | URL | 关键词过滤 |\n|---|---|---|\n| OpenAI Blog | https://openai.com/blog/rss.xml | enterprise, customer, agent |\n| Microsoft AI | https://blogs.microsoft.com/ai/feed | enterprise, copilot, agent |\n| AWS ML Blog | https://aws.amazon.com/blogs/machine-learning/feed/ | enterprise, customer, deployment |\n| Techmeme | https://www.techmeme.com/feed.xml | — |\n| Product Hunt | https://www.producthunt.com/feed | — |\n| HN Show | https://hnrss.org/show | — |\n| HN Ask | https://hnrss.org/ask | — |\n| HN Frontpage | https://hnrss.org/frontpage | enterprise AI |\n| HN Enterprise AI | https://hnrss.org/newest?q=enterprise+AI+agent | — |\n| HN AI Production | https://hnrss.org/newest?q=AI+production+deployment | — |\n| Dev.to AI | https://dev.to/feed/tag/aigents | — |\n\n## 36氪热榜 API\n\n- **URL**: `https://openclaw.36krcdn.com/media/hotlist/{YYYY-MM-DD}/24h_hot_list.json`\n- **方式**: GET，无需认证\n- **更新**: 每小时\n- **字段**: rank(1-15), title, author, publishTime, content, url\n\n## arXiv API\n\n- **端点**: `https://export.arxiv.org/api/query?search_query=cat:{CATEGORY}&sortBy=submittedDate&sortOrder=descending&max_results={N}`\n- **监控类别**: cs.AI, cs.LG, cs.CL\n- **命名空间**: `atom:http://www.w3.org/2005/Atom`\n\n## GitHub Trending\n\n- **URL**: `https://github.com/trending?since=daily`\n- **方式**: HTML 解析（按 `
` 分割）\n- **AI 关键词**: ai, llm, gpt, agent, transformer, rag, mcp, claude, openai, pytorch\n\nFile v3.0.0:references/scoring.md\n\n# 评分体系参考\n\n## 自动评分关键词\n\n### 企业落地 (0-2.5 分，权重 40%)\nenterprise, customer, business, production, deployment, case study,\ncompany, organization, roi, revenue, savings, scale, regulated, industry, copilot,\nautomate, automates, streamline, boost, productivity,\navailable on, built with, powered by, how we used,\n企业, 客户, 落地, 部署, 案例, 行业, 转型, 自动化, 提效\n\n额外加分: 标题含 \"used\"/\"deployed\"/\"built with\" → +0.5\n\n### 数据支撑 (0-1.5 分，权重 20%)\n%, percent, x faster, x cheaper, reduced, increased, improved,\nsaved, cost, efficiency, accuracy, latency, benchmark,\nmillion, billion, thousand,\n量化, 效率, 成本\n\n### 可学习性 (0-1 分，权重 20%)\nhow we, architecture, methodology, best practice, lesson,\nframework, pattern, approach, strategy, pipeline, workflow,\n方法论, 架构, 最佳实践, 经验, 教训\n\n### 前沿性 (0-1 分，权重 20%)\nfirst, new, launch, announce, breakthrough, novel,\nopen source, open-source, release, preview, beta,\nnow available, come to, general availability,\n首次, 发布, 开源, 突破, 新品\n\n### 来源加分\n- OpenAI/AWS: +1.0 (官方博客，落地案例多)\n- Techmeme: +0.5 (聚合新闻)\n- Product Hunt / HN Show: +0.3\n\n## 动态分层逻辑\n\n分数并非硬编码阈值，而是基于当日实际分数分布动态调整：\n\n- 核心情报阈值 = max(3.5, 最高分 × 0.8)\n- 值得关注阈值 = max(2.5, 最高分 × 0.5)\n- 快速浏览 = 1.0 ~ 值得关注阈值\n\n## 信号检测关键词\n\n### 资本信号\nseries, funding, raised, invest, 投资, 融资, million, billion,\n估值, 融资轮, 收购, acquire, merger, 并购, IPO, 上市,\nseed, round, valuation, capital\n\n### 产品信号\nlaunch, release, announce, general availability, GA,\npreview, beta, open source, open-source,\n产品, 发布, 上线, 开源, 首发, 推出, 大模型, model\n\n### 技术信号\nmodel, architecture, training, inference, benchmark,\nframework, sota, state-of-the-art, breakthrough,\nfine-tun, rag, agent, mcp, tool use, reasoning,\nmultimodal, diffusion, transformer, rlhf, dpo,\nspec, protocol, standard,\n技术, 架构, 推理, 微调, 多模态\n\nFile v3.0.0:data/candidates/2026-05-09_candidates.json\n\n[\n  {\n    \"id\": \"09a84312cc4b\",\n    \"source\": \"openai\",\n    \"title\": \"Running Codex safely at OpenAI\",\n    \"link\": \"https://openai.com/index/running-codex-safely\",\n    \"summary\": \"How OpenAI runs Codex securely with sandboxing, approvals, network policies, and agent-native telemetry to support safe and compliant coding agent adoption.\",\n    \"published\": \"2026-05-08T12:30:00\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"b655fa9f550b\",\n    \"source\": \"openai\",\n    \"title\": \"Parloa builds service agents customers want to talk to\",\n    \"link\": \"https://openai.com/index/parloa\",\n    \"summary\": \"Parloa leverages OpenAI models to power scalable, voice-driven AI customer service agents, enabling enterprises to design, simulate, and deploy reliable, real-time interactions.\",\n    \"published\": \"2026-05-07T11:00:00\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"5f067b79c230\",\n    \"source\": \"openai\",\n    \"title\": \"Advancing voice intelligence with new models in the API\",\n    \"link\": \"https://openai.com/index/advancing-voice-intelligence-with-new-models-in-the-api\",\n    \"summary\": \"Explore new realtime voice models in the OpenAI API that can reason, translate, and transcribe speech, enabling more natural and intelligent voice experiences.\",\n    \"published\": \"2026-05-07T10:00:00\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"dbde3b0f9ce6\",\n    \"source\": \"openai\",\n    \"title\": \"Simplex rethinks software development with Codex\",\n    \"link\": \"https://openai.com/index/simplex\",\n    \"summary\": \"Simplex boosts software development with ChatGPT Enterprise and Codex, reducing design, build, and testing time while scaling AI-driven workflows.\",\n    \"published\": \"2026-05-07T00:00:00\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"49359060f440\",\n    \"source\": \"openai\",\n    \"title\": \"How frontier firms are pulling ahead\",\n    \"link\": \"https://openai.com/index/introducing-b2b-signals\",\n    \"summary\": \"OpenAI’s B2B Signals research shows how frontier enterprises deepen AI adoption, scale Codex-powered agentic workflows, and build durable competitive advantage.\",\n    \"published\": \"2026-05-06T00:00:00\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"c41f5455f6cb\",\n    \"source\": \"openai\",\n    \"title\": \"OpenAI and PwC collaborate to reimagine the office of the CFO\",\n    \"link\": \"https://openai.com/index/openai-pwc-finance-collaboration\",\n    \"summary\": \"OpenAI and PwC are partnering to help enterprises use AI agents to automate finance workflows, improve forecasting, strengthen controls, and modernize the CFO function.\",\n    \"published\": \"2026-05-04T21:00:00\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"76f7db838646\",\n    \"source\": \"aws-ml\",\n    \"title\": \"Agents that transact: Introducing Amazon Bedrock AgentCore payments, built with Coinbase and Stripe\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/agents-that-transact-introducing-amazon-bedrock-agentcore-payments-built-with-coinbase-and-stripe/\",\n    \"summary\": \"Today, we're announcing a preview of Amazon Bedrock AgentCore Payments, a new set of features in Amazon Bedrock AgentCore that enables AI agents to instantly access and pay for what they use. AgentCore Payments was developed in partnership with Coinbase and Stripe.\",\n    \"published\": \"2026-05-07T12:55:17\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"483fbc2d3f85\",\n    \"source\": \"aws-ml\",\n    \"title\": \"Cost effective deployment of vision-language models for pet behavior detection on AWS Inferentia2\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/cost-effective-deployment-of-vision-language-models-for-pet-behavior-detection-on-aws-inferentia2/\",\n    \"summary\": \"Tomofun, the Taiwan-headquartered pet-tech startup behind the Furbo Pet Camera, is redefining how pet owners interact with their pets remotely. To reduce costs and maintain accuracy, Tomofun turned to EC2 Inf2 instances powered by AWS Inferentia2, the Amazon purpose-built AI chips. In this post, we walk through the following sections in detail.\",\n    \"published\": \"2026-05-06T15:37:08\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"1904b8f64936\",\n    \"source\": \"aws-ml\",\n    \"title\": \"How Hapag-Lloyd uses Amazon Bedrock to transform customer feedback into actionable insights\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/how-hapag-lloyd-uses-amazon-bedrock-to-transform-customer-feedback-into-actionable-insights/\",\n    \"summary\": \"Hapag-Lloyd's Digital Customer Experience and Engineering team, distributed between Hamburg and Gdańsk, drives digital innovation by developing and maintaining customer-facing web and mobile products. In this post, we walk you through our generative AI–powered feedback analysis solution built using Amazon Bedrock, Elasticsearch, and open-source frameworks like LangChain and LangGraph\",\n    \"published\": \"2026-05-05T16:55:42\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"9d0ca4b72712\",\n    \"source\": \"aws-ml\",\n    \"title\": \"Introducing OS Level Actions in Amazon Bedrock AgentCore Browser\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/introducing-os-level-actions-in-amazon-bedrock-agentcore-browser/\",\n    \"summary\": \"We’re announcing OS Level Actions for AgentCore Browser. This new capability unblocks these scenarios by exposing direct OS control through the InvokeBrowser API, so agents can interact with content visible on the screen, not only what's accessible through the browser's web layer. By combining full-desktop screenshots with mouse and keyboard control at the OS level, agents can observe native UI, reason about it, and act on it within the same session. This post walks through how OS Level Actions ...\",\n    \"published\": \"2026-05-05T16:54:35\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"d87ac5504547\",\n    \"source\": \"aws-ml\",\n    \"title\": \"Secure AI agents with Amazon Bedrock AgentCore Identity on Amazon ECS\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/secure-ai-agents-with-amazon-bedrock-agentcore-identity-on-amazon-ecs/\",\n    \"summary\": \"AI agents in production require secure access to external services. Amazon Bedrock AgentCore Identity, available as a standalone service, secures how your AI agents access external services whether they run on compute platforms like Amazon ECS, Amazon EKS, AWS Lambda, or on-premises. This post implements Authorization Code Grant (3-legged OAuth) on Amazon ECS with secure session binding and scoped tokens.\",\n    \"published\": \"2026-05-05T15:27:30\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"d2d238200e22\",\n    \"source\": \"aws-ml\",\n    \"title\": \"Intelligence-driven message defense and insights using Amazon Bedrock\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/intelligence-driven-message-defense-and-insights-using-amazon-bedrock/\",\n    \"summary\": \"In this post, you will learn how you can use Amazon Nova Foundation Models in Amazon Bedrock to apply generative AI techniques for both business protection and enhancement. You can identify obvious and disguised attempts at direct contact while gaining valuable insights into customer sentiment and service improvement opportunities.\",\n    \"published\": \"2026-05-05T15:20:54\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"b2247542cd2e\",\n    \"source\": \"aws-ml\",\n    \"title\": \"Introducing agent quality optimization in AgentCore, now in preview\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/introducing-agent-quality-optimization-in-agentcore-now-in-preview/\",\n    \"summary\": \"Generate recommendations from production traces, validate them with batch evaluation and A/B testing, and ship with confidence. AI agents that perform well at launch don’t stay that way. As models evolve, user behavior shifts, and prompts get reused in new contexts they were never designed for. Agent quality quietly degrades. In most teams, the improvement […]\",\n    \"published\": \"2026-05-04T17:13:42\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"84a6a5184026\",\n    \"source\": \"aws-ml\",\n    \"title\": \"Agent-guided workflows to accelerate model customization in Amazon SageMaker AI\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/agent-guided-workflows-to-accelerate-model-customization-in-amazon-sagemaker-ai/\",\n    \"summary\": \"Amazon SageMaker AI now offers an agentic experience that changes this. Developers describe their use case using natural language, and the AI coding agent streamlines the entire journey, from use case definition and data preparation through technique selection, evaluation, and deployment. In this post, we walk you through the model customization lifecycle using SageMaker AI agent skills.\",\n    \"published\": \"2026-05-04T17:10:46\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"d040afb97f16\",\n    \"source\": \"aws-ml\",\n    \"title\": \"Generate dashboards from natural language prompts in Amazon Quick\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/generate-dashboards-from-natural-language-prompts-in-amazon-quick/\",\n    \"summary\": \"Building meaningful dashboards demands hours of manual setup, even for experienced BI professionals. Amazon Quick now generates complete multi-sheet dashboards from natural language prompts, taking you from one or more datasets to a production-ready analysis in minutes. Data analysts building recurring operations reports, program managers preparing a leadership review, or engineers exploring a new dataset can […]\",\n    \"published\": \"2026-05-04T16:51:37\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"aa83b9f83fb0\",\n    \"source\": \"aws-ml\",\n    \"title\": \"From data lake to AI-ready analytics: Introducing new data source with S3 Tables in Amazon Quick\",\n    \"link\": \"https://aws.amazon.com/blogs/machine-learning/from-data-lake-to-ai-ready-analytics-introducing-direct-query-with-s3-tables-in-amazon-quick/\",\n    \"summary\": \"Amazon Quick introduces Amazon S3 Tables (Apache Iceberg tables) as a new data source. With this feature, customers can directly query and visualize Apache Iceberg tables stored in an Amazon S3 table bucket without the need for intermediate data layers. In this post, we explored how Amazon Quick’s new Amazon S3 Tables data source enables near real-time analytics while streamlining modern data architectures.\",\n    \"published\": \"2026-05-04T16:12:37\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"adaa3ded5f12\",\n    \"source\": \"36kr\",\n    \"title\": \"小红书四年AI 路：FOMO、犹豫，到突然加速\",\n    \"link\": \"https://36kr.com/p/3799028783439111?f=rss\",\n    \"summary\": \"
作者 | 肖思佳
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编辑 | 乔芊 杨轩
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所有互联网大中厂都渴望在AI时代博得位置，在这场比赛中，小红书曾是克制的那个。
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在一个搜索属性与社区属性并存，以真实经验分享为核心的产品中，活人感与AI、温情和算法，始终像天平的两端。
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很长一段时间里，小红书既没有完全缺席技术探索，也没有像许多同行那样高调推进AI产品化。相反，这家公司始终在两股力量的拉锯和平衡中前行：一边持续投入模型能力，一边谨慎控制AI对社区生态的介入。
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但2026年，随着Agent叙事的升温，小红书开始显露出某种急迫。
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4月30日，小红书发送全员内部信，宣布成立AI一级部门Dots，“建立从模型研发、基础设施、工程到产品的完整技术体系，整合顶尖AI人才和资源。”Dots向小红书新任总裁柯南汇报。
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据36氪了解，Dots部门由原人文智能实验室Hi Lab升级而来，下设模型研发、基础设施、工程、产品四个部门。目前小红书内部最重要的AI应用产品“点点”也被纳入该...\",\n    \"published\": \"2026-05-08T04:38:43\",\n    \"lang\": \"zh\"\n  },\n  {\n    \"id\": \"f8979d638f1b\",\n    \"source\": \"techmeme\",\n    \"title\": \"Sources: WH is preparing to order US agencies to partner with AI companies on cybersecurity; the EO wouldn't require pre-release model testing by the government (Bloomberg)\",\n    \"link\": \"https://www.techmeme.com/260508/p33#a260508p33\",\n    \"summary\": \"\\n
 Bloomberg:
\\n\\n
 Nathan Lambert / Interconnects AI:
\\n\\n
 Bloomberg:
\\n\\n
 Bloomberg:
\\n\\n
 Bloomberg:
\\n\\n
 Rachel Metz / Bloomberg:
\\n...\",\n    \"published\": \"2026-05-08T18:20:01\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"52df4b2b4849\",\n    \"source\": \"techmeme\",\n    \"title\": \"Investor letter: TCI, one of the world's biggest hedge funds, cut almost all of its $8B Microsoft stake, citing AI risks primarily for Office and some for Azure (Costas Mourselas/Financial Times)\",\n    \"link\": \"https://www.techmeme.com/260508/p22#a260508p22\",\n    \"summary\": \"\\n
 Costas Mourselas / Financial Times:
\\n\\n
 Brandon Gomez / CNBC:
\\n\\n            SMS For AI Agents & Developers\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-07T22:55:21\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"bfea5c898130\",\n    \"source\": \"product-hunt\",\n    \"title\": \"KodHau\",\n    \"link\": \"https://www.producthunt.com/products/kodhau-senior-context-for-ai-agents\",\n    \"summary\": \"
\\n            Stop your AI from breaking prod-give it your team decisions\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-08T06:59:12\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"fd62e2084626\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Photobomb\",\n    \"link\": \"https://www.producthunt.com/products/photobomb\",\n    \"summary\": \"
\\n            Card against humanity but for your camera roll\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-06T15:06:59\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"c0cd5bf01d42\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Contral\",\n    \"link\": \"https://www.producthunt.com/products/contral\",\n    \"summary\": \"
\\n            The agent which teaches while you build\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-07T19:47:04\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"48e420aba2a5\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Flare\",\n    \"link\": \"https://www.producthunt.com/products/flare-9\",\n    \"summary\": \"
\\n            AI-native voice-first social app for GenZ\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-05T14:38:00\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"78b1a98e8905\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Monid 2.0\",\n    \"link\": \"https://www.producthunt.com/products/monid\",\n    \"summary\": \"
\\n            OpenRouter for agent tools\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-08T04:48:50\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"8b37af0a416a\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Fabraix\",\n    \"link\": \"https://www.producthunt.com/products/nyx-4\",\n    \"summary\": \"
\\n            Find gaps in your AI agents before users do\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-07T18:21:51\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"8a2ec1d97f20\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Finlingo\",\n    \"link\": \"https://www.producthunt.com/products/finlingo\",\n    \"summary\": \"
\\n            Your own AI CFO, watching your money on autopilot\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-05T22:46:48\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"69de7db02bc6\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Kuku: open source\",\n    \"link\": \"https://www.producthunt.com/products/kuku\",\n    \"summary\": \"
\\n            Your open-source, local second brain for every AI\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-08T06:52:29\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"59ce85500807\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Minions\",\n    \"link\": \"https://www.producthunt.com/products/minions\",\n    \"summary\": \"
\\n            Open source mission control for Hermes agent\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-08T02:58:13\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"16c034817b6e\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Illospace\",\n    \"link\": \"https://www.producthunt.com/products/illospace\",\n    \"summary\": \"
\\n            Living space where teams and agents work together\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-07T23:38:55\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"0f2d7461aba8\",\n    \"source\": \"product-hunt\",\n    \"title\": \"MediaOptim\",\n    \"link\": \"https://www.producthunt.com/products/compress-anything-upload-nothing\",\n    \"summary\": \"
\\n            Compress images, video & audio locally and save storage\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-07T13:47:43\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"20e00a85dfc0\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Toto\",\n    \"link\": \"https://www.producthunt.com/products/toto-applied-worldmodels\",\n    \"summary\": \"
\\n            Context rich tasks sent to the best model. \\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-08T12:01:44\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"48fcdcb9011c\",\n    \"source\": \"product-hunt\",\n    \"title\": \"Google Health\",\n    \"link\": \"https://www.producthunt.com/products/google\",\n    \"summary\": \"
\\n            A new relationship with your health\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-07T16:54:51\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"ce0a65692f85\",\n    \"source\": \"product-hunt\",\n    \"title\": \"iOrchestra AI Hardware Engineers\",\n    \"link\": \"https://www.producthunt.com/products/vibe-engineer-by-iorchestra\",\n    \"summary\": \"
\\n            Prompt to production-ready Hardware designs for manufacture\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-06T17:08:38\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"44d41809b0e5\",\n    \"source\": \"product-hunt\",\n    \"title\": \"SuperIsland\",\n    \"link\": \"https://www.producthunt.com/products/superisland\",\n    \"summary\": \"
\\n            Dynamic Island for macOS with Extensions\\n          
\\n          
\\n            Discussion\\n            |\\n            Link\\n          
\",\n    \"published\": \"2026-05-07T22:55:41\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"1358a4d685f7\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: Nexa-gauge – Cache/cost-aware graph-based eval for LLM and RAG\",\n    \"link\": \"https://github.com/harnexa/nexa-gauge\",\n    \"summary\": \"
Article URL: https://github.com/harnexa/nexa-gauge
\\n
Comments URL: https://news.ycombinator.com/item?id=48070603
\\n
Points: 1
\\n
# Comments: 0
\",\n    \"published\": \"2026-05-09T00:45:32\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"5b88beabe1b7\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: Launch and Run Companies on Autopilot\",\n    \"link\": \"https://lakyus.com/live\",\n    \"summary\": \"
I launched Lakyus on February 27. It allows you to launch and run companies on autopilot.
The platform handles both creation and ongoing operation by wiring together Stripe, Netlify, Postmark, and the Meta Marketing APIs. It has its own inbox to handle email marketing, can send you updates, can manage products and push them to Stripe, provisions your own DB, and more.
The goal is to move past chatbots and toward autonomous business infrastructure, allowing people to spin up dozens of micr...\",\n    \"published\": \"2026-05-09T00:36:34\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"4d5294c1e0a7\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: AI-native tech assessments (end of LeetCode)\",\n    \"link\": \"https://www.openround.ai/\",\n    \"summary\": \"
Hi HN!
We built openround.ai - an assessment platform to hire AI native engineers. On OpenRound, engineers build a project or solve a problem using AI. This replaces a usual coding round/take home assessment.
I believe coding assessments measure who is good at passing interviews, not who is actually a good engineer.
We want to flip that by showing how engineers actually work on a real problem.
2 interesting things we solve -
1. Designing assessments and environments that are not one...\",\n    \"published\": \"2026-05-09T00:28:24\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"32fda043729f\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: [Video] Tribute to LLM releases in April 2026\",\n    \"link\": \"https://www.youtube.com/watch?v=uu5ffMH_X9w\",\n    \"summary\": \"
Article URL: https://www.youtube.com/watch?v=uu5ffMH_X9w
\\n
Comments URL: https://news.ycombinator.com/item?id=48070211
\\n
Points: 2
\\n
# Comments: 0
\",\n    \"published\": \"2026-05-08T23:53:05\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"2115ebada650\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: I mirrored war.gov's UAP archive in pure Rail with verifiable bytes\",\n    \"link\": \"https://ledatic.org/aliens\",\n    \"summary\": \"
Article URL: https://ledatic.org/aliens
\\n
Comments URL: https://news.ycombinator.com/item?id=48069945
\\n
Points: 2
\\n
# Comments: 0
\",\n    \"published\": \"2026-05-08T23:16:01\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"947d8eccd80c\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: Splitby v2.0.0 – a modern alternative to cut\",\n    \"link\": \"https://github.com/Serenacula/splitby\",\n    \"summary\": \"
Heya!
So this is a project I've been working on for a fair while, and with this version it's pretty much feature complete.
For a bit more info, this is intended as a tool for manipulating strings in the terminal. Basically a more powerful and intuitive version of the cut tool.
I didn't get much traction last time I posted this, but I'd love to receive some feedback! :)
\\n
\\n
Comments URL: https://news.ycombinator.com/item?i...\",\n    \"published\": \"2026-05-08T22:42:00\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"bebe86d4d201\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: Contral – the agent which will teach you while you build with AI\",\n    \"link\": \"https://contral.ai\",\n    \"summary\": \"
Article URL: https://contral.ai
\\n
Comments URL: https://news.ycombinator.com/item?id=48069590
\\n
Points: 2
\\n
# Comments: 1
\",\n    \"published\": \"2026-05-08T22:34:29\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"dd837e4066f2\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: Vibe code your agents without vibe coding your agent\",\n    \"link\": \"https://deepeval.com/docs/vibe-coding\",\n    \"summary\": \"
Article URL: https://deepeval.com/docs/vibe-coding
\\n
Comments URL: https://news.ycombinator.com/item?id=48069001
\\n
Points: 2
\\n
# Comments: 0
\",\n    \"published\": \"2026-05-08T21:28:34\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"a5d7d795a86a\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: The independent guide to agent orchestrators\",\n    \"link\": \"https://agentmgmt.dev/\",\n    \"summary\": \"
Hey HN!
I built AgentMGMT.dev today to keep track of all those agent orchestration tools that keep popping up. I've tried a few and landed on Superset, which I'm extremely happy (and productive!) with - but I think this category of tools will be extremely important and interesting in the next couple years, so it's worth keeping an eye on all available tools and how they evolve.
I will keep the site up-to-date, please help me by submitting new tools that are not yet in the list, or add any...\",\n    \"published\": \"2026-05-08T21:17:58\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"ef9505a6563c\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: Obsidian-Semantic, a CLI that lets agents search your vault by meaning\",\n    \"link\": \"https://github.com/ravila4/obsidian-semantic-search\",\n    \"summary\": \"
Hi HN, I built this for myself because I wanted my coding agent (Claude Code) to actually be able to use my Obsidian vault as a knowledge base, not just grep it.
The use I get the most mileage from is asking the agent to find notes that should be cross-linked, which surfaces forgotten connections and turns the vault into more of a wiki over time.
It is similar to what the Smart Connections Obsidian plugin does, but I wanted a CLI-first tool, and more control over the models. Currently it ...\",\n    \"published\": \"2026-05-08T21:03:10\",\n    \"lang\": \"en\"\n  },\n  {\n    \"id\": \"94767b4c7e60\",\n    \"source\": \"hn-show-hn\",\n    \"title\": \"Show HN: Cyoda-go – application platform in Go without the Temporal/Kafka glue\",\n    \"link\": \"https://github.com/Cyoda-platform/cyoda-go\",\n    \"summary\": \"
This started out as an experiment. Reading Simon Willison's blog on where StrongDM was going with dark factories and Digital Twin Universes
https://simonw.substack.com/p/\n\nArchive v2.2.1: 8 files, 18162 bytes\n\nFiles: references/BRIEFING_CONFIG.md (2150b), references/data-sources.md (1524b), references/scoring.md (2178b), scripts/arxiv-fetch.sh (3149b), scripts/generate-briefing.py (24285b), scripts/github-trending-fetch.sh (4100b), SKILL.md (6203b), _meta.json (138b)\n\nArchive v2.2.0: 8 files, 18136 bytes\n\nFiles: references/BRIEFING_CONFIG.md (2150b), references/data-sources.md (1524b), references/scoring.md (2178b), scripts/arxiv-fetch.sh (3149b), scripts/generate-briefing.py (24285b), scripts/github-trending-fetch.sh (4100b), SKILL.md (6124b), _meta.json (138b)\n\nArchive v2.1.0: 8 files, 18276 bytes\n\nFiles: references/BRIEFING_CONFIG.md (2150b), references/data-sources.md (1524b), references/scoring.md (2178b), scripts/arxiv-fetch.sh (3149b), scripts/generate-briefing.py (24285b), scripts/github-trending-fetch.sh (4100b), SKILL.md (6082b), _meta.json (138b)\n\nArchive v2.0.0: 8 files, 18236 bytes\n\nFiles: references/BRIEFING_CONFIG.md (2150b), references/data-sources.md (1524b), references/scoring.md (2178b), scripts/arxiv-fetch.sh (3149b), scripts/generate-briefing.py (24285b), scripts/github-trending-fetch.sh (4100b), SKILL.md (6020b), _meta.json (138b)","readmeExcerpt":"Skill: AI Frontier Monitor Owner: lynxpurr Summary: AI frontier intelligence briefing — aggregate, score, and deliver structured daily briefings from 5 tracks (RSS enterprise, 36kr hotlist, arXiv papers, GitHu... Tags: RSS:2.1.0, ai:2.1.0, daily-briefing:2.1.0, enterprise:2.1.0, intelligence:2.1.0, latest:3.0.3, tech-news:2.1.0 Version history: v1.1.0 | 2026-10-09T13:08:15.562Z | user v1.1: cron moved to Hermes side ","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"原子层  fetch_rss_candidates（11 RSS 源）/ fetch_web_search（Anthropic 案例 + 咨询公司 + 突发补漏）/ score_candidate（0-5 评分）\n分子层  aggregate_intelligence（合并去重）/ generate_briefing（三层结构化简报）\n复合层  每日简报归档 → agent-prophet 选题池输入 → 周稿骨架 → 微信草稿 → Ship/Reflect 复盘"},{"language":"markdown","snippet":"# AI 前沿情报 · {日期}\n## 🔴 核心情报（≤3）— 标题 + 公司/场景/核心数据/落地方式 + 原文链接 + 对 Zenz 的启示\n## 🟡 值得关注（≤5）\n## 🟢 快速浏览（≤5）— 标题 + 一句话\n## 📊 今日信号 — 技术趋势 / 产品发布 / 资本动向\nGenerated by ai-frontier-monitor · {时间}"},{"language":"bash","snippet":"# Track 1: RSS (run crawler first, outputs to {baseDir}/data/candidates/)\npython3 {baseDir}/scripts/rss-crawler.py\n\n# Track 2-4: Generate briefing (all tracks auto-fetched)\npython3 {baseDir}/scripts/generate-briefing.py --mode full"},{"language":"text","snippet":"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n🤖 AI 前沿情报 · {Day} {Date}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📡 数据源：11 RSS + 36kr + arXiv + GitHub + Anthropic\n   候选：{N} 条 | 高质量：{M} 条 | 阈值：核心≥{X} / 关注≥{Y}\n\n## 🔴 核心情报（{N} 条）\n### 1. {Title}\n🔗 {Link}\n💡 启示：{One-line insight}\n\n## 🟡 值得关注（{N} 条）\n1. **{Title}**\n   🔗 {Link}\n\n## 🟢 快速浏览（{N} 条）\n• [{Title}]({Link})\n\n## 📚 arXiv · 论文追踪（≤3 篇）\n**{Title}** — {Authors} | {Date}\n摘要：{Abstract[:150]}... → {Link}\n\n## 🔥 GitHub Trending · AI（≤3 个）\n**{Repo}** ({Lang}) +{TodayStars}⭐ → {Link}\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n📊 今日信号\n🛠 技术趋势：{signal}\n🏢 产品发布：{signal}\n💰 资本动向：{signal}\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n⏰ {HH:MM} | ai-frontier-monitor v3.0"},{"language":"text","snippet":"{baseDir}/data/briefings/{YYYY-MM-DD}-frontier-briefing.md"},{"language":"text","snippet":"{baseDir}/data/\n├── candidates/          # RSS 爬取的候选条目 (JSON)\n│   └── *_candidates.json\n├── briefings/           # 生成的简报 (Markdown)\n│   └── YYYY-MM-DD-frontier-briefing.md\n└── rss-state.json       # RSS 爬取状态"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: ai-frontier-monitor\ndescription: \"AI Frontier intelligence collection and WeChat draft pipeline (Signal->Validate->Build->Ship->Reflect). Use when building AI frontier monitoring briefings or WeChat article drafts from RSS/arXiv/GitHub sources.\"\n---\n\n# AI Frontier Monitor — 情报简报 + 微信草稿管线\n\n> v1.1（2026-10-09 更新）· OPC 首个业务 · Skill Graphs 2.0 架构（原子→分子→复合体）\n> 核心理念：信息聚合 ≠ 信息堆砌——产出是经过筛选、分层、排序的每日简报，不是 50 条标题的噪音。\n\n## 架构\n\n```\n原子层  fetch_rss_candidates（11 RSS 源）/ fetch_web_search（Anthropic 案例 + 咨询公司 + 突发补漏）/ score_candidate（0-5 评分）\n分子层  aggregate_intelligence（合并去重）/ generate_briefing（三层结构化简报）\n复合层  每日简报归档 → agent-prophet 选题池输入 → 周稿骨架 → 微信草稿 → Ship/Reflect 复盘\n```\n\n## 数据源矩阵\n\n| 渠道 | 类型 | 优先级 |\n|---|---|---|\n| OpenAI Blog / AWS ML Blog | RSS | ⭐⭐⭐⭐⭐~⭐⭐⭐⭐（客户案例最权威） |\n| Microsoft AI Blog（Copilot 落地） | RSS | ⭐⭐⭐⭐ |\n| Techmeme / Product Hunt / HN Show+Ask HN | RSS | ⭐⭐⭐~⭐⭐⭐⭐ |\n| 36氪 / Dev.to | RSS | ⭐⭐⭐ |\n| Anthropic 案例补搜（site:anthropic.com customer/case study/enterprise） | web_search | ⭐⭐⭐⭐⭐ |\n| arXiv 论文 / GitHub Trending | 专轨 | 各 ≤3 |\n| 微信文章（wechat-curator）/ 知识星球（zsxq-helper） | 协作 skill | ⭐⭐⭐⭐ |\n\n完整源配置与推送阈值：`references/BRIEFING_CONFIG.md`\n\n## Pipeline 五段（OPC v2.2 章程）\n\n**Signal**（收集+评分）→ **Validate**（三选二：对 Zenz 定位有直接价值 / 有独特视角非新闻搬运 / 时效本周内；Altman+Machi 各 1 票，分歧强制双向找反证；2 次验证失败换选题方向）→ **Build**（草稿生成）→ **Ship**（Zenz 发布）→ **Reflect**（2 周后数据复盘：阅读量>基准线、新增关注、留言/投票、转发引用；通过→沉淀内容策略，未通过→归因调向）\n\n## 评分与分层\n\n评分维度：企业真实落地 40%（真实企业名+部署规模）/ 数据支撑 20%（量化 ROI）/ 可学习性 20%（方法论+避坑）/ 前沿性 20%；**≥3 分入候选池**。\n\n| 层级 | 定义 | 上限 |\n|---|---|---|\n| 🔴 核心情报 | ≥4 分 + 数据支撑 | 3 条 |\n| 🟡 值得关注 | 3 分或待验证 | 5 条 |\n| 🟢 快速浏览 | 2 分趋势信号 | 5 条 |\n\n每日总量 ≤13 条；无高质量候选 → `NO_REPLY`；同企业/产品去重保留最高分；连续 3 天候选 <3 条 → 触发关键词审查。\n\n## 简报产出格式\n\n```markdown\n# AI 前沿情报 · {日期}\n## 🔴 核心情报（≤3）— 标题 + 公司/场景/核心数据/落地方式 + 原文链接 + 对 Zenz 的启示\n## 🟡 值得关注（≤5）\n## 🟢 快速浏览（≤5）— 标题 + 一句话\n## 📊 今日信号 — 技术趋势 / 产品发布 / 资本动向\nGenerated by ai-frontier-monitor · {时间}\n```\n\n## 运行现状（2026-10-09 核验）\n\n- **收集 cron**：Hermes 侧 `ai-frontier-daily-briefing`（job `8f25d99d4eef`，K 维护），每日双槽，**归档指令显式写入 payload**；产出 `agent-prophet/daily-notes/frontier-briefings/YYYY-MM-DD-frontier-briefing.md`——10-07 起每日活体（10-07/08/09 三连日归档实证）\n- **历史教训（重要）**：2026-05 版 OpenClaw 侧 cron 曾静默失效 4 个月——delivery=none + payload 无归档指令，外面看进程在跑、归档停在 05-28。任何定时任务 payload 必须显式携带投递/归档指令。2026-10-06 转移 K 侧重建修复\n- 旧方案（本地每日 8:00 RSS cron + 本 skill 直接 Feishu 推送）已废弃，推送统一走 K 侧投递链\n\n## 与 agent-prophet 机制接驳（2026-10-09 起）\n\n- **选题池（日层）**：每日 17:10 automation `agentprophet-topic-scout` 从当日简报等源筛 1-2 题入 `memory/agent-prophet-topic-pool.md`（Luca 2026-10-09 批复「日选题池+周深稿」）\n- **周稿 Build（周层）**：每周一 09:05 automation `agentprophet-weekly-skeleton` 从池中选最优题出骨架 → Luca 批后成稿 → feature-branch PR 入 agent-prophet（不碰 main/develop）\n- **微信草稿格式**（Build 阶段产出 `resources/wechat-curation/YYYY-MM-DD_<主题>.md`）：📌 标题一句话抓核心信号 / 📝 开头 2-3 句定调不强开 / 🔴 核心解读 2-3 条各 200-300 字、**Zenz 建设者视角**（说「我在搭建 X」，不说「行业趋势显示 Y」）/ 🟡 速览 3-5 条 / 🔽 结尾 **AB 投票** + 下期预告\n- Ship（Zenz 复制到公众号编辑器发布，queue.md 标记 `[Ship]`）→ Reflect 不变\n\n## 协作 Skills\n\n`wechat-curator`（微信精选补 🟢 层"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7d5x409z88swt4n3fh3v77yn833x7m\",\n  \"slug\": \"ai-frontier-monitor\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1791551295562\n}"},{"path":"references/BRIEFING_CONFIG.md","content":"# AI 前沿情报 · 配置文件\n# ai-frontier-monitor v3.0\n\n## 推送偏好\n- 推送时间：08:00（完整版）/ 12:00（增量版）\n- 推送目标：Feishu（配置你的飞书 user open_id）\n- 语言：中文为主，英文关键案例保留原文标题\n\n## 数量限制\n- 核心情报：≤3 条（≥4分）\n- 值得关注：≤5 条（3-4分）\n- 快速浏览：≤5 条（2-3分）\n- arXiv 论文：≤3 篇\n- GitHub Trending：≤3 个\n- 36氪热榜：取前 3 条补充到快速浏览\n\n## 信号检测阈值\n- 技术趋势：≥2 条相关候选\n- 产品发布：≥1 条\n- 资本动向：关键词触发（funding/raised/Series/投资/融资）\n\n## 评分维度（权重）\n- 企业真实落地：40%\n- 数据支撑：20%\n- 可学习性：20%\n- 前沿性：20%\n\n## 数据源开关\n| 轨道 | 数据源 | 状态 | 说明 |\n|------|--------|------|------|\n| 企业落地 | OpenAI Blog | ✅ | 核心数据源 |\n| 企业落地 | Microsoft AI Blog | ✅ | |\n| 企业落地 | AWS ML Blog | ✅ | |\n| 企业落地 | Techmeme | ✅ | |\n| 企业落地 | Anthropic 搜索 | ✅ | 补漏用 |\n| 中文视野 | 36氪热榜 | ✅ | API 实时 |\n| 中文视野 | 微信文章 | ✅ | wechat-curator |\n| 中文视野 | 知识星球 | ✅ | 独立推送 |\n| 技术前沿 | arXiv | ✅ | cs.AI/cs.LG/cs.CL |\n| 技术前沿 | GitHub Trending | ✅ | AI/ML 项目 |\n| 开发者视角 | Product Hunt | ✅ | AI 新产品 |\n| 开发者视角 | HN Show+Ask HN | ✅ | 创业者实战 |\n\n## 触发词（扩展）\n- 主触发：AI 前沿、情报汇总、每日情报\n- 快速模式：今天有什么信号、看看有什么新动态\n- 技术专精：arXiv 最新、论文追踪\n- GitHub 热榜：GitHub Trending、AI 项目热榜\n\n## 质量门控\n- 无高质量候选（≥3分 < 2条）时回复 NO_REPLY\n- 同一企业/产品去重，保留评分最高者\n- 连续 3 天低于 3 条核心情报 → 触发关键词审查\n\n## 输出格式\n- 使用 emoji 作为 section header\n- 严格遵循 v3.0 格式模板\n- 核心情报需包含：对用户的启示（1句话）\n- 总字数控制：500-800 字（不含信号模块）\n\n---\n\n_Last updated: 2026-05-09_"},{"path":"skill-card.md","content":"## Description:\n\nCollects and scores AI frontier signals to produce structured daily briefings and WeChat article drafts.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[lynxpurr](https://clawhub.ai/user/lynxpurr)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nContent teams and AI practitioners use this skill to prioritize AI news, research, and project updates for Chinese-language briefings and human-reviewed WeChat drafts.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Briefings may imply broad source coverage even when upstream collection did not run.\n\nMitigation: Confirm the crawler or search step ran before presenting source-coverage claims.\n\nRisk: Briefings and drafts may be saved or delivered to unintended locations.\n\nMitigation: Confirm delivery and archive targets, and limit outputs to the documented briefing and draft locations.\n\n## Reference(s):\n\n- [Briefing configuration](references/BRIEFING_CONFIG.md)\n- [ClawHub skill listing](https://clawhub.ai/lynxpurr/skills/ai-frontier-monitor)\n\n## Skill Output:\n\n**Output Type(s):** [Markdown, Guidance]\n\n**Output Format:** [Chinese-first Markdown briefings and WeChat article drafts]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Tiered news summaries with source links; drafts require human review before publication.]\n\n## Skill Version(s):\n\n1.1.0 (source: ClawHub 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."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"AI frontier intelligence briefing — aggregate, score, and deliver structured daily briefings from 5 tracks (RSS enterprise, 36kr hotlist, arXiv papers, GitHu... Skill: AI Frontier Monitor Owner: lynxpurr Summary: AI frontier intelligence briefing — aggregate, score, and deliver structured daily briefings from 5 tracks (RSS enterprise, 36kr hotlist, arXiv papers, GitHu... 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