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Codex、WorkBuddy 等 Agent 已经能读取 PDF。面对扫描件、多栏排版、跨页表格和公式时，解析质量仍会直接影响后续的检索、问答和数据提取。 Mac 上可以本地运行的 PDF parser 很多，PaddleOCR、GLM-OCR、MinerU 等工具的安装方式和运行环境各不相同。文档类型变化后，想换一个 parser 重新处理，通常还要重新配置。 DocDot @docdotai 把多个本地 parser 集中到了同一个 CLI 中管理。 官方安装命令： curl -fsSL | bash 安装 DocDot 后，可以在 NanoDoc、PaddleOCR、GLM-OCR、MinerU、LiteParse 之间安装和切换，并将 PDF 解析为 Markdown 或 JSON。后续还会继续增加新的 parser。 DocDot 也提供了 Web Mode。运行 `docdot web` 后，可以在浏览器中并排比较 3 个 parser 的结果。遇到复杂表格、公式或特殊排版时，可以根据实际输出选择更合适的解析器。 根据官方说明，安装程序还能扫描本机的 Codex、Claude Code、OpenClaw、Hermes 等 Agent，并配置相应的 Skill；需要接入其他应用时，也可以通过 MCP 调用。解析在本机运行，PDF 无需上传，目前主要面向 Apple Silicon Mac。 我在 Mac 上安装了 DocDot 和 NanoDoc，并准备了一页包含中英文、表格和公式的 PDF。 首次启动完成模型编译后，再次解析同一份 PDF 用时约 2.3 秒。正文和公式基本准确，表格结构得到保留，小字号表头仍有少量误识别。 这只是一份单页样本，不能替代完整的性能测试。官方技术报告使用了 4,231 页、64 类文档进行比较，NanoDoc 的综合质量和 Table TEDS 在参测解析器中均排名第一。 DocDot 今天在 Product Hunt 上线：

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Video foundation models (e.g., Seedance 1.0 → 1.5 → 2.0; V-JEPA 1.0 → 2.0 → 2.1) keep getting stronger, while public descriptions of how their training data is built keep getting shorter. ⚒️ We built VidaForge, an open-source, five-stage video data pipeline that turns raw video collections into training-ready datasets. In an academic lab, studying video data recipes begins with a lot of tedious engineering: handling broken videos and transcode failures, keeping large jobs resumable, tracking every clip, and packaging the result for training. Across ingestion, segmentation, selection, annotation, and training dataset packaging, data moves through: raw videos → standardized videos → clips → curated clips → annotated clips → training datasets At every step, we can inspect what happened to each video or clip. This lets us see which samples changed under a data recipe and trace a training dataset back through the pipeline. VidaForge currently connects processed data to two video foundation model pretraining paths: Wan video generation through NeMo-AutoModel, and self-supervised video representation learning through the official V-JEPA2 repository. We ran VidaForge end to end on 200K videos, producing over 700K clips. From these clips, we built Selected-200K, Mixed-200K, and Rejected-200K datasets for Wan2.1-1.3B and V-JEPA2.1-1B pretraining. In these early runs, the three datasets produced different training behavior: data selection appeared in Wan2.1-1.3B eval curves and V-JEPA2.1-1B training stability. Pipeline, experiments, open data, and project resources in the thread below ↓