A reliable Python tool designed to organize messy Google Photos Takeout exports. It addresses common issues found in Takeout downloads, such as inconsistent JSON sidecar naming, truncated filenames due to Windows path limits, duplicate files sharing single metadata files, and incorrect timestamps.
Key features include:
- Organization by date structure (YYYY/MM)
- Preservation of original album structures
- Robust matching for various JSON sidecar naming variants
- MD5 hash-based duplicate detection
- Support for EXIF fallback and filename pattern extraction
- Dry-run mode to preview changes without writing files
Key features include:
- Organization by date structure (YYYY/MM)
- Preservation of original album structures
- Robust matching for various JSON sidecar naming variants
- MD5 hash-based duplicate detection
- Support for EXIF fallback and filename pattern extraction
- Dry-run mode to preview changes without writing files
This article proposes the DataBook, a design pattern that utilizes Markdown to bridge the gap between large-scale RDF knowledge graphs and small, ephemeral, task-specific semantic content. By combining YAML frontmatter for metadata, inline identifiers for addressability, and typed fenced code blocks for data payloads, DataBooks create self-describing and portable semantic artifacts. The authors argue that this approach allows for a microdatabase model where structured data can exist without the overhead of a full triple store.
Key points include:
The use of Markdown as a substrate for semantic infrastructure.
Defining the microdatabase for small-scale, non-indexed knowledge work.
Inverting the LLM role to act as a transformation engine within a DataBook pipeline.
Implementing provenance through process stamps in YAML metadata.
Managing complex dependencies via manifest DataBooks and build graphs.
Supporting secure data transfer through designed-in encryption profiles.
Key points include:
The use of Markdown as a substrate for semantic infrastructure.
Defining the microdatabase for small-scale, non-indexed knowledge work.
Inverting the LLM role to act as a transformation engine within a DataBook pipeline.
Implementing provenance through process stamps in YAML metadata.
Managing complex dependencies via manifest DataBooks and build graphs.
Supporting secure data transfer through designed-in encryption profiles.
This tutorial demonstrates how to combine LLM embeddings, TF-IDF vectors, and metadata features into a single Scikit-learn pipeline for document retrieval and search. It covers generating embeddings with Sentence Transformers, calculating TF-IDF, handling metadata, and building a combined retrieval system.
Anna's Archive has backed up Spotify's metadata and music files (~300TB), creating the largest publicly available music metadata database (256 million tracks, 186 million ISRCs) and a "preservation archive" for music.
LLMII uses a local LLM to label metadata and index images. It does not rely on a cloud service or database. A visual language model runs on your computer and is used to create captions and keywords for images in a directory tree. The generated information is then added to each image file's metadata.
Google’s John Mueller downplayed the usefulness of LLMs.txt, comparing it to the keywords meta tag, as AI bots aren’t currently checking for the file and it opens potential for cloaking.
In Thunderbird email client, tags are used to organize and group messages manually or through filters. They can be assigned and removed via the context menu, toolbar, or message header, and are case-insensitive. Tags are stored differently depending on whether you use POP or IMAP accounts, and their color is determined by the lowest-numbered tag assigned. The article also discusses differences between tags and labels, interoperability with other email clients, and various add-ons that enhance tag functionality.
