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Source channel @githubtrending · Post #15401 · Jan 8

#python#agent#agentic_ai#agentic_framework#agentic_workflow#ai#ai_agents#ai_companion#ai_roleplay#benchmark#framework#llm#mcp#memory#open_source#python#sandbox MemU lets AI systems take in conversations, documents, and media, turn them into structured memories, and store them in a clear three-layer file system. It offers both fast embedding search and deeper LLM-based retrieval, works with many data types, and supports cloud or self-hosted setups with simple APIs. This helps you build AI agents that truly remember past interactions, retrieve the right context when needed, and improve over time, making your applications more accurate, personal, and efficient. https://github.com/NevaMind-AI/memU

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djangoproject

@djangoproject · Post #99 · 07/14/2016, 04:57 AM

https://github.com/daleroberts/tv tv ("#textview") is a small tool to quickly view high-resolution multi-band imagery directly in your terminal. It was designed for working with (very large) #satellite imagery data over a low-bandwidth connection. For example, you can directly visualise a Himawari 8 (11K x 11K pixel) image of the Earth directly from its URL: It is built upon the wonderful #GDAL library so it is able to load a large variety of image formats (GeoTiff, PNG, Jpeg, NetCDF, ...) and subsample the image as it reads from disk so it can handle very large files quickly. It has the ability to read filenames (or URLs) from stdin and load files directly from URLs without writing locally to disk. Command line options are styled after gdal_translate such as: -b to specify the bands (and ordering) to use, -srcwin xoff yoff xsize ysize to view a subset of the image, -r to specify the subsampling algorithm (nearest, bilinear, cubic, cubicspline, lanczos, average, mode).