{"repo":"SmooSenseAI/smoosense","free":true,"listed":false,"github":"https://github.com/SmooSenseAI/smoosense","clone":"git clone https://github.com/SmooSenseAI/smoosense.git","description":"Interactively browse multimodal tabular data","language":"TypeScript","stars":115,"topics":["analytics","exploratory-data-analysis","exploratory-data-visualizations","multimodal","visualization"],"license":"Apache-2.0","category":"analytics","readme_excerpt":"Landing Page Read Docs SmooSense is a web-based application for exploring and analyzing large-scale multi-modal tabular data. It provides an intuitive interface for working with CSV, Parquet, and other data formats with powerful SQL querying capabilities. Feature highlights - Natively visualize multimodal data (images, videos, json, bbox, image mask, 3d assets etc) - Effortlessly look at distribution. Automatic drill-through from statistics to random samples. - Graphical and interactive slice-n-dice of your dataset. - Large scale support for 100 million rows on your laptop. - Easy to integrate; SmooSense directly work with table file (parquet, csv, jsonl, etc) - Low cost. Free and open source to use on your laptop. Compute efficient when deployed. Read more: How to use CLI Install uv, and then In terminal, cd into the folder containing your data files, and then run sense Jupyter Notebook Inside Jupyter notebook: License SmooSense Python SDK is licensed under Apache 2.0 . This is a permissive open source license that allows you to: - ✅ Use SmooSense for any purpose, including commercial use - ✅ Modify and distribute the software - ✅ Use it in proprietary software - ✅ Deploy it in production environments - ✅ Include it as a dependency in your projects See the full LICENSE file for complete terms and conditions.","default_branch":null,"files":null,"tree":[],"storefront":"/r/SmooSenseAI","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/SmooSenseAI/smoosense/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}