Alternatives
Products that do what Cartwright – Automating detection of geographic and temporal features does
Cartwright is a data profiler that identifies and categorizes spatial and temporal features. Cartwright uses deep learning, natural language processing, and a variety of heuristics to determine whether a column in a dataset contains spatial or temporal information and, if so, what is specifically contained. Cartwright was built to automate complex data pipelines for heterogenous climate and geopolitical data that are generally oriented around geospatial and temporal features (think maps and time series). The challenge that Cartwright solves is automatically detecting those features so they…
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Hey, guys. I've just made a plugin which turns your pandas dataframe into a tableau-style component. It allows you to explore the dataframe with easy drag-and-drop UI. You can use PyGWalker in Jupyter, Google Colab, or even Kaggle Notebook to easily explore your data and generate interactive visualizations. PyGWalker (pronounced like "Pig Walker", just for fun) is named as an abbreviation of "Python binding of Graphic Walker". Here are some links to check it out: The Github Repo: https://github.com/Kanaries/pygwalker Use PyGWalker in Kaggle:…
2023 · github.com
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Hi! We are excited to announce the second release of Desbordante — an open-source, high-performance data profiler that is capable of discovering and validating many different patterns in data using various algorithms. Unlike existing data profilers, Desbordante focuses on discovering complex patterns in data, which are notoriously hard to extract. Since its inception in 2019, it has become the fastest open-source tool for these tasks. It also offers an array of patterns which have no alternative implementations. With this release, Desbordante now supports 17 types of patterns, such as:…
2024 · github.com
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I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license:…
2025 · nature.com
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2022 · shalerb.org
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TLDR: I’ve made a transformer model and a wrapper library that segments text into meaningful semantic chunks. The current text splitting approaches rely on heuristics (although one can use neural embedder to group semantically related sentences). I propose a fully neural approach to semantic chunking. I took the base distilbert model and trained it on a bookcorpus to split concatenated text paragraphs into original paragraphs. Basically it’s a token classification task. Model fine-tuning took day and a half on a 2x1080ti. The library could be used as a text splitter module in a RAG system or…
2025 · github.com
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Feb 2026 · guidelabs.ai
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Creating data visualizations with AI nowadays often means chat, chat and more chats...and writing long prompts can be annoying while they are also not the most effective way to describe your visualization designs. Data Formulator blends UI interaction with natural language so that you can create visualizations with AI much more effectively! You can: * create rich visualizations beyond initial datasets, where AI helps transforming and visualizing data along the way * iterate your designs and dive deeper using data threads, a new way to manage your conversation with AI. Here is a demo video:…
2024 · github.com
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We’ve built a Python SDK for running inference on foundation models designed for time-series and tabular data. They are new SOTA models for time-series and tabular tasks and work out of the box. They do not require model training or feature engineering. The link to the GitHub repository is: https://github.com/S-FM/faim-python-client
Dec 2025 · github.com
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Hello HN. I've always found writing data visualisation scripts boring and repetitive in data science workflows earlier in my career, so I built this tool to automate it. The available methods are based on my experience in econometrics where histograms and scatterplots were the starting points to check data distributions. The link is to the documentation and the app is freely available at https://visprex.com, and if you're curious about the implementation it's open source at https://github.com/visprex/visprex. I'd appreciate any comments and feedback!
2024 · docs.visprex.com
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I built a system that monitors ~200,000 news RSS feeds in near real-time and clusters related articles to show how stories spread across the web. It uses Snowflake’s Arctic model for embeddings and HNSW for fast similarity search. Each “story cluster” shows who published first, how fast it propagated, and how the narrative evolved as more outlets picked it up. Would love feedback on the architecture, scaling approach, and any ways to make the clusters more accurate or useful. Live demo: https://yandori.io/news-flow/
Nov 2025 · yandori.io
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Data Painter is RATH's new feature to interact with your data. It helps you to specify complex visual patterns and find explanations and potential causals. It allows you to do on-flight data labeling, cleaning or even create new features does not exited in original dataset. Everything can be done with a brush tool(painter), You can even play with your data with your fingers on mobile. RATH is an open-source alternative to Tableau, but with more automations. Feedbacks and suggestions are appreciated. RATH's Github: https://github.com/Kanaries/Rath RATH's HomePage:…
2023 · docs.kanaries.net
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This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to…
2024 · github.com
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2022 · github.com
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