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Southampton 1-4 Manchester City: Erling Haaland scores sublime bicycle kick in comfortable win

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Erling Haaland scores a superb bicycle kick as Manchester City reduce the gap to Premier League leaders Arsenal to five points with a comfortable victory at relegation-threatened Southampton.

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Friday, April 7, 2023

Thursday, April 6, 2023

New top story on Hacker News: Show HN: Machato — A native macOS client for ChatGPT

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Show HN: Machato — A native macOS client for ChatGPT
6 by fliife | 0 comments on Hacker News.
Machato is a tiny ChatGPT client that supports all basic features, and more ! I was frustrated with other native implementations that focused on quick access but didn't give easy access to a history of what has been said. Since ChatGPT is a learning tool for me, I'm always ruffling through past conversations. The client supports markdown rendering as well as LaTeX. Feel free to try it out ! For those who see this thread early, you can use the promo code EARLY_BIRD to get a free lifetime license. Let me know if some aspects can be improved or if there are features you'd like to see implemented in native clients.

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Wednesday, April 5, 2023

New top story on Hacker News: Show HN: Quadratic – Open-Source Spreadsheet with Python, AI (WASM and WebGL)

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Show HN: Quadratic – Open-Source Spreadsheet with Python, AI (WASM and WebGL)
36 by davidkircos | 13 comments on Hacker News.
Hi, I am David Kircos. The Founder of Quadratic ( https://QuadraticHQ.com ), an open-source spreadsheet application that supports Python, SQL (coming soon), AI Prompts, and classic Formulas. Unlike other spreadsheets, Quadratic has an infinite canvas (like Figma). As a result, you can pinch and zoom to navigate large data sets, and everything renders smoothly at 60fps. Our vision is to build a place where your team can collaborate on data analysis. You can write Python, AI Prompts, and Formulas in one spreadsheet feeding each other data and updating automatically. Quadratic is built using WebGL and Rust WASM. To render a large grid of cells smoothly, we tile the spreadsheet similar to google maps. If you are interested in the technical details, check us out on GitHub ( https://ift.tt/35e8N7D ) You can use AI to help you write Python and then run the code directly in Quadratic. Then, we feed the result back to the AI model so it can follow along, help you debug, and modify your existing code. AI can also be used to directly generate data onto the sheet with prompts. It knows the context of what's on the sheet and how the data it's inserting fits in. Try it out. SQL is coming soon... stay tuned!

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New top story on Hacker News: Show HN: Want something better than k-means? Try BanditPAM

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Show HN: Want something better than k-means? Try BanditPAM
20 by motiwari | 0 comments on Hacker News.
Want something better than k-means? I'm happy to announce our SOTA k-medoids algorithm from NeurIPS 2020, BanditPAM, is now publicly available! `pip install banditpam` or `install.packages("banditpam")` and you're good to go! k-means is one of the most widely-used algorithms to cluster data. However, it has several limitations: a) it requires the use of L2 distance for efficient clustering, which also b) restricts the data you're clustering to be vectors, and c) doesn't require the means to be datapoints in the dataset. Unlike in k-means, the k-medoids problem requires cluster centers to be actual datapoints, which permits greater interpretability of your cluster centers. k-medoids also works better with arbitrary distance metrics, so your clustering can be more robust to outliers if you're using metrics like L1. Despite these advantages, most people don't use k-medoids because prior algorithms were too slow. In our NeurIPS 2020 paper, BanditPAM, we sped up the best known algorithm from O(n^2) to O(nlogn) by using techniques from multi-armed bandits. We were inspired by prior research that demonstrated many algorithms can be sped up by sampling the data intelligently, instead of performing exhaustive computations. We've released our implementation, which is pip- and CRAN-installable. It's written in C++ for speed, but callable from Python and R. It also supports parallelization and intelligent caching at no extra complexity to end users. Its interface also matches the sklearn.cluster.KMeans interface, so minimal changes are necessary to existing code. PyPI: https://ift.tt/Y1bNVBu CRAN: https://ift.tt/ahqLcBN Repo: https://ift.tt/o10YBp7 Paper: https://ift.tt/PfZ42v7 If you find our work valuable, please consider starring the repo or citing our work. These help us continue development on this project. I'm Mo Tiwari (motiwari.com), a PhD student in Computer Science at Stanford University. A special thanks to my collaborators on this project, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, and Ilan Shomorony, as well as the author of the R package, Balasubramanian Narasimhan. (This is my first time posting on HN; I've read the FAQ before posting, but please let me know if I broke any rules)

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