The best Hacker News stories from Show from the past day
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Show HN: Generate startup ideas based on HN comments
Show HN: Generate startup ideas based on HN comments
Show HN: ADHD-friendly Pomodoro web app
Show HN: ADHD-friendly Pomodoro web app
Show HN: ADHD-friendly Pomodoro web app
Show HN: ADHD-friendly Pomodoro web app
Show HN: Tabby – A self-hosted GitHub Copilot
I would like to introduce Tabby, which is a self-hosted alternative to GitHub Copilot that you can integrate into your hardware. While GitHub Copilot has made coding more efficient and less time-consuming by assisting developers with suggestions and completing code, it raises concerns around privacy and security.<p>Tabby is in its early stages, and we are excited to receive feedback from the community.<p>Its Github repository is located here: <a href="https://github.com/TabbyML/tabby">https://github.com/TabbyML/tabby</a>.<p>We have also deployed the latest docker image to Huggingface for a live demo: <a href="https://huggingface.co/spaces/TabbyML/tabby" rel="nofollow">https://huggingface.co/spaces/TabbyML/tabby</a>.<p>Tabby is built on top of the popular Hugging Face Transformers / Triton FasterTransformer backend and is designed to be self-hosted, providing you with complete control over your data and privacy. In Tabby's next feature iteration, you can fine-tune the model to meet your project requirements.
Show HN: Tabby – A self-hosted GitHub Copilot
I would like to introduce Tabby, which is a self-hosted alternative to GitHub Copilot that you can integrate into your hardware. While GitHub Copilot has made coding more efficient and less time-consuming by assisting developers with suggestions and completing code, it raises concerns around privacy and security.<p>Tabby is in its early stages, and we are excited to receive feedback from the community.<p>Its Github repository is located here: <a href="https://github.com/TabbyML/tabby">https://github.com/TabbyML/tabby</a>.<p>We have also deployed the latest docker image to Huggingface for a live demo: <a href="https://huggingface.co/spaces/TabbyML/tabby" rel="nofollow">https://huggingface.co/spaces/TabbyML/tabby</a>.<p>Tabby is built on top of the popular Hugging Face Transformers / Triton FasterTransformer backend and is designed to be self-hosted, providing you with complete control over your data and privacy. In Tabby's next feature iteration, you can fine-tune the model to meet your project requirements.
Show HN: Tabby – A self-hosted GitHub Copilot
I would like to introduce Tabby, which is a self-hosted alternative to GitHub Copilot that you can integrate into your hardware. While GitHub Copilot has made coding more efficient and less time-consuming by assisting developers with suggestions and completing code, it raises concerns around privacy and security.<p>Tabby is in its early stages, and we are excited to receive feedback from the community.<p>Its Github repository is located here: <a href="https://github.com/TabbyML/tabby">https://github.com/TabbyML/tabby</a>.<p>We have also deployed the latest docker image to Huggingface for a live demo: <a href="https://huggingface.co/spaces/TabbyML/tabby" rel="nofollow">https://huggingface.co/spaces/TabbyML/tabby</a>.<p>Tabby is built on top of the popular Hugging Face Transformers / Triton FasterTransformer backend and is designed to be self-hosted, providing you with complete control over your data and privacy. In Tabby's next feature iteration, you can fine-tune the model to meet your project requirements.
Show HN: Tabby – A self-hosted GitHub Copilot
I would like to introduce Tabby, which is a self-hosted alternative to GitHub Copilot that you can integrate into your hardware. While GitHub Copilot has made coding more efficient and less time-consuming by assisting developers with suggestions and completing code, it raises concerns around privacy and security.<p>Tabby is in its early stages, and we are excited to receive feedback from the community.<p>Its Github repository is located here: <a href="https://github.com/TabbyML/tabby">https://github.com/TabbyML/tabby</a>.<p>We have also deployed the latest docker image to Huggingface for a live demo: <a href="https://huggingface.co/spaces/TabbyML/tabby" rel="nofollow">https://huggingface.co/spaces/TabbyML/tabby</a>.<p>Tabby is built on top of the popular Hugging Face Transformers / Triton FasterTransformer backend and is designed to be self-hosted, providing you with complete control over your data and privacy. In Tabby's next feature iteration, you can fine-tune the model to meet your project requirements.
Show HN: Tabby – A self-hosted GitHub Copilot
I would like to introduce Tabby, which is a self-hosted alternative to GitHub Copilot that you can integrate into your hardware. While GitHub Copilot has made coding more efficient and less time-consuming by assisting developers with suggestions and completing code, it raises concerns around privacy and security.<p>Tabby is in its early stages, and we are excited to receive feedback from the community.<p>Its Github repository is located here: <a href="https://github.com/TabbyML/tabby">https://github.com/TabbyML/tabby</a>.<p>We have also deployed the latest docker image to Huggingface for a live demo: <a href="https://huggingface.co/spaces/TabbyML/tabby" rel="nofollow">https://huggingface.co/spaces/TabbyML/tabby</a>.<p>Tabby is built on top of the popular Hugging Face Transformers / Triton FasterTransformer backend and is designed to be self-hosted, providing you with complete control over your data and privacy. In Tabby's next feature iteration, you can fine-tune the model to meet your project requirements.
Show HN: AI-Less Hacker News
Lately I've felt exhausted due to the deluge of AI/GPT posts on hacker news, and have seen similar grumblings. I threw together this frontend that filters out anything with the phrases AI, LLM, GPT, or LLaMa for use until the hype dies down a bit.<p>Before anyone asks, yes I did try to use ChatGPT to help, and while the code it provided was helpful, it needed some heavy bug-fixing.<p>Edit: One other note I forgot to mention. The favicon is generated by Stable Diffusion, I asked it to generate an "Aritificial Intelligence Favicon", and then I added the red circle with line through it.
Show HN: AI-Less Hacker News
Lately I've felt exhausted due to the deluge of AI/GPT posts on hacker news, and have seen similar grumblings. I threw together this frontend that filters out anything with the phrases AI, LLM, GPT, or LLaMa for use until the hype dies down a bit.<p>Before anyone asks, yes I did try to use ChatGPT to help, and while the code it provided was helpful, it needed some heavy bug-fixing.<p>Edit: One other note I forgot to mention. The favicon is generated by Stable Diffusion, I asked it to generate an "Aritificial Intelligence Favicon", and then I added the red circle with line through it.
Show HN: Quadratic – Open-Source Spreadsheet with Python, AI (WASM and WebGL)
Hi, I am David Kircos. The Founder of Quadratic (<a href="https://QuadraticHQ.com" rel="nofollow">https://QuadraticHQ.com</a>), an open-source spreadsheet application that supports Python, SQL (coming soon), AI Prompts, and classic Formulas.<p>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.<p>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.<p>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 (<a href="https://github.com/quadratichq/quadratic/">https://github.com/quadratichq/quadratic/</a>)<p>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.<p>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.<p>SQL is coming soon... stay tuned!
Show HN: Quadratic – Open-Source Spreadsheet with Python, AI (WASM and WebGL)
Hi, I am David Kircos. The Founder of Quadratic (<a href="https://QuadraticHQ.com" rel="nofollow">https://QuadraticHQ.com</a>), an open-source spreadsheet application that supports Python, SQL (coming soon), AI Prompts, and classic Formulas.<p>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.<p>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.<p>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 (<a href="https://github.com/quadratichq/quadratic/">https://github.com/quadratichq/quadratic/</a>)<p>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.<p>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.<p>SQL is coming soon... stay tuned!
Show HN: Quadratic – Open-Source Spreadsheet with Python, AI (WASM and WebGL)
Hi, I am David Kircos. The Founder of Quadratic (<a href="https://QuadraticHQ.com" rel="nofollow">https://QuadraticHQ.com</a>), an open-source spreadsheet application that supports Python, SQL (coming soon), AI Prompts, and classic Formulas.<p>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.<p>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.<p>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 (<a href="https://github.com/quadratichq/quadratic/">https://github.com/quadratichq/quadratic/</a>)<p>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.<p>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.<p>SQL is coming soon... stay tuned!
Show HN: Want something better than k-means? Try BanditPAM
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!<p>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.<p>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.<p>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.<p>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.<p>PyPI: <a href="https://pypi.org/project/banditpam" rel="nofollow">https://pypi.org/project/banditpam</a><p>CRAN: <a href="https://cran.r-project.org/web/packages/banditpam/index.html" rel="nofollow">https://cran.r-project.org/web/packages/banditpam/index.html</a><p>Repo: <a href="https://github.com/motiwari/BanditPAM">https://github.com/motiwari/BanditPAM</a><p>Paper: <a href="https://arxiv.org/abs/2006.06856" rel="nofollow">https://arxiv.org/abs/2006.06856</a><p>If you find our work valuable, please consider starring the repo or citing our work. These help us continue development on this project.<p>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.<p>(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)
Show HN: Want something better than k-means? Try BanditPAM
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!<p>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.<p>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.<p>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.<p>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.<p>PyPI: <a href="https://pypi.org/project/banditpam" rel="nofollow">https://pypi.org/project/banditpam</a><p>CRAN: <a href="https://cran.r-project.org/web/packages/banditpam/index.html" rel="nofollow">https://cran.r-project.org/web/packages/banditpam/index.html</a><p>Repo: <a href="https://github.com/motiwari/BanditPAM">https://github.com/motiwari/BanditPAM</a><p>Paper: <a href="https://arxiv.org/abs/2006.06856" rel="nofollow">https://arxiv.org/abs/2006.06856</a><p>If you find our work valuable, please consider starring the repo or citing our work. These help us continue development on this project.<p>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.<p>(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)
Show HN: Want something better than k-means? Try BanditPAM
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!<p>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.<p>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.<p>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.<p>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.<p>PyPI: <a href="https://pypi.org/project/banditpam" rel="nofollow">https://pypi.org/project/banditpam</a><p>CRAN: <a href="https://cran.r-project.org/web/packages/banditpam/index.html" rel="nofollow">https://cran.r-project.org/web/packages/banditpam/index.html</a><p>Repo: <a href="https://github.com/motiwari/BanditPAM">https://github.com/motiwari/BanditPAM</a><p>Paper: <a href="https://arxiv.org/abs/2006.06856" rel="nofollow">https://arxiv.org/abs/2006.06856</a><p>If you find our work valuable, please consider starring the repo or citing our work. These help us continue development on this project.<p>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.<p>(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)
Show HN: Live coaching app for remote SWE interviews, uses Whisper and GPT-4
Posting from a throwaway account to maintain privacy.<p>This project is a salvo against leetcode-style interviews that require candidates to study useless topics and confidently write code in front of a live audience, in order to get a job where none of that stuff matters.<p>Cheetah is an AI-powered macOS app designed to assist users during remote software engineering interviews by providing real-time, discreet coaching and integration with CoderPad. It uses Whisper for audio transcription and GPT-4 to generate hints/answers. The UI is intentionally minimal to allow for discreet use during a video call.<p>It was fun dipping into the world of LLMs, prompt chaining, etc. I didn't find a Swift wrapper for whisper.cpp, so in the repo there's also a barebones Swift framework that wraps whisper.cpp and is designed for real-time transcription on M1/M2.<p>I'll be around if anyone has questions or comments!