The best Hacker News stories from Show from the past day
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Show HN: Improved freemusicdemixer – AI music demixing in the browser
Hi HN,<p>Last time I showed free-music-demixer, which people seemed to enjoy. It was a static website with a Javascript + WASM module to perform music demixing (or music source separation) using an AI model UMX-L (Open-Unmix) running client-side in the browser.<p>Since then, I have overhauled the project and made several improvements:<p>- The demixing/separation quality is higher now, since I implemented the missing post-processing step<p>- Memory usage is lower now by performing a custom segmented inference with a streaming LSTM, which should allow larger tracks (or, dare I say, arbitrarily-large tracks)<p>- There is a batch upload feature now to demix an entire folder of songs (and provide zip files of the stems)<p>- There are now dev logs printed to the website to show the progress better
Show HN: Improved freemusicdemixer – AI music demixing in the browser
Hi HN,<p>Last time I showed free-music-demixer, which people seemed to enjoy. It was a static website with a Javascript + WASM module to perform music demixing (or music source separation) using an AI model UMX-L (Open-Unmix) running client-side in the browser.<p>Since then, I have overhauled the project and made several improvements:<p>- The demixing/separation quality is higher now, since I implemented the missing post-processing step<p>- Memory usage is lower now by performing a custom segmented inference with a streaming LSTM, which should allow larger tracks (or, dare I say, arbitrarily-large tracks)<p>- There is a batch upload feature now to demix an entire folder of songs (and provide zip files of the stems)<p>- There are now dev logs printed to the website to show the progress better
Show HN: Victor, a browser-optimized vector database written in Rust
Hey HN,<p>My friend Sam and I were bored one weekend, and wanted to make something self-contained but still useful. We ended up with Victor [0], a vector database designed to run in the browser. It’s written in Rust, which gives us access to things like super-fast and compact serialization and deserialization, but it’s compiled to WASM and uses web-sys to interact with the browser’s JS APIs to write the database contents to disk.<p>We worked hard on designing it in a way that makes sense for the browser. It uses the private origin file system [1] and stores the vectors in a compact format (one byte per vector dimension).<p>Since storage space is at a premium in the browser, we also provide built-in PCA to allow you to compress your vectors. It’s completely transparent to the user and automatically kicks in once a vector storage file exceeds 10mb. We’re trying to think of a better way to design this API, so let us know any ideas you have!<p>[0]: <a href="https://github.com/not-pizza/victor/">https://github.com/not-pizza/victor/</a><p>[1]: <a href="https://developer.mozilla.org/en-US/docs/Web/API/File_System_API/Origin_private_file_system" rel="nofollow noreferrer">https://developer.mozilla.org/en-US/docs/Web/API/File_System...</a>
Show HN: Victor, a browser-optimized vector database written in Rust
Hey HN,<p>My friend Sam and I were bored one weekend, and wanted to make something self-contained but still useful. We ended up with Victor [0], a vector database designed to run in the browser. It’s written in Rust, which gives us access to things like super-fast and compact serialization and deserialization, but it’s compiled to WASM and uses web-sys to interact with the browser’s JS APIs to write the database contents to disk.<p>We worked hard on designing it in a way that makes sense for the browser. It uses the private origin file system [1] and stores the vectors in a compact format (one byte per vector dimension).<p>Since storage space is at a premium in the browser, we also provide built-in PCA to allow you to compress your vectors. It’s completely transparent to the user and automatically kicks in once a vector storage file exceeds 10mb. We’re trying to think of a better way to design this API, so let us know any ideas you have!<p>[0]: <a href="https://github.com/not-pizza/victor/">https://github.com/not-pizza/victor/</a><p>[1]: <a href="https://developer.mozilla.org/en-US/docs/Web/API/File_System_API/Origin_private_file_system" rel="nofollow noreferrer">https://developer.mozilla.org/en-US/docs/Web/API/File_System...</a>
Show HN: Deploying subdomain-based routing like github.io
Show HN: Deploying subdomain-based routing like github.io
Show HN: Kr8s – a Python client library for Kubernetes
Hey folks. I'm the author of kr8s.<p>I’ve been working on kr8s for a while now and one of my core goals is to build a Python library for Kubernetes that is the most simple, readable and produces the most maintainable code. It should enable folks to write clean code when working with the Kubernetes API.<p>If you're interested in how it compares with other libraries then check out [this post](<a href="https://jacobtomlinson.dev/posts/2023/comparison-of-kr8s-vs-other-python-libraries-for-kubernetes/" rel="nofollow noreferrer">https://jacobtomlinson.dev/posts/2023/comparison-of-kr8s-vs-...</a>).<p>Happy to answer any questions you might have in the comments here .
Show HN: Kr8s – a Python client library for Kubernetes
Hey folks. I'm the author of kr8s.<p>I’ve been working on kr8s for a while now and one of my core goals is to build a Python library for Kubernetes that is the most simple, readable and produces the most maintainable code. It should enable folks to write clean code when working with the Kubernetes API.<p>If you're interested in how it compares with other libraries then check out [this post](<a href="https://jacobtomlinson.dev/posts/2023/comparison-of-kr8s-vs-other-python-libraries-for-kubernetes/" rel="nofollow noreferrer">https://jacobtomlinson.dev/posts/2023/comparison-of-kr8s-vs-...</a>).<p>Happy to answer any questions you might have in the comments here .
Show HN: Kr8s – a Python client library for Kubernetes
Hey folks. I'm the author of kr8s.<p>I’ve been working on kr8s for a while now and one of my core goals is to build a Python library for Kubernetes that is the most simple, readable and produces the most maintainable code. It should enable folks to write clean code when working with the Kubernetes API.<p>If you're interested in how it compares with other libraries then check out [this post](<a href="https://jacobtomlinson.dev/posts/2023/comparison-of-kr8s-vs-other-python-libraries-for-kubernetes/" rel="nofollow noreferrer">https://jacobtomlinson.dev/posts/2023/comparison-of-kr8s-vs-...</a>).<p>Happy to answer any questions you might have in the comments here .
Show HN: Lantern – a PostgreSQL vector database for building AI applications
We are excited to share Lantern! Lantern is a PostgreSQL vector database extension for building AI applications.
Install and use our extension here: <a href="https://github.com/lanterndata/lantern">https://github.com/lanterndata/lantern</a><p>We have the most complete feature set of all the PostgreSQL vector database extensions. Our database is built on top of usearch — a state of the art implementation of HNSW, the most scalable and performant algorithm for handling vector search.<p>There’s three key metrics we track. CREATE INDEX time, SELECT throughput, and SELECT latency. We match or outperform pgvector and pg_embedding (Neon) on all of these metrics.<p>** Here’s what we support today **<p>- Creating an AI application end to end without leaving your database (example: <a href="https://github.com/ezra-varady/lanterndb-semantic-image-search">https://github.com/ezra-varady/lanterndb-semantic-image-sear...</a>)<p>- Embedding generation for popular use cases (CLIP model, Hugging Face models, custom model)<p>- Interoperability with pgvector's data type, so anyone using pgvector can switch to Lantern<p>- Parallel index creation capabilities -- Support for creating the index outside of the database and inside another instance allows you to create an index without interrupting database workflows.<p>** Here’s what’s coming soon **<p>- Cloud-hosted version of Lantern<p>- Templates and guides for building applications for different industries<p>- Tools for generating embeddings (support for third party model API's, more local models)<p>- Support for version control and A/B test embeddings<p>- Autotuned index type that will choose appropriate index creation parameters<p>- 1 byte and 2 byte vector elements, and up to 8000 dimensional vectors support<p>** Why we started Lantern today **<p>There's dozens of vector databases on the market, but no enterprise option built on top of PostgreSQL. We think it's super important to build on top of PostgreSQL<p>- Developers know how to use PostgreSQL.<p>- Companies already store their data on PostgreSQL.<p>- Standalone vector databases have to rebuild all of what PostgreSQL has built for the past 30-years, including all of the optimizations on how to best store and access data.<p>We are open source and excited to have community contributors!
Looking forward to hearing your feedback!
Show HN: Lantern – a PostgreSQL vector database for building AI applications
We are excited to share Lantern! Lantern is a PostgreSQL vector database extension for building AI applications.
Install and use our extension here: <a href="https://github.com/lanterndata/lantern">https://github.com/lanterndata/lantern</a><p>We have the most complete feature set of all the PostgreSQL vector database extensions. Our database is built on top of usearch — a state of the art implementation of HNSW, the most scalable and performant algorithm for handling vector search.<p>There’s three key metrics we track. CREATE INDEX time, SELECT throughput, and SELECT latency. We match or outperform pgvector and pg_embedding (Neon) on all of these metrics.<p>** Here’s what we support today **<p>- Creating an AI application end to end without leaving your database (example: <a href="https://github.com/ezra-varady/lanterndb-semantic-image-search">https://github.com/ezra-varady/lanterndb-semantic-image-sear...</a>)<p>- Embedding generation for popular use cases (CLIP model, Hugging Face models, custom model)<p>- Interoperability with pgvector's data type, so anyone using pgvector can switch to Lantern<p>- Parallel index creation capabilities -- Support for creating the index outside of the database and inside another instance allows you to create an index without interrupting database workflows.<p>** Here’s what’s coming soon **<p>- Cloud-hosted version of Lantern<p>- Templates and guides for building applications for different industries<p>- Tools for generating embeddings (support for third party model API's, more local models)<p>- Support for version control and A/B test embeddings<p>- Autotuned index type that will choose appropriate index creation parameters<p>- 1 byte and 2 byte vector elements, and up to 8000 dimensional vectors support<p>** Why we started Lantern today **<p>There's dozens of vector databases on the market, but no enterprise option built on top of PostgreSQL. We think it's super important to build on top of PostgreSQL<p>- Developers know how to use PostgreSQL.<p>- Companies already store their data on PostgreSQL.<p>- Standalone vector databases have to rebuild all of what PostgreSQL has built for the past 30-years, including all of the optimizations on how to best store and access data.<p>We are open source and excited to have community contributors!
Looking forward to hearing your feedback!
Show HN: Lantern – a PostgreSQL vector database for building AI applications
We are excited to share Lantern! Lantern is a PostgreSQL vector database extension for building AI applications.
Install and use our extension here: <a href="https://github.com/lanterndata/lantern">https://github.com/lanterndata/lantern</a><p>We have the most complete feature set of all the PostgreSQL vector database extensions. Our database is built on top of usearch — a state of the art implementation of HNSW, the most scalable and performant algorithm for handling vector search.<p>There’s three key metrics we track. CREATE INDEX time, SELECT throughput, and SELECT latency. We match or outperform pgvector and pg_embedding (Neon) on all of these metrics.<p>** Here’s what we support today **<p>- Creating an AI application end to end without leaving your database (example: <a href="https://github.com/ezra-varady/lanterndb-semantic-image-search">https://github.com/ezra-varady/lanterndb-semantic-image-sear...</a>)<p>- Embedding generation for popular use cases (CLIP model, Hugging Face models, custom model)<p>- Interoperability with pgvector's data type, so anyone using pgvector can switch to Lantern<p>- Parallel index creation capabilities -- Support for creating the index outside of the database and inside another instance allows you to create an index without interrupting database workflows.<p>** Here’s what’s coming soon **<p>- Cloud-hosted version of Lantern<p>- Templates and guides for building applications for different industries<p>- Tools for generating embeddings (support for third party model API's, more local models)<p>- Support for version control and A/B test embeddings<p>- Autotuned index type that will choose appropriate index creation parameters<p>- 1 byte and 2 byte vector elements, and up to 8000 dimensional vectors support<p>** Why we started Lantern today **<p>There's dozens of vector databases on the market, but no enterprise option built on top of PostgreSQL. We think it's super important to build on top of PostgreSQL<p>- Developers know how to use PostgreSQL.<p>- Companies already store their data on PostgreSQL.<p>- Standalone vector databases have to rebuild all of what PostgreSQL has built for the past 30-years, including all of the optimizations on how to best store and access data.<p>We are open source and excited to have community contributors!
Looking forward to hearing your feedback!
Show HN: Permify Now Supports Attribute-Based Access Control
Hey Everyone,<p>Almost year ago we launched Permify an open-source authorization service to build fine-grained and scalable user permissions and access control systems over here(<a href="https://news.ycombinator.com/item?id=32096610">https://news.ycombinator.com/item?id=32096610</a>)<p>My co-founders and I had a enterprise solution agency where we worked with a bunch of fortune 500 companies.<p>Each projects we made we have to re-invent the wheel for access control and authorization.<p>Not having a easy to integrate, scalable and a granular system that can fulfill the requirements of these enterprise companies was a pain in the ass for us. As well as, platform engineers and software architects in these teams as well.<p>So, we start working on a centralized authorization piece which makes authorization less scary for these architects. We learned a lot from google-zanzibar, OPA, and XACML.<p>Yet we wanted make Permify unique. Permify become a true ReBAC system where you can create a structured authorization logic.<p>But there was a missing piece in which makes you build attribute based access rights such as based on date, number of times, amount of money, etc.<p>Today we are launching that piece with Permify ABAC.<p>We would love to get your feedback on this!!!<p>Feel free roast or toast :)
Show HN: A surprisingly effective way to predict token importance in LLM prompts
We explored a novel method to gauge the significance of tokens in prompts given to large language models, without needing direct model access. Essentially, we just did an ablation study on the prompt using cosine similarity of the embeddings as the measure. We got surprisingly promising results when comparing this really simple approach to integrated gradients. Curious to hear thoughts from the community!
Show HN: Mavex.ai – Your Personal AI Executive Assistant
Mavy is your personal AI Executive Assistant which helps in scheduling and calendar management. Looking for early adopters.
Show HN: GitHub Plugin that generates launch tweets when you ship new code
Hey HN!<p>I just shipped a project I’ve been working on called Buildshare.<p>Demo: <a href="https://www.youtube.com/watch?v=CsacqlCHlzE">https://www.youtube.com/watch?v=CsacqlCHlzE</a><p>How does it work?<p>1. Enter some info about a product that you’re building
2. Connect your Github + install a plugin on the repository of the product
3. Every time you ship new code (by either merging a pull request or making a commit), you get a tweet generated automatically with GPT to announce the new feature.<p>Generated tweets follow best practices that are optimised for grabbing people’s attention and making them want to check out what you’ve built.<p>Hope you like it, and would love to hear any suggestions or feedback! :D
Show HN: Code Indexer Loop
This is a project I recently worked on at Definitive. We’re hoping to make it easier to semantically query (changing) source code files. We’d love to see people use it as a building block. If you have any suggestions please leave a comment or open a GitHub issue!
Show HN: Code Indexer Loop
This is a project I recently worked on at Definitive. We’re hoping to make it easier to semantically query (changing) source code files. We’d love to see people use it as a building block. If you have any suggestions please leave a comment or open a GitHub issue!
Show HN: Rental data supplied by tenants in Ireland, searchable by all
I created <a href="https://www.howmuchrent.com" rel="nofollow noreferrer">https://www.howmuchrent.com</a> last Friday to help bring this kind of transparency to Ireland, allowing people to submit their rents. Would love to get any HN feedback on the idea/website.
Show HN: Rental data supplied by tenants in Ireland, searchable by all
I created <a href="https://www.howmuchrent.com" rel="nofollow noreferrer">https://www.howmuchrent.com</a> last Friday to help bring this kind of transparency to Ireland, allowing people to submit their rents. Would love to get any HN feedback on the idea/website.