The best Hacker News stories from Show from the past week
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Show HN: Stock Photos Using Stable Diffusion
Hi HN, this is an early version of what we’re imagining as a truly functional stock photo platform using Stable Diffusion.<p>We’re doing our best to hide the customization prompts on the back end so users are able to quickly search for pre-existing generated photos, or create new ones that would ideally work as well.<p>If we keep going with it, in future versions we’d like to add voting, better tags, and more varied prompts, or maybe whatever you recommend!
Show HN: Stock Photos Using Stable Diffusion
Hi HN, this is an early version of what we’re imagining as a truly functional stock photo platform using Stable Diffusion.<p>We’re doing our best to hide the customization prompts on the back end so users are able to quickly search for pre-existing generated photos, or create new ones that would ideally work as well.<p>If we keep going with it, in future versions we’d like to add voting, better tags, and more varied prompts, or maybe whatever you recommend!
Show HN: uFuzzy.js – A tiny, efficient fuzzy search that doesn't suck
Hello HN!<p>I became frustrated with the unpredictible/poor match quality and opaqueness of "relevance scores" in existing fuzzy and fulltext search libs, so I tried something different and this is the result. The main selling point is the result quality / ordering, with best-in-class memory overhead and excellent performance being bonuses. The API is pretty stable at this point, but looking for feedback before committing to 1.0.<p>TL;DR<p>The test corpus is a 4MB json file with 162k words/phrases, so give it a second for initial download. You can also drag/drop your own text/json corpus into the UI to try it against your own dataset.<p>Live demo/compare with a few other libs (there are many more in the codebase, in various states of completion, WIP):<p><a href="https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uFuzzy,fuzzysort,QuickScore,Fuse&search=super ma" rel="nofollow">https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...</a><p>In isolation for perf assessment:<p><a href="https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uFuzzy&search=super ma" rel="nofollow">https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...</a><p>To increase fuzziness and get broader results, try setting intraMax=1 (core) and enable outOfOrder (userland):<p><a href="https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uFuzzy&outOfOrder&intraMax=1&search=super ma" rel="nofollow">https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...</a><p>Also play with the sortPreset selector to swap out the default Array.sort() for one in userland that prioritizes typehead-ness (the resultset remains identical).<p>Still TODO:<p><pre><code> - Example of stripping diacritics
- Example of using non-latin charsets
- Example of prefix-caching to improve typeahead perf even further
- Example of poor man's document search (matching multiple object properties)
</code></pre>
That's all, thanks!
Show HN: uFuzzy.js – A tiny, efficient fuzzy search that doesn't suck
Hello HN!<p>I became frustrated with the unpredictible/poor match quality and opaqueness of "relevance scores" in existing fuzzy and fulltext search libs, so I tried something different and this is the result. The main selling point is the result quality / ordering, with best-in-class memory overhead and excellent performance being bonuses. The API is pretty stable at this point, but looking for feedback before committing to 1.0.<p>TL;DR<p>The test corpus is a 4MB json file with 162k words/phrases, so give it a second for initial download. You can also drag/drop your own text/json corpus into the UI to try it against your own dataset.<p>Live demo/compare with a few other libs (there are many more in the codebase, in various states of completion, WIP):<p><a href="https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uFuzzy,fuzzysort,QuickScore,Fuse&search=super ma" rel="nofollow">https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...</a><p>In isolation for perf assessment:<p><a href="https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uFuzzy&search=super ma" rel="nofollow">https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...</a><p>To increase fuzziness and get broader results, try setting intraMax=1 (core) and enable outOfOrder (userland):<p><a href="https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uFuzzy&outOfOrder&intraMax=1&search=super ma" rel="nofollow">https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...</a><p>Also play with the sortPreset selector to swap out the default Array.sort() for one in userland that prioritizes typehead-ness (the resultset remains identical).<p>Still TODO:<p><pre><code> - Example of stripping diacritics
- Example of using non-latin charsets
- Example of prefix-caching to improve typeahead perf even further
- Example of poor man's document search (matching multiple object properties)
</code></pre>
That's all, thanks!
Show HN: uFuzzy.js – A tiny, efficient fuzzy search that doesn't suck
Hello HN!<p>I became frustrated with the unpredictible/poor match quality and opaqueness of "relevance scores" in existing fuzzy and fulltext search libs, so I tried something different and this is the result. The main selling point is the result quality / ordering, with best-in-class memory overhead and excellent performance being bonuses. The API is pretty stable at this point, but looking for feedback before committing to 1.0.<p>TL;DR<p>The test corpus is a 4MB json file with 162k words/phrases, so give it a second for initial download. You can also drag/drop your own text/json corpus into the UI to try it against your own dataset.<p>Live demo/compare with a few other libs (there are many more in the codebase, in various states of completion, WIP):<p><a href="https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uFuzzy,fuzzysort,QuickScore,Fuse&search=super ma" rel="nofollow">https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...</a><p>In isolation for perf assessment:<p><a href="https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uFuzzy&search=super ma" rel="nofollow">https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...</a><p>To increase fuzziness and get broader results, try setting intraMax=1 (core) and enable outOfOrder (userland):<p><a href="https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uFuzzy&outOfOrder&intraMax=1&search=super ma" rel="nofollow">https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...</a><p>Also play with the sortPreset selector to swap out the default Array.sort() for one in userland that prioritizes typehead-ness (the resultset remains identical).<p>Still TODO:<p><pre><code> - Example of stripping diacritics
- Example of using non-latin charsets
- Example of prefix-caching to improve typeahead perf even further
- Example of poor man's document search (matching multiple object properties)
</code></pre>
That's all, thanks!
ButtFish – Transmit Morse Code of chess moves to your butt
ButtFish – Transmit Morse Code of chess moves to your butt
Show HN: Depot – fast, remote Docker container builds
Hey HN! We’re Kyle and Jacob and we are excited to show you Depot (<a href="https://depot.dev" rel="nofollow">https://depot.dev</a>) and get your feedback!
Depot is a hosted Docker container build service, providing fully managed remote builds from CI and from your terminal. We support both Intel and Arm builds natively.<p>As application and platform engineers, we have experienced the challenge of keeping Docker container build times fast. From optimizing and reoptimizing Dockerfiles, to implementing layer caching in CI, to running & maintaining custom runners for multi-platform images.<p>Still today, there are limitations with the available tools. CI runners are ephemeral, and saving and loading cache tarballs is slow. CI providers are resource constrained, with limited CPUs and disk space to dedicate to fast builds. And with the increasing popularity of Arm devices like M1, Graviton, etc, building multi-platform images requires slow emulation or self-hosted infrastructure.<p>We created Depot to directly address those limitations. Depot provides managed VMs running BuildKit, the backing build engine for Docker. Each VM includes 4 CPUs, 8GB of memory, and a persistent 50GB SSD cache disk. We launch both native Intel and native Arm machines, on Fly.io for Intel builds and AWS for Arm.<p>We have built a depot CLI that embeds the Docker buildx build library, implementing the same CLI flags, so developers can send their builds to Depot VMs just by replacing `docker buildx build` with `depot build`. We also have a depot/build-push-action GitHub Action that can be swapped for docker/build-push-action in CI.<p>The combination of native CPUs, fast networks, and persistent disks significantly lowers build time — we’ve seen speedups of 2-3x on optimized projects, and as much as a 12x speedup with some of our customers.<p>We believe that today we are the only hosted CI or build service offering the ability to natively build multi-platform Docker images (--platform linux/amd64,linux/arm64) without emulation.<p>We are still early though, and would love your feedback.<p>You can sign up without a credit card at <a href="https://depot.dev/sign-up" rel="nofollow">https://depot.dev/sign-up</a> to access a free project with thirty days of unlimited build minutes to try it out.
Show HN: Depot – fast, remote Docker container builds
Hey HN! We’re Kyle and Jacob and we are excited to show you Depot (<a href="https://depot.dev" rel="nofollow">https://depot.dev</a>) and get your feedback!
Depot is a hosted Docker container build service, providing fully managed remote builds from CI and from your terminal. We support both Intel and Arm builds natively.<p>As application and platform engineers, we have experienced the challenge of keeping Docker container build times fast. From optimizing and reoptimizing Dockerfiles, to implementing layer caching in CI, to running & maintaining custom runners for multi-platform images.<p>Still today, there are limitations with the available tools. CI runners are ephemeral, and saving and loading cache tarballs is slow. CI providers are resource constrained, with limited CPUs and disk space to dedicate to fast builds. And with the increasing popularity of Arm devices like M1, Graviton, etc, building multi-platform images requires slow emulation or self-hosted infrastructure.<p>We created Depot to directly address those limitations. Depot provides managed VMs running BuildKit, the backing build engine for Docker. Each VM includes 4 CPUs, 8GB of memory, and a persistent 50GB SSD cache disk. We launch both native Intel and native Arm machines, on Fly.io for Intel builds and AWS for Arm.<p>We have built a depot CLI that embeds the Docker buildx build library, implementing the same CLI flags, so developers can send their builds to Depot VMs just by replacing `docker buildx build` with `depot build`. We also have a depot/build-push-action GitHub Action that can be swapped for docker/build-push-action in CI.<p>The combination of native CPUs, fast networks, and persistent disks significantly lowers build time — we’ve seen speedups of 2-3x on optimized projects, and as much as a 12x speedup with some of our customers.<p>We believe that today we are the only hosted CI or build service offering the ability to natively build multi-platform Docker images (--platform linux/amd64,linux/arm64) without emulation.<p>We are still early though, and would love your feedback.<p>You can sign up without a credit card at <a href="https://depot.dev/sign-up" rel="nofollow">https://depot.dev/sign-up</a> to access a free project with thirty days of unlimited build minutes to try it out.
Show HN: Refurb – A tool for refurbishing and modernizing Python codebases
Show HN: Refurb – A tool for refurbishing and modernizing Python codebases
Show HN: Get conversational practice in over 20 languages by talking to an AI
Hi everyone,<p>Let me introduce you to Quazel, where we want to enable people to talk their way to fluency.<p>We have all tried various language learning apps and tools, however, one aspect of language learning current services are really bad at is conversational practice. You might get a chat-like interface, but in the end, the conversation partner will only respond with a predefined "if the users say X I say Y".<p>With Quazel that's completely different. In completely dynamic and unscripted conversation you can talk about pretty much anything you want. For example, you can try ordering food at a restaurant and even hold a philosophical discussion with Socrates. Additionally, you can analyze the grammar of your responses or use hints to help you out when you get stuck.<p>We want to change how languages are learned from a grammar-centric approach to a more natural, conversation-focused one.
Show HN: Get conversational practice in over 20 languages by talking to an AI
Hi everyone,<p>Let me introduce you to Quazel, where we want to enable people to talk their way to fluency.<p>We have all tried various language learning apps and tools, however, one aspect of language learning current services are really bad at is conversational practice. You might get a chat-like interface, but in the end, the conversation partner will only respond with a predefined "if the users say X I say Y".<p>With Quazel that's completely different. In completely dynamic and unscripted conversation you can talk about pretty much anything you want. For example, you can try ordering food at a restaurant and even hold a philosophical discussion with Socrates. Additionally, you can analyze the grammar of your responses or use hints to help you out when you get stuck.<p>We want to change how languages are learned from a grammar-centric approach to a more natural, conversation-focused one.
Show HN: A Little Tool to Visualize Guitar Chords
Show HN: A Little Tool to Visualize Guitar Chords
Show HN: Jot: Rapid note management for the terminal, inspired by Obsidian
Show HN: Ezno, a type checker for JavaScript and optimiser for React
Show HN: Ezno, a type checker for JavaScript and optimiser for React
Show HN: Ezno, a type checker for JavaScript and optimiser for React
Show HN: Rocketry – Statement-based scheduling framework for Python