The best Hacker News stories from Show from the past day

from

All Show

from the past*

day week month

with these search terms:

Create follow Go back

Latest posts:

Show HN: An API that takes a URL and returns a file with browser screenshots

Show HN: SQLite disk page explorer

Show HN: SQLite disk page explorer

Show HN: SQLite disk page explorer

Show HN: Polaris, a self-hosted music streaming server in Rust

I'm happy to announce the release of Polaris 0.15.0 . Polaris is a self-hosted music streaming server, to enjoy your music collection from any computer or mobile device. It is free and open-source software, without any kind of premium version.<p>This release is the biggest one since Polaris' humble beginnings 9 years ago. It's technically not a rewrite but there is not much code that was left untouched. The highlights are:<p>- New visual design for the web client (screenshots[1]) - Ability to browse the music collection by artist/album/genre, not only by file - Support for multi-value fields in song metadata (eg. multiple artists on the same song) - Revamped search functionality, now supporting per-field queries and boolean operators - Android client now supports search and playlists (and all the new features from this release)<p>See the changelog[2] for a complete list of improvements.<p>This release is a result of many months of work. I hope all the love and effort I put into it are visible in the end result .<p>[1]: <a href="https://github.com/agersant/polaris/discussions/227">https://github.com/agersant/polaris/discussions/227</a> [2]: <a href="https://github.com/agersant/polaris/blob/master/CHANGELOG.md">https://github.com/agersant/polaris/blob/master/CHANGELOG.md</a>

Show HN: Language Learning from YouTube content

Hi!<p>I'm living as an expat in France and I had a hard time following the news. So I've been working on Fluentsubs that turns YouTube content into a language learning experience.<p>YouTube videos up to 20 minutes can be transcribed and listened to. At the same time I made it easy to lookup certain words and automatically add spaced repetition cards using the given context. This means that you can also rehearse with a real voice and not with AI dubs.<p>This works especially well for the somewhat smaller languages that have a small track on Duolingo, and now are desperately looking for content (Norwegian, Finnish, Swedish, etc.)<p>I'd love to hear your thoughts and feedback: <a href="https://fluentsubs.com" rel="nofollow">https://fluentsubs.com</a>

Show HN: Apitally – A simple, privacy-focused API monitoring and analytics tool

G’day Hacker News, I’m Simon Gurcke, the sole founder of Apitally (<a href="https://apitally.io" rel="nofollow">https://apitally.io</a>).<p>I’m building a simple API monitoring and analytics tool for Python / Node.js apps. It helps users understand API usage and performance, spot issues early and troubleshoot effectively when something goes wrong.<p>Features include:<p>- <i>Dashboards:</i> Provide insights into API traffic, errors, performance and consumers.<p>- <i>Request logging:</i> Opt-in and highly configurable in terms of what data is logged. Users can drill down from aggregated metrics to individual requests (proven to be super helpful when troubleshooting issues).<p>- <i>Custom alerts:</i> Based on 14 different API metrics with notifications delivered via email, Slack or Microsoft Teams.<p>- <i>Validation error tracking:</i> Captures metrics about which fields failed validation and why. Works for web frameworks with built-in validation (e.g. FastAPI with pydantic), or that integrate with popular third-party validation libraries (e.g. Zod for Hono).<p>- <i>Server error tracking:</i> Captures exception details and stack traces for 500 error responses. An integration with the Sentry SDK also captures event IDs, allowing users to click through to the relevant Sentry issue for more context.<p>I first started developing Apitally to scratch my own itch. While working at a health tech company where I was responsible for API-based software products, I became frustrated with the monitoring tools we had in place - Datadog and the ELK stack. They were too complex for my API-centric use cases, and often a pain to use.<p>As a result, I focused on making Apitally as simple as possible. This involved not just refining the UX of the dashboard, but also optimizing the developer experience with the open-source SDKs:<p>- <a href="https://github.com/apitally/apitally-py">https://github.com/apitally/apitally-py</a> - Python SDK (supports FastAPI, Flask, Django, Litestar, Starlette)<p>- <a href="https://github.com/apitally/apitally-js">https://github.com/apitally/apitally-js</a> - Node.js SDK (supports Express, NestJS, Fastify, Koa, Hono)<p>My other focus was on data privacy, as that is a strict requirement in the healthcare industry. By default, Apitally doesn’t capture any sensitive data - metrics are aggregated on the client side (similar to Prometheus) and sent in the background in regular intervals.<p>The hardest part has been implementing integrations for various web frameworks and supporting a wide range of versions. I learned a lot about the inner workings of web frameworks in the process. Good test coverage and an extensive test matrix were really important to not break people’s production APIs with buggy middleware.<p>Apitally’s backend is built in Python and runs on a small Kubernetes cluster on DigitalOcean. It uses PostgreSQL and ClickHouse to store data and NATS JetStream as a message queue. I chose NATS for being lightweight and its exactly-once processing capabilities. I’m also impressed by ClickHouse’s performance given the low hardware specs of my server (4 vCPUs, 8 GB RAM).<p>Apitally is free to use for small hobby projects (with limitations), and I offer two paid tiers for $39 and $119 (USD) per month. The dashboard has a demo mode, allowing people to explore the product without having to set up their own app first.<p>Thank you for reading about my bootstrapped indie product. Please let me know your thoughts and questions in the comments.

Show HN: How good is your color vision? Find out in my new game

Show HN: Matle – A Daily Chess Puzzle Inspired by Wordle

Matle[1], a daily puzzle game that combines chess and Wordle mechanics. You have to guess a hidden checkmate.<p>How it works: 1. You’re given a board with five hidden squares. 2. Guess the pieces in those squares. 3. You must form a checkmate!<p>Hints work like Wordle: Correct piece & position Correct piece, wrong position ⬜ Wrong piece<p>[1] <a href="http://www.matle.io/" rel="nofollow">http://www.matle.io/</a>

Show HN: Matle – A Daily Chess Puzzle Inspired by Wordle

Matle[1], a daily puzzle game that combines chess and Wordle mechanics. You have to guess a hidden checkmate.<p>How it works: 1. You’re given a board with five hidden squares. 2. Guess the pieces in those squares. 3. You must form a checkmate!<p>Hints work like Wordle: Correct piece & position Correct piece, wrong position ⬜ Wrong piece<p>[1] <a href="http://www.matle.io/" rel="nofollow">http://www.matle.io/</a>

Show HN: I indexed 10M Shopify products to build an API

Show HN: Calculate Your Revenue

Show HN: Marksmith – a GitHub-style Markdown editor for Ruby on Rails

Show HN: Marksmith – a GitHub-style Markdown editor for Ruby on Rails

Show HN: Marksmith – a GitHub-style Markdown editor for Ruby on Rails

Show HN: Mandarin Word Segmenter with Translation

I've built mandoBot, a web app that segments and translates Mandarin Chinese text. This is a Django API (using Django-Ninja and PostgreSQL) and a NextJS front-end (with Typescript and Chakra). For a sample of what this app does, head to <a href="https://mandobot.netlify.app/?share_id=e8PZ8KFE5Y" rel="nofollow">https://mandobot.netlify.app/?share_id=e8PZ8KFE5Y</a>. This is my presentation of the first chapter of a classic story from the Republican era of Chinese fiction, Diary of a Madman by Lu Xun. Other chapters are located in the "Reading Room" section of the app.<p>This app exists because reading Mandarin is very hard for learners (like me), since Mandarin text does not separate words using spaces in the same way Western languages do. But extensive reading is the most effective way to learn vocabulary and grammar. Thus, learning Mandarin by reading requires first memorizing hundreds or thousands of words, before you can even know where one word ends and the next word begins.<p>I'm solving this problem by allowing users to input Mandarin text, which is then computationally segmented and machine translated by my server, which also adds dictionary definitions for each word and character. The hard part is the segmentation: it turns out that "Chinese Word Segmentation"[0] is <i>the</i> central problem in Chinese Natural Language Processing; no current solutions reach 100% accuracy, whether they're from Stanford[1], Academia Sinica[2], or Tsing Hua University[3]. This includes every LLM currently available.<p>I could talk about this for hours, but the bottom line is that this app is a way to develop my full-stack skills; the backend should be fast, accurate, secure, well-tested, and well-documented, and the front-end should be pretty, secure, well-tested, responsive, and accessible. I am the sole developer, and I'm open to any comments and suggestions: roberto.loja+hn@gmail.com<p>Thanks HN!<p>[0] <a href="https://en.wikipedia.org/wiki/Chinese_word-segmented_writing" rel="nofollow">https://en.wikipedia.org/wiki/Chinese_word-segmented_writing</a><p>[1] <a href="https://nlp.stanford.edu/software/segmenter.shtml" rel="nofollow">https://nlp.stanford.edu/software/segmenter.shtml</a><p>[2] <a href="https://ckip.iis.sinica.edu.tw/project/ws" rel="nofollow">https://ckip.iis.sinica.edu.tw/project/ws</a><p>[3] <a href="http://thulac.thunlp.org/" rel="nofollow">http://thulac.thunlp.org/</a>

Show HN: Mandarin Word Segmenter with Translation

I've built mandoBot, a web app that segments and translates Mandarin Chinese text. This is a Django API (using Django-Ninja and PostgreSQL) and a NextJS front-end (with Typescript and Chakra). For a sample of what this app does, head to <a href="https://mandobot.netlify.app/?share_id=e8PZ8KFE5Y" rel="nofollow">https://mandobot.netlify.app/?share_id=e8PZ8KFE5Y</a>. This is my presentation of the first chapter of a classic story from the Republican era of Chinese fiction, Diary of a Madman by Lu Xun. Other chapters are located in the "Reading Room" section of the app.<p>This app exists because reading Mandarin is very hard for learners (like me), since Mandarin text does not separate words using spaces in the same way Western languages do. But extensive reading is the most effective way to learn vocabulary and grammar. Thus, learning Mandarin by reading requires first memorizing hundreds or thousands of words, before you can even know where one word ends and the next word begins.<p>I'm solving this problem by allowing users to input Mandarin text, which is then computationally segmented and machine translated by my server, which also adds dictionary definitions for each word and character. The hard part is the segmentation: it turns out that "Chinese Word Segmentation"[0] is <i>the</i> central problem in Chinese Natural Language Processing; no current solutions reach 100% accuracy, whether they're from Stanford[1], Academia Sinica[2], or Tsing Hua University[3]. This includes every LLM currently available.<p>I could talk about this for hours, but the bottom line is that this app is a way to develop my full-stack skills; the backend should be fast, accurate, secure, well-tested, and well-documented, and the front-end should be pretty, secure, well-tested, responsive, and accessible. I am the sole developer, and I'm open to any comments and suggestions: roberto.loja+hn@gmail.com<p>Thanks HN!<p>[0] <a href="https://en.wikipedia.org/wiki/Chinese_word-segmented_writing" rel="nofollow">https://en.wikipedia.org/wiki/Chinese_word-segmented_writing</a><p>[1] <a href="https://nlp.stanford.edu/software/segmenter.shtml" rel="nofollow">https://nlp.stanford.edu/software/segmenter.shtml</a><p>[2] <a href="https://ckip.iis.sinica.edu.tw/project/ws" rel="nofollow">https://ckip.iis.sinica.edu.tw/project/ws</a><p>[3] <a href="http://thulac.thunlp.org/" rel="nofollow">http://thulac.thunlp.org/</a>

Show HN: Mandarin Word Segmenter with Translation

I've built mandoBot, a web app that segments and translates Mandarin Chinese text. This is a Django API (using Django-Ninja and PostgreSQL) and a NextJS front-end (with Typescript and Chakra). For a sample of what this app does, head to <a href="https://mandobot.netlify.app/?share_id=e8PZ8KFE5Y" rel="nofollow">https://mandobot.netlify.app/?share_id=e8PZ8KFE5Y</a>. This is my presentation of the first chapter of a classic story from the Republican era of Chinese fiction, Diary of a Madman by Lu Xun. Other chapters are located in the "Reading Room" section of the app.<p>This app exists because reading Mandarin is very hard for learners (like me), since Mandarin text does not separate words using spaces in the same way Western languages do. But extensive reading is the most effective way to learn vocabulary and grammar. Thus, learning Mandarin by reading requires first memorizing hundreds or thousands of words, before you can even know where one word ends and the next word begins.<p>I'm solving this problem by allowing users to input Mandarin text, which is then computationally segmented and machine translated by my server, which also adds dictionary definitions for each word and character. The hard part is the segmentation: it turns out that "Chinese Word Segmentation"[0] is <i>the</i> central problem in Chinese Natural Language Processing; no current solutions reach 100% accuracy, whether they're from Stanford[1], Academia Sinica[2], or Tsing Hua University[3]. This includes every LLM currently available.<p>I could talk about this for hours, but the bottom line is that this app is a way to develop my full-stack skills; the backend should be fast, accurate, secure, well-tested, and well-documented, and the front-end should be pretty, secure, well-tested, responsive, and accessible. I am the sole developer, and I'm open to any comments and suggestions: roberto.loja+hn@gmail.com<p>Thanks HN!<p>[0] <a href="https://en.wikipedia.org/wiki/Chinese_word-segmented_writing" rel="nofollow">https://en.wikipedia.org/wiki/Chinese_word-segmented_writing</a><p>[1] <a href="https://nlp.stanford.edu/software/segmenter.shtml" rel="nofollow">https://nlp.stanford.edu/software/segmenter.shtml</a><p>[2] <a href="https://ckip.iis.sinica.edu.tw/project/ws" rel="nofollow">https://ckip.iis.sinica.edu.tw/project/ws</a><p>[3] <a href="http://thulac.thunlp.org/" rel="nofollow">http://thulac.thunlp.org/</a>

Show HN: Haystack Code Reviewer – Perform code reviews on a canvas

Hi HN!<p>We’re building Haystack Code Reviewer, a tool that lays out code diffs for a GitHub pull request on an interactive canvas. Instead of scrolling through diffs line-by-line, you can view all changes in a more connected, visual format – similar to viewing a call graph. We hope this will make it easier and less cognitively taxing to understand how different changes across files work together.<p>For a quick overview, check out our short demo video: <a href="https://www.youtube.com/watch?v=QeOz70x0WPE" rel="nofollow">https://www.youtube.com/watch?v=QeOz70x0WPE</a>. If you would like to give it a spin, head over to <a href="https://haystackeditor.dev" rel="nofollow">https://haystackeditor.dev</a>, click the “Review pull request button” in the top toolbar, and load any pull request via URL or pick a pull request from a dropdown.<p>We built Haystack Code Reviewer because we found pull requests difficult to review in a pure textual format — especially when hopping between multiple files or trying to break down complex changes. Oftentimes, pull request authors would have to specifically structure their commits so that code reviews would be easier to tackle, which is a time-consuming and error-prone process. Our goal is to make any pull request easy to understand at a glance, and reduce the effort needed from both reviewers and authors to craft a good code review.<p>Haystack Code Reviewer works on private repositories! We have authentication to ensure that someone cannot open the server for your pull request without having access to that pull request on GitHub. For additional security, we plan to build self-hosting soon. Please contact us if you’re interested in this.<p>Alternatively, a completely local option would be to download desktop Haystack and then navigate to your pull request from there. This is great for trying out the feature without exposing any data on the cloud!<p>In the near future, we plan to:<p>1. Introduce step-by-step navigation to guide reviewers through each part of the changeset<p>2. Allow for self-hosting<p>We’d love to hear your thoughts, suggestions, and any feedback on our approach or potential features!

Show HN: Haystack Code Reviewer – Perform code reviews on a canvas

Hi HN!<p>We’re building Haystack Code Reviewer, a tool that lays out code diffs for a GitHub pull request on an interactive canvas. Instead of scrolling through diffs line-by-line, you can view all changes in a more connected, visual format – similar to viewing a call graph. We hope this will make it easier and less cognitively taxing to understand how different changes across files work together.<p>For a quick overview, check out our short demo video: <a href="https://www.youtube.com/watch?v=QeOz70x0WPE" rel="nofollow">https://www.youtube.com/watch?v=QeOz70x0WPE</a>. If you would like to give it a spin, head over to <a href="https://haystackeditor.dev" rel="nofollow">https://haystackeditor.dev</a>, click the “Review pull request button” in the top toolbar, and load any pull request via URL or pick a pull request from a dropdown.<p>We built Haystack Code Reviewer because we found pull requests difficult to review in a pure textual format — especially when hopping between multiple files or trying to break down complex changes. Oftentimes, pull request authors would have to specifically structure their commits so that code reviews would be easier to tackle, which is a time-consuming and error-prone process. Our goal is to make any pull request easy to understand at a glance, and reduce the effort needed from both reviewers and authors to craft a good code review.<p>Haystack Code Reviewer works on private repositories! We have authentication to ensure that someone cannot open the server for your pull request without having access to that pull request on GitHub. For additional security, we plan to build self-hosting soon. Please contact us if you’re interested in this.<p>Alternatively, a completely local option would be to download desktop Haystack and then navigate to your pull request from there. This is great for trying out the feature without exposing any data on the cloud!<p>In the near future, we plan to:<p>1. Introduce step-by-step navigation to guide reviewers through each part of the changeset<p>2. Allow for self-hosting<p>We’d love to hear your thoughts, suggestions, and any feedback on our approach or potential features!