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Showing posts with the label Hacker News

Show HN: Build a SQLite satabase from your Reddit data https://ift.tt/ywQalEk

Show HN: Build a SQLite satabase from your Reddit data With Reddit's upcoming API changes[0], I got nervous that I'd no longer be able to access the many posts and comments I've left there over the years. Inspired by the Dogsheep projects[1], this CLI lets you immediately pull your most recent 1k comments & 1k posts (the max allowed by the paged API) into a nicely-structured SQLite database. It's perfect for loading into Datasette for nice viewing & full-text search of your content. Taking it a step further, the project's killer feature is the ability to import data from GDPR archives. This allows you to store your full Reddit history (including deleted comments and posts on removed subreddits). I hope you find this tool useful! I'll be around to answer questions and field comments (or feel free to open a GH issue). [0]: https://ift.tt/7freIRv... [1]: https://ift.tt/WkEYjGM https://ift.tt/9Ug2fsF May 24, 2023 at 11:35PM
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Show HN: Yakread – An RSS reader powered by machine learning https://ift.tt/iknhEF4

Show HN: Yakread – An RSS reader powered by machine learning This is a web-based reading app I've been working on since August. The main differentiator is that Yakread uses machine learning to rank the articles in your feed: as you click on articles from a particular RSS/newsletter subscription, other articles from that subscription will tend to be ranked higher in the future (via a bandit algorithm). Yakread also uses ML to recommend articles that other users have read, so your feed will have articles in it even before you sign up and add your own subscriptions. For the recommendations, I'm using the collaborative filtering implementation from Spark MLlib[1]. I model RSS feeds instead of individual articles: when you click an article, that counts as a "point" for that article's RSS feed; at recommendation time, the algorithm first selects an RSS feed to recommend, and then it picks one of the popular/recent articles from that feed. To counter popularity bias, I h...
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Show HN: iCal RSVP Helper https://ift.tt/FvJZXBk

Show HN: iCal RSVP Helper Hi HN, We use HEY for Work at my startup, which doesn't have a calendar integration. This is mostly fine, as we can simply use our private calendars, but has a major drawback: We can't reply to invitations in an easy way. I made this microsite to help with this problem until they implement simple Yes/No/Tentative buttons in HEY. It's fully client-side, and gives you the ICS-file you need to attach in an email, and the correct address it needs to be sent to as well - just select your file and RSVP. Hope it's useful for someone else too! https://rsvp.klungo.no/ May 24, 2023 at 12:19PM
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Show HN: File-by-file AI-generated comments for your codebase https://ift.tt/M4Qcx5I

Show HN: File-by-file AI-generated comments for your codebase My friends and I were complaining about having to decipher incomprehensible code one day and decided to pass the code through GPT to see if it could write easily understandable comments to help us out. It turns out that GPT can but it was still a hassle to generate comments for large files. So we decided to develop a basic web application that automatically integrates with your Github repository, generate comments, create a pull request and send you an email when it is all done. There is definitely a lot more that can be done but we wanted to gain feedback on whether this is a problem that you face too. Do you often find it challenging to understand complex code? Do you have difficulties in writing informative comments? And if so, would you find value in a tool that can automatically generate comments for your code? Really appreciate any feedback and suggestions! Thanks in advance! https://ift.tt/ubZrJz8 May 23, 2023 at 07:1...
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Show HN: I made Hazumi, a modern HN reader for iOS https://ift.tt/FsBaHYI

Show HN: I made Hazumi, a modern HN reader for iOS Hello HN, I'm thrilled to announce the launch of my first app, Hazumi, a Hacker News reader, available for free on iOS! I’ve been lurking on HN for around 2 years while working on this project seeing people post amazing HN reader apps of their own. I’ve always figured Hazumi wasn’t good enough yet to post here but if I wait around until I’m ready, I’ll probably never share so here goes… Hazumi is a modern Hacker News app for your pocket with some intentional limitations. Stories are displayed on a simple colourful card, with images, that condenses all the information you need at a glance. Out of the box you get accessibility features for system settings like light/dark mode and text size. Others are controlled in app for opening stories in a Web View vs externally via Safari, defaulting to Reader Mode and reducing animation bounciness. You can browse top, new and best sections, limited to 30 stories each, which refresh every hour o...
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Show HN: I made an iOS HN app to navigate large threads without getting lost https://ift.tt/qRPlOf0

Show HN: I made an iOS HN app to navigate large threads without getting lost I was struggling with navigating HN discussions using existing solutions, so I decided to implement a completely different approach, think of it as depth-first reading vs breadth-first reading. Visually it looks like swipeable stacks of comments and it offers several advantages over traditional interfaces: - Comment width doesn't get narrower no matter how deep in the comment tree you are - You always see the parent of the comment you're currently reading - Swiping allows you to move in and out of subtrees with animated transitions that you fully control - You can easily skip subtrees that don't interest you by scrolling As a result it's easier to maintain the context and to keep track of where you are in the discussion tree. The app is fully featured, it does all the things that you would expect it to do, and there's extra: custom boards, search, in-thread search, anchors, reading list, re...
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Show HN: I built a web app for learning Vim from the browser as a 17-year-old https://ift.tt/96mtVTS

Show HN: I built a web app for learning Vim from the browser as a 17-year-old Hey HN! After my own experiences with learning Vim, I wanted to skip the frustrating process of configuring a new tool before even learning how to use it. In an attempt to solve this problem, I started working on Vim Ninja, a web app that would allow developers to learn Vim through interactive lessons in the browser. It’s been a couple of months, and I’m proud to say that I’ve finally released https://VimNinja.com ! Check out a demo of the app here: https://youtu.be/reukQHKqMZE . On the technical side of things, I used SvelteKit to build the entire app and Tailwind, which turned out to be an amazing decision. I actually really like SvelteKit’s filesystem-based router as well as Svelte’s brevity, and Tailwind actually makes styling a fun task for me. I’m using CodeMirror 6 as a base for Vim Ninja’s code editor, and I really prefer it over more feature-packed alternatives like the Monaco Editor, which is what I...
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Show HN: WikTok – A Recommendation UI for Wikipedia https://ift.tt/YRtjqwx

Show HN: WikTok – A Recommendation UI for Wikipedia Hi HN, WikTok is a UI for Wikipedia that lets you quickly swipe (or use your arrow keys) to navigate between random and recommended articles (based on the previous articles you interacted most with). It's just a fun project I hacked together this weekend, so may be a little rough around the edges, but I'd love to get your thoughts. Let me know if you have any suggestions (or find any interesting articles!) Cheers, https://wiktok.org/ May 22, 2023 at 01:59PM
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Show HN: Horizon – Programmatic Prompt Generation and LLM Configurations https://ift.tt/cpZYTtn

Show HN: Horizon – Programmatic Prompt Generation and LLM Configurations Hi HN. I heard you like dev tools and AI, so we wanted to share our project that we’ve been working on. We’re working on Horizon [1] - a higher level abstraction for LLMs so that developers can spend less time trying to grapple with LLMs to make them work and more time with users. This is the starting feature set which takes an auto-ML approach to identify the optimal LLM model, hyperparameters, and prompt - instead of just giving you the tooling to figure it out yourself. You can read more about it in our documentations. Our view is that as LLMs become increasingly commoditized and prompts become easier to develop all the while fine-tuning is not realistic for the many organization out there - the configuration, optimization, and management will become much more difficult at-scale (consist with Andrej’s recent post [3]). The idea came as we were trying to build LLM apps (think sales outreach, ppt generation, supp...
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