The best Hacker News stories from Show from the past day
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Show HN: HN Update – Hourly news broadcast of top HN stories
I feel like it was inevitable, with the recent buzz around NotebookLM. I'm just surprised that it hasn't been done yet.
Show HN: HN Update – Hourly news broadcast of top HN stories
I feel like it was inevitable, with the recent buzz around NotebookLM. I'm just surprised that it hasn't been done yet.
Show HN: HN Update – Hourly news broadcast of top HN stories
I feel like it was inevitable, with the recent buzz around NotebookLM. I'm just surprised that it hasn't been done yet.
Show HN: I wrote an autodiff in C++ and implemented LeNet with it
Show HN: Desktop app displaying files on a 2D canvas
Hey all! I always wanted to arrange my text/markdown/pdf files on a 2D canvas and visualize them without opening all the windows. An extra feature I added is also visualizing folders within - so kind of a 3D visualization? It was also important to be an offline desktop app, rather than online tool like Miro or Mural, because once I edit files in Sublime or AdobePDF then I want changes to sync in the canvas right away.<p>Some technical points and lessons learned: being Android developer helped a lot with this project since I used Kotlin Multiplatform with Compose Desktop renderer (actually skiko). It runs on JVM under the hood, which was exciting at first since I can use the app on all of my Mac/Windows/Linux machines. Right? Wrong. One lesson I learned wasn’t “write once - run everywhere”, it was “write once - test everywhere; repeat”. On the other hand, using Kotlin Multiplatform will allow me easily to port to Android and port the logic to iOS.<p>Anyways, I released LimanDoc v1.0.3, still in Proof-Of-Concept, so I hope to get some feedback and features you think would be helpful.<p>I was thinking these features would be great for future releases:
- adding a local LLM support to search/summarize your docs, books, videos, etc;
- sync on local network (including future mobile apps)
- Templates, groups, and better diagram integration like in Drawio.
Show HN: Desktop app displaying files on a 2D canvas
Hey all! I always wanted to arrange my text/markdown/pdf files on a 2D canvas and visualize them without opening all the windows. An extra feature I added is also visualizing folders within - so kind of a 3D visualization? It was also important to be an offline desktop app, rather than online tool like Miro or Mural, because once I edit files in Sublime or AdobePDF then I want changes to sync in the canvas right away.<p>Some technical points and lessons learned: being Android developer helped a lot with this project since I used Kotlin Multiplatform with Compose Desktop renderer (actually skiko). It runs on JVM under the hood, which was exciting at first since I can use the app on all of my Mac/Windows/Linux machines. Right? Wrong. One lesson I learned wasn’t “write once - run everywhere”, it was “write once - test everywhere; repeat”. On the other hand, using Kotlin Multiplatform will allow me easily to port to Android and port the logic to iOS.<p>Anyways, I released LimanDoc v1.0.3, still in Proof-Of-Concept, so I hope to get some feedback and features you think would be helpful.<p>I was thinking these features would be great for future releases:
- adding a local LLM support to search/summarize your docs, books, videos, etc;
- sync on local network (including future mobile apps)
- Templates, groups, and better diagram integration like in Drawio.
Show HN: What happens if you make a crossword out of Reddit r/gaming
As a fan of dense New York Times-style crosswords, I challenged myself to create topic-specific puzzles. It turns out that generating crosswords and efficiently placing words is a non-trivial computational problem.<p>I started the project, "Joystick Jargon" combining traditional crossword elements with gaming-related vocabulary. Here's the technical process behind it:<p>1. Data Source: Used a 3.8 Million Rows Reddit dataset from Hugging Face (<a href="https://huggingface.co/datasets/webis/tldr-17" rel="nofollow">https://huggingface.co/datasets/webis/tldr-17</a>).<p>2. Data Filtering: Narrowed down to gaming-related subreddits (r/gaming, r/dota2, r/leagueoflegends).<p>3. Keyword Extraction: Employed ML techniques, specifically BERT-embeddings and cosine similarity, to extract keywords from the subreddits.<p>4. Data Preprocessing: Cleaned up data unsuitable for crossword puzzles.<p>5. Grid Generation: Implemented a heuristic crossword algorithm to create grids and place words efficiently.<p>6. Clue Generation: Utilized a Large Language Model to generate context-aware clues for the placed words.<p>The resulting system creates crossword puzzles that blend traditional elements with gaming terminology, achieving about a 50-50 mix.<p>This project is admittedly overengineered for its purpose, but it was an interesting exploration into natural language processing, optimization algorithms, and the intersection of traditional word games with modern gaming culture.<p>A note on content: Since the data source is Reddit, some mature language may appear in the puzzles. Manual filtering was minimal to preserve authenticity.<p>You can try the puzzles here: <<a href="https://capsloq.de/crosswords/joystick-jargon" rel="nofollow">https://capsloq.de/crosswords/joystick-jargon</a>><p>I'm curious about the HN community's thoughts on this approach to puzzle generation? What other domains might benefit from similar computational techniques for content creation?
Show HN: What happens if you make a crossword out of Reddit r/gaming
As a fan of dense New York Times-style crosswords, I challenged myself to create topic-specific puzzles. It turns out that generating crosswords and efficiently placing words is a non-trivial computational problem.<p>I started the project, "Joystick Jargon" combining traditional crossword elements with gaming-related vocabulary. Here's the technical process behind it:<p>1. Data Source: Used a 3.8 Million Rows Reddit dataset from Hugging Face (<a href="https://huggingface.co/datasets/webis/tldr-17" rel="nofollow">https://huggingface.co/datasets/webis/tldr-17</a>).<p>2. Data Filtering: Narrowed down to gaming-related subreddits (r/gaming, r/dota2, r/leagueoflegends).<p>3. Keyword Extraction: Employed ML techniques, specifically BERT-embeddings and cosine similarity, to extract keywords from the subreddits.<p>4. Data Preprocessing: Cleaned up data unsuitable for crossword puzzles.<p>5. Grid Generation: Implemented a heuristic crossword algorithm to create grids and place words efficiently.<p>6. Clue Generation: Utilized a Large Language Model to generate context-aware clues for the placed words.<p>The resulting system creates crossword puzzles that blend traditional elements with gaming terminology, achieving about a 50-50 mix.<p>This project is admittedly overengineered for its purpose, but it was an interesting exploration into natural language processing, optimization algorithms, and the intersection of traditional word games with modern gaming culture.<p>A note on content: Since the data source is Reddit, some mature language may appear in the puzzles. Manual filtering was minimal to preserve authenticity.<p>You can try the puzzles here: <<a href="https://capsloq.de/crosswords/joystick-jargon" rel="nofollow">https://capsloq.de/crosswords/joystick-jargon</a>><p>I'm curious about the HN community's thoughts on this approach to puzzle generation? What other domains might benefit from similar computational techniques for content creation?
How to leverage Claude's capabilities with interactive visualization
Show HN: Gomponents, HTML components in pure Go
Hey everyone! I just released v1.0.0 of gomponents last week. It's an HTML component builder in pure Go, with a DSL-like HTML syntax. It's been 4 years in the making, and makes it really easy to build HTML in your web apps.
Show HN: Gomponents, HTML components in pure Go
Hey everyone! I just released v1.0.0 of gomponents last week. It's an HTML component builder in pure Go, with a DSL-like HTML syntax. It's been 4 years in the making, and makes it really easy to build HTML in your web apps.
Show HN: I made a site to quick identify any plant and learn how to care for it
I'm still working on it, curious but what feature may be most valuable for you? What do you think about personalised care instructions, and an interactive chat feature for each plant.
Show HN: I made a site to quick identify any plant and learn how to care for it
I'm still working on it, curious but what feature may be most valuable for you? What do you think about personalised care instructions, and an interactive chat feature for each plant.
Show HN: Go Plan9 Memo
A quick dive into the Plan9 assembly I picked up while developing my SIMD package for Go, and how it led to a 450% performance boost in calculations.
Show HN: Go Plan9 Memo
A quick dive into the Plan9 assembly I picked up while developing my SIMD package for Go, and how it led to a 450% performance boost in calculations.
Show HN: Go Plan9 Memo
A quick dive into the Plan9 assembly I picked up while developing my SIMD package for Go, and how it led to a 450% performance boost in calculations.
Show HN: Clean News - A cleaner curation of world news events
Hello HN,<p>This a small side project I built a few months ago and I find myself using it quite often to stay up to date so I thought I should share.<p>Its updated every few hours.<p>Feedback welcome.<p><a href="https://cleannews.fyi" rel="nofollow">https://cleannews.fyi</a>
Show HN: Sava OS – A desktop interface for your web browser
Hello, I'm Owen, co-founder of Sava OS.<p>I think you've heard this a thousand times by now; "We spend most of our time on the web browser, yet nothing has changed." And then a "revolutionary" product comes out that puts our links & tabs collapsed on the side, with some extra features. Magical, right :)?<p>Well, we tried a lot of these products, and we also tried building one ourselves about 8years ago. But we felt like no UI can handle the same kind of organization our desktop can, and that's when the idea first came to our mind ~5years ago. For the past year, we worked on the side to build the MVP you see today. But along these years, a lot of thoughts kept popping up, and that's why this product has an OS in it's name (it's still cooking :).<p>Unlike other desktop-like products that are accessible on the browser, Sava OS is not only built and made to run natively on the web browser, but it actually has some useful features to help with your browsing management - and that's only the beginning.<p>There's still a lot to consider when it comes to shaping a modern, desktop-like UI that meets today's needs.. We’ve got some exciting ideas and aim to go beyond the traditional approach.<p>We would really love to hear your take on this.
Show HN: Automated smooth Nth order derivatives of noisy data
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (<a href="https://github.com/hugohadfield/bayesfilter/">https://github.com/hugohadfield/bayesfilter/</a>) so this felt like a fun and very useful first thing to try it out on! If people find kalmangrad useful I would be more than happy to add a few more features etc. and I would be very grateful if people sent in any bugs they spot.. Thanks!
Show HN: Automated smooth Nth order derivatives of noisy data
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (<a href="https://github.com/hugohadfield/bayesfilter/">https://github.com/hugohadfield/bayesfilter/</a>) so this felt like a fun and very useful first thing to try it out on! If people find kalmangrad useful I would be more than happy to add a few more features etc. and I would be very grateful if people sent in any bugs they spot.. Thanks!