Show HN: I Built a Telegraph Simulator https://ift.tt/WBRqdeL

Show HN: I Built a Telegraph Simulator https://ift.tt/DZIwsMT March 5, 2025 at 03:30AM

Show HN: Ask AI Paul Graham (Open Sourced) https://ift.tt/csQo2pI

Show HN: Ask AI Paul Graham (Open Sourced) https://ift.tt/VCordRU March 8, 2025 at 12:10AM

Show HN: Open-source, native audio turn detection model https://ift.tt/Rclh9tS

Show HN: Open-source, native audio turn detection model https://ift.tt/5I1dz47 March 6, 2025 at 11:50PM

Show HN: DataBridge: Rule-Based Metadata Extraction, PII Redaction, and More https://ift.tt/LfJwoKi

Show HN: DataBridge: Rule-Based Metadata Extraction, PII Redaction, and More https://ift.tt/J2ijNop March 6, 2025 at 08:12PM

Show HN: I made an app to like or dislike famous quotes https://ift.tt/9w5Ukct

Show HN: I made an app to like or dislike famous quotes https://ift.tt/ORzD1kt March 4, 2025 at 03:25AM

Show HN: Bayleaf – Building a low-profile wireless split keyboard https://ift.tt/xAXGj4u

Show HN: Bayleaf – Building a low-profile wireless split keyboard Hey HN, I built a wireless, split, ultra-low profile keyboard from scratch called Bayleaf. As a beginner I learned all things electronics, PCB-building, designing for manufacturing, and many other hardware-related skills to put this together. This case study dives into the build process and of course the final result, hope you enjoy! https://ift.tt/abm7zeq March 4, 2025 at 08:30PM

Show HN: We created a music MMO that can scale 10x of Roblox https://ift.tt/4vruF0I

Show HN: We created a music MMO that can scale 10x of Roblox https://ift.tt/zdQA3oG March 5, 2025 at 12:35AM

Show HN: Time travel debugging AI for more reliable vibe coding https://ift.tt/pFtTuwo

Show HN: Time travel debugging AI for more reliable vibe coding Hi HN, I'm the CEO at https://replay.io . We've been building a time travel debugger for web apps for several years now (previous HN post: https://ift.tt/IOSDfjP ) and are combining our tech with AI to automate the debugging process. AIs are really good at writing code but really bad at debugging -- it's amazing to use Claude to prompt an app into existence, and pretty frustrating when that app doesn't work right and Claude is all thumbs fixing the problem. The basic reason for this is a lack of context. People can use devtools to understand what's going on in the app, but AIs struggle here. With a recording of the app its behavior becomes a giant database for querying using RAG. We've been giving Claude tools to explore and understand what happens in a Replay recording, from basic stuff like seeing console messages to more advanced analysis of React, control dependencies, and dataflow. For now this is behind a chat API ( https://ift.tt/mhbc7CV ). We recently launched Nut ( https://nut.new ) as an open source project which uses this tech for building apps through prompting (vibe coding), similar to e.g. https://bolt.new and https://v0.dev . We want Nut to fix bugs effectively (cracking nuts, so to speak) and are working to make it a reliable tool for building complete production grade apps. It's been pretty neat to see Nut fixing bugs that totally stump the AI otherwise. Each of the problems below has a short video but you can also load the associated project and try it yourself. - Exception thrown from a catch block unmounts the entire app: https://ift.tt/1dycGAE - A settings button doesn't work because its modal component isn't always created: https://ift.tt/tzpombc - An icon is really tiny due to sizing constraints imposed by other elements: https://ift.tt/BSVG39g - Loading doesn't finish due to a problem initializing responsive UI state: https://ift.tt/mQdhjcs - Infinite rendering loop caused by a missing useCallback: https://ift.tt/xA9P16D Nut is completely free. You get some free uses or can add an API key, and we're also offering unlimited free access for folks who can give us feedback we'll use to improve Nut. Email me at hi@replay.io if you're interested. For now Nut is best suited for building frontends but we'll be rolling out more full stack features in the next few weeks. I'd love to know what you think! https://nut.new March 5, 2025 at 12:23AM

Show HN: FlakeUI https://ift.tt/43XkAHc

Show HN: FlakeUI https://ift.tt/UVsKTfo March 3, 2025 at 10:59AM

Show HN: Generating Random Art in Haskell https://ift.tt/3VszUda

Show HN: Generating Random Art in Haskell https://ift.tt/Rj8QSU4 March 3, 2025 at 11:53PM

Show HN: A Transformer model that preserves logical equivalence https://ift.tt/bwEX1x5

Show HN: A Transformer model that preserves logical equivalence https://ift.tt/cOGMVAR March 3, 2025 at 02:11AM

Show HN: Mmar – open-source, zero-dependancy, cross-platform HTTP tunneling https://ift.tt/b17Vf5W

Show HN: Mmar – open-source, zero-dependancy, cross-platform HTTP tunneling Hey HN! For the past couple of months, I've been working on and off on a cool project I'm excited to share. mmar (pronounced "ma-mar") is an open-source, zero dependency, cross platform and self-hostable HTTP tunnel built in Go. It allows you to easily expose your localhost to the world on a public URL. You can easily create an HTTP tunnel right away for free on a randomly generated subdomain on "*.mmar.dev" if you don't feel like self-hosting. This isn't something new, in fact there's quite a few of alternative HTTP tunneling tools out there. mmar is my attempt to optimize for a super easy developer experience and simplified implementation. None the less, I had a blast building it and I think developers could find it pretty useful. Additionally, I documented the whole process of building mmar through devlogs. You can read about the thought process and implementation details here ( https://ift.tt/pjOmFrs ). If I would suggest one devlog to read, I highly recommend devlog 5 ( https://ift.tt/CKxbHqX ). I describe how I built a (very) basic DNS server just to run simulation tests for mmar (a bit of an overkill, but a fantastic learning experience). I dive deep into the DNS protocol and explain why I needed to implement it. Finally, I would love to hear your thoughts and feedback. If you try mmar out, let me know! https://ift.tt/XKvp956 March 3, 2025 at 01:28AM

Show HN: Fast Transition from Firefox to Librewolf https://ift.tt/G3EVSZd

Show HN: Fast Transition from Firefox to Librewolf After looking at various browser alternatives to Firefox (my daily driver for years), I decided to try LibreWolf and the transition was trivial on a Debian based system (by HN standards). My extensions even ran without logging in (YMMV). First install LibreWolf: sudo apt update && sudo apt install extrepo -y sudo extrepo enable librewolf sudo apt update && sudo apt install librewolf -y Second: After closing Firefox, copy Firefox profile (in ~/.mozilla/firefox/) to Librevox profile (in ~/.librewolf/). Note: I copied the profile into the default profile (as seen in about:profiles) not default-default. I then launched the profile and all my tabs were restored, bookmarks, logins, etc. I will update if something seems broken. March 2, 2025 at 11:44PM

Show HN: Schedual https://ift.tt/cnlD8eb

Show HN: Schedual No nonsense tasks. https://schedual.app/ March 2, 2025 at 01:10AM

Show HN: LLM Token Visualizer – How big is 128k token input https://ift.tt/JtLbhYu

Show HN: LLM Token Visualizer – How big is 128k token input https://ift.tt/M62q9Hs March 2, 2025 at 01:14AM

Show HN: Open-source tool that send UI feedback with context https://ift.tt/Cqvi27h

Show HN: Open-source tool that send UI feedback with context https://ift.tt/uksr9E8 March 2, 2025 at 01:11AM

Show HN: I built an app to convert ChatGPT Deep Research to PDFs with footnotes https://ift.tt/7QFy3mk

Show HN: I built an app to convert ChatGPT Deep Research to PDFs with footnotes Whilst ChatGPT Deep Research is very useful for generating in-depth reports, it's time consuming to copy, reformat the text (thousands of words) and clean referenced hyperlinks for use in a professional context. Out of frustration, I built deep research docs to help save time by automating the reformatting, cleaning links, footnote references, and conversion to shareable PDF format. Hopefully this helps you save time to focus on meaningful work. Let me know your feedback. https://ift.tt/1kQu4Zo March 1, 2025 at 06:22PM

Show HN: Torii – a framework agnostic authentication library for Rust https://ift.tt/CApP0re

Show HN: Torii – a framework agnostic authentication library for Rust https://ift.tt/kWTw47J March 1, 2025 at 04:46AM

Show HN: Find out if you qualify for an O-1 visa https://ift.tt/q5ElBmS

Show HN: Find out if you qualify for an O-1 visa https://o1pathways.com/ March 1, 2025 at 03:49AM

Show HN: Betting game puzzle (Hamming neighbor sum in linear time) https://ift.tt/9LSQAjd

Show HN: Betting game puzzle (Hamming neighbor sum in linear time) In Spain, there's a betting game called La Quiniela: https://ift.tt/KrUeApM Players predict the outcome of 14 football matches (home win, draw, away win). You win money if you get at least 10 correct, and the prize amount depends on the number of winners. Since all bets are public, the number of winners and the corresponding payouts can be estimated for each of the 3^14 possible outcomes. We can also estimate their probabilities using bookmaker odds, allowing us to compute the expected value for each prediction. As a side project, I wanted to analyze this, but ran into a computational bottleneck: to evaluate a prediction, I had to sum the values of all its Hamming neighbors up to distance 4. That’s nearly 20,000 neighbors per prediction (1+28+364+2912+16016=19321): S_naive = sum from k=0 to r of [(d! / ((d-k)! * k!)) * (q-1)^k] (d=14, q=3, r=4) This took days to run in my first implementation. Optimizing and doing it with matrices brought it down to 20 minutes—still too slow (im running it in GAS with 6 minutes limit). For a while, I used a heuristic: start from a random prediction, check its 28 nearest neighbors, move to the highest-value one, and repeat until no improvement is possible within distance 3. It worked surprisingly well. But I kept thinking about how to solve the problem properly. Eventually, I realized that partial sums could be accumulated efficiently by exploiting overlaps: if two predictions A and B share neighbors, their shared neighbors can be computed once and reused. This is achieved through a basic transformation that I implemented using reshape, roll, and flatten (it is probably not the most efficient implementation but it is the clearest), which realigns the matrix by applying an offset in dimension i. This transformation has two key properties that enable reducing the number of summations from 19,321 to just 101: - T(T(space, d1), d2) = T(T(space, d2), d1) - T(space1, d) + T(space2, d) = T(space1+space2, d) Number of sums would be the result of this expression: S_PSA = 1 + (d - (r-1)/2) * r * (q-1) I've generalized the algorithm for any number of dimensions, elements per dimension, and summation radius. The implementation is in pure NumPy. I have uploaded the code to colab, github and an explanation in my blog. Apparently, this falls under Hamming neighbor summation, but I haven't found similar approaches elsewhere (maybe I'm searching poorly). If you know or you've worked on something similar, I'd love to hear your thoughts! colab: https://ift.tt/pFVkozG... github: https://ift.tt/8qe3E9R blog: https://ift.tt/gzfQhmt... March 1, 2025 at 02:03AM