Show HN: A website that heatmaps your city based on your housing preferences https://ift.tt/X0lm7ct

Show HN: A website that heatmaps your city based on your housing preferences For the past few months, I've been working on a website that answers two different questions: - Where in my city have the best travel times to all the things and people I care about? - Given a listing, how far is it from all the things and people I care about? Personally this was fueled by my own frustrations when I was apartment hunting in NYC. I was frustrating to have to juggle so many Google Maps tabs when I was evaluating a listing, and it was also annoying to not have full confidence that I was even searching in the right places. I wanted to be close to work, a Trader Joe's, and a major park. Given that public transportation networks can sometimes make close things hard to get to and far things easy to get to, it's not always obvious whether a neighborhood actually even fits my criteria or not! The overarching goal of theretowhere.com is to allow you to make more informed moving decisions while also making things more convenient than they are today. https://ibb.co/pBsX2HjN It can generate detailed travel time breakdowns for individual listings and addresses, making it easier to determine whether a listing is worth applying for without juggling Google Maps tabs. This is great for questions like “How far is this apartment from my friends, work and dancing gyms?” https://ibb.co/mVBjwPrJ It also has the powerful ability to heatmap a city based on which parts of it are close or not to the people and places you care about. This is great for questions like “Where in the city would I be reasonably close to work, friends and a woodworking studio?” https://ibb.co/vCynPSRK You can add these heatmaps to sites like Zillow and Streeteasy to make things super convenient (this was very fun to make). The main thing that's on my mind is whether this is useful or not. Like, is this something you would actually use? I also have other ideas I'd like to eventually intergrate into this (crime heatmaps, noise heatmaps, etc) https://ift.tt/bWTHtSm February 7, 2025 at 11:53PM

Show HN: Heap Explorer https://ift.tt/onHdRvb

Show HN: Heap Explorer I wrote a little LD_PRELOAD library that makes it easy to inspect and interact with a running program's glibc heap. It's fun to pause processes, free a bunch of their allocations, then resume them. Most of the time, the processes continue as though nothing happened, but sometimes they do interesting things :) https://ift.tt/ONbiRmj February 6, 2025 at 10:24AM

Show HN: Embedding WebAssembly in QR Codes https://ift.tt/FpwEo7J

Show HN: Embedding WebAssembly in QR Codes https://ift.tt/hrDiTZQ February 6, 2025 at 08:13PM

Show HN: Kindly RSS, a self-hostable RSS app designed for e-ink devices https://ift.tt/FU8Cyb0

Show HN: Kindly RSS, a self-hostable RSS app designed for e-ink devices In the last few weeks I've been working on a RSS application designed to be used in e-ink devices such as Kindle, through the device's web browser. It's a self-hostable app optimized for running on low-end hardware (such as Raspberry Pi, I actually run it on a 3b model). The project is in its early stages of development. It is usable, but you may (and probably will :P) encounter bugs from time to time. I did it for myself (I like to read at night before going to sleep but I don't like to use my phone at that time). I thought people could find it useful so I worked on it a little bit more to publish it. At the moment it can only be run by downloading and compiling the source code or using the docker image (in the repo and the landing page there is a curl that executes the script to run the container, manual instructions can be found in the repo's README). Repo: https://ift.tt/YhOW9yb Dockerhub: https://ift.tt/i5W8Ar0 Thank you for reading! I'd love to hear your thoughts and suggestions. https://kindlyrss.app/ February 6, 2025 at 02:16AM

Show HN: How good is your color vision? Find out in my new game https://ift.tt/prPjNdz

Show HN: How good is your color vision? Find out in my new game https://ift.tt/eOEd7Vm February 2, 2025 at 05:33AM

Show HN: Grammarly-like extension for any language https://ift.tt/UmRZFA1

Show HN: Grammarly-like extension for any language https://ift.tt/cgj6i31 February 6, 2025 at 12:11AM

Show HN: CoPlay – Enabling In-Room Xbox Gaming for Children's Hospitals https://ift.tt/aL30piP

Show HN: CoPlay – Enabling In-Room Xbox Gaming for Children's Hospitals Hey everybody, My name is Brady. I'm the creator of CoPlay. Think of it as an MDM/fleet management for xbox accounts and devices. Pediatric hospitals want to allow their patients to play and connect with friends, family and other patients but they have lacked the tools to facilitate and manage this in the past. A friend and I found this problem while volunteering at our local children's hospital. We are now in 6 pediatric hospitals across the US. Yup, it's a niche. But it's a cool niche. I'm sharing this to get feedback, answer questions, contribute to this amazing community and most importantly, HOPEFULLY FIND SOMEONE THAT CAN GET US CONNECTED TO SOME OF THE HIGHER UPS AT MICROSOFT/XBOX. If you have any connections at all please reach out. We believe there is an opportunity for a beautiful partnership there. Sorry for shouting ;) https://ift.tt/jf8RxZO February 5, 2025 at 02:08AM

Show HN: Haystack Code Reviewer – Perform code reviews on a canvas https://ift.tt/49X38Ig

Show HN: Haystack Code Reviewer – Perform code reviews on a canvas Hi HN! 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. For a quick overview, check out our short demo video: https://www.youtube.com/watch?v=QeOz70x0WPE . If you would like to give it a spin, head over to https://ift.tt/F4NPLfw , 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. 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. 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. 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! In the near future, we plan to: 1. Introduce step-by-step navigation to guide reviewers through each part of the changeset 2. Allow for self-hosting We’d love to hear your thoughts, suggestions, and any feedback on our approach or potential features! https://ift.tt/v2KQUjC February 4, 2025 at 10:32PM

Show HN: Mandarin Word Segmenter with Translation https://ift.tt/0QwZ9st

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 https://ift.tt/tQIe1nu . 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. 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. 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 the 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. 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 Thanks HN! [0] https://ift.tt/p9TDIcx [1] https://ift.tt/cIJGq1p [2] https://ift.tt/AnZpR38 [3] https://ift.tt/KQvs2tV https://ift.tt/U4Fck8o February 4, 2025 at 11:26PM

Show HN: I indexed 10M Shopify products to build an API https://ift.tt/LojhZ8V

Show HN: I indexed 10M Shopify products to build an API https://ift.tt/wK8ao79 February 4, 2025 at 02:35AM

Show HN: Calculate Your Revenue https://ift.tt/gaqR01m

Show HN: Calculate Your Revenue https://ift.tt/MbXapd4 February 3, 2025 at 04:17PM

Show HN: Surf.new – An open-source alternative to OpenAI Operator https://ift.tt/DAulHbX

Show HN: Surf.new – An open-source alternative to OpenAI Operator https://ift.tt/N06fhj2 February 4, 2025 at 12:51AM

Show HN: AI text editor with suggested edits in diff view https://ift.tt/XwfHjnK

Show HN: AI text editor with suggested edits in diff view https://www.potext.com February 3, 2025 at 11:41PM

Show HN: Made a tiling manager Linux-XFCE to roughly copy Snap-Layout in Windows https://ift.tt/yYqS10z

Show HN: Made a tiling manager Linux-XFCE to roughly copy Snap-Layout in Windows Title says all that needs to said about it, admittedly it is stupid to "copy" any Windows feature in Linux but here we are...it is not exactly made for use by extensive audience but just a rough work of it would love any suggestion/critique on it ... https://ift.tt/iOJPUum February 3, 2025 at 10:13AM

Show HN: I Built a Platform to Buy and Sell GitHub Repositories https://ift.tt/z5rDI2Q

Show HN: I Built a Platform to Buy and Sell GitHub Repositories Hey HN, I built a platform that allows developers to buy and sell GitHub repositories using private forking. The idea is to help indie developers, open-source maintainers, and teams monetize their work while ensuring buyers get fully functional projects with minimal hassle. Many developers create great projects but lack the time or resources to maintain them. Instead of letting them fade away, why not sell them to someone who wants to continue the work? Here is how it works: - Sellers list theis GitHub repos in the platform - Buyers purchase repos - Buyers automatically added as collaborators and can fork the repo Check it out here: https://gittrader.com https://ift.tt/gbzl4wx February 3, 2025 at 06:07AM

Show HN: Random Art Generator in Haskell https://ift.tt/0kCLNmt

Show HN: Random Art Generator in Haskell https://ift.tt/ZOuDVEQ February 3, 2025 at 02:11AM

Show HN: Modest – musical harmony library for Lua https://ift.tt/scaBPgh

Show HN: Modest – musical harmony library for Lua This is a project I've been building in my spare time over the past few months. It's a library that provides methods for working with musical harmony ‒ intervals, notes, chords. For example, it can parse almost any chord symbol (Fmaj7, CminMaj9, etc) and turn it into notes, or it can identify a chord from a given set of notes. I started this project with the idea of using formal grammar to parse chord symbols. I wanted to use it instead of a hand-written parser, which is the common approach among similar libraries. Lua caught my attention because of Lpeg, a Parsing Expression Grammar library that is both fast and easy to use. An additional motivation for using Lua was the lack of comparable libraries for it, even though the language is commonly used in audio programming. However, despite being a Lua library, the project itself is written in Fennel — a "lispy" language that transpiles to Lua. Fennel has features that make writing code for the Lua platform much more pleasant: a concise syntax, macros, and destructuring — a feature Lua sorely lacks! In the process, I definitely learned a lot about music theory, although my new knowledge is quite one-sided. By working on this library, I know a thing or two about types and structure of chords, but I learned almost nothing about their composition and transformation. Perhaps these will be the directions I explore next in the project. https://ift.tt/isQJfCS February 2, 2025 at 04:02PM

Show HN: I built a full mulimodal LLM by merging multiple models into one https://ift.tt/OuFZHbo

Show HN: I built a full mulimodal LLM by merging multiple models into one https://ift.tt/EsW63ye February 2, 2025 at 12:44PM

Show HN: ESP32 RC Cars https://ift.tt/S1b0rVg

Show HN: ESP32 RC Cars This is a projected I started that blends both the fun of playing a split screen multiplayer driving game and controlling real rc cars. The cars can also be controlled via bluetooth gamepads and is meant to be easily hackable. https://ift.tt/tB4IXQP February 2, 2025 at 12:21AM

Show HN: I hacked LLMs to work like scikit-learn https://ift.tt/hHoWVYm

Show HN: I hacked LLMs to work like scikit-learn Working with LLMs in existing pipelines can often be bloated, complex, and slow. That's why I created FlashLearn , a streamlined library that mirrors the user experience of scikit-learn. It follows a pipeline-like structure allowing you to "fit" (learn) skills from sample data or instructions, and "predict" (apply) these skills to new data, returning structured results. High-Level Concept Flow: Your Data --> Load Skill / Learn Skill --> Create Tasks --> Run Tasks --> Structured Results --> Downstream Steps Installation: pip install flashlearn Learning a New "Skill" from Sample Data Just like a fit/predict pattern in scikit-learn, you can quickly "learn" a custom skill from minimal (or no!) data. Here's an example where we create a skill to evaluate the likelihood of purchasing a product based on user comments: from flashlearn.skills.learn_skill import LearnSkill from flashlearn.client import OpenAI # Instantiate your pipeline "estimator" or "transformer", similar to a scikit-learn model learner = LearnSkill(model_name="gpt-4o-mini", client=OpenAI()) data = [ {"comment_text": "I love this product, it's everything I wanted!"}, {"comment_text": "Not impressed... wouldn't consider buying this."}, # ... ] # Provide instructions and sample data for the new skill skill = learner.learn_skill( data, task=( "Evaluate how likely the user is to buy my product based on the sentiment in their comment, " "return an integer 1-100 on key 'likely_to_buy', " "and a short explanation on key 'reason'." ), ) # Save skill to use in pipelines skill.save("evaluate_buy_comments_skill.json") Input Is a List of Dictionaries Simply wrap each record into a dictionary, much like feature dictionaries in typical ML workflows: user_inputs = [ {"comment_text": "I love this product, it's everything I wanted!"}, {"comment_text": "Not impressed... wouldn't consider buying this."}, # ... ] Run in 3 Lines of Code - Concurrency Built-in up to 1000 calls/min # Suppose we previously saved a learned skill to "evaluate_buy_comments_skill.json". skill = GeneralSkill.load_skill("evaluate_buy_comments_skill.json") tasks = skill.create_tasks(user_inputs) results = skill.run_tasks_in_parallel(tasks) print(results) Get Structured Results Here's an example of structured outputs mapped to indexes of your original list: { "0": { "likely_to_buy": 90, "reason": "Comment shows strong enthusiasm and positive sentiment." }, "1": { "likely_to_buy": 25, "reason": "Expressed disappointment and reluctance to purchase." } } Pass on to the Next Steps You can use each record’s output for downstream tasks such as storing results in a database or filtering high-likelihood leads: # Suppose 'flash_results' is the dictionary with structured LLM outputs for idx, result in flash_results.items(): desired_score = result["likely_to_buy"] reason_text = result["reason"] # Now do something with the score and reason, e.g., store in DB or pass to next step print(f"Comment #{idx} => Score: {desired_score}, Reason: {reason_text}") https://ift.tt/YT4tzQx February 1, 2025 at 10:09PM