Show HN: File-by-file AI-generated comments for your codebase https://ift.tt/JoqnWf9

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/nsATrKX May 23, 2023 at 07:46PM

Show HN: I'm open sourcing Harmonic, the Android Hacker News client https://ift.tt/xbXKwZT

Show HN: I'm open sourcing Harmonic, the Android Hacker News client https://ift.tt/ydLbhs8 May 23, 2023 at 10:26PM

Show HN: Blotter – An interactive, never ending music video https://ift.tt/Wfy9Cvm

Show HN: Blotter – An interactive, never ending music video One day I was listening to a playlist and wished there could be some cool visuals to go along with it. Blotter is a proof of concept I hacked together that does a bit of audio recognition combined with a few generative AI models (both text and img) to create visuals that are relevant to the song. The video stream is generated in real time at 24fps - you can try it yourself by requesting visuals in the Twitch chat using the "!v" command! Right now it's mostly a fun hack project, but I am tinkering with new model architectures for higher fidelity video as well as an interactive tool so people can make videos with their own audio files. I'd love to hear any feedback or suggestions, thanks! https://ift.tt/W5EiX0a May 23, 2023 at 03:51AM

Show HN: A Simple Load Testing CLI Built in Go https://ift.tt/oQLUk28

Show HN: A Simple Load Testing CLI Built in Go There are plenty of good tools for load testing such as JMeter, Minigun, and plenty more - but they all have their own set of options to learn. I built this one to support a simpler workflow where you just paste your curl request (or other cli command) after your `spam` config (literally just `spam -r 2 -- curl www.google.com). It's pretty barebones but I'm 100% going to use it in my day to day - figured I'd share it here if it helped anyone else! https://ift.tt/mkWR97w May 22, 2023 at 01:02AM

Show HN: Planit Earth – Your AI-Powered Travel Planner https://ift.tt/usd8FNn

Show HN: Planit Earth – Your AI-Powered Travel Planner Greetings Hacker News family! I'm excited to share with you the first version of our AI-powered travel planner. I'm a digital nomad/tech geek who loves traveling but admittedly am lazy about trip planning, so I created a tool built on ChatGPT that could help me jump start that. I simply enter a destination, the # of days, a budget preference, and it spits out a full travel itinerary with highlighted points of interest. The problem: Trip planning can be overwhelming and time consuming. While some may find excitement in planning, many folks are overwhelmed by the prospect of figuring out where to go and what to do once you arrive at your destination. The typically requires copious amounts of research in order to uncover the major attractions, determine their relative proximities, and weave everything together into an actionable travel itinerary. To complicate things, everyone has different preferences for the types of activities they would like to partake in. The solution: Planit Earth aims to help travelers shortcut this process by leveraging the power of AI to generate personalized itineraries with just a click. The vision over time is for Planit Earth to become the go-to resource for your trip planning needs. Look forward to hearing your thoughts! https://ift.tt/KdDtC2r May 22, 2023 at 03:00AM

Show HN: Thousands of shadertoy.com creations on one scrollable grid https://ift.tt/V9kLKiG

Show HN: Thousands of shadertoy.com creations on one scrollable grid https://ift.tt/vXkgB69 May 21, 2023 at 08:35PM

Show HN: Drift, a self-hostable Gist and pastebin https://ift.tt/H451yXB

Show HN: Drift, a self-hostable Gist and pastebin https://drift.lol/ May 21, 2023 at 04:37AM

Show HN: What Electrons Look Like https://ift.tt/DdgJbIi

Show HN: What Electrons Look Like https://ift.tt/BlegPCN May 20, 2023 at 11:32PM

Show HN: pg-bulk-ingest – Bulk ingest into PostgreSQL with high-watermarking https://ift.tt/ca29Z0p

Show HN: pg-bulk-ingest – Bulk ingest into PostgreSQL with high-watermarking https://ift.tt/210hFaT May 20, 2023 at 11:13PM

Show HN: Todo PWA with Pomodoro with keyboard friendly https://ift.tt/TtIiwUB

Show HN: Todo PWA with Pomodoro with keyboard friendly https://ift.tt/0UuH1lc May 20, 2023 at 02:05AM

Show HN: PAKman – A new build system built around Alpine Linux Packages https://ift.tt/VRpfglI

Show HN: PAKman – A new build system built around Alpine Linux Packages https://ift.tt/ojVazbY May 16, 2023 at 07:53PM

Show HN: Jesth – Next-level human-readable data serialization format https://ift.tt/jJCprZN

Show HN: Jesth – Next-level human-readable data serialization format Hi HN ! I'm Alex, a tech enthusiast. I'm excited to show you Jesth, a next-level human-readable data serialization format. This project started out as a markup language for writing the docstrings of functions that would ultimately be consumed by a documentation generator. Basically the idea was to split a docstring into sections like Description and Parameters. Each section would consist of a header in square brackets and a body (lines of text between two headers). Here's what a docstring for a sum function would look like: This function takes in two integers a and b and returns their sum. [parameters] - a: First integer - b: Second integer [return] Sum of a and b The Description section in the example above is actually an anonymous section, i.e., a section with an empty header. Meanwhile, I was thinking of a way to automate part of my dev workflow by storing in a file commands grouped into tasks such as project creation, build, testing, release, et cetera. Similarly with the markup language for my documentation generator, I would use square brackets to define the tasks. Thus, a task would consist of a header and a body which would be a list of commands to be executed sequentially. I built this project and named it Backstage. Here is a hypothetical backstage.tasks file: [release] & test & generate_doc & git_stuff & build # upload to PyPI $ twine upload --skip-existing dist/* [git_stuff] $ git add . $ git commit -m {message} $ git push origin master The example above is illustrative only and would not work. It contains 2 sections "release" and "git_stuff". Running the "release" task from the command line is equivalent to sequentially executing the commands in the "release" section. The documentation generator and the scripting language, despite the obvious similarity in their formats, did not share any parsing code. So, to stop repeating myself, I created a file format and its library named Jesth which stands for "Just Extract Sections Then Hack". The library acts as an incomplete INI file parser that only hands the programmer the sections (as headers and their associated bodies which are lists of strings). No further interpretation of the data is done by the parser, allowing the programmer to unleash their creativity through useful hacks. In its latest iteration, Jesth has matured and also includes a proper and extensively tested hack to convert a compatible section into a dictionary data structure, making Jesth my de facto preferred format for config files. I find Jesth more readable than TOML, YAML, and JSON. Here, encoding a dictionary data structure in its own section with another section containing a prompt for ChatGPT: [prompt] I want you to act as a detective story writer. I will provide you with two dictionary data structures representing the profiles of two people. Your goal is to write a thrilling neo-noir story. My first request is: "guess who the killer and victim is from the profiles, then build a story that includes every detail of the profiles". [profile] # This section can be converted into a dictionary data structure name = 'Jane Doe' birthday = 2000-12-23Z10:17:37Z photo_jpg = (bin) VGhpcyBpcyBub3QgYSBwaG90by4uLiBCdXQgdGhhbmsgeW91 IGZvciB5b3VyIGludGVsbGVjdHVhbCBjdXJpb3NpdHkgOyk= --- books = (dict) romance = (list) 'Happy Place' 'Romantic Comedy' sci-fi = (list) 'Dune' 'Neuromancer' epitaph = (text) According to the law of conservation of energy, no a bit of you is gone; you are just less orderly. --- [profile] name = 'John Doe' birthday = null books = (list) 'American Predator' 'Mindhunter: Inside the FBI's Elite Serial Crime Unit' You can learn more by reading the project's README and playing with the demo. Let me know what you think of this project. https://ift.tt/tIp3cul May 18, 2023 at 10:38PM

Show HN: Metatype – an open-source HTTP/GraphQL query engine for APIs and data https://ift.tt/J1R8gGf

Show HN: Metatype – an open-source HTTP/GraphQL query engine for APIs and data Hey everyone, we built Metatype [0] as a different way to build APIs and backends based on 3 parts: 1. a Python package to build virtual graphs connecting all components of your stack (think databases, third-parties, and existing systems) 2. an HTTP/GraphQL query engine built in Rust/Deno that authorizes and optimizes queries over the graphs 3. a CLI to provide a nice developer experience and fast feedback cycle We developed this platform to tackle some of the challenges we often saw in tech teams we worked with: - most developers (especially backend) still spend too much time on tasks with no real added value (crud, data validation, compliance, etc.) - when growing a product, it is difficult keeping up with business needs and remaining agile/innovative with technology (especially when there is limited funding) We believe it provides multiple advantages over more traditional approaches: - it offers multiple runtimes [1] with pre-defined operations (e.g. Prisma) and can replace the needs for an ad-hoc backend - when the project grows, you can easily introduce new APIs or break existing ones in smaller parts while keeping the same interface - you can write complex business logic directly in Typescript or Python and run them directly inside the query engine - most of the frontend are today built on composable components, this brings a similar approach to backend development - third-parties APIs can be easily integrated, providing you visibility and control over them - it is interoperable with existing systems, and can be introduced step by step - it can be easily self-hosted or customized according to your needs The project is now in public beta, and is run in production by a few companies. We are looking to collect more feedback and early users to help us improve the platform. For the tech curious reader, we also had to build a few new open source pieces to make this happen: - a WASI runtime for Python [2] to run Python code inside WASM inside the engine - a task runner [3] supporting live reload and managing dependencies as DAGs - a way to inject secrets [4] from your preferred vaults into the engine [0]: http://metatype.dev [1]: https://ift.tt/2gVmWHj [2]: https://ift.tt/57DrXHK [3]: https://ift.tt/d38C4aE [4]: https://ift.tt/HL4Q6Cx https://ift.tt/Ts05PaL May 18, 2023 at 01:55AM

Show HN: LogSpend – Gain full visibility and control over your OpenAI costs https://ift.tt/wgK9INd

Show HN: LogSpend – Gain full visibility and control over your OpenAI costs https://logspend.com/ May 18, 2023 at 01:06AM

Show HN: A little web server in C https://ift.tt/jgNUdMP

Show HN: A little web server in C A little web server written in C for Linux. Supports: CGI, Reverse Proxy. Single threaded using I/O multiplexing (select). https://ift.tt/PBKbD5X May 17, 2023 at 01:07AM

Show HN: Neucards – Privacy based digital contact card https://ift.tt/6q1xPYz

Show HN: Neucards – Privacy based digital contact card Neucards is an end-to-end encrypted contact information sharing and updating iOS app that protects your identity while letting you keep in touch with people. I started working on neucards as a side project more than ten years ago, and I decided three years ago to go full-time and try to build a community around it. There are two major problems that neucards addresses. First, most people end up with contact lists that are hopelessly out of date. Over time, people move, change jobs, or add social profiles and unless they tell you, chances are you could lose touch. Second, your contact information ends up in the wrong hands. There has been a huge increase in robocalls, unsolicited emails, data breaches, and online scams that is driven by accessing a person's contact info. Even worse, with AI now being able to imitate a person's voice or other mannerisms, knowledge about the connections you have with others can be used against you. Neucards automatically updates your contact information for anyone who has your digital contact card. You control your contact information and who has access. This is possible because of end-to-end encryption. Neucards brings the same level of protection for your contact information as Signal or WhatsApp does for your chats. Privacy is built it. But, even with these protections, you can share your contact info with anyone. As an example, here is a link to my Social card: https://ift.tt/ukGN6Yb... I'm excited about how much neucards has grown and what I have planned for the future to do even more to protect people's privacy. If you have any comments, please let me know. Brad https://ift.tt/8NogPA1 https://ift.tt/8NogPA1 May 16, 2023 at 10:30PM

Show HN: My solar-powered, ePaper digital photo frame https://ift.tt/HuqPlng

Show HN: My solar-powered, ePaper digital photo frame This is version 2 of my ongoing heirloom device project, a digital photo frame built with the goal of lasting longer than your typical gadget. There's a part of me that wishes to commercialize a polished version of this product, but the more I speak to people, the more I become convinced that I belong to a very small minority. https://ift.tt/kwz2Ul3 May 16, 2023 at 10:06PM

Show HN: Legend-State 1.0 – The fastest React state library https://ift.tt/GzDZmt0

Show HN: Legend-State 1.0 – The fastest React state library After almost a year of development and iterating, we just released Legend-State 1.0. It's the fastest React state library and is very easy to use, based on Observables (Signals) with fine-grained reactivity and built-in persistence. I'd love to know what you think, and I'm also happy to answer any general JavaScript performance questions if you want since I've gone very deep into optimizing . https://ift.tt/5B4DXFN https://ift.tt/GRkBHdx May 16, 2023 at 04:36AM

Show HN: Hat-syslog – Syslog Server with real time web UI https://ift.tt/IJL7Q9B

Show HN: Hat-syslog – Syslog Server with real time web UI https://ift.tt/XGAyfVS May 16, 2023 at 03:35AM

Show HN: Test, fix, and improve your ML models https://ift.tt/Rnvdi32

Show HN: Test, fix, and improve your ML models Hey HN, my name is Vikas, and my cofounders Rish, Gabe and I are building Openlayer: http://openlayer.com/ Openlayer is an ML testing, evaluation, and observability platform designed to help teams pinpoint and resolve issues in their models. We were ML engineers experiencing the struggle that goes into properly evaluating models, making them robust to the myriad of unexpected edge cases they encounter in production, and understanding the reasons behind their mistakes. It was like playing an endless game of whack-a-mole with Jupyter notebooks and CSV files — fix one issue and another pops up. This shouldn’t be the case. Error analysis is vital to establishing guardrails for AI and ensuring fairness across model predictions. Traditional software testing platforms are designed for deterministic systems, where a given input produces an expected output. Since ML models are probabilistic, testing them reliably has been a challenge. What sets Openlayer apart from other companies in the space is our end-to-end approach to tackling both pre- and post-deployment stages of the ML pipeline. This "shift-left" approach emphasizes the importance of thorough validation before you ship, rather than relying solely on monitoring after you deploy. Having a strong evaluation process pre-ship means fewer bugs for your users, shorter and more efficient dev-cycles, and lower chances of getting into a PR disaster or having to recall a model. Openlayer provides ML teams and individuals with a suite of powerful tools to understand models and data beyond your typical metrics. The platform offers insights about the quality of your training and validation sets, the performance of your model across subpopulations of your data, and much more. Each of these insights can be turned into a “goal.” As you commit new versions of your models and data, you can see how your model progresses towards these goals, as you guard against regressions you may have otherwise not picked up on and continually raise the bar. Here's a quick rundown of the Openlayer workflow: 1. Add a hook in your training / data ingestion pipeline to upload your data and model predictions to Openlayer via our API 2. Explore insights about your models and data and create goals around them [1] 3. Diagnose issues with the help of our platform, using powerful tools like explainability (e.g. SHAP values) to get actionable recommendations on how to improve 4. Track the progress over time towards your goals with our UI and API and create new ones to keep improving We've got a free sandbox for you to try out the platform today! You can sign up here: https://ift.tt/ZgbNWOA . We are also soon adding support for even more ML tasks, so please reach out if your use case is not supported and we can add you to a waitlist. Give Openlayer a spin and join us in revolutionizing ML development for greater efficiency and success. Let us know what you think, or if you have any questions about Openlayer or model evaluation in general. [1] A quick run-down of the categories of goals you can track: - Integrity goals measure the quality of your validation and training sets - Consistency goals guard against drift between your datasets - Performance goals evaluate your model's performance across subpopulations of the data - Robustness goals stress-test your model using synthetic data to uncover edge cases - Fairness goals help you understand biases in your model on sensitive populations https://ift.tt/NYbO3J6 May 15, 2023 at 11:05PM