Show HN: React-decorated-text (first NPM lib, seeking code review) https://ift.tt/f28vYH9

Show HN: React-decorated-text (first NPM lib, seeking code review) https://ift.tt/dGWQLDq May 9, 2024 at 12:47AM

Show HN: Browser-based knitting (pattern) software https://ift.tt/7xIpXVy

Show HN: Browser-based knitting (pattern) software I wrote some simple open source web-based app to (1) dynamically compute knitting patterns (based on input parameters, such as the exact desired size), and (2) display these patterns and help me keep track of which row I'm on (as I start knitting), similar to minimalist "row counters" that other knitters use. It also gives you a simple visualization of the shape of what you're knitting. You can see it in action at https://ift.tt/jR3stuJ (and read about it in https://ift.tt/way8TO7 ). Right now I only implemented on simple pattern: Sophie scarfs. After knitting one that came out somewhat … asymmetric, I decided to just write some software to help me (1) easily adjust the length/width of the scarf (using Bézier curves), and (2) keep track of which row I'm on (so that I can make sure I apply increases/decreases at the right places). In the future, I expect to extend this with many other knitting patterns for other types of items. The application is 100% browser (JavaScript, tested in only in Chrome in Linux/Android) based (no server-side component): all state is kept in the URL hash. I've used it to knit two scarves, including https://ift.tt/TOnMSLi . The current state of knitting patterns is far from optional, stuck in pre-computer times. Perhaps knitters are not the most technically minded group. Most knitters just download patterns as PDF files. These files will show multiple numbers from which you should choose one depending on the size you're knitting, saying things like "Purl 24 (32 38 42 50 64) times" (you're supposed to pick the right number depending on the size you're knitting). They'll say things like "repeat rows 4 to 6 sixteen times". I think software can display patterns much better (including not being limited to a few pre-selected sizes, but letting you choose the _exact_ size you want, and adjusting everything accordingly), and keep track of your progress much more easily. For example, for my scarf, the user inputs the desired number of rows (based on the desired length, which makes the pattern agnostic to the needle size), and the software computes where to apply increases/decreases. I have many other ideas for improvements (e.g., track how much time I've spent in each row, show a clock), but I figured I'd share this early and ask for feedback. Hopefully there are other fellow knitters in HN. :-) Check it out and let me know what you think! https://ift.tt/way8TO7 May 9, 2024 at 01:29AM

Show HN: I made DevOps as productized service https://ift.tt/Cq0BIW4

Show HN: I made DevOps as productized service I'm normal contractor programmer, for the past few years doing mainly DevOps on AWS. I noticed lot of smaller companies don't have enough work for full-time devops person nor they have cloud/aws knowledge on the team. Lot of them has similar needs. So my thinking is I can help them for flat montly fee to do all the infrastructure on AWS for them. Obviously this is no way to get rich but my hope is to streamline my process and avoid chasing invoices etc. What do you think? https://ift.tt/dkfpDnY May 8, 2024 at 06:29AM

Show HN: I made a better Perplexity for developers https://ift.tt/QrDyoUn

Show HN: I made a better Perplexity for developers Hi HN, I am Jiayuan, and I'm here to introduce a tool we've been building over the past few months: Devv ( https://devv.ai ). In simple terms, it is an AI-powered search engine specifically designed for developers. Now, you might ask, with so many AI search engines already available—Perplexity, You.com, Phind, and several open-source projects—why do we need another one? We all know that Generative Search Engines are built on RAG (Retrieval-Augmented Generation)[1] combined with Large Language Models (LLMs). Most of the products mentioned above use indexes from general search engines (like Google/Bing APIs), but we've taken a different approach. We've created a vertical search index focused on the development domain, which includes: - Documents: These are essentially the single source of truth for programming languages or libraries; I believe many of you are users of Dash ( https://kapeli.com/dash ) or devdocs ( https://devdocs.io/ ). - Code: While not natural language, code contains rich contextual information. If you have a question related to the Django framework, nothing is more convincing than code snippets from Django's repository. - Web Search: We still use data from search engines because these results contain additional contextual information. Our reasons for doing this include: - The quality of the index is crucial to the RAG system; its effectiveness determines the output quality of the entire system. - We focus more on the Index (RAG) rather than LLMs because LLMs evolve rapidly; even models performing well today may be superseded by better ones in a few months, and fine-tuning an LLM now has relatively low costs. - All players are currently exploring what kind of LLM product works best; we hope to contribute some different insights ourselves (and plan to open source parts of our underlying infrastructure in return for contributions back into open source communities). Some brief product features: - Three modes: - Fast mode: Offers quick answers within seconds. - Agent mode: For complex queries where Devv Agent infers your question before selecting appropriate solutions. - GitHub mode(currently in beta): Links directly with your own GitHub repositories allowing inquiries about specific codebases. - Clean & intuitive UI/UX design. - Currently only available as web version but Chrome extension & VSCode plugin planned soon! Technical details regarding how we build our Index: - Documents section involves crawling most documentation sources using scripts inspired by devdocs project’s crawler logic then slicing them up according function/symbol dimensions before embedding into vector databases; - Codes require special treatment beyond just embeddings alone hence why custom parsers were developed per language type extracting logical structures within repos such as architectural layouts calling relationships between functions definitions etc., semantically processed via LMM; - Web searches combine both selfmade indices targeting developer niches alongside traditional API based methods. We crawled relevant sites including blogs forums tech news outlets etc.. For the Agent Mode, we have actually developed a multi-agent framework. It first categorizes the user's query and then selects different agents based on these categories to address the issues. These various agents employ different models and solution steps. Future Plans: - Build a more comprehensive index that includes internal context (The Devv for Teams version will support indexing team repositories, documents, issue trackers for Q&A) - Fully localized: All of the above technologies can be executed locally, ensuring privacy and security through complete localization. Devv is still in its very early stages and can be used without logging in. We welcome everyone to experience it and provide feedback on any issues; we will continue to iterate on it. [1]: https://ift.tt/PX8sZVN https://devv.ai May 8, 2024 at 05:19AM

Show HN: Tracexec – TUI for tracing execve and pre-exec behavior https://ift.tt/fLYg5v8

Show HN: Tracexec – TUI for tracing execve and pre-exec behavior tracexec helps you to figure out what and how programs get executed when you execute a command. It's useful for debugging build systems, catching fd leaks, understanding what shell scripts actually do, figuring out what programs does a proprietary software run, etc. https://ift.tt/jcyukoK May 8, 2024 at 02:04AM

Show HN: AI climbing coach – visualize how to climb any route based on your body https://ift.tt/VDgi26J

Show HN: AI climbing coach – visualize how to climb any route based on your body I made SABR - an AI model that helps you visualize the beta/technique on any route, based on your body parameters. You can input a video of you climbing any route, in any orientation or lighting condition (it's truly versatile!). SABR then creates a virtual avatar of your body shape and uses it to climb the route you're climbing. Then, you can compare/contrast. You can see the demo here: https://www.youtube.com/watch?v=cnvNPWoYZz4 Will be open sourcing the model, backend, and frontend codebase soon! https://climbing.ai/ May 5, 2024 at 10:09PM

Show HN: Visual debugger for Rails system tests https://ift.tt/7aObC92

Show HN: Visual debugger for Rails system tests Hey all, I've been working on this side project to get a Cypress.io-like experience, but for Ruby developers. It's plug-n-play with Capybara system tests, with the following features: - visualize assertions/commands as they happen - view all API requests, errors, and logs in the timeline - pause/continue on any step - rewind through history with a recorded video This is brand new, so looking for people to start trying it out and leave feedback. https://ift.tt/vpd9G8Q May 7, 2024 at 03:32AM