The best Hacker News stories from Show from the past day
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Show HN: Open-source Postman alternative with type safety
Hello! This is Jeane and Samuel and we’re building RecipeUI. RecipeUI is an open source Postman alternative that uses TypeScript to statically type and autocomplete requests.<p>We built this because current API tools don’t deal with the fact that some APIs are just painful to work with. For us, it’s usually error after error as we try to figure out how to properly form the first request.<p>We recorded a demo to show you how TypeScript helps us autocomplete a request correctly.
<a href="https://youtu.be/O_Mly_p-g5s" rel="nofollow noreferrer">https://youtu.be/O_Mly_p-g5s</a><p>How does our platform improve the developer experience? The analogy is similar to using a statically typed language vs dynamically typed. Most API tools are dynamically typed. You’re guessing the params and relying on the request to magically work at runtime, only for you to go back to stack overflow or the docs when it doesn’t.<p>We take the approach of defining parameters and the schema first. When you add a new parameter, you need to mention upfront if it’s required and what type it is (integer, string, boolean). While this can be painful in the beginning, it will save you and anyone you share this with the hassle of understanding how this API works.<p>Our app is cross-platform on web and desktop. Our desktop app is <20mb and built on top of Rust with Tauri, NextJS, and Supabase. We open source our code because we want to be transparent about how API requests and secrets are handled (all local IndexDB).<p>I built the first version of this at Robinhood when my colleagues were sharing bash scripts and internal APIs on slack to test things. I wanted to make it easier for anyone to use an API quickly and made use of our OpenAPI specs to generate a nice autocomplete API tool. Soon after, the Options team, then the Crypto team, and then the whole eng org at Robinhood adopted this tool!<p>Try it out at recipeui.com! Please star us on GitHub if you like the product <a href="https://github.com/RecipeUI/RecipeUI">https://github.com/RecipeUI/RecipeUI</a>.
Show HN: Open-source Postman alternative with type safety
Hello! This is Jeane and Samuel and we’re building RecipeUI. RecipeUI is an open source Postman alternative that uses TypeScript to statically type and autocomplete requests.<p>We built this because current API tools don’t deal with the fact that some APIs are just painful to work with. For us, it’s usually error after error as we try to figure out how to properly form the first request.<p>We recorded a demo to show you how TypeScript helps us autocomplete a request correctly.
<a href="https://youtu.be/O_Mly_p-g5s" rel="nofollow noreferrer">https://youtu.be/O_Mly_p-g5s</a><p>How does our platform improve the developer experience? The analogy is similar to using a statically typed language vs dynamically typed. Most API tools are dynamically typed. You’re guessing the params and relying on the request to magically work at runtime, only for you to go back to stack overflow or the docs when it doesn’t.<p>We take the approach of defining parameters and the schema first. When you add a new parameter, you need to mention upfront if it’s required and what type it is (integer, string, boolean). While this can be painful in the beginning, it will save you and anyone you share this with the hassle of understanding how this API works.<p>Our app is cross-platform on web and desktop. Our desktop app is <20mb and built on top of Rust with Tauri, NextJS, and Supabase. We open source our code because we want to be transparent about how API requests and secrets are handled (all local IndexDB).<p>I built the first version of this at Robinhood when my colleagues were sharing bash scripts and internal APIs on slack to test things. I wanted to make it easier for anyone to use an API quickly and made use of our OpenAPI specs to generate a nice autocomplete API tool. Soon after, the Options team, then the Crypto team, and then the whole eng org at Robinhood adopted this tool!<p>Try it out at recipeui.com! Please star us on GitHub if you like the product <a href="https://github.com/RecipeUI/RecipeUI">https://github.com/RecipeUI/RecipeUI</a>.
Show HN: Open-source Postman alternative with type safety
Hello! This is Jeane and Samuel and we’re building RecipeUI. RecipeUI is an open source Postman alternative that uses TypeScript to statically type and autocomplete requests.<p>We built this because current API tools don’t deal with the fact that some APIs are just painful to work with. For us, it’s usually error after error as we try to figure out how to properly form the first request.<p>We recorded a demo to show you how TypeScript helps us autocomplete a request correctly.
<a href="https://youtu.be/O_Mly_p-g5s" rel="nofollow noreferrer">https://youtu.be/O_Mly_p-g5s</a><p>How does our platform improve the developer experience? The analogy is similar to using a statically typed language vs dynamically typed. Most API tools are dynamically typed. You’re guessing the params and relying on the request to magically work at runtime, only for you to go back to stack overflow or the docs when it doesn’t.<p>We take the approach of defining parameters and the schema first. When you add a new parameter, you need to mention upfront if it’s required and what type it is (integer, string, boolean). While this can be painful in the beginning, it will save you and anyone you share this with the hassle of understanding how this API works.<p>Our app is cross-platform on web and desktop. Our desktop app is <20mb and built on top of Rust with Tauri, NextJS, and Supabase. We open source our code because we want to be transparent about how API requests and secrets are handled (all local IndexDB).<p>I built the first version of this at Robinhood when my colleagues were sharing bash scripts and internal APIs on slack to test things. I wanted to make it easier for anyone to use an API quickly and made use of our OpenAPI specs to generate a nice autocomplete API tool. Soon after, the Options team, then the Crypto team, and then the whole eng org at Robinhood adopted this tool!<p>Try it out at recipeui.com! Please star us on GitHub if you like the product <a href="https://github.com/RecipeUI/RecipeUI">https://github.com/RecipeUI/RecipeUI</a>.
Show HN: Puck – Open-source visual editor for React
Hey hackers, OP here!<p>I've been dipping in and out of this problem space for the last few years with many of my clients.<p>Puck sits somewhere between an old-school WYSIWYG-powered CMS and headless one, allowing content teams to author content using real React components.<p>Traditional CMS solutions were flexible but often resulted in page that completely broke the brand guidelines. Headless CMS solutions are a fantastic way of controlling brand by restricting UI changes to developers, but makes layout changes restrictive and slow as developers often need to get involved.<p>Puck provides a visual editor for React that can sit on top of your existing headless CMS (or act as standalone). We've been dog-fooding it on a few pages at <a href="https://measured.co" rel="nofollow noreferrer">https://measured.co</a> and on <a href="https://wellpaid.io" rel="nofollow noreferrer">https://wellpaid.io</a>. So far, so good<p>The API is built for React, which allows FE devs to quickly integrate their existing component and add some form fields for author input, or connect it to a headless CMS of choice.<p>It's open-source under MIT, and pairs nicely with Next.js (check out the demo application). Next in the pipeline: support for multi-column layouts, richer demos, new plugins.<p>Looking forward to hearing your comments!
Show HN: Puck – Open-source visual editor for React
Hey hackers, OP here!<p>I've been dipping in and out of this problem space for the last few years with many of my clients.<p>Puck sits somewhere between an old-school WYSIWYG-powered CMS and headless one, allowing content teams to author content using real React components.<p>Traditional CMS solutions were flexible but often resulted in page that completely broke the brand guidelines. Headless CMS solutions are a fantastic way of controlling brand by restricting UI changes to developers, but makes layout changes restrictive and slow as developers often need to get involved.<p>Puck provides a visual editor for React that can sit on top of your existing headless CMS (or act as standalone). We've been dog-fooding it on a few pages at <a href="https://measured.co" rel="nofollow noreferrer">https://measured.co</a> and on <a href="https://wellpaid.io" rel="nofollow noreferrer">https://wellpaid.io</a>. So far, so good<p>The API is built for React, which allows FE devs to quickly integrate their existing component and add some form fields for author input, or connect it to a headless CMS of choice.<p>It's open-source under MIT, and pairs nicely with Next.js (check out the demo application). Next in the pipeline: support for multi-column layouts, richer demos, new plugins.<p>Looking forward to hearing your comments!
Show HN: Puck – Open-source visual editor for React
Hey hackers, OP here!<p>I've been dipping in and out of this problem space for the last few years with many of my clients.<p>Puck sits somewhere between an old-school WYSIWYG-powered CMS and headless one, allowing content teams to author content using real React components.<p>Traditional CMS solutions were flexible but often resulted in page that completely broke the brand guidelines. Headless CMS solutions are a fantastic way of controlling brand by restricting UI changes to developers, but makes layout changes restrictive and slow as developers often need to get involved.<p>Puck provides a visual editor for React that can sit on top of your existing headless CMS (or act as standalone). We've been dog-fooding it on a few pages at <a href="https://measured.co" rel="nofollow noreferrer">https://measured.co</a> and on <a href="https://wellpaid.io" rel="nofollow noreferrer">https://wellpaid.io</a>. So far, so good<p>The API is built for React, which allows FE devs to quickly integrate their existing component and add some form fields for author input, or connect it to a headless CMS of choice.<p>It's open-source under MIT, and pairs nicely with Next.js (check out the demo application). Next in the pipeline: support for multi-column layouts, richer demos, new plugins.<p>Looking forward to hearing your comments!
Show HN: TTop – System monitoring tool with historical data, triggers and TUI
It is not top/htop replacement because of historical snapshots which can help you to find problems back in time
Show HN: Cross-platform dotfiles manager written in Rust
Show HN: Recognize license plates using fine-tuned yolov8, OCR and IP camera
Hey, just a work related project I made, which could be open sourced :D<p>If you're looking for an example on how to use/fine-tune yolov8, I feel like taking a look at this repo and reading the README could help you get up to speed (also linked some nice refs)!<p>This is actually a full rewrite of a proprietary project I made (and documented on my site) like a year ago, will do some finishing touches (write blog post about it, mark the old version deprecated, record a tutorial on how to set it up on an Ubuntu server, etc, etc) in the following month, but felt like sharing it now, cuz I consider it done<p>The only proprietary part is the client, which receives the images and does stuff with db (has to interact with internal APIs, so there's no reason to make it oss anyways). Also, the client contains only the business logic, all of the fun ai/web server stuff is fully open under AGPL-3.0 (and an example client without the business logic is available ... in rust btw xdd).
Show HN: Rapidgzip – Truly Parallel Gzip Decompression with 10 GB/s
I have posted a much earlier version of this over a year ago [0].<p>Since then a lot has changed. Obviously, the name has changed. This happened for the paper publication [1].<p>I have also optimized the speed, integrated ISA-L for special cases, limited the compression-ratio-dependent maximum memory consumption, and finally added parallelized CRC32 computation, which adds ~5% overhead no matter the number of cores used. At this point, I am leaning towards calling it production-ready although there are still many ideas for improvements.<p>Redoing the benchmarks of the older Show HN, would look like this:<p><pre><code> time pigz -d -c 4GiB-base64.gz | wc -c # real ~13.4 s -> ~320 MB/s
time rapidgzip -d -c 4GiB-base64.gz | wc -c # real ~1.26 s -> ~3.4 GB/s
</code></pre>
However, at this point, the piping itself becomes a problem. Rapidgzip is actually slightly faster than cat when comparing the piped bandwidth! E.g., compare these additional benchmarks:<p><pre><code> time cat 4GiB-base64.gz | wc -c # real ~1.06 s -> ~3.1 GB/s
time fcat 4GiB-base64.gz | wc -c # real ~0.41 s -> ~8.0 GB/s
time rapidgzip -o /dev/null -d 4GiB-base64.gz # real ~0.68 s -> ~6.5 GB/s
</code></pre>
fcat is an alternative cat implementation that uses vmsplice to speed up piping. According to the ReadMe it currently is broken, but it works fine on my system and piping it to md5sum yields consistent results [2].<p>So, at this point, I/O and actually also allocations have become a limiting factor and if you want full speed, you would have to interface with the rapidgzip library interface directly (in C++ or via the Python bindings) and process the decompressed data in memory.<p>The project ReadMe contains further benchmarks with Silesia and FASTQ data and scaling up to 128 cores, for which rapidgzip achieves 12 GB/s for Silesia and 24 GB/s when an index has been created with --export-index and is used with --import-index.<p>It can also be tested with ratarmount 0.14.0, which now uses rapidgzip as a backend by default for .gz and .tar.gz files [3].<p>[0] <a href="https://news.ycombinator.com/item?id=32366959">https://news.ycombinator.com/item?id=32366959</a>
[1] <a href="https://dl.acm.org/doi/10.1145/3588195.3592992" rel="nofollow noreferrer">https://dl.acm.org/doi/10.1145/3588195.3592992</a>
[2] <a href="https://github.com/mre/fcat">https://github.com/mre/fcat</a>
[3] <a href="https://github.com/mxmlnkn/ratarmount">https://github.com/mxmlnkn/ratarmount</a>
Show HN: MonoDevelop
Show HN: finetune LLMs via the Finetuning Hub
Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs.<p>To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into:
- Performance
- Time to train an LLM
- Cost to train an LLM
- Inference (throughput / latency / cost per token)<p>For each LLM, my aim is to benchmark them for popular tasks, i.e., classification and summarization. Moreover, I would like to compare them against each other.<p>So far, I have benchmarked Flan-T5-Large, Falcon-7B and RedPajama and have found them to be very efficient in low-data situations, i.e., when there are very few annotated samples. Llama2-7B/13B and Writer’s Palmyra are in the pipeline.<p>But there’s so many LLMs out there! In case this work interests you, would be great to join forces.<p>GitHub repo attached — feedback is always welcome :)<p>Happy hacking!
Show HN: finetune LLMs via the Finetuning Hub
Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs.<p>To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into:
- Performance
- Time to train an LLM
- Cost to train an LLM
- Inference (throughput / latency / cost per token)<p>For each LLM, my aim is to benchmark them for popular tasks, i.e., classification and summarization. Moreover, I would like to compare them against each other.<p>So far, I have benchmarked Flan-T5-Large, Falcon-7B and RedPajama and have found them to be very efficient in low-data situations, i.e., when there are very few annotated samples. Llama2-7B/13B and Writer’s Palmyra are in the pipeline.<p>But there’s so many LLMs out there! In case this work interests you, would be great to join forces.<p>GitHub repo attached — feedback is always welcome :)<p>Happy hacking!
Show HN: A .bit gateway for resolving ipfs:// to https://
bit.site dynamically captures all requests to *.bit.site, automatically resolving the IPFS/IPNS/Skynet content hashes for the corresponding .bit account records. It returns the relevant static content via HTTPS, enabling users to access decentralized networks with zero configuration on modern browsers.<p>At the same time, we provide global CDN acceleration, striving to enhance the availability of decentralized content.
Show HN: A .bit gateway for resolving ipfs:// to https://
bit.site dynamically captures all requests to *.bit.site, automatically resolving the IPFS/IPNS/Skynet content hashes for the corresponding .bit account records. It returns the relevant static content via HTTPS, enabling users to access decentralized networks with zero configuration on modern browsers.<p>At the same time, we provide global CDN acceleration, striving to enhance the availability of decentralized content.
Show HN: Automating Job Search with AI
This is a personal experiment that uses LLMs to rank unstructured job posting data based on user-defined criteria. Traditional job search platforms rely on rigid filtering systems, but many users lack such concrete criteria.<p>One of the superpowers of LLMs is understanding unstructured data, like the job postings in the monthly "Ask HN: who's hiring" threads. So I built a little tool that lets you define your preferences in a more natural way and then rates each job postings based on the relevance.<p>You can define what you're looking for in simple terms and get a custom list ranked by relevance. It's not flawless (especially with cheaper models like gpt-3.5), but it's a lot better than searching through hundreds of listings manually.
Show HN: Extract an RSS feed from almost anything
Howdy! RSSfeedASAP scratches my own itch. I run a regional podcasting directory which gets dozens of messy submission for podcasts. Often they don't even include an xml file and me being a good samaritan I sometimes do the manual work and find it myself. I got tired of that manual work and decided to build a microapp.<p>RSSfeedASAP is this app and I decided to release it in case someone else finds any use in it.
Show HN: Extract an RSS feed from almost anything
Howdy! RSSfeedASAP scratches my own itch. I run a regional podcasting directory which gets dozens of messy submission for podcasts. Often they don't even include an xml file and me being a good samaritan I sometimes do the manual work and find it myself. I got tired of that manual work and decided to build a microapp.<p>RSSfeedASAP is this app and I decided to release it in case someone else finds any use in it.
Show HN: Extract an RSS feed from almost anything
Howdy! RSSfeedASAP scratches my own itch. I run a regional podcasting directory which gets dozens of messy submission for podcasts. Often they don't even include an xml file and me being a good samaritan I sometimes do the manual work and find it myself. I got tired of that manual work and decided to build a microapp.<p>RSSfeedASAP is this app and I decided to release it in case someone else finds any use in it.
Show HN: Modular Diffusion – A modular Python library for diffusion models
Hello everyone! I've been working on this project for a few months as part of my thesis in Machine Learning. It's meant to be a library that provides an easy-to-use but flexible API to design and train Diffusion Models. I decided to make it because I wanted to quickly prototype a Diffusion Model but there were no good tools to do it with. I think it really can help people prototype their own Diffusion Models a lot faster and only in a few lines of code.<p>The base idea is to have a Model class that takes different modules corresponding to the different aspects of the Diffusion Model process (noise schedule, noise type, denoising network, loss function, guidance, etc.) and allow the user to mix and match different modules to achieve different results. The library ships with a bunch of prebuilt modules and the plan is to add many more. I also made it super easy to implement your own modules, you just need to extend from one of the base classes available.<p>Contrary to HuggingFace Diffusers, this library is focused on designing and training your own Diffusion Models rather than finetuning pretrained ones (although this is possible).<p>I would really appreciate your feedback.