The best Hacker News stories from Show from the past week
Latest posts:
Show HN: Textual Markdown – a Markdown “browser” in the terminal
Hi HN,<p>This is a TUI app which displays interactive Markdown documents. Interactive in the sense that you can scroll code fences / tables / and click links. There's a Table of Contents extracted from the MD, and a very rudimentary browser like forward + back.<p>I'm thinking it could be the starting point for a variety of hypertext like applications in the terminal.<p>Very much a work in progress.
Show HN: Textual Markdown – a Markdown “browser” in the terminal
Hi HN,<p>This is a TUI app which displays interactive Markdown documents. Interactive in the sense that you can scroll code fences / tables / and click links. There's a Table of Contents extracted from the MD, and a very rudimentary browser like forward + back.<p>I'm thinking it could be the starting point for a variety of hypertext like applications in the terminal.<p>Very much a work in progress.
A self-updating list of the most current useragents
Hi Hacker News!<p>I made a site which displays the most common useragents found on the web.<p>The site updates weekly with data sourced from the server access logs of another site I run in order to give an accurate picture of the devices and browsers being used on the web.<p>I do a lot of web scraping in my work and it's this group of people who I had in mind when creating the site.<p>The data is presented as useragent, browser, os, and relative percentage of occurence. It can be viewed as a table on the site or via json in the API.<p>Please let me know your thoughts or feedback and I hope you find it useful!<p>Thanks!
Show HN: Forma – An efficient vector-graphics renderer
Show HN: Forma – An efficient vector-graphics renderer
Show HN: Readwise Reader, an all-in-one reading app
Hey HN, cofounder of Readwise here. We've been working on this cross-platform reader app for about 2 years, excited to finally share it in public beta.<p>Probably the most notable thing that makes Reader unique is that it supports almost any content type you could want to save/read/highlight:<p>* web pages<p>* emails/newsletters<p>* PDFs<p>* ePubs<p>* twitter threads<p>* youtube videos (with transcripts)<p>* RSS feeds<p>With all of your knowledge content in one place, we built powerful reading and highlighting, as well as a bunch of novel triage/organization features, so you can actually consume & stay on top of that content!<p>There are also a lot of advanced features too, such as text-to-speech, GPT3 questions/summaries, super powerful highlighting (that includes markup and images), complex filtering/search (with our own query language), sleek mobile triage UI, keyboard shortcuts for reading/everything, integrations with note-taking apps, a browser extension for both saving pages and highlighting them, and much more.<p>If anyone's interested in more product details, as well as our business model, etc, we wrote a detailed launch post: <a href="https://blog.readwise.io/the-next-chapter-of-reader-public-beta/" rel="nofollow">https://blog.readwise.io/the-next-chapter-of-reader-public-b...</a><p>Predicting a common question: Reader is part of the Readwise subscription pricing right now in beta -- there's a 30 day free trial and then it's paid at ~$8usd/month. We also promise to not raise this price for existing subscribers.<p>Reader is also fairly technically interesting -- our iOS, Android and webapp all work fully offline and sync your reading data/progress with eachother. Our search on web is built with wasm sqlite. We have a fairly intense pipeline for cleaning web articles (removing ads/styling). We share lot of modules around syncing/highlighting across all platforms, etc...<p>Happy to answer any questions :)
Show HN: We scaled Git to support 1 TB repos
I’ve been in the MLOps space for ~10 years, and data is still the hardest unsolved open problem. Code is versioned using Git, data is stored somewhere else, and context often lives in a 3rd location like Slack or GDocs. This is why we built XetHub, a platform that enables teams to treat data like code, using Git.<p>Unlike Git LFS, we don’t just store the files. We use content-defined chunking and Merkle Trees to dedupe against everything in history. This allows small changes in large files to be stored compactly. Read more here: <a href="https://xethub.com/assets/docs/how-xet-deduplication-works" rel="nofollow">https://xethub.com/assets/docs/how-xet-deduplication-works</a><p>Today, XetHub works for 1 TB repositories, and we plan to scale to 100 TB in the next year. Our implementation is in Rust (client & cache + storage) and our web application is written in Go. XetHub includes a GitHub-like web interface that provides automatic CSV summaries and allows custom visualizations using Vega. Even at 1 TB, we know downloading an entire repository is painful, so we built git-xet mount - which, in seconds, provides a user-mode filesystem view over the repo.<p>XetHub is available today (Linux & Mac today, Windows coming soon) and we would love your feedback!<p>Read more here:<p>- <a href="https://xetdata.com/blog/2022/10/15/why-xetdata" rel="nofollow">https://xetdata.com/blog/2022/10/15/why-xetdata</a><p>- <a href="https://xetdata.com/blog/2022/12/13/introducing-xethub" rel="nofollow">https://xetdata.com/blog/2022/12/13/introducing-xethub</a>
Show HN: Pg_CRDT – an experimental CRDT extension for Postgres
This is an experimental extension for CRDTs, pg_crdt: GitHub repo[0]. It supports Yjs/Yrs and Automerge.<p>The linked blog post [1] describes how we're thinking about this extension in a Supabase context. I want to emphasise this part: "pg_crdt has not been released onto the Supabase platform (and it may never be). We’re considering many options for offline-sync/support and, while CRDTs will undoubtedly factor in, we’re not sure if this is the right approach."<p>[0] GitHub repo: <a href="https://github.com/supabase/pg_crdt" rel="nofollow">https://github.com/supabase/pg_crdt</a><p>[1] Blog post: <a href="https://supabase.com/blog/postgres-crdt" rel="nofollow">https://supabase.com/blog/postgres-crdt</a>
Show HN: Pg_CRDT – an experimental CRDT extension for Postgres
This is an experimental extension for CRDTs, pg_crdt: GitHub repo[0]. It supports Yjs/Yrs and Automerge.<p>The linked blog post [1] describes how we're thinking about this extension in a Supabase context. I want to emphasise this part: "pg_crdt has not been released onto the Supabase platform (and it may never be). We’re considering many options for offline-sync/support and, while CRDTs will undoubtedly factor in, we’re not sure if this is the right approach."<p>[0] GitHub repo: <a href="https://github.com/supabase/pg_crdt" rel="nofollow">https://github.com/supabase/pg_crdt</a><p>[1] Blog post: <a href="https://supabase.com/blog/postgres-crdt" rel="nofollow">https://supabase.com/blog/postgres-crdt</a>
Show HN: Pynecone – Web Apps in Pure Python
Hello, we just launched the alpha release of Pynecone - a way to build full-stack web apps in pure Python. The framework is easy to get started with even without previous web dev experience and is completely open source / free to use.<p>We made Pynecone for Python devs who want to make web apps, but don’t want the overhead of having to learn or use Javascript. We wanted more flexibility than existing Python frameworks like Streamlit/Dash that don't allow the user to make real, customizable web apps.<p>With Pynecone, you can make anything from a small data science/python project to a full-scale, multi page web app. We have over 60+ built-in components and are adding more.<p>We are actively trying to grow this project so no matter you skill level we welcome contributions! Open up an issue if you find missing features/bugs or contribute to existing issue.
Show HN: LearnGPT – Browse and share ChatGPT examples
Show HN: LearnGPT – Browse and share ChatGPT examples
Show HN: Web search using a ChatGPT-like model that can cite its sources
We’ve trained a generative AI model to browse the web and answer questions/retrieve code snippets directly. Unlike ChatGPT, it has access to primary sources and is able to cite them when you hover over an answer (click on the text to go to the source being cited). We also show regular Bing results side-by-side with our AI answer.<p>The model is an 11-billion parameter T5-derivative that has been fine-tuned on feedback given on hundreds of thousands of searches done (anonymously) on our platform. Giving the model web access lessens its burden to need to store a snapshot of human knowledge within its parameters. Rather, it knows how to piece together primary sources in a natural and informative way. Using our own model is also an order of magnitude cheaper than relying on GPT.<p>A drawback to aligning models to web results is that they are less inclined to generate complete solutions/answers to questions where good primary sources don’t exist. Answers generated without underlying citable sources can be more creative but are prone to errors. In the future, we will show both types of answers.<p>Examples:<p><a href="https://beta.sayhello.so/search?q=set+cookie+in+fastapi" rel="nofollow">https://beta.sayhello.so/search?q=set+cookie+in+fastapi</a><p><a href="https://beta.sayhello.so/search?q=What+did+Paul+Graham+learn+from+users" rel="nofollow">https://beta.sayhello.so/search?q=What+did+Paul+Graham+learn...</a><p><a href="https://beta.sayhello.so/search?q=How+to+get+command+line+parameters+in+Rust" rel="nofollow">https://beta.sayhello.so/search?q=How+to+get+command+line+pa...</a><p><a href="https://beta.sayhello.so/search?q=why+did+Elon+Musk+buy+twitter" rel="nofollow">https://beta.sayhello.so/search?q=why+did+Elon+Musk+buy+twit...</a><p>Would love to hear your thoughts.
Show HN: Port of OpenAI's Whisper model in C/C++
Hi HN,<p>OpenAI recently released a model for automatic speech recognition called Whisper [0]. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++. The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies. The Github project is here:<p><a href="https://github.com/ggerganov/whisper.cpp" rel="nofollow">https://github.com/ggerganov/whisper.cpp</a><p>With this implementation I can very easily build and run the model - <i>“make base.en”</i>. It also allows me to run it on a wide range of devices. For example, I have provided examples of running the model on an iPhone, Raspberry Pi 4 and even in a web page via WebAssembly!<p>The implementation runs fully on the CPU and utilizes FP16, AVX intrinsics on x86 architectures and NEON + Accelerate framework on Apple Silicon. The latter is especially efficient and I observe that the inference is about 2-3 times faster compared to the current PyTorch implementation provided by OpenAI when running it on my MacBook M1 Pro. The WASM port utilizes SIMD 128-bit intrinsics - a feature supported in some modern web browsers [1].<p>I am very happy with the performance that I observe on Apple Silicon devices. I didn’t expect that the Accelerate framework [2] (i.e. CBLAS) offers such a dramatic performance boost for matrix multiplications so I was very pleasantly surprised! To enable the framework in your C/C++ projects, all you have to do is add <i>`-framework Accelerate`</i> to your clang command-line flags.<p>This entire exercise of implementing the Whisper model was very interesting to me and helped me understand a lot about how the transformer architecture works. I also got a lot of positive feedback from people finding and using my project. We brainstormed on a lot of interesting tools that can potentially be created with this library (such as speech-to-text plugin for Vim, RPi4 voice assistant, WASM chat bot, etc). If interested, checkout the “Examples” section and the “Show and tell” discussions for some ideas!<p>Would love to know what you think about this project and about your experience with using the Accelerate framework in any of your projects.
Cheers!<p>[0] <a href="https://github.com/openai/whisper" rel="nofollow">https://github.com/openai/whisper</a><p>[1] <a href="https://chromestatus.com/feature/6533147810332672" rel="nofollow">https://chromestatus.com/feature/6533147810332672</a><p>[2] <a href="https://developer.apple.com/documentation/accelerate" rel="nofollow">https://developer.apple.com/documentation/accelerate</a>
Show HN: Domain Name Search with AI
In my exploration of OpenAI, I just created a domain-name search that takes business description as an input, and generates interesting domain names for it. It then uses DNSimple API to check if .com is available.<p>In my view it is a much easier way to find a suitable domain, as the AI thinks of a much large pool of possible names than my own brain. SmartyNames found its own name, using the tool itself.<p>Hope you enjoy it! <a href="https://smartynames.com/" rel="nofollow">https://smartynames.com/</a>
Tell HN: My child's first program
Last night, I introduced my kid to programming. We'd done some stuff with Mindstorms before, but she never really caught the bug for it. But for some reason, last night when I showed her some simple Python scripting to solve math problems and write to the console, she was enthralled.<p>After guiding her through a few things, she took the laptop off for a while and then came back with her first program, giggling like a maniac<p><pre><code> you='WOW!!!'
fart='So many poops!'
print(you,fart)
</code></pre>
I'm pretty proud :D
Tell HN: Giving ChatGPT access to a real terminal
So, I guess this is the inevitable conclusion with LLMs. Connect them to a real terminal and let them act on real-world objects... I honestly don't know whether I like the idea or not, but I guess it's good to have this conversation now while it is only a marginally better version of tldr.<p>But you can already use it do do simple tasks like cleaning old files, figuring out what machine you're running on or even perform and summarize portscan results.<p>It should go without saying that this should be done on VMs and every command is confirmed and checked by the user...<p>tldr: browsing: enabled
Show HN: Whole Git repo was made with ChatGPT
Show HN: Whole Git repo was made with ChatGPT
Show HN: Chrome extension to display ChatGPT response besides Google Search