The best Hacker News stories from Show from the past day
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Show HN: SmoothWAN a simple home internet bonding router using Speedify
Show HN: SmoothWAN a simple home internet bonding router using Speedify
Show HN: I made my personal website a Pokémon-style minigame using Phaser 3
Repo here: <a href="https://github.com/ariroffe/personal-website" rel="nofollow">https://github.com/ariroffe/personal-website</a>
Show HN: I made my personal website a Pokémon-style minigame using Phaser 3
Repo here: <a href="https://github.com/ariroffe/personal-website" rel="nofollow">https://github.com/ariroffe/personal-website</a>
Show HN: I made my personal website a Pokémon-style minigame using Phaser 3
Repo here: <a href="https://github.com/ariroffe/personal-website" rel="nofollow">https://github.com/ariroffe/personal-website</a>
Show HN: A simple framework for writing a web scraper using Python decorators
Show HN: Sci-Hub Scholar – Firefox Extension Update v1.2
Sci-Hub Scholar is a browser extension that takes Google Scholar search results and tries to point them at Sci-Hub, where they can be read freely. The main selling point for this extension versus others is that it works right on the results page, rather than the article page at the pay-walled website.<p>One night, I decided I was going to do some research, only to find every Google Scholar Result behind a paywall. Trying to find the link on Sci-Hub, I ran into a bunch of issues:<p>* <a href="https://whereisscihub.now.sh/" rel="nofollow">https://whereisscihub.now.sh/</a> is down<p>* Annoying to copy/paste title to Sci-Hub<p>* Didn't want to be presented with another set of search results from my search results.<p>I've seen some other Sci-Hub extensions, most notably <a href="https://openaccessbutton.org/" rel="nofollow">https://openaccessbutton.org/</a>. However, most of these require you to go to the article page, then click the extension's button to finally be redirected. I thought I could do better.<p>I recently did an update pass to update with some features others have added and requested.<p>New Features for v1.2.0:<p>* Added support for all Google Subdomains. You can now use this extension on Google Scholar websites for any country!<p>* DOI is now listed next to the article title for articles where the DOI was found<p>* If the title's URL was updated to Sci-Hub, the Icon to the left will now point to the original article.<p>* More accurate DOI lookups!<p>Issues:<p>* Currently, I can't validate that the article exists on Sci-Hub, due to the way Firefox handles website permissions for extensions. I do not want to request permissions for all domains, but since the Sci-Hub domain can change, this is difficult.<p>* I'm afraid to port this to Chrome, because I don't want to lose my Google Account over something like this.<p>* It's a hassle to support manifest v2 for Firefox and manifest v3 for chrome. Haven't found a good workflow setup for developing for both browser platforms at the same time, from one codebase.<p>I welcome any feedback or recommendations on the issues. I also have planned features, and am open to contributions! The extension is all open source and can be found at: <a href="https://github.com/djfdat/sci-hub-scholar" rel="nofollow">https://github.com/djfdat/sci-hub-scholar</a><p>I hope this helps some people get access to the information they need!
Show HN: Graphsignal – Machine learning profiler for training and inference
Hi HN, I'm the founder of Graphsignal (<a href="https://graphsignal.com" rel="nofollow">https://graphsignal.com</a>). Graphsignal is a machine learning profiler. We've created it to make ML profiling simple and usable. It provides performance summaries, ML operation and kernel level statistics as well as detailed resource usage information necessary for making training and inference faster and more efficient.<p>Profilers help fix performance issues, improve user experience and reduce computation costs. Such improvements benefit machine learning profoundly; model training jobs that run for hours or days could be made much shorter and inference latency could be reduced resulting in significantly lower costs and improved user experience.<p>I realized the benefits in one of my previous projects, where the model would have to be trained regularly and be used for inference on huge amount of data. Having spent last decade developing profiling and monitoring tools, it seemed logical for me to use a profiler for the task. But since the training and inference were running remotely, I had a hard time using existing ML profilers.<p>TensorFlow and PyTorch provide built-in ML profilers, which utilize NVIDIA's profiling interface (CUPTI) under the hood for GPU profiling. One way to use those profilers is via locally installed TensorBoard or by logging the profiles.<p>In turn, Graphsignal Profiler (<a href="https://github.com/graphsignal/graphsignal" rel="nofollow">https://github.com/graphsignal/graphsignal</a>) uses the built-in profilers as well as other tools to enable automatic profiling in any environment, including notebooks, training pipelines, periodic batch jobs, model serving and so on, without installing additional servers/software. It also allows teams to share and collaborate online. Basically, the profiles along with environment and usage information are be automatically recorded and sent to Graphsignal where they are available for analysis.<p>Trying it out is easy: 1) sign up for a free account; 2) add the profiler to your ML code and run it; 3) see and analyze the profiles at graphsignal.com. Everything is described in the Quick Start Guide <a href="https://graphsignal.com/docs/profiler/quick-start/" rel="nofollow">https://graphsignal.com/docs/profiler/quick-start/</a>.<p>I'm very excited to show it to you here and will appreciate any thoughts, comments and feedback!
Show HN: Hubfs – File System for GitHub
Show HN: Hubfs – File System for GitHub
Show HN: Asmle – Wordle in 512 Bytes
Show HN: Asmle – Wordle in 512 Bytes
Show HN: Hide promoted tweets and sponsored content
Show HN: Hide promoted tweets and sponsored content
Show HN: Hide promoted tweets and sponsored content
Show HN: Google Maps Shadow Calculator
Show HN: Google Maps Shadow Calculator
Show HN: Google Maps Shadow Calculator
Show HN: Prepform – AI and spaced-repetition to optimize learning
Hi, I'm Eric and I'm the founder and lead developer of Prepform.<p>A high-quality education helped me pursue my interests and achieve my goals. I started Prepform so students of all backgrounds have access to the same kind of education.<p>I grew up in Southern California, surrounded by dozens of SAT prep programs, and I swear I must have gone to all of them. Different programs followed different styles and techniques, but the strategy they shared was to create a study plan and review mistakes.<p>A study plan is
taking a diagnostic test,<p>setting a target score,<p>creating a study schedule,<p>identifying mistakes, and finally<p>reviewing those mistakes.<p>I wanted to take this structure and optimize it with machine learning, while accounting for elements of human learning and memory.
I'm a big fan of SuperMemo, a memorization technique developed by Piotr Wozniak, where you review material just as you're about to forget it. Cognitive psychology tells us human forgetting follows a pattern, but Piotr quantified this behavior to identify the precise moment forgetting happens.<p>The goal was to build on his research with AI and tailor it to not only test prep but to the individual student, and make it the engine of the study plan.<p>The result is Blended Prep, which guides students to internalize knowledge rather than memorize material, and gives them the best chance to ace their next exam.<p>I'm so excited to share this with the HN community, and would love to know what you think. You can try it out at <a href="https://prepform.com" rel="nofollow">https://prepform.com</a>. Thanks for reading.
Show HN: Prepform – AI and spaced-repetition to optimize learning
Hi, I'm Eric and I'm the founder and lead developer of Prepform.<p>A high-quality education helped me pursue my interests and achieve my goals. I started Prepform so students of all backgrounds have access to the same kind of education.<p>I grew up in Southern California, surrounded by dozens of SAT prep programs, and I swear I must have gone to all of them. Different programs followed different styles and techniques, but the strategy they shared was to create a study plan and review mistakes.<p>A study plan is
taking a diagnostic test,<p>setting a target score,<p>creating a study schedule,<p>identifying mistakes, and finally<p>reviewing those mistakes.<p>I wanted to take this structure and optimize it with machine learning, while accounting for elements of human learning and memory.
I'm a big fan of SuperMemo, a memorization technique developed by Piotr Wozniak, where you review material just as you're about to forget it. Cognitive psychology tells us human forgetting follows a pattern, but Piotr quantified this behavior to identify the precise moment forgetting happens.<p>The goal was to build on his research with AI and tailor it to not only test prep but to the individual student, and make it the engine of the study plan.<p>The result is Blended Prep, which guides students to internalize knowledge rather than memorize material, and gives them the best chance to ace their next exam.<p>I'm so excited to share this with the HN community, and would love to know what you think. You can try it out at <a href="https://prepform.com" rel="nofollow">https://prepform.com</a>. Thanks for reading.