Show HN: Motivational Twitter Account https://ift.tt/2ErZ859

Show HN: Motivational Twitter Account https://twitter.com/cosmicsoulforge August 24, 2020 at 07:09AM

Show HN: Acorn – a back end design tool/low-code platform https://ift.tt/31l4OXM

Show HN: Acorn – a back end design tool/low-code platform https://ift.tt/3hny7P3 August 24, 2020 at 07:08AM

Show HN: Creating a web app that looks like an old operational system https://ift.tt/32hf6Hv

Show HN: Creating a web app that looks like an old operational system https://ift.tt/34sVeUC August 24, 2020 at 03:56AM

Show HN: VPN startup introduces new and unique features to VPN market https://ift.tt/3j8NNpA

Show HN: VPN startup introduces new and unique features to VPN market Hi everyone, We recently launched our VPN service into a stable release. Our VPN works differently to traditional VPN services. Some highlights are; 1. Automatic Regioning - Connect to a VPN exit-node and streaming services are unblocked from around the world automatically. There is no need to switch regions. 2. Custom DNS - Filter by Adult, Malware, Ads and Social Networks. This also allows you to set up your own custom block lists which you create. In addition to that, you can use your own DNS and allow the VPN to keep the DNS rules in place ( or not, depending on your needs ). 3. Device Profiles - Add up to 100 profiles to your account. This allows you to have set rules for every device you own. This is very useful for families with young children as well as adults who want to tweak their network setup. 4. Advanced Port Forwarding - Allows you to set the forwarded port, taking away the need to modify your applications. It also has a permanent URL to allow you to access your device regardless of which server you are connected to. This also caters for device profiles. If you would like to find out more about these features you can do so at www.oeck.com/manual/ In addition to all of those features the VPN is high-security. All of our hardware ( including the routers ) are owned by us. You can test out the service completely free for 6 hours ( no payment information required ). We would love to hear feedback and what you all think of it. URL - https://www.oeck.com/ Regards, Peter @ Oeck. August 24, 2020 at 03:31AM

Show HN: Boethius, smart flashcards for the classical liberal arts https://ift.tt/31p9wUt

Show HN: Boethius, smart flashcards for the classical liberal arts https://www.boethi.us/ August 24, 2020 at 01:50AM

Show HN: DrugSheet – Keep up with the clinical trials on Covid-19 https://ift.tt/32nrxBA

Show HN: DrugSheet – Keep up with the clinical trials on Covid-19 https://drugsheet.com/ August 24, 2020 at 12:33AM

Show HN: Strikr – Simple Remote Technical Interviews with Code Execution https://ift.tt/34nrngp

Show HN: Strikr – Simple Remote Technical Interviews with Code Execution https://strikr.co August 23, 2020 at 11:32PM

Show HN: An embeddable Lisp implemented in Rust, supporting native interop https://ift.tt/31rogCl

Show HN: An embeddable Lisp implemented in Rust, supporting native interop https://ift.tt/34paxxt August 23, 2020 at 09:40PM

Show HN: ASimpleGallery, a Python powered photo gallery website generator https://ift.tt/3j89mqk

Show HN: ASimpleGallery, a Python powered photo gallery website generator https://ift.tt/3hokcIj August 23, 2020 at 08:52PM

Show HN: Boook.link – Share a book with links to all stores https://ift.tt/31jAcG3

Show HN: Boook.link – Share a book with links to all stores https://boook.link August 23, 2020 at 07:27PM

Show HN: How we adapted our classrooms for videoconferencing https://ift.tt/3hnrKuN

Show HN: How we adapted our classrooms for videoconferencing https://ift.tt/3j1V9Lu August 23, 2020 at 05:35PM

Launch HN: Depict.ai (YC S20) - Product recommendations for any e-commerce store https://ift.tt/31mdoFN

Launch HN: Depict.ai (YC S20) - Product recommendations for any e-commerce store Hey there! We are Oliver and Anton, and are founders at Depict.ai. We help online stores challenge Amazon by building recommender systems that don't require any sales or behavioral data at all. Today, most recommender systems are based on a class of methods commonly called ‘collaborative filtering’ - which means that they generate recommendations based on a users’ past behavior. This method is successfully used by Amazon and Netflix (see the https://ift.tt/1O6ygl7 ). They are also very unsuccessfully used by smaller companies that lack the critical mass of historical behavioral data required to use those models effectively. This generally results in the cold start problem ( https://ift.tt/3l8qDS5... ) and a worse customer experience. We solve this by not focusing on understanding the customer but instead focus on understanding the product. The way we do this is with machine learning techniques that create vector representations of products based on the products’ images and descriptions, and recommend matching using these vector representations. More specifically, we have found a way to scrape the web and then train massive neural networks on e-commerce products. This makes it possible to leverage large amounts of product metadata to make truly impressive recommendations for any e-commerce store. One analogy we like is that just as almost no single company has enough sales or behavioral data to consistently predict, for instance, credit card frauds on their own, almost no e-commerce company has enough data to generate good recommendations based only on their own information. Stripe can make excellent fraud detection models by pooling transactions from many smaller companies, and we can do the same thing for personalizing e-commerce stores by pooling product metadata. Through A/B-tests we have proved that we can increase top-line revenue with 4-6% for almost any e-commerce store. To prove our value we offer the tests and setup 100% for free. We make money by taking a cut of the revenue uplift we generate in the A/B-tests. We have also found that the sales and decision cycle gets much shorter by being independent of customer's user data. You can see us live at Staples Nordics and kitchentime.com, among others. Oliver and I have several years of experience applying recommender systems within e-commerce and education respectively and felt uneasy about a winner-takes-it-all development where the largest companies could use their data supremacy to out-personalize any smaller company. Our goal is to build a company that can offer the best personalization to any e-commerce store, not just the ones with enough data. Do you think our approach seems interesting, crazy, lazy or somewhere in the middle? We’d love any feedback - please feel free to shoot us comments below or DM, we’ll be here to answer your thoughts and gather feedback! /Depict.ai-team August 23, 2020 at 05:05PM

Show HN: An Android launcher based purely on touch gestures https://ift.tt/2QhLe8j

Show HN: An Android launcher based purely on touch gestures https://ift.tt/31mxZd2 August 23, 2020 at 04:31PM

Show HN: Django REST Framework Boilerplate with JWT and Swagger https://ift.tt/2EqkVdu

Show HN: Django REST Framework Boilerplate with JWT and Swagger https://ift.tt/34pr8kJ August 23, 2020 at 01:34PM

Show HN: Pingr – Uptime Monitoring https://ift.tt/3jb3Jb7

Show HN: Pingr – Uptime Monitoring https://pingr.io August 23, 2020 at 01:31PM

Show HN: Shieldon 2.0 Released Today https://ift.tt/32kkuJQ

Show HN: Shieldon 2.0 Released Today https://ift.tt/34owHQt August 23, 2020 at 08:23AM