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Show HN: BokehCamera – Blur webcam background photorealistically using RealSense
Show HN: BokehCamera – Blur webcam background photorealistically using RealSense
Show HN: BokehCamera – Blur webcam background photorealistically using RealSense
Show HN: BokehCamera – Blur webcam background photorealistically using RealSense
Show HN: Excel for Writers
Show HN: Excel for Writers
Show HN: Excel for Writers
Show HN: I made an app to draw stickers for iMessage, Discord, and Slack
Show HN: I made an app to draw stickers for iMessage, Discord, and Slack
Show HN: I made an app to draw stickers for iMessage, Discord, and Slack
Show HN: Blur Webcam Background on Linux
Show HN: Blur Webcam Background on Linux
Show HN: Blur Webcam Background on Linux
Show HN: Dotplan Online
Show HN: Pylectronics – Reproduce digital electronics in Python
Show HN: Pylectronics – Reproduce digital electronics in Python
Show HN: NLP Flashcards for Most of the Internet
Hello HN! We're Sam and Kanyes. We're building an extension to help you remember what you read online. We're calling it Ferret [1].<p>When you open Ferret on an HTML page, it generates recall-based questions + answers to reinforce key concepts with NLP. Consider the following toy example where we open Ferret on an explanation of Bayesian statistics. [2]<p>Q: What does the frequentist interpretation view probability as?
A: the limit of the relative frequency of an event after many trials<p>Q: What is often computed in Bayesian statistics using mathematical optimization methods?
A:The maximum a posteriori<p>We do this by (1) Parsing the DOM tree of an HTML page for <p> tags on the client, and segmenting these into preprocessed chunks (2) Performing inference on question-generation with a T5-base model pretrained on SQuAD (3) Extractive question-answering with the chunk & question we've generated with RoBERTa, also pretrained on SQuAD.<p>No GPT-3 here— where's the fun in an API call when you can do it yourself. Ferret is built as a React.JS app deployed as a chrome extension, with models hosted on AWS Sagemaker.<p>Finally, why could this be helpful?
Human memory is lossy. Psychologists have shown for forever that your memory can be modeled with a forgetting curve. If you don't attempt to retain knowledge, you'll likely lose it. But most of the content we read online (technical blog posts, documentation, course notes, articles) gets ingested and quickly forgotten. We're interested in low-friction approaches to helping people better remember this content , starting with fellow engineers who depend on their ability to remember key concepts to do the best job.<p>We've open-sourced the full repo and are actively responding to PRs + issues. [3]. You can read more about the technical + product challenges we faced if that interests you as well. [4]<p>We appreciate all feedback and suggestions!<p>[1]https://chrome.google.com/webstore/detail/ferret/mjnmolplinickaigofdpejfgfoehnlbh
[2] https://en.wikipedia.org/wiki/Bayesian_statistics<p>[3] https://github.com/kanyesthaker/qgqa-flashcards<p>[4] https://samgorman.notion.site/Ferret-c7508ec65df841859d1f84e518fcf21d
Show HN: NLP Flashcards for Most of the Internet
Hello HN! We're Sam and Kanyes. We're building an extension to help you remember what you read online. We're calling it Ferret [1].<p>When you open Ferret on an HTML page, it generates recall-based questions + answers to reinforce key concepts with NLP. Consider the following toy example where we open Ferret on an explanation of Bayesian statistics. [2]<p>Q: What does the frequentist interpretation view probability as?
A: the limit of the relative frequency of an event after many trials<p>Q: What is often computed in Bayesian statistics using mathematical optimization methods?
A:The maximum a posteriori<p>We do this by (1) Parsing the DOM tree of an HTML page for <p> tags on the client, and segmenting these into preprocessed chunks (2) Performing inference on question-generation with a T5-base model pretrained on SQuAD (3) Extractive question-answering with the chunk & question we've generated with RoBERTa, also pretrained on SQuAD.<p>No GPT-3 here— where's the fun in an API call when you can do it yourself. Ferret is built as a React.JS app deployed as a chrome extension, with models hosted on AWS Sagemaker.<p>Finally, why could this be helpful?
Human memory is lossy. Psychologists have shown for forever that your memory can be modeled with a forgetting curve. If you don't attempt to retain knowledge, you'll likely lose it. But most of the content we read online (technical blog posts, documentation, course notes, articles) gets ingested and quickly forgotten. We're interested in low-friction approaches to helping people better remember this content , starting with fellow engineers who depend on their ability to remember key concepts to do the best job.<p>We've open-sourced the full repo and are actively responding to PRs + issues. [3]. You can read more about the technical + product challenges we faced if that interests you as well. [4]<p>We appreciate all feedback and suggestions!<p>[1]https://chrome.google.com/webstore/detail/ferret/mjnmolplinickaigofdpejfgfoehnlbh
[2] https://en.wikipedia.org/wiki/Bayesian_statistics<p>[3] https://github.com/kanyesthaker/qgqa-flashcards<p>[4] https://samgorman.notion.site/Ferret-c7508ec65df841859d1f84e518fcf21d
Show HN: A C# library to help you enforce a Given-When-Then structured Unit test
I always strive to write better, clean and readable code. But I often find unit tests are hard to read, and especially harder to quickly identify what are the important pieces, or even what the test is testing about.<p>So I came up with this lightweight library to help enforce unit tests with a Given-When-Then structure. I hope you find this useful. Any feedback are welcome.<p>https://github.com/cobrakai-lab/Cobrakai.GWTUnit
Show HN: A C# library to help you enforce a Given-When-Then structured Unit test
I always strive to write better, clean and readable code. But I often find unit tests are hard to read, and especially harder to quickly identify what are the important pieces, or even what the test is testing about.<p>So I came up with this lightweight library to help enforce unit tests with a Given-When-Then structure. I hope you find this useful. Any feedback are welcome.<p>https://github.com/cobrakai-lab/Cobrakai.GWTUnit