Hit men aren't what you think
6 by tzs | 5 comments on Hacker News.
Friday, December 6, 2024
Thursday, December 5, 2024
Wednesday, December 4, 2024
New top story on Hacker News: Show HN: I combined spaced repetition with emails so you can remember anything
Show HN: I combined spaced repetition with emails so you can remember anything
15 by iskrataa | 3 comments on Hacker News.
Hey HN, I am a student shipping apps in my free time. This is my 4th for the year! Non-fic books and podcasts have been part of my life for years now but I always struggled with remembering what I’ve read or listened to. I wanted it to stick even after years. My notes list grew large but I never really revisited them. That’s why I created GinkgoNotes. You can enter notes you want to recall and leave it to the app to create a personalised (based on spaced repetition) email schedule. That means you’ll get your notes emailed to you a couple of times exactly when you should read them again (based on Ebbinghaus's Forgetting Curve) so it’s certain that you’ll remember them. I hope this will be helpful as it was for me. Would love some feedback! Iskren
15 by iskrataa | 3 comments on Hacker News.
Hey HN, I am a student shipping apps in my free time. This is my 4th for the year! Non-fic books and podcasts have been part of my life for years now but I always struggled with remembering what I’ve read or listened to. I wanted it to stick even after years. My notes list grew large but I never really revisited them. That’s why I created GinkgoNotes. You can enter notes you want to recall and leave it to the app to create a personalised (based on spaced repetition) email schedule. That means you’ll get your notes emailed to you a couple of times exactly when you should read them again (based on Ebbinghaus's Forgetting Curve) so it’s certain that you’ll remember them. I hope this will be helpful as it was for me. Would love some feedback! Iskren
Tuesday, December 3, 2024
Monday, December 2, 2024
Sunday, December 1, 2024
New top story on Hacker News: Show HN: Vicinity – Fast, Lightweight Nearest Neighbors with Flexible Back Ends
Show HN: Vicinity – Fast, Lightweight Nearest Neighbors with Flexible Back Ends
12 by Pringled | 0 comments on Hacker News.
We’ve just open-sourced Vicinity, a lightweight approximate nearest neighbors (ANN) search package that allows for fast experimentation and comparison of a larger number of well known algorithms. Main features: - Lightweight: the base package only uses Numpy - Unified interface: use any of the supported algorithms and backends with a single interface: HNSW, Annoy, FAISS, and many more algorithms and libraries are supported - Easy evaluation: evaluate the performance of your backend with a simple function to measure queries per second vs recall - Serialization: save and load your index for persistence After working with a large number of ANN libraries over the years, we found it increasingly cumbersome to learn the interface, features, quirks, and limitations of every library. After writing custom evaluation code to measure the speed and performance for the 100th time to compare libraries, we decided to build this as a way to easily use a large number of algorithms and libraries with a unified, simple interface that allows for quick comparison and evaluation. We are curious to hear your feedback! Are there any algorithms that are missing that you use? Any extra evaluation metrics that are useful?
12 by Pringled | 0 comments on Hacker News.
We’ve just open-sourced Vicinity, a lightweight approximate nearest neighbors (ANN) search package that allows for fast experimentation and comparison of a larger number of well known algorithms. Main features: - Lightweight: the base package only uses Numpy - Unified interface: use any of the supported algorithms and backends with a single interface: HNSW, Annoy, FAISS, and many more algorithms and libraries are supported - Easy evaluation: evaluate the performance of your backend with a simple function to measure queries per second vs recall - Serialization: save and load your index for persistence After working with a large number of ANN libraries over the years, we found it increasingly cumbersome to learn the interface, features, quirks, and limitations of every library. After writing custom evaluation code to measure the speed and performance for the 100th time to compare libraries, we decided to build this as a way to easily use a large number of algorithms and libraries with a unified, simple interface that allows for quick comparison and evaluation. We are curious to hear your feedback! Are there any algorithms that are missing that you use? Any extra evaluation metrics that are useful?