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498 by wuschel | 286 comments on News.
New best story on News: Ask HN: YouTube Channels for the Intellectually Curious
Ask HN: YouTube Channels for the Intellectually Curious
649 by maxutility | 281 comments on News.
As someone who is intellectually curious and has had great success finding articles on sites like Hacker News and blogs like kottke.org, I haven’t yet figured out where to find the most interesting content on YouTube. Let me know in the comments if you have found certain channels and creators to be particularly rewarding to follow, or if there are other ways you’ve found to consistently track down good content.
649 by maxutility | 281 comments on News.
As someone who is intellectually curious and has had great success finding articles on sites like Hacker News and blogs like kottke.org, I haven’t yet figured out where to find the most interesting content on YouTube. Let me know in the comments if you have found certain channels and creators to be particularly rewarding to follow, or if there are other ways you’ve found to consistently track down good content.
New best story on News: Ask HN: What are some cool but obscure data structures you know about?
Ask HN: What are some cool but obscure data structures you know about?
756 by Uptrenda | 360 comments on News.
I'm very interested in what types of interesting data structures are out there HN. Totally your preference. I'll start: bloom filters. Lets you test if a value is definitely NOT in a list of pre-stored values (or POSSIBLY in a list - with adjustable probability that influences storage of the values.) Good use-case: routing. Say you have a list of 1 million IPs that are black listed. A trivial algorithm would be to compare every element of the set with a given IP. The time complexity grows with the number of elements. Not so with a bloom filter! A bloom filter is one of the few data structures whose time complexity does not grow with the number of elements due to the 'keys' not needing to be stored ('search' and 'insert' is based on the number of hash functions.) Bonus section: Golomb Coded Sets are similar to bloom filters but the storage space is much smaller. Worse performance though.
756 by Uptrenda | 360 comments on News.
I'm very interested in what types of interesting data structures are out there HN. Totally your preference. I'll start: bloom filters. Lets you test if a value is definitely NOT in a list of pre-stored values (or POSSIBLY in a list - with adjustable probability that influences storage of the values.) Good use-case: routing. Say you have a list of 1 million IPs that are black listed. A trivial algorithm would be to compare every element of the set with a given IP. The time complexity grows with the number of elements. Not so with a bloom filter! A bloom filter is one of the few data structures whose time complexity does not grow with the number of elements due to the 'keys' not needing to be stored ('search' and 'insert' is based on the number of hash functions.) Bonus section: Golomb Coded Sets are similar to bloom filters but the storage space is much smaller. Worse performance though.
New best story on Hacker News: Ask HN: What are some cool but obscure data structures you know about?
Ask HN: What are some cool but obscure data structures you know about?
750 by Uptrenda | 359 comments on
I'm very interested in what types of interesting data structures are out there HN. Totally your preference. I'll start: bloom filters. Lets you test if a value is definitely NOT in a list of pre-stored values (or POSSIBLY in a list - with adjustable probability that influences storage of the values.) Good use-case: routing. Say you have a list of 1 million IPs that are black listed. A trivial algorithm would be to compare every element of the set with a given IP. The time complexity grows with the number of elements. Not so with a bloom filter! A bloom filter is one of the few data structures whose time complexity does not grow with the number of elements due to the 'keys' not needing to be stored ('search' and 'insert' is based on the number of hash functions.) Bonus section: Golomb Coded Sets are similar to bloom filters but the storage space is much smaller. Worse performance though.
750 by Uptrenda | 359 comments on
I'm very interested in what types of interesting data structures are out there HN. Totally your preference. I'll start: bloom filters. Lets you test if a value is definitely NOT in a list of pre-stored values (or POSSIBLY in a list - with adjustable probability that influences storage of the values.) Good use-case: routing. Say you have a list of 1 million IPs that are black listed. A trivial algorithm would be to compare every element of the set with a given IP. The time complexity grows with the number of elements. Not so with a bloom filter! A bloom filter is one of the few data structures whose time complexity does not grow with the number of elements due to the 'keys' not needing to be stored ('search' and 'insert' is based on the number of hash functions.) Bonus section: Golomb Coded Sets are similar to bloom filters but the storage space is much smaller. Worse performance though.
New best story on News: Ask HN: What are some cool but obscure data structures you know about?
Ask HN: What are some cool but obscure data structures you know about?
738 by Uptrenda | 353 comments .
I'm very interested in what types of interesting data structures are out there HN. Totally your preference. I'll start: bloom filters. Lets you test if a value is definitely NOT in a list of pre-stored values (or POSSIBLY in a list - with adjustable probability that influences storage of the values.) Good use-case: routing. Say you have a list of 1 million IPs that are black listed. A trivial algorithm would be to compare every element of the set with a given IP. The time complexity grows with the number of elements. Not so with a bloom filter! A bloom filter is one of the few data structures whose time complexity does not grow with the number of elements due to the 'keys' not needing to be stored ('search' and 'insert' is based on the number of hash functions.) Bonus section: Golomb Coded Sets are similar to bloom filters but the storage space is much smaller. Worse performance though.
738 by Uptrenda | 353 comments .
I'm very interested in what types of interesting data structures are out there HN. Totally your preference. I'll start: bloom filters. Lets you test if a value is definitely NOT in a list of pre-stored values (or POSSIBLY in a list - with adjustable probability that influences storage of the values.) Good use-case: routing. Say you have a list of 1 million IPs that are black listed. A trivial algorithm would be to compare every element of the set with a given IP. The time complexity grows with the number of elements. Not so with a bloom filter! A bloom filter is one of the few data structures whose time complexity does not grow with the number of elements due to the 'keys' not needing to be stored ('search' and 'insert' is based on the number of hash functions.) Bonus section: Golomb Coded Sets are similar to bloom filters but the storage space is much smaller. Worse performance though.
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