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Based on our record, Every Noice at Once seems to be a lot more popular than LittleSis. While we know about 422 links to Every Noice at Once, we've tracked only 23 mentions of LittleSis. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Https://littlesis.org/ LittleSis* is a free database of who-knows-who at the heights of business and government * opposite of Big Brother We're a grassroots watchdog network connecting the dots between the world's most powerful people and organizations. - Source: Hacker News / 9 months ago
Check the list then observe all the directors connections and investment history here. Source: 10 months ago
Theres a website that tries to track down who is connected to who and what companies sponsor certain politicians. unfotunately, ive never used it and can't say as to how accurate/biased/conspiratorial it is (they don't have info on politicians in my country) (its called littlesis.org for people who dont like clicking on links). Source: 11 months ago
Https://littlesis.org is a great site for seeing what are called "interlocking directorates". Source: 11 months ago
The more people eat fast food and binge drink the more they profit. Check the fraternal connections on some of the corporate teams and they all lead back to frats with a long history of abusing the country and pedophila etc. Source: about 1 year ago
I see this in https://everynoise.com/#updates > 2024-01-05 status update: With my layoff from Spotify on 2023-12-04, I lost the internal data-access required for ongoing updates to many parts of this site. Most of this, as a result, is now a static snapshot of what, for now, will be the final state from the site's 10-year history and evolution, hosted on my own server. Some pieces may get disabled and reenabled... - Source: Hacker News / 9 days ago
Anyone aware of a similar feature for foobar2000? I have an extensive library mostly tagged from Discogs, including release IDs. In theory, this should be sufficient to cluster music by genres, pull similar releases from Discogs "similar" feature and correlate data from https://everynoise.com. Obviously, in case of album mixed genres things will mix up, but I'm not sure there's a model that can correlate existing... - Source: Hacker News / 25 days ago
The article mentions Glenn McDonald's musical genre page (https://everynoise.com/, no longer refreshing with new Spotify data) as an example of a flexible graph-like exploration format, without being burdened by explicit connections. The author also has a thorough description of pros and cons of the general concept. - Source: Hacker News / 4 months ago
This is from Glenn McDonald's blog, founder of "Every Noise at Once". He was laid off from Spotify (discussed here briefly [0]) --- https://everynoise.com/ is now in "archival copy" mode [1][2]. Super sad to read / see this. [0] https://news.ycombinator.com/item?id=38650917 [2] https://twitter.com/EveryNoise/status/1736086849339244935. - Source: Hacker News / 5 months ago
Data exported using: https://benjaminbenben.com/lastfm-to-csv/ Album art compiled using: https://www.neverendingchartrendering.org/ Genre data compiled using: http://organizeyourmusic.playlistmachinery.com/# https://everynoise.com/ https://www.tunemymusic.com/transfer Gender, year and country of origin information manually compiled using Last.fm and wikipedia. Data analysis done in excel and image created in GIMP. Source: 5 months ago
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