Final trailer for Coyote vs. Acme: #CoyoteVsAcme #movies
The Right Wing: Do not hire based on skin color! You must hire the best person for the job!!!
Chris Nolan: Got it! I've cast my #odyssey movie based on who is best for the job.
The Right Wing: No, not like that!
#movies
When Matt Damon found out in this movie they don’t send people to help him get home and he has to help himself get home.
#movies #Odyssey
dbpedia_starring: DBpedia film-actor network
A bipartite network of movies and the actors that played in them, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 157184 nodes and 281396 edges.
Tags: Economic, Employment, Unweighted
https://network…
I think pairing Dragon: The Bruce Lee Story as a double feature with The Adventures of Priscilla, Queen of the Desert might make people less friendly towards that latter.
#Movies
moviegalaxies: Moviegalaxies, movies 410-466 (2018)
Social graphs for over 700 movies from the moviegalaxies.com website. Each node represents a character in a movie and each edge is a same-scene appearance between two characters in that movie. The weight gives the number of same-scene appearances. Networks are extracted from movie scripts automatically.
This network has 12 nodes and 42 edges.
Tags: Social, Fictional, Weighted
I will be using this insult everywhere now.
#TheOdyssey #meme #movies
Senator Mark Kelly, former astronaut, explains his top 3 space movies.
Do they overlap with your favorites?
#Space #MarkKelly #movies
I just can’t fathom why I would care whether an actor is male, or female, or trans, or whatever. I am here to see the movie and the characters in it….
If an actor is cast to be a character then it is probably because that seemed like a good fit to the director.
So… my job is really only to decide if I like the movie and the characters. Like every movie. 🤷♂️
(I have not seen the movie currently at the centre of the “controversy “)
#theOdyssey #transphobia #movies
movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
A profile of Sony CEO Hiroki Totoki, who aims to transform the company into a business focused on music, movies, video games, and the tech that underpins them (Jason Douglas/Wall Street Journal)
https://www.wsj.com/business/media/sony-ce
Stop naming your movies "Brick", we already have enough of those. Thank you.
Boots Riley must believe good movies have the same metric as bad code – WTFs per minute, https://www.osnews.com/story/19266/wtfsm/ – and i gotta say it really works for him.
Graduation Day (1981)
Speaking of graduations...
The new wave band Felony performs “Gangsters of Rock” at the 55min mark in this slasher movie. I love that band!
This film was made for about $250k but grossed more $24million 🤯
Oh and Vanna White makes an appearance.
#music #movies
2Guys_1Movie
We are just two guys that love to watch movies and talk about them...
Great Australian Pods Podcast Directory: #AusPods
movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
My kids wanted to watch a Blink-182 video ("ahaha they run naked") which led me to think about the preponderance of "college" in US culture.
College movies, college music, college as definition of success (even for dropouts) and so many other examples.
In other words, almost our entire world is broadly based on what male twentysomethings find ideal.
And we wonder why everything's going to shit?
Just read someone’s opinion that the Steam Machine is bad because it doesn’t “have a disc drive for movies and music”, and I’m wondering if they just woke up from a decade-long coma or something
Bei @… wird 'Good Luck to You, Leo Grande' als Komödie beschrieben. Ich sehe da eher die schmerzhafte Häutung einer Frau, die ein Leben lang in konservativen Rollenstereotypen gefangen war.
Sehr großartig, wie immer: Emma Thompson.
Jetzt online bei Eurer Bibliothek.
Sad to learn about the passing of Sam Neill. Who knows what my life would be like if I wasn't watching Event Horizon on one fateful evening in 2005? Still one of my favorite movies.
https://www.nbcnews.com/news/obituaries/sam-neill-ac…
Loved Backrooms. Found it cool as hell. One of the best flicks I've seen in a WHILE. Bizarre, all kinds of unexplained shit-- just everything I love about movies. Highly recommend.
#horror
As the implementation of AI disrupts dozens of industries,
it isn’t surprising that a crop of recent television shows and movies are depicting the villainy of this rapidly changing technology.
After all, screenwriters have been at the forefront of this fight.
“We were on the front lines as both the people who are being stolen from,
and the people whose work is being used to eliminate them,”
Carolyn Lipka, who co-wrote Hacks’ QuikScribbl episode, told The Flytr…
movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
The case to buy physical media: Sony is removing all purchased Studio Canal movies from users libraries - no refunds - including:
▫️ Terminator 2: Judgment Day
▫️ Apocalypse Now: Final Cut
▫️ Hot Fuzz
▫️ Total Recall
▫️ Rambo: First Blood
▫️ Paddington
▫️ Moonlight
▫️ Bridget Jones’s Diary
☑️ Sony Removes Hundreds of Purchased PlayStation Store Movies From Customer Libraries
Got movies on my Linux laptop. But my AirPods don't connect to the laptop and I want to enjoy the audio. So I'm installing JellyFin server now to stream to my iPad ... I love it when open systems allow me to solve problems on my own.
I love, how 'The Odyssey' referenced 'Just a Man' and the beginning of 'Polyphemus' from 'Epic: The Musical', btw 🙂↕️💕
#TheOdyssey #EpicTheMusical #Movies
movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
Watched #Idiocracy (2006) today for the first time while at the gym. It's one of these movies that are both great and horrible at the same time. It also kinda feels like a prophecy... 🥴
MINI MOVIE REVIEW
"Remarkably Bright Creatures"
This movie was like if a Hallmark or Lifetime movie became watchable and had a budget.
It's simple and fun, and Sally Field still has it.
My wife, as usual, told me I should read the book because it's much better.
#Movies #Netflix
🕰️ Those were the days!
Real animation done by a real human.
Today marks 20 YEARS since "Animator vs. Animation" by Alan Becker was posted to https://www.newgrounds.com/movies
RE: https://mastodon.gamedev.place/@protopop/117073920365882773
This is really beautiful work! 😍
Though as an aside, in the bigger picture: My general problem with this kind of realism in games & movies (and it's bringing tears to my eye…
I went down a rabbit hole of reading plot synopsis of gay porn movies. Don’t ask me how I got started on that… but I will say they are very creative.
🔊 #NowPlaying on #BBCRadio3:
#ClassicalMixtape
- Proms special: classical music and movies
The first of four Classical Mixtapes featuring music in this year's BBC Proms, including works by Rodrigo, LIli Boulanger, Mozart, and Ravel.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002yml5
🤚 Getting a 2160p screen to watch 2160p movies.
👉️ Getting a 2160p screen to watch 1080p movies in split-screen with working.
This might be my favorite movie review ever.
> One of the worst films I have a very screen. Terrible writing, acting and terrible dialogue.
#Movies
Man, I still remember, "Save the cheerleader. Save the World" very vividly. This is so sad.
☑️ Hayden Panettiere dead: Star of 'Heroes,' 'Nashville' was 36 - Los Angeles Times
https://www.latimes.com/entertainment-arts
I wonder if there is a correlation between the fact I’ve watched more horror movies since I’ve transitioned.
#trans
Unqualified Movie Reviews
Patrick and Benji review movies and tv shows...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/unqualified-movie-reviews/
moviegalaxies: Moviegalaxies, movies 410-466 (2018)
Social graphs for over 700 movies from the moviegalaxies.com website. Each node represents a character in a movie and each edge is a same-scene appearance between two characters in that movie. The weight gives the number of same-scene appearances. Networks are extracted from movie scripts automatically.
This network has 47 nodes and 219 edges.
Tags: Social, Fictional, Weighted
Cyril Cyril & Le Syndicat du Futur - "Le gros Hit" (2026)
If you know me, you know I love silly and absurd tv, movies, music, etc. So I was psyched to hear this groovy, energetic song from The Geneva-based experimental folk/jazz duo Cyril Cyril.
They literally handed the microphone over to their own kids and the result is a delightfully surreal, playful, and minimalist French indie-pop track.
The lyrics are 😂 and the deadpan delivery against the danceable m…
Hawaiian Airlines should rename themselves to "Audio Announcement in Progress" because this is the dialog passengers see no fewer than 20 times a flight - interrupting TV/movies, meals, sleep, etc.
And it's rarely safety-related. The mid-flight Hawaiian Air Miles Credit Card pitch is the icing on the cake.
#hawaiianairlines
moviegalaxies: Moviegalaxies, movies 410-466 (2018)
Social graphs for over 700 movies from the moviegalaxies.com website. Each node represents a character in a movie and each edge is a same-scene appearance between two characters in that movie. The weight gives the number of same-scene appearances. Networks are extracted from movie scripts automatically.
This network has 70 nodes and 299 edges.
Tags: Social, Fictional, Weighted
I haven’t seen the Odyssey movie yet, but this is an interesting commentary on it and the zeitgeist were in.
#movies
Apparently, I am sincerely asking a coworker "Who is Tom Holland?" years old. 👴
#movies
When the guy in movies won’t turn around so all you see is his juicy butt.
It's #LetterboxdFriday and I actually have a #Last4Watched this week. Didn't personally enjoy Twisted or Undertone.
#movies #film
Off to the local Limelight Cinema with a friend tomorrow morning (Tuesday) to see the new Star Wars movie. Tradition dictates coffee and cinnamon donuts for our film viewing snack.
#HalPriceTuesday #Movies #StarWars
dbpedia_starring: DBpedia film-actor network
A bipartite network of movies and the actors that played in them, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 157184 nodes and 281396 edges.
Tags: Economic, Employment, Unweighted
https://network…
I neeeeed to see the Mile End Kicks movie. Apparently they have lots of artists covering every song from Jagged Little Pill.
and a lot of the smaller artists I already know.
#music #movies
movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
Haven't been able to do as many movies lately but I've done a few, enough for a #LastFourWatched for #LetterboxdFriday this week. Worldbreaker I saw a week or two ago, it got criticism but it wasn't bad. Little Brother was hilarious, and I'm now over half done with Evil Dead Burn, I'v…
Finally watched Snakes on a Plane and loved it. Now I’m watching Encino Man. Enjoying some comedy movies. How is your Sunday?
movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
dbpedia_starring: DBpedia film-actor network
A bipartite network of movies and the actors that played in them, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 157184 nodes and 281396 edges.
Tags: Economic, Employment, Unweighted
https://network…
douban: Douban friendship network (2009)
A friendship network among users on Douban.com, a Chinese website providing recommendations for books, music, and movies.
This network has 154908 nodes and 327162 edges.
Tags: Social, Online, Unweighted
https://networks.skewed.de/net/douban…
moviegalaxies: Moviegalaxies, movies 410-466 (2018)
Social graphs for over 700 movies from the moviegalaxies.com website. Each node represents a character in a movie and each edge is a same-scene appearance between two characters in that movie. The weight gives the number of same-scene appearances. Networks are extracted from movie scripts automatically.
This network has 42 nodes and 109 edges.
Tags: Social, Fictional, Weighted
dbpedia_starring: DBpedia film-actor network
A bipartite network of movies and the actors that played in them, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 157184 nodes and 281396 edges.
Tags: Economic, Employment, Unweighted
https://network…
dbtropes_feature: Artistic works and their tropes
A bipartite network of artistic works (movies, novels, etc.) and their tropes (stylistic conventions or devices), as extracted from tvtropes.org. The date of this snapshot is uncertain.
This network has 152093 nodes and 3232134 edges.
Tags: Informational, Relatedness, Unweighted
https…
moviegalaxies: Moviegalaxies, movies 410-466 (2018)
Social graphs for over 700 movies from the moviegalaxies.com website. Each node represents a character in a movie and each edge is a same-scene appearance between two characters in that movie. The weight gives the number of same-scene appearances. Networks are extracted from movie scripts automatically.
This network has 32 nodes and 80 edges.
Tags: Social, Fictional, Weighted
douban: Douban friendship network (2009)
A friendship network among users on Douban.com, a Chinese website providing recommendations for books, music, and movies.
This network has 154908 nodes and 327162 edges.
Tags: Social, Online, Unweighted
https://networks.skewed.de/net/douban…
dbpedia_starring: DBpedia film-actor network
A bipartite network of movies and the actors that played in them, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 157184 nodes and 281396 edges.
Tags: Economic, Employment, Unweighted
https://network…
moviegalaxies: Moviegalaxies, movies 410-466 (2018)
Social graphs for over 700 movies from the moviegalaxies.com website. Each node represents a character in a movie and each edge is a same-scene appearance between two characters in that movie. The weight gives the number of same-scene appearances. Networks are extracted from movie scripts automatically.
This network has 50 nodes and 169 edges.
Tags: Social, Fictional, Weighted
douban: Douban friendship network (2009)
A friendship network among users on Douban.com, a Chinese website providing recommendations for books, music, and movies.
This network has 154908 nodes and 327162 edges.
Tags: Social, Online, Unweighted
https://networks.skewed.de/net/douban…
movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
dbpedia_starring: DBpedia film-actor network
A bipartite network of movies and the actors that played in them, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 157184 nodes and 281396 edges.
Tags: Economic, Employment, Unweighted
https://network…
dbtropes_feature: Artistic works and their tropes
A bipartite network of artistic works (movies, novels, etc.) and their tropes (stylistic conventions or devices), as extracted from tvtropes.org. The date of this snapshot is uncertain.
This network has 152093 nodes and 3232134 edges.
Tags: Informational, Relatedness, Unweighted
https…
moviegalaxies: Moviegalaxies, movies 410-466 (2018)
Social graphs for over 700 movies from the moviegalaxies.com website. Each node represents a character in a movie and each edge is a same-scene appearance between two characters in that movie. The weight gives the number of same-scene appearances. Networks are extracted from movie scripts automatically.
This network has 25 nodes and 91 edges.
Tags: Social, Fictional, Weighted
dbtropes_feature: Artistic works and their tropes
A bipartite network of artistic works (movies, novels, etc.) and their tropes (stylistic conventions or devices), as extracted from tvtropes.org. The date of this snapshot is uncertain.
This network has 152093 nodes and 3232134 edges.
Tags: Informational, Relatedness, Unweighted
https…
moviegalaxies: Moviegalaxies, movies 410-466 (2018)
Social graphs for over 700 movies from the moviegalaxies.com website. Each node represents a character in a movie and each edge is a same-scene appearance between two characters in that movie. The weight gives the number of same-scene appearances. Networks are extracted from movie scripts automatically.
This network has 38 nodes and 96 edges.
Tags: Social, Fictional, Weighted