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We recently started watching Les Revenants on Netflix and, along with its lovely landscape shots, it has the most beautiful opening credits! It’s a must watch for sure!

shelovesyoublog:

My eyes are raining lololol 💻

shelovesyoublog:

My eyes are raining lololol 💻

louelladeville:

Real talk

louelladeville:

Real talk

It’s quick but the effect used on Crystal Reed at :05 and Holland Roden at :21 is a great little addition to the Teen Wolf opener!

How many steps does it take to author a DVD?

It takes A LOT. We’re trying to find a way to streamline our DVD authoring process and for the April release, it was quite painful. It was so painful that it was keeping me up at night. That is NOT allowed. I hate having something I can’t control control me like that. Every time I would close my eyes, I would see the assets map in my mind and imagine mistakes (where there weren’t any, btw) and experience sleepless night after sleepless night for thinking these damn things will NEVER get out in time. Sure enough, they got out the door but not without some errors in the assets themselves. How to we make this better?

Currently, we export out of Avid with a “Master” ProRes Quick Time, take that QT and put it into Sorenson Squeeze and set our destinations: DVD, iPod, web streaming, and whatever else we might think we need.
What sucks about this is that when there’s an error in the asset, like, say, a typo or missing lower thirds or something, we do the process all over again: ProRes QT, Squeeze DVD, iPod, web, etc.
Then there’s the asset refresh that sometimes throws my buttons off…
I say “sometimes” because for the first few asset refreshes, there were 1 or 2 (or 5) programs whose assets became unlinked with their menu buttons. That was what I like to call the “Great Button Disaster of January - February 2014”.
What contributed to that was the menus being changed more than once. I kept trying to put numbers and times on it: We have 9 programs with 4 menus each, which equals 45 menus total. It took me two days to design the first set of 45 menus and another 2 days to re-design them. Those were the worst days of it because DSP doesn’t like Photoshop’s layer styles but our menu design kinda needs it, so whenever menu text had to change, so did the PSD, which changed the button connections in DSP. I’m not sure what kept throwing the connections off because the layer numbers in the PSD didn’t change after the re-design. Needless to say, it was such a nightmare…

Anyways, now that that nightmare is over, I wanted to share ONE (out of NINE) DVD processes. I blurred out some crucial text since this isn’t supposed to be “live” until April but just imagine doing this nine times. It’s not that I’m new to the process or anything - I used to do it before (when there were only seven programs) but this is rough. Especially when DSP doesn’t want to burn from within. Oh no, it wants to crash during the last leg of the burn so an extra piece in the labyrinth includes finalizing the DSP project (connections, buttons, menus, assets, etc) and then making a disk image so that it burns properly through third-party burning software.

Yep, it doesn’t get anymore complicated than that! Or maybe it does? I’m sure it’s no walk in the park for even the most seasoned DVD Authors. I’m imaging my nightmares will become less and less with each round I do it. Here’s hoping…

theatlantic:

How Netflix Reverse Engineered Hollywood

If you use Netflix, you’ve probably wondered about the specific genres that it suggests to you. Some of them just seem so specific that it’s absurd. Emotional Fight-the-System Documentaries? Period Pieces About Royalty Based on Real Life? Foreign Satanic Stories from the 1980s?
If Netflix can show such tiny slices of cinema to any given user, and they have 40 million users, how vast did their set of “personalized genres” need to be to describe the entire Hollywood universe?
This idle wonder turned to rabid fascination when I realized that I could capture each and every microgenre that Netflix’s algorithm has ever created. 
Through a combination of elbow grease and spam-level repetition, we discovered that Netflix possesses not several hundred genres, or even several thousand, but 76,897 unique ways to describe types of movies.
There are so many that just loading, copying, and pasting all of them took the little script I wrote more than 20 hours. 
We’ve now spent several weeks understanding, analyzing, and reverse-engineering how Netflix’s vocabulary and grammar work. We’ve broken down its most popular descriptions, and counted its most popular actors and directors. 
To my (and Netflix’s) knowledge, no one outside the company has ever assembled this data before.
What emerged from the work is this conclusion: Netflix has meticulously analyzed and tagged every movie and TV show imaginable. They possess a stockpile of data about Hollywood entertainment that is absolutely unprecedented. The genres that I scraped and that we caricature above are just the surface manifestation of this deeper database.
Read more. [Image: @darth]

theatlantic:

How Netflix Reverse Engineered Hollywood

If you use Netflix, you’ve probably wondered about the specific genres that it suggests to you. Some of them just seem so specific that it’s absurd. Emotional Fight-the-System Documentaries? Period Pieces About Royalty Based on Real Life? Foreign Satanic Stories from the 1980s?

If Netflix can show such tiny slices of cinema to any given user, and they have 40 million users, how vast did their set of “personalized genres” need to be to describe the entire Hollywood universe?

This idle wonder turned to rabid fascination when I realized that I could capture each and every microgenre that Netflix’s algorithm has ever created. 

Through a combination of elbow grease and spam-level repetition, we discovered that Netflix possesses not several hundred genres, or even several thousand, but 76,897 unique ways to describe types of movies.

There are so many that just loading, copying, and pasting all of them took the little script I wrote more than 20 hours. 

We’ve now spent several weeks understanding, analyzing, and reverse-engineering how Netflix’s vocabulary and grammar work. We’ve broken down its most popular descriptions, and counted its most popular actors and directors. 

To my (and Netflix’s) knowledge, no one outside the company has ever assembled this data before.

What emerged from the work is this conclusion: Netflix has meticulously analyzed and tagged every movie and TV show imaginable. They possess a stockpile of data about Hollywood entertainment that is absolutely unprecedented. The genres that I scraped and that we caricature above are just the surface manifestation of this deeper database.

Read more. [Image: @darth]


Greta Gerwig (x)

“When I was 12, I realized that I would never be a professional dancer. In ballet, if your body is not right, it becomes self-flagellation, which is, I guess, its own pleasure, but that wasn’t enough for me. Before I quit, I played Clara in The Nutcracker with the Sacramento Ballet, which was a very big deal. My teacher said to me, ‘You know, they only gave you the part because of your personality.’ For the rest of my life, that tape has been in my head: I feel like everything I’ve achieved was because of my personality, rather than my ability.”

Greta Gerwig (x)

“When I was 12, I realized that I would never be a professional dancer. In ballet, if your body is not right, it becomes self-flagellation, which is, I guess, its own pleasure, but that wasn’t enough for me. Before I quit, I played Clara in The Nutcracker with the Sacramento Ballet, which was a very big deal. My teacher said to me, ‘You know, they only gave you the part because of your personality.’ For the rest of my life, that tape has been in my head: I feel like everything I’ve achieved was because of my personality, rather than my ability.”

mishawinsexster:

cinematography otps → kubrick + one point perspective