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VillageRoadShow production studio files for bankruptcy
Village Roadshow (prod company/financier: Wonka, the Matrix series, and Ocean’s 11) has filed for bankruptcy.
It’s a rough indicator of where we are in 2025 when one of the last independent production companies working with the studios goes under.
Here’s their balance sheet:
$400 M in library value of 100+ films (89 of which they co-own with Warner Bros.)
$500 M – $1bn total debt
$1.4 M in debt to WGA, whose members were told to stop working with Roadshow in December
$794 K owed to Bryan Cranston’s prod company
$250 K owed to Sony Pictures TV
$300 K/month overhead
The crowning expense that brought down this 36-year-old production company is the $18 M in (unpaid) legal fees from a lengthy and currently unresolved arbitration with their long-time partner Warner Bros, who they’ve had a co-financing arrangement since the late 90s.
Roadshow sued when WBD released their Matrix Resurrections (2021) film in theaters and on Max simultaneously, causing Roadshow to withhold their portion of the $190 M production costs.
Due to mounting financial pressures, Village Roadshow’s CEO, Steve Mosko, a veteran film and TV exec, left the company in January.
Now, this all falls on the shoulders of Jim Moore, CEO of Vine, an equity firm that owns Village Roadshow, as well as Luc Besson’s prod company EuropaCorp.
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Google Gemini Robotics
For safety considerations, Google mentions a “layered, holistic approach” that maintains traditional robot safety measures like collision avoidance and force limitations. The company describes developing a “Robot Constitution” framework inspired by Isaac Asimov’s Three Laws of Robotics and releasing a dataset unsurprisingly called “ASIMOV” to help researchers evaluate safety implications of robotic actions.
This new ASIMOV dataset represents Google’s attempt to create standardized ways to assess robot safety beyond physical harm prevention. The dataset appears designed to help researchers test how well AI models understand the potential consequences of actions a robot might take in various scenarios. According to Google’s announcement, the dataset will “help researchers to rigorously measure the safety implications of robotic actions in real-world scenarios.”
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Personalize Anything – For Free with Diffusion Transformer
https://fenghora.github.io/Personalize-Anything-Page
Customize any subject with advanced DiT without additional fine-tuning.
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Google Gemini 2.0 Flash new AI model extremely proficient at removing watermarks from images
Gemini 2.0 Flash won’t just remove watermarks, but will also attempt to fill in any gaps created by a watermark’s deletion. Other AI-powered tools do this, too, but Gemini 2.0 Flash seems to be exceptionally skilled at it — and free to use.
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Stability.ai – Introducing Stable Virtual Camera: Multi-View Video Generation with 3D Camera Control
Capabilities
Stable Virtual Camera offers advanced capabilities for generating 3D videos, including:
- Dynamic Camera Control: Supports user-defined camera trajectories as well as multiple dynamic camera paths, including: 360°, Lemniscate (∞ shaped path), Spiral, Dolly Zoom In, Dolly Zoom Out, Zoom In, Zoom Out, Move Forward, Move Backward, Pan Up, Pan Down, Pan Left, Pan Right, and Roll.
- Flexible Inputs: Generates 3D videos from just one input image or up to 32.
- Multiple Aspect Ratios: Capable of producing videos in square (1:1), portrait (9:16), landscape (16:9), and other custom aspect ratios without additional training.
- Long Video Generation: Ensures 3D consistency in videos up to 1,000 frames, enabling seamless
Model limitations
In its initial version, Stable Virtual Camera may produce lower-quality results in certain scenarios. Input images featuring humans, animals, or dynamic textures like water often lead to degraded outputs. Additionally, highly ambiguous scenes, complex camera paths that intersect objects or surfaces, and irregularly shaped objects can cause flickering artifacts, especially when target viewpoints differ significantly from the input images.
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Survivorship Bias: The error resulting from systematically focusing on successes and ignoring failures. How a young statistician saved his planes during WW2.
A young statistician saved their lives.
His insight (and how it can change yours):
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During World War II, the U.S. wanted to add reinforcement armor to specific areas of its planes.
Analysts examined returning bombers, plotted the bullet holes and damage on them (as in the image below), and came to the conclusion that adding armor to the tail, body, and wings would improve their odds of survival.
But a young statistician named Abraham Wald noted that this would be a tragic mistake. By only plotting data on the planes that returned, they were systematically omitting the data on a critical, informative subset: The planes that were damaged and unable to return.
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Photography basics: Lumens vs Candelas (candle) vs Lux vs FootCandle vs Watts vs Irradiance vs Illuminance
https://www.translatorscafe.com/unit-converter/en-US/illumination/1-11/
The power output of a light source is measured using the unit of watts W. This is a direct measure to calculate how much power the light is going to drain from your socket and it is not relatable to the light brightness itself.
The amount of energy emitted from it per second. That energy comes out in a form of photons which we can crudely represent with rays of light coming out of the source. The higher the power the more rays emitted from the source in a unit of time.
Not all energy emitted is visible to the human eye, so we often rely on photometric measurements, which takes in account the sensitivity of human eye to different wavelenghts
Details in the post
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