BREAKING NEWS
LATEST POSTS
-
Thomas Müller nv-tlabs GEN3C – 3D-Informed World-Consistent Video Generation with Precise Camera Control
https://github.com/nv-tlabs/GEN3C
Load a picture, define a camera path in 3D, and then render a photoreal video.
-
AI and the Law – Disney, NBCU sue Midjourney over copyright infringement
https://www.axios.com/2025/06/11/disney-nbcu-midjourney-copyright
Why it matters: It’s the first legal action that major Hollywood studios have taken against a generative AI company.
The complaint, filed in a U.S. District Court in central California, accuses Midjourney of both direct and secondary copyright infringement by using the studios’ intellectual property to train their large language model and by displaying AI-generated images of their copyrighted characters. -
ComfyRun – A fully open source and self-hosted solution to run your ComfyUI workflows at blazing fast speeds on cloud GPUs
https://github.com/punitda/ComfyRun
Best suited for individuals who want to
- Run complex workflows in seconds on the powerful GPUs like A10G, A100, and H100
- Experiment with any workflows you find across web without worrying about breaking your local ComfyUI environment
- Edit workflows on the go
-
Python Windows environment requirements vs apps and custom venv installs
Think of Python like a big toolkit of tools (the interpreter and all its libraries). On Windows, you need to install that toolkit in one place so the operating system knows “Here’s where Python lives.” Once that’s in place, each application can make its own little copy of the toolkit (a venv) to keep its dependencies separate. Here’s why this setup is necessary:
(more…) -
Google Stitch – Transform ideas into UI designs for mobile and web applications
https://stitch.withgoogle.com/
Stitch is available for free of charge with certain usage limits. Each user receives a monthly allowance of 350 generations using Flash mode and 50 generations using Experimental mode. Please note that these limits are subject to change.
FEATURED POSTS
-
What the Boeing 737 MAX’s crashes can teach us about production business – the effects of commoditisation
Airplane manufacturing is no different from mortgage lending or insulin distribution or make-believe blood analyzing software (or VFX?) —another cash cow for the one percent, bound inexorably for the slaughterhouse.
The beginning of the end was “Boeing’s 1997 acquisition of McDonnell Douglas, a dysfunctional firm with a dilapidated aircraft plant in Long Beach and a CEO (Harry Stonecipher) who liked to use what he called the “Hollywood model” for dealing with engineers: Hire them for a few months when project deadlines are nigh, fire them when you need to make numbers.” And all that came with it. “Stonecipher’s team had driven the last nail in the coffin of McDonnell’s flailing commercial jet business by trying to outsource everything but design, final assembly, and flight testing and sales.”
It is understood, now more than ever, that capitalism does half-assed things like that, especially in concert with computer software and oblivious regulators.
There was something unsettlingly familiar when the world first learned of MCAS in November, about two weeks after the system’s unthinkable stupidity drove the two-month-old plane and all 189 people on it to a horrific death. It smacked of the sort of screwup a 23-year-old intern might have made—and indeed, much of the software on the MAX had been engineered by recent grads of Indian software-coding academies making as little as $9 an hour, part of Boeing management’s endless war on the unions that once represented more than half its employees.
Down in South Carolina, a nonunion Boeing assembly line that opened in 2011 had for years churned out scores of whistle-blower complaints and wrongful termination lawsuits packed with scenes wherein quality-control documents were regularly forged, employees who enforced standards were sabotaged, and planes were routinely delivered to airlines with loose screws, scratched windows, and random debris everywhere.
Shockingly, another piece of the quality failure is Boeing securing investments from all airliners, starting with SouthWest above all, to guarantee Boeing’s production lines support in exchange for fair market prices and favorite treatments. Basically giving Boeing financial stability independently on the quality of their product. “Those partnerships were but one numbers-smoothing mechanism in a diversified tool kit Boeing had assembled over the previous generation for making its complex and volatile business more palatable to Wall Street.”
-
Embedding frame ranges into Quicktime movies with FFmpeg
QuickTime (.mov) files are fundamentally time-based, not frame-based, and so don’t have a built-in, uniform “first frame/last frame” field you can set as numeric frame IDs. Instead, tools like Shotgun Create rely on the timecode track and the movie’s duration to infer frame numbers. If you want Shotgun to pick up a non-default frame range (e.g. start at 1001, end at 1064), you must bake in an SMPTE timecode that corresponds to your desired start frame, and ensure the movie’s duration matches your clip length.
How Shotgun Reads Frame Ranges
- Default start frame is 1. If no timecode metadata is present, Shotgun assumes the movie begins at frame 1.
- Timecode ⇒ frame number. Shotgun Create “honors the timecodes of media sources,” mapping the embedded TC to frame IDs. For example, a 24 fps QuickTime tagged with a start timecode of 00:00:41:17 will be interpreted as beginning on frame 1001 (1001 ÷ 24 fps ≈ 41.71 s).
Embedding a Start Timecode
QuickTime uses a
tmcd
(timecode) track. You can bake in an SMPTE track via FFmpeg’s-timecode
flag or via Compressor/encoder settings:- Compute your start TC.
- Desired start frame = 1001
- Frame 1001 at 24 fps ⇒ 1001 ÷ 24 ≈ 41.708 s ⇒ TC 00:00:41:17
- FFmpeg example:
ffmpeg -i input.mov \ -c copy \ -timecode 00:00:41:17 \ output.mov
This adds a timecode track beginning at 00:00:41:17, which Shotgun maps to frame 1001.
Ensuring the Correct End Frame
Shotgun infers the last frame from the movie’s duration. To end on frame 1064:
- Frame count = 1064 – 1001 + 1 = 64 frames
- Duration = 64 ÷ 24 fps ≈ 2.667 s
FFmpeg trim example:
ffmpeg -i input.mov \ -c copy \ -timecode 00:00:41:17 \ -t 00:00:02.667 \ output_trimmed.mov
This results in a 64-frame clip (1001→1064) at 24 fps.