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Runway Aleph
https://runwayml.com/research/introducing-runway-aleph
Generate New Camera Angles
Generate the Next Shot
Use Any Style to Transfer to a Video
Change Environments, Locations, Seasons and Time of Day
Add Things to a Scene
Remove Things from a Scene
Change Objects in a Scene
Apply the Motion of a Video to an Image
Alter a Character’s Appearance
Recolor Elements of a Scene
Relight Shots
Green Screen Any Object, Person or Situation
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Mike Wong – AtoMeow – A Blue noise image stippling in Processing
https://github.com/mwkm/atoMeow
https://www.shadertoy.com/view/7s3XzX
This demo is created for coders who are familiar with this awesome creative coding platform. You may quickly modify the code to work for video or to stipple your own Procssing drawings by turning them into
PImageand run the simulation. This demo code also serves as a reference implementation of my article Blue noise sampling using an N-body simulation-based method. If you are interested in 2.5D, you may mod the code to achieve what I discussed in this artist friendly article.Convert your video to a dotted noise.
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Aitor Echeveste – Free CG and Comp Projection Shot, Download the Assets & Follow the Workflow
What’s Included:
- Cleaned and extended base plates
- Full Maya and Nuke 3D projection layouts
- Bullet and environment CG renders with AOVs (RGB, normals, position, ID, etc.)
- Explosion FX in slow motion
- 3D scene geometry for projection
- Camera + lensing setup
- Light groups and passes for look development
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Tauseef Fayyaz About readable code – Clean Code Practices
𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝗖𝗹𝗲𝗮𝗻 𝗖𝗼𝗱𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀:
🔹 Code Readability & Simplicity – Use meaningful names, write short functions, follow SRP, flatten logic, and remove dead code.
→ Clarity is a feature.
🔹 Function & Class Design – Limit parameters, favor pure functions, small classes, and composition over inheritance.
→ Structure drives scalability.
🔹 Testing & Maintainability – Write readable unit tests, avoid over-mocking, test edge cases, and refactor with confidence.
→ Test what matters.
🔹 Code Structure & Architecture – Organize by features, minimize global state, avoid god objects, and abstract smartly.
→ Architecture isn’t just backend.
🔹 Refactoring & Iteration – Apply the Boy Scout Rule, DRY, KISS, and YAGNI principles regularly.
→ Refactor like it’s part of development.
🔹 Robustness & Safety – Validate early, handle errors gracefully, avoid magic numbers, and favor immutability.
→ Safe code is future-proof.
🔹 Documentation & Comments – Let your code explain itself. Comment why, not what, and document at the source.
→ Good docs reduce team friction.
🔹 Tooling & Automation – Use linters, formatters, static analysis, and CI reviews to automate code quality.
→ Let tools guard your gates.
🔹 Final Review Practices – Review, refactor nearby code, and avoid cleverness in the name of brevity.
→ Readable code is better than smart code. -
Mark Theriault “Steamboat Willie” – AI Re-Imagining of a 1928 Classic in 4k
I ran Steamboat Willie (now public domain) through Flux Kontext to reimagine it as a 3D-style animated piece. Instead of going the polished route with something like W.A.N. 2.1 for full image-to-video generation, I leaned into the raw, handmade vibe that comes from converting each frame individually. It gave it a kind of stop-motion texture, imperfect, a bit wobbly, but full of character.
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Microsoft DAViD – Data-efficient and Accurate Vision Models from Synthetic Data
Our human-centric dense prediction model delivers high-quality, detailed (depth) results while achieving remarkable efficiency, running orders of magnitude faster than competing methods, with inference speeds as low as 21 milliseconds per frame (the large multi-task model on an NVIDIA A100). It reliably captures a wide range of human characteristics under diverse lighting conditions, preserving fine-grained details such as hair strands and subtle facial features. This demonstrates the model’s robustness and accuracy in complex, real-world scenarios.
https://microsoft.github.io/DAViD
The state of the art in human-centric computer vision achieves high accuracy and robustness across a diverse range of tasks. The most effective models in this domain have billions of parameters, thus requiring extremely large datasets, expensive training regimes, and compute-intensive inference. In this paper, we demonstrate that it is possible to train models on much smaller but high-fidelity synthetic datasets, with no loss in accuracy and higher efficiency. Using synthetic training data provides us with excellent levels of detail and perfect labels, while providing strong guarantees for data provenance, usage rights, and user consent. Procedural data synthesis also provides us with explicit control on data diversity, that we can use to address unfairness in the models we train. Extensive quantitative assessment on real input images demonstrates accuracy of our models on three dense prediction tasks: depth estimation, surface normal estimation, and soft foreground segmentation. Our models require only a fraction of the cost of training and inference when compared with foundational models of similar accuracy.
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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-timecodeflag 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.movThis 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.movThis results in a 64-frame clip (1001→1064) at 24 fps.
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Aider.chat – A free, open-source AI pair-programming CLI tool
Aider enables developers to interactively generate, modify, and test code by leveraging both cloud-hosted and local LLMs directly from the terminal or within an IDE. Key capabilities include comprehensive codebase mapping, support for over 100 programming languages, automated git commit messages, voice-to-code interactions, and built-in linting and testing workflows. Installation is straightforward via pip or uv, and while the tool itself has no licensing cost, actual usage costs stem from the underlying LLM APIs, which are billed separately by providers like OpenAI or Anthropic.
Key Features
- Cloud & Local LLM Support
Connect to most major LLM providers out of the box, or run models locally for privacy and cost control aider.chat. - Codebase Mapping
Automatically indexes all project files so that even large repositories can be edited contextually aider.chat. - 100+ Language Support
Works with Python, JavaScript, Rust, Ruby, Go, C++, PHP, HTML, CSS, and dozens more aider.chat. - Git Integration
Generates sensible commit messages and automates diffs/undo operations through familiar git tooling aider.chat. - Voice-to-Code
Speak commands to Aider to request features, tests, or fixes without typing aider.chat. - Images & Web Pages
Attach screenshots, diagrams, or documentation URLs to provide visual context for edits aider.chat. - Linting & Testing
Runs lint and test suites automatically after each change, and can fix issues it detects
- Cloud & Local LLM Support
FEATURED POSTS
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sRGB vs REC709 – An introduction and FFmpeg implementations
1. Basic Comparison
- What they are
- sRGB: A standard “web”/computer-display RGB color space defined by IEC 61966-2-1. It’s used for most monitors, cameras, printers, and the vast majority of images on the Internet.
- Rec. 709: An HD-video color space defined by ITU-R BT.709. It’s the go-to standard for HDTV broadcasts, Blu-ray discs, and professional video pipelines.
- Why they exist
- sRGB: Ensures consistent colors across different consumer devices (PCs, phones, webcams).
- Rec. 709: Ensures consistent colors across video production and playback chains (cameras → editing → broadcast → TV).
- What you’ll see
- On your desktop or phone, images tagged sRGB will look “right” without extra tweaking.
- On an HDTV or video-editing timeline, footage tagged Rec. 709 will display accurate contrast and hue on broadcast-grade monitors.
2. Digging Deeper
Feature sRGB Rec. 709 White point D65 (6504 K), same for both D65 (6504 K) Primaries (x,y) R: (0.640, 0.330) G: (0.300, 0.600) B: (0.150, 0.060) R: (0.640, 0.330) G: (0.300, 0.600) B: (0.150, 0.060) Gamut size Identical triangle on CIE 1931 chart Identical to sRGB Gamma / transfer Piecewise curve: approximate 2.2 with linear toe Pure power-law γ≈2.4 (often approximated as 2.2 in practice) Matrix coefficients N/A (pure RGB usage) Y = 0.2126 R + 0.7152 G + 0.0722 B (Rec. 709 matrix) Typical bit-depth 8-bit/channel (with 16-bit variants) 8-bit/channel (10-bit for professional video) Usage metadata Tagged as “sRGB” in image files (PNG, JPEG, etc.) Tagged as “bt709” in video containers (MP4, MOV) Color range Full-range RGB (0–255) Studio-range Y′CbCr (Y′ [16–235], Cb/Cr [16–240])
Why the Small Differences Matter
(more…) - What they are
