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Bloomberg – Sam Altman on ChatGPT’s First Two Years, Elon Musk and AI Under Trump
https://www.bloomberg.com/features/2025-sam-altman-interview
One of the strengths of that original OpenAI group was recruiting. Somehow you managed to corner the market on a ton of the top AI research talent, often with much less money to offer than your competitors. What was the pitch?
The pitch was just come build AGI. And the reason it worked—I cannot overstate how heretical it was at the time to say we’re gonna build AGI. So you filter out 99% of the world, and you only get the really talented, original thinkers. And that’s really powerful. If you’re doing the same thing everybody else is doing, if you’re building, like, the 10,000th photo-sharing app? Really hard to recruit talent.
OpenAI senior executives at the company’s headquarters in San Francisco on March 13, 2023, from left: Sam Altman, chief executive officer; Mira Murati, chief technology officer; Greg Brockman, president; and Ilya Sutskever, chief scientist. Photographer: Jim Wilson/The New York Times
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LG 45GX990A – The world’s first bendable gaming monitor
The monitor resembles a typical thin flat screen when in its home position, but it can flex its 45-inch body to 900R curvature in the blink of an eye.
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DMesh++ – An Efficient Differentiable Mesh for Complex Shapes
https://sonsang.github.io/dmesh2-project
An efficient differentiable mesh-based method that can effectively handle complex 2D and 3D shapes. For instance, it can be used for reconstructing complex shapes from point clouds and multi-view images.
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Nvidia unveils $3,000 desktop AI computer for home LLM researchers
https://arstechnica.com/ai/2025/01/nvidias-first-desktop-pc-can-run-local-ai-models-for-3000
https://www.nvidia.com/en-us/project-digits
Some smaller open-weights AI language models (such as Llama 3.1 70B, with 70 billion parameters) and various AI image-synthesis models like Flux.1 dev (12 billion parameters) could probably run comfortably on Project DIGITS, but larger open models like Llama 3.1 405B, with 405 billion parameters, may not. Given the recent explosion of smaller AI models, a creative developer could likely run quite a few interesting models on the unit.
DIGITS’ 128GB of unified RAM is notable because a high-power consumer GPU like the RTX 4090 has only 24GB of VRAM. Memory serves as a hard limit on AI model parameter size, and more memory makes room for running larger local AI models.
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Gaussian Splatting OFX plugin for Nuke
https://radiancefields.com/gaussian-splatting-in-nuke
https://aescripts.com/gaussian-splatting-for-nuke
Features
- Import .ply files in Nuke.
- Support Compressed .ply files from SuperSplat
- Crop with Spherical or Box shape.
- Crop with Y Plane.
- Combine up to 10 models in the scene.
- Colorize with Ramp using Spherical or Box shape.
- Reveal model with Opacity Ramp.
- Animate Splat Scale with Spherical or Box shape.
- Each model can be distorted with Noise.
- Render Depth Pass for 3D compose.
- Color correction for each model.
- Real-time with GPU
- Export scene
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ComfyUI + InstaID SDXL – Face and body swap tutorials
https://github.com/cubiq/ComfyUI_InstantID
https://github.com/cubiq/ComfyUI_InstantID/tree/main/examples
https://github.com/deepinsight/insightface
Unofficial version https://github.com/ZHO-ZHO-ZHO/ComfyUI-InstantID
Installation details under the post
(more…)
FEATURED POSTS
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TurboSquid move towards supporting AI against its own policies
https://www.turbosquid.com/ai-3d-generator
The AI is being trained using a mix of Shutterstock 2D imagery and 3D models drawn from the TurboSquid marketplace. However, it’s only being trained on models that artists have approved for this use.
People cannot generate a model and then immediately sell it. However, a generated 3D model can be used as a starting point for further customization, which could then be sold on the TurboSquid marketplace. However, models created using our generative 3D tool—and their derivatives—can only be sold on the TurboSquid marketplace.
TurboSquid does not accept AI-generated content from our artists
As AI-powered tools become more accessible, it is important for us to address the impact AI has on our artist community as it relates to content made licensable on TurboSquid. TurboSquid, in line with its parent company Shutterstock, is taking an ethically responsible approach to AI on its platforms. We want to ensure that artists are properly compensated for their contributions to AI projects while supporting customers with the protections and coverage issued through the TurboSquid license.In order to ensure that customers are protected, that intellectual property is not misused, and that artists’ are compensated for their work, TurboSquid will not accept content uploaded and sold on our marketplace that is generated by AI. Per our Publisher Agreement, artists must have proven IP ownership of all content that is submitted. AI-generated content is produced using machine learning models that are trained using many other creative assets. As a result, we cannot accept content generated by AI because its authorship cannot be attributed to an individual person, and we would be unable to ensure that all artists who were involved in the generation of that content are compensated.
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Types of AI Explained in a few Minutes – AI Glossary
1️⃣ 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗜) – The broadest category, covering automation, reasoning, and decision-making. Early AI was rule-based, but today, it’s mainly data-driven.
2️⃣ 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗠𝗟) – AI that learns patterns from data without explicit programming. Includes decision trees, clustering, and regression models.
3️⃣ 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗡𝗡) – A subset of ML, inspired by the human brain, designed for pattern recognition and feature extraction.
4️⃣ 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗗𝗟) – Multi-layered neural networks that drives a lot of modern AI advancements, for example enabling image recognition, speech processing, and more.
5️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 – A revolutionary deep learning architecture introduced by Google in 2017 that allows models to understand and generate language efficiently.
6️⃣ 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 (𝗚𝗲𝗻𝗔𝗜) – AI that doesn’t just analyze data—it creates. From text and images to music and code, this layer powers today’s most advanced AI models.
7️⃣ 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗣𝗿𝗲-𝗧𝗿𝗮𝗶𝗻𝗲𝗱 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 (𝗚𝗣𝗧) – A specific subset of Generative AI that uses transformers for text generation.
8️⃣ 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠) – Massive AI models trained on extensive datasets to understand and generate human-like language.
9️⃣ 𝗚𝗣𝗧-4 – One of the most advanced LLMs, built on transformer architecture, trained on vast datasets to generate human-like responses.
🔟 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 – A specific application of GPT-4, optimized for conversational AI and interactive use.
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VFX pipeline – Render Wall management topics
1: Introduction Title: Managing a VFX Facility’s Render Wall
- Briefly introduce the importance of managing a VFX facility’s render wall.
- Highlight how efficient management contributes to project timelines and overall productivity.
2: Daily Overview Title: Daily Management Routine
- Monitor Queues: Begin each day by reviewing render queues to assess workload and priorities.
- Resource Allocation: Allocate resources based on project demands and available hardware.
- Job Prioritization: Set rendering priorities according to project deadlines and importance.
- Queue Optimization: Adjust queue settings to maximize rendering efficiency.
3: Resource Allocation Title: Efficient Resource Management
- Hardware Utilization: Distribute rendering tasks across available machines for optimal resource usage.
- Balance Workloads: Avoid overloading specific machines while others remain underutilized.
- Consider Off-Peak Times: Schedule resource-intensive tasks during off-peak hours to enhance overall performance.
4: Job Prioritization Title: Prioritizing Rendering Tasks
- Deadline Sensitivity: Give higher priority to tasks with imminent deadlines to ensure timely delivery.
- Critical Shots: Identify shots crucial to the project’s narrative or visual impact for prioritization.
- Dependent Shots: Sequence shots that depend on others should be prioritized together.
5: Queue Optimization and Reporting Title: Streamlining Render Queues
- Dependency Management: Set up dependencies to ensure shots are rendered in the correct order.
- Error Handling: Implement automated error detection and requeueing mechanisms.
- Progress Tracking: Regularly monitor rendering progress and update stakeholders.
- Data Management: Archive completed renders and remove redundant data to free up storage.
- Reporting: Provide daily reports on rendering status, resource usage, and potential bottlenecks.
6: Conclusion Title: Enhancing VFX Workflow
- Effective management of a VFX facility’s render wall is essential for project success.
- Daily monitoring, resource allocation, job prioritization, queue optimization, and reporting are key components.
- A well-managed render wall ensures efficient production, timely delivery, and overall project success.
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HDR and Color
https://www.soundandvision.com/content/nits-and-bits-hdr-and-color
In HD we often refer to the range of available colors as a color gamut. Such a color gamut is typically plotted on a two-dimensional diagram, called a CIE chart, as shown in at the top of this blog. Each color is characterized by its x/y coordinates.
Good enough for government work, perhaps. But for HDR, with its higher luminance levels and wider color, the gamut becomes three-dimensional.
For HDR the color gamut therefore becomes a characteristic we now call the color volume. It isn’t easy to show color volume on a two-dimensional medium like the printed page or a computer screen, but one method is shown below. As the luminance becomes higher, the picture eventually turns to white. As it becomes darker, it fades to black. The traditional color gamut shown on the CIE chart is simply a slice through this color volume at a selected luminance level, such as 50%.
Three different color volumes—we still refer to them as color gamuts though their third dimension is important—are currently the most significant. The first is BT.709 (sometimes referred to as Rec.709), the color gamut used for pre-UHD/HDR formats, including standard HD.
The largest is known as BT.2020; it encompasses (roughly) the range of colors visible to the human eye (though ET might find it insufficient!).
Between these two is the color gamut used in digital cinema, known as DCI-P3.
sRGB
D65
