https://www.colour-science.org/anders-langlands/
This page compares images rendered in Arnold using spectral rendering and different sets of colourspace primaries: Rec.709, Rec.2020, ACES and DCI-P3. The SPD data for the GretagMacbeth Color Checker are the measurements of Noburu Ohta, taken from Mansencal, Mauderer and Parsons (2014) colour-science.org.
Best alternatives to Adobe:
https://github.com/KenneyNL/Adobe-Alternatives
In software development, “technical debt” is a term used to describe the accumulation of shortcuts, suboptimal solutions, and outdated code that occur as developers rush to meet deadlines or prioritize immediate goals over long-term maintainability. While this concept initially seems abstract, its consequences are concrete and can significantly affect the security, usability, and stability of software systems.
Technical debt arises when software engineers choose a less-than-ideal implementation in the interest of saving time or reducing upfront effort. Much like financial debt, these decisions come with an interest rate: over time, the cost of maintaining and updating the system increases, and more effort is required to fix problems that stem from earlier choices. In extreme cases, technical debt can slow development to a crawl, causing future updates or improvements to become far more difficult than they would have been with cleaner, more scalable code.
One of the most significant threats posed by technical debt is the vulnerability it creates in terms of software security. Outdated code often lacks the latest security patches or is built on legacy systems that are no longer supported. Attackers can exploit these weaknesses, leading to data breaches, ransomware, or other forms of cybercrime. Furthermore, as systems grow more complex and the debt compounds, identifying and fixing vulnerabilities becomes increasingly challenging. Failing to address technical debt leaves an organization exposed to security risks that may only become apparent after a costly incident.
Technical debt also affects the user experience. Systems burdened by outdated code often become clunky and slow, leading to poor usability. Engineers may find themselves continuously patching minor issues rather than implementing larger, user-centric improvements. Over time, this results in a product that feels antiquated, is difficult to use, or lacks modern functionality. In a competitive market, poor usability can alienate users, causing a loss of confidence and driving them to alternative products or services.
Stability is another critical area impacted by technical debt. As developers add features or make updates to systems weighed down by previous quick fixes, they run the risk of introducing bugs or causing system crashes. The tangled, fragile nature of code laden with technical debt makes troubleshooting difficult and increases the likelihood of cascading failures. Over time, instability in the software can erode both the trust of users and the efficiency of the development team, as more resources are dedicated to resolving recurring issues rather than innovating or expanding the system’s capabilities.
While technical debt can provide short-term gains by speeding up initial development, the long-term costs are much higher. Unaddressed technical debt can lead to project delays, escalating maintenance costs, and an ever-widening gap between current code and modern best practices. The more technical debt accumulates, the harder and more expensive it becomes to address. For many companies, failing to pay down this debt eventually results in a critical juncture: either invest heavily in refactoring the codebase or face an expensive overhaul to rebuild from the ground up.
Technical debt is an unavoidable aspect of software development, but understanding its perils is essential for minimizing its impact on security, usability, and stability. By actively managing technical debt—whether through regular refactoring, code audits, or simply prioritizing long-term quality over short-term expedience—organizations can avoid the most dangerous consequences and ensure their software remains robust and reliable in an ever-changing technological landscape.
https://github.com/jedypod/debayer
The only required dependency is oiiotool. However other “debayer engines” are also supported.
https://depthcrafter.github.io/
We innovate DepthCrafter, a novel video depth estimation approach, by leveraging video diffusion models. It can generate temporally consistent long depth sequences with fine-grained details for open-world videos, without requiring additional information such as camera poses or optical flow.
Kiosk streamlines resource management. With tailored filtering, customizable organization, and seamless integration into Maya, Houdini, Blender and Cinema4D. Maintain one library for them all!
https://fabianstrube.gumroad.com/l/kiosk-library
This repository is a collection of simple USD projects. Each project shows off a single feature or group of USD features.
https://github.com/ColinKennedy/USD-Cookbook
These stubs are designed to be used with a type checker like mypy
to provide static type checking of python code, as well as to provide analysis and completion in IDEs like PyCharm and VSCode (with Pylance).
HTMLrev by Devluc provides free HTML website templates built with vanilla CSS, Bootstrap, Tailwind, React, Vue, Nextjs, Nuxt, Svelte, Gatsby, Laravel, Astro and more.
It connects Nuke with the ComfyUI server, any plugin that comes out in ComfyUI can be used in nuke, rotos with sam, rescaling, image generation, inpaintins, normal generator, the nodes are IPAdapter, ControlNet, AnimateDiff, Flux etc.
https://github.com/vinavfx/nuke_comfyui
https://www.rankred.com/convert-2d-images-to-3d/
https://world.hey.com/dhh/software-estimates-have-never-worked-and-never-will-a41a9c71
The fundamental problem is that as soon as a type of software development becomes so routine that it would be possible to estimate, it turns into a product or a service you can just buy rather than build.