Spline Path Control is a simple tool designed to make it easy to create motion controls. It allows you to create and animate shapes that follow splines, and then export the result as a .webm video file. This project was created to simplify the process of generating control videos for tools like VACE. Use it to control the motion of anything (camera movement, objects, humans etc) all without extra prompting.
MiniMax-Remover is a fast and effective video object remover based on minimax optimization. It operates in two stages: the first stage trains a remover using a simplified DiT architecture, while the second stage distills a robust remover with CFG removal and fewer inference steps.
intelligence (AI) is likely to impact job salaries rather than eliminating jobs entirely. The primary argument is that AI will erode the skill premium traditionally commanded by high-skilled workers. This erosion happens through three key mechanisms:
Skill Premium on Specialized Tasks: AI enables low-skilled workers to perform tasks at a level comparable to high-skilled workers, making skilled workers more substitutable and reducing their wage premium.
Skill Premium on Learning Advantages: AI’s ability to continuously learn and improve from vast amounts of data threatens professions that rely on continuous learning and skill development. For example, in healthcare, AI can absorb and replicate the learning and expertise of doctors, diminishing their unique value.
Skill Premium on Managerial Advantages: AI agents can take over managerial tasks like planning and resource allocation, which have traditionally required human intervention. As AI becomes more sophisticated, even complex managerial roles might lose their premium as AI performs these functions more efficiently.
These factors collectively lead to a commoditization of skills, reducing the relative advantage and salary premium of traditionally high-skilled and managerial roles. The article emphasizes that while AI may not replace jobs outright, it will significantly affect how jobs are valued and compensated.
Spectral sensitivity of eye is influenced by light intensity. And the light intensity determines the level of activity of cones cell and rod cell. This is the main characteristic of human vision. Sensitivity to individual colors, in other words, wavelengths of the light spectrum, is explained by the RGB (red-green-blue) theory. This theory assumed that there are three kinds of cones. It’s selectively sensitive to red (700-630 nm), green (560-500 nm), and blue (490-450 nm) light. And their mutual interaction allow to perceive all colors of the spectrum.
While the human eye has red, green, and blue-sensing cones, those cones are cross-wired in the retina to produce a luminance channel plus a red-green and a blue-yellow channel, and it’s data in that color space (known technically as “LAB”) that goes to the brain. That’s why we can’t perceive a reddish-green or a yellowish-blue, whereas such colors can be represented in the RGB color space used by digital cameras.
The back of the retina is covered in light-sensitive neurons known as cone cells and rod cells. There are three types of cone cells, each sensitive to different ranges of light. These ranges overlap, but for convenience the cones are referred to as blue (short-wavelength), green (medium-wavelength), and red (long-wavelength). The rod cells are primarily used in low-light situations, so we’ll ignore those for now.
When light enters the eye and hits the cone cells, the cones get excited and send signals to the brain through the visual cortex. Different wavelengths of light excite different combinations of cones to varying levels, which generates our perception of color. You can see that the red cones are most sensitive to light, and the blue cones are least sensitive. The sensitivity of green and red cones overlaps for most of the visible spectrum.
Here’s how your brain takes the signals of light intensity from the cones and turns it into color information. To see red or green, your brain finds the difference between the levels of excitement in your red and green cones. This is the red-green channel.
To get “brightness,” your brain combines the excitement of your red and green cones. This creates the luminance, or black-white, channel. To see yellow or blue, your brain then finds the difference between this luminance signal and the excitement of your blue cones. This is the yellow-blue channel.
From the calculations made in the brain along those three channels, we get four basic colors: blue, green, yellow, and red. Seeing blue is what you experience when low-wavelength light excites the blue cones more than the green and red.
Seeing green happens when light excites the green cones more than the red cones. Seeing red happens when only the red cones are excited by high-wavelength light.
Here’s where it gets interesting. Seeing yellow is what happens when BOTH the green AND red cones are highly excited near their peak sensitivity. This is the biggest collective excitement that your cones ever have, aside from seeing pure white.
Notice that yellow occurs at peak intensity in the graph to the right. Further, the lens and cornea of the eye happen to block shorter wavelengths, reducing sensitivity to blue and violet light.
