Dario Amodei, CEO of Anthropic, envisions a future where AI systems are not only powerful but also aligned with human values. After leaving OpenAI, Amodei co-founded Anthropic to tackle the safety challenges of AI, aiming to create systems that are both intelligent and ethical. One of the key methods Anthropic employs is “Constitutional AI,” a training approach that instills AI models with a set of core principles derived from universally accepted documents like the United Nations Declaration of Human Rights.
GaiaNet is a decentralized computing infrastructure that enables everyone to create, deploy, scale, and monetize their own AI agents that reflect their styles, values, knowledge, and expertise. It allows individuals and businesses to create AI agents. Each GaiaNet node provides
a web-based chatbot UI.
an OpenAI compatible API. See how to use a GaiaNet node as a drop-in OpenAI replacement in your favorite AI agent app.
This grounding helps increase accuracy and reduce the common issue of AI-generated inaccuracies or “hallucinations.” This technique is commonly known as “Retrieval Augmented Generation”, or RAG.
LARS aims to be the ultimate open-source RAG-centric LLM application. Towards this end, LARS takes the concept of RAG much further by adding detailed citations to every response, supplying you with specific document names, page numbers, text-highlighting, and images relevant to your question, and even presenting a document reader right within the response window. While all the citations are not always present for every response, the idea is to have at least some combination of citations brought up for every RAG response and that’s generally found to be the case.
An open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K.
The new material provides an energy density—the amount that can be squeezed into a given space—of 1,000 watt-hours per liter, which is about 100 times greater than TDK’s current battery in mass production.
TDK has 50 to 60 percent global market share in the small-capacity batteries that power smartphones and is targeting leadership in the medium-capacity market, which includes energy storage devices and larger electronics such as drones.
Blender 3 updated Intel® Open Image Denoise to version 1.4.2 which improved many artifacts in render, even separating into passes, but still loses a lot of definition when used in standard mode, DENOISER COMP separates passes and applies denoiser only in the selected passes and generates the final pass (beauty) keeping much more definition as can be seen in the videos.
Sourcetree and GitHub Desktop are both free, GUI-based Git clients aimed at simplifying version control for developers. While they share the same core purpose—making Git more accessible—they differ in features, UI design, integration options, and target audiences.
One problem with sRGB is that in a gradient between blue and white, it becomes a bit purple in the middle of the transition. That’s because sRGB really isn’t created to mimic how the eye sees colors; rather, it is based on how CRT monitors work. That means it works with certain frequencies of red, green, and blue, and also the non-linear coding called gamma. It’s a miracle it works as well as it does, but it’s not connected to color perception. When using those tools, you sometimes get surprising results, like purple in the gradient.
There were also attempts to create simple models matching human perception based on XYZ, but as it turned out, it’s not possible to model all color vision that way. Perception of color is incredibly complex and depends, among other things, on whether it is dark or light in the room and the background color it is against. When you look at a photograph, it also depends on what you think the color of the light source is. The dress is a typical example of color vision being very context-dependent. It is almost impossible to model this perfectly.
I based Oklab on two other color spaces, CIECAM16 and IPT. I used the lightness and saturation prediction from CIECAM16, which is a color appearance model, as a target. I actually wanted to use the datasets used to create CIECAM16, but I couldn’t find them.
IPT was designed to have better hue uniformity. In experiments, they asked people to match light and dark colors, saturated and unsaturated colors, which resulted in a dataset for which colors, subjectively, have the same hue. IPT has a few other issues but is the basis for hue in Oklab.
In the Munsell color system, colors are described with three parameters, designed to match the perceived appearance of colors: Hue, Chroma and Value. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. Modern color spaces and models, such as CIELAB, Cam16 and Björn Ottosson own Oklab, are very similar in their construction.
By far the most used color spaces today for color picking are HSL and HSV, two representations introduced in the classic 1978 paper “Color Spaces for Computer Graphics”. HSL and HSV designed to roughly correlate with perceptual color properties while being very simple and cheap to compute.
Today HSL and HSV are most commonly used together with the sRGB color space.
One of the main advantages of HSL and HSV over the different Lab color spaces is that they map the sRGB gamut to a cylinder. This makes them easy to use since all parameters can be changed independently, without the risk of creating colors outside of the target gamut.
The main drawback on the other hand is that their properties don’t match human perception particularly well.
Reconciling these conflicting goals perfectly isn’t possible, but given that HSV and HSL don’t use anything derived from experiments relating to human perception, creating something that makes a better tradeoff does not seem unreasonable.
With this new lightness estimate, we are ready to look into the construction of Okhsv and Okhsl.
