LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
featured
Apple ARKit
Made With ARKit
Google ARCore
Snap Art
Facebook AR Studio
SketchAR
AirMeasure
Shopify AR
Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
Apple ARKit PandasBased on our record, Pandas seems to be a lot more popular than Apple ARKit. While we know about 198 links to Pandas, we've tracked only 6 mentions of Apple ARKit. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Apple has quite nice page with docs at the bottom: https://developer.apple.com/augmented-reality/. Source: 11 months ago
Feels like you're grasping at straws to dismiss them. If you think lower weight, not-grainy MR, six years of a public AR SDK, far better computing units, and an existing high-quality software ecosystem are "not noticeable", I'm left wondering what you think is noticeable. Source: about 1 year ago
If you're looking to build a more advanced application, there are plenty of useful resources for all major technologies. For mobile apps, the best places to get started are docs for Google ARCore and Apple ARKit. Both platforms work with popular gaming engines like Unity and Unreal Engine. - Source: dev.to / over 2 years ago
ARKit is Apple's (A)ugmented (R)eality development (K)it. It takes the output from Unity and displays it in the goggles/headset the guy is wearing to see all this. Well, what a camera pointed at the display sees. Source: over 2 years ago
Google and Apple have already released their augmented reality development platforms, ARCore or ARKit, enabling the seamless integration of the digital and physical worlds. - Source: dev.to / over 2 years ago
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience. - Source: dev.to / 13 days ago
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method. - Source: dev.to / 7 days ago
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / 2 months ago
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 5 months ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
Made With ARKit - Hand-picked curation of the coolest stuff made with ARKit
NumPy - NumPy is the fundamental package for scientific computing with Python
Google ARCore - Google Augmented Reality SDK
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Snap Art - Snap's augmented reality platform
OpenCV - OpenCV is the world's biggest computer vision library