Search Results for "feature selection"

...The applets are structured with a focus on simplicity: each presents a prompt input, minimal UI logic, and inline display of the resulting output or widget (e.g. generated text, images). They are built to illustrate best practices (e.g. safety guards, prompt templates, streaming UI updates) rather than production feature sets. The repo supplies a CLI or script to scaffold new applet templates, letting developers spin up small Gemini-powered components quickly. Each applet includes configuration parameters (API keys, model selection, prompt parameters) in a secure but flexible format. Because applets are meant to be composable, they adopt a modular plugin architecture so you can integrate one into a Next.js, Flutter, or native app with minimal glue.

...It also allows to Crop and Compress the Image based on Aspect Ratio, Resolution, and Image Size. Almost 90% of the apps that I have developed have an Image upload feature. Along with the image selection, Sometimes I needed a crop feature for my profile image for that I've used uCrop. Most of the time I need to compress the image as the image captured from the camera is more than 5-10 MBs and sometimes we have a requirement to upload images with specific resolution/size, in that case, image compression is the way to go option. ...

QuoJS is a lightweight JavaScript library aimed at building mobile-first web interfaces with a focus on touch interactions and simple DOM utilities. It provides a compact, jQuery-like API for element selection, traversal, and manipulation, but trims the surface area to keep payloads small for mobile browsers. A core feature set centers on high-level touch gestures—such as tap, double-tap, swipe, pinch, and long-tap—abstracting away platform quirks so developers can attach handlers consistently across devices. The library embraces modern browser features and CSS3 where available, offering helpers that make common mobile UI patterns feel responsive and fluid. ...

DeepDream is a small, educational repository that accompanies Google’s original “Inceptionism” blog post by providing a runnable IPython/Jupyter notebook that demonstrates how to “dream” through a convolutional neural network. The notebook shows how to take a trained vision model and iteratively amplify patterns the network detects, producing the hallmark surreal, hallucinatory visuals. It walks through loading a pretrained network, selecting layers and channels to maximize, computing...