Tag: ai

PixLab App UI/UX Now Available: Generate Mobile App Screens with AI

We’re excited to announce the public release of PixLab App UI/UX, a brand-new AI-powered platform that turns your screen design ideas into clean, production-ready code, instantly.

PixLab App UI/UX

From prompt to mobile UI code in seconds.

🚀 What is PixLab App UI/UX?

PixLab App UI/UX is an AI-assisted code generation tool that allows developers and designers to:

  • Describe a screen layout using natural language or a sketch
  • Instantly generate UI code for:
    • Flutter
    • React Native
    • Jetpack Compose
    • SwiftUI

This tool is perfect for rapid prototyping, wireframing, and bridging the gap between design and development.

How It Works

APP UX Frameworks

  1. Open the App: Visit app-ux.pixlab.io
  2. Write a Prompt: Example: “A login screen with email and password fields, a sign-in button, and a forgot password link”
  3. Pick Your Framework: Choose Flutter, React Native, Jetpack Compose, or SwiftUI.
  4. Get Code Instantly: The app outputs clean, reusable code ready to integrate into your mobile app.

✨ Key Features

  • Prompt-Based UI Generation
    Just describe what you want — no manual coding required to start.
  • Multi-Framework Support
    Code is generated for all major mobile app development frameworks.
  • Faster Prototyping
    Build high-fidelity mockups and POCs within minutes.
  • Developer Friendly
    Copy/paste directly into your project — fully readable and modifiable code.
  • Consistent Design Language
    Follows modern UI best practices and component conventions.

Who Is It For?

  • App developers needing a quick starting point
  • UI/UX designers looking to preview or validate screen ideas
  • Agencies & startups aiming to speed up MVP delivery
  • Educators & students exploring mobile design fundamentals

Try It Now

Final Thoughts

PixLab App UI/UX bridges the gap between design and code — giving creators the freedom to focus on ideas while we handle the heavy lifting.

Try it today, and let AI accelerate your mobile development workflow. Your next app screen is only one prompt away.

Explore the New PixLab Vision Platform VLM API Endpoints

PixLab’s Vision VLM Platform introduces a groundbreaking set of Vision Language Model (VLM) endpoints that combine natural language processing and computer vision in a simple, developer-friendly API suite.

PixLab Vision Platform

From querying images and parsing complex documents to generating PDFs and extracting ID data, the PixLab VLM API makes it easy to integrate intelligent image and document analysis into your own apps.

Vision Language Model Endpoints

These endpoints allow your application to understand images and video frames with natural language intelligence.

  • /query – Ask natural language questions about images and receive contextual answers
  • /describe – Get a natural language description of an image
  • /tagimg – Retrieve tags describing the image content
  • /detect – Detect and localize objects in the image
  • /vocr – OCR via vision models for printed text
  • /nsfw – Detect explicit content in media

Unstructured Document Parsing

Parse unstructured documents like invoices, receipts, and contracts using VLM-powered tools.

  • /llm-parse – Extract data from complex document layouts using a user-defined schema

Embedding APIs

Turn your content into machine-understandable vectors for search, indexing, and matching.

  • /txt-embed – Generate semantic embeddings from raw text
  • /img-embed – Generate vector embeddings from images

These endpoints are perfect for building your own AI-powered similarity search or recommendation systems.

Image Processing & Background Removal

PixLab also provides classic computer vision capabilities enhanced by AI.

  • /bg-remove – Remove background or unwanted objects from images
  • /docscan – Scan and extract JSON data from over 11,000 supported ID document types from 200+ countries
  • /nsfw – Pixelate or block NSFW content automatically

PDF Generation & Conversion

Create and manipulate PDF documents programmatically using these SDK-free endpoints:

  • /pdfgen – Generate media-rich PDFs from HTML or Markdown
  • /pdftoimg – Convert PDF files into image previews

LLM Tool Calling Infrastructure

PixLab provides built-in tools for enhancing your LLM pipeline:

  • /llm-tool – Get a list of tools callable from your LLM
  • /tool-call – Execute a tool call based on LLM output

These endpoints enable your LLM agent to execute functions dynamically and return results within the same context.

System Tools & Metadata

Helpful utility endpoints for checking server health and supported formats:

  • /status – View current system status
  • /about – Get PixLab version & license info
  • /extension – Retrieve supported file extensions

Explore the Full API Suite

PixLab offers over 150 RESTful endpoints for vision, media, and document automation tasks. Visit the following links to dive deeper:

Final Thoughts

Whether you're working on an AI productivity suite, eKYC onboarding, or document automation pipeline, PixLab’s VLM API delivers powerful, production-ready tools in minutes. All endpoints are accessible via secure HTTP requests and require no proprietary SDKs.

Get started by signing up for an API key at PixLab Console and explore what's possible with Vision Language Models.

Build smarter apps — faster.

Resolving Liveness Detection with FACEIO's Advanced AI Models

Liveness detection is a critical component in biometric security systems, ensuring that the face being scanned is from a live person rather than a photograph, video, or mask. FACEIO, a leader in facial authentication technology, addresses these challenges with its advanced AI models. This blog post explores how FACEIO resolves liveness detection issues, leveraging sophisticated algorithms and security practices.

FACEIO Landing page

What is Liveness Detection?

Liveness detection is a security feature used in facial recognition systems to verify that the scanned face is from a live individual. It prevents spoofing attacks by detecting natural facial movements and responses. Various techniques employed in liveness detection include:

  • Motion Analysis: Detects natural movements such as blinking and head movements.
  • Texture Analysis: Differentiates between the texture of live skin and that of a photograph or screen.
  • Challenge-Response: Prompts the user to perform actions like blinking or smiling to prove liveness.

Implementing Liveness Detection with FACEIO

FACEIO provides an easy-to-integrate solution for facial authentication with built-in liveness detection. Here is a step-by-step guide to activate liveness detection on your FACEIO Application:

  1. Setting Up FACEIO:

  2. Integrating FACEIO SDK:

  3. Initializing FACEIO:

  4. Implementing Liveness Detection:

    • Activate Face Anti-Spoofing first by connecting to the FACEIO Console, then Navigate to the SECURITY tab from the manager main view. Once the target application selected. Activate the Protect Against Deep-Fakes & Face Spoof Attempts security option as shown below:

      async function performLivenessCheck() {
 try {
     const response = await faceio.authenticate({ action: "liveness-check" });
     if (response.livenessScore > 0.9) {
         console.log("User authenticated successfully");
     } else {
         console.warn("Liveness check failed");
     }
 } catch (error) {
     console.error("Liveness check failed", error);
 }
}

Advanced Security Features of FACEIO

FACEIO's commitment to security extends beyond basic liveness detection. Here are some of the advanced features that enhance its security:

  • Multifactor Authentication (MFA): Combine facial recognition with other authentication methods for added security.
  • Customizable User Prompts: Tailor instructions and prompts shown to users during the liveness check.
  • Analytics and Reporting: Access detailed reports on authentication attempts to monitor and improve system performance.

Best Practices for Secure Implementation

FACEIO provides several security best practices to ensure robust protection against spoofing and other security threats:

  • Reject Weak PIN Codes: Ensure users create strong PIN codes during enrollment.
  • Prevent Duplicate Enrollment: Avoid multiple enrollments by the same user.
  • Protect Against Deepfakes: Use liveness detection to counteract spoofing attempts.
  • Forbid Minors: Prevent minors from enrolling in the application.
  • Always Ask for PIN Code: Require PIN code confirmation during authentication for added security.
  • Enforce PIN Code Uniqueness: Ensure each user's PIN code is unique.
  • Ignore Obscured Faces: Reject partially masked or poorly lit faces.
  • Reject Missing HTTP Headers: Prevent requests without proper origin or referer headers.
  • Restrict Domain and Country: Limit widget instantiation to specific domains and countries.
  • Enable Webhooks: Use webhooks for real-time updates on user interactions.

Conclusion

Integrating FACEIO for liveness detection and facial authentication in JavaScript significantly enhances digital security. Its robust API and user-friendly JavaScript library make it easy for developers to implement biometric authentication, preventing spoofing and unauthorized access. FACEIO's advanced features and best practices ensure both security and user experience are prioritized, making it a valuable addition to any web application's defense against modern threats.

For more information, visit the FACEIO website.

Published in: Level Up Coding

Launching FACEIO's New Age Verification Widget: Elevating Digital Experiences

PixLab is thrilled to announce the launch of FACEIO's pioneering Age Verification Widget, a remarkable addition to the FACEIO SDK suite, specifically designed for web and mobile platforms. This state-of-the-art widget provides real-time age verification, ensuring that users can access content suitable for their age group, thereby promoting a safer digital environment.

FACEIO Age Verification

Key Features and Benefits

  • Instant Verification: The FACEIO Widget verifies user age in milliseconds, using advanced facial analysis to swiftly differentiate minors from adults.
  • Enhanced Compliance: With precise age distinction, businesses can effortlessly adhere to regional and global age-related regulations, minimizing legal risks.
  • Seamless Integration: Developers can easily incorporate the widget into existing platforms with minimal effort, supported by comprehensive documentation available on our Integration Guide and Developer Guides.

A Tool for All Digital Arenas

Whether you're managing an online gaming site, a digital marketplace, or a content streaming service, the Age Verification Widget is your solution to maintaining age integrity online. It ensures that each user's experience is not only compliant with legal standards but also tailored to their age-specific needs.

Future-Proof Your Platform

As digital interactions become increasingly personalized and regulated, integrating robust age verification technology is more crucial than ever. The FACEIO Age Verification Widget is more than just a tool—it's an investment in your platform's future, safeguarding your operations and enhancing user trust.

For more details on how to get started, visit our detailed documentation provided in the Integration Guide and the Developer Guides.

New Gender/Age Classification Model Deployed

Here at PixLab, we recently deployed on production, a brand new gender/age classification model available to our customers via the FACEMOTION API endpoint.

gender age detection

  • The new model implementation is based on the ResNet-50 convolutional neural network (CNN) that is 50 layers deep. The network can easily classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals.

  • The reference, implementation paper is from: Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019 (https://arxiv.org/abs/1801.07698).

  • The Python/PHP code samples listed below should be able to easily output the age estimation, gender, and emotion pattern by just looking at the facial shape of any present human face in a given picture or video frame using our new classifcation model.

Python Code

  • FACEMOTION is the sole endpoint needed to perform such a task. It should output the rectangle coordinates for each detected human face that you can pass verbatim if desired to other processing endpoints like CROP or MOGRIFY plus the age estimation, gender and emotion pattern of the target face based on its facial shape.
  • Finally, all of our production ready, code samples are available to consult at our samples page or the PixLab Gihtub repository.

Announcing PixLab Annotate - An Online Image Annotation Tool

The PixLab Computer Vision team is pleased to announce the immediate availability of PixLab Annotate. A web based image annotation, labeling & segmentation tool for Machine Learning model training tasks and beyond...

Annotate Features Set:

  • Rectangle, Polygon, Zoom & Drag labeling tool.
  • Consistent JSON output accepted by most Machine Learning frameworks.
  • Optimized for instance segmentation (Mask R-CNN, etc).
  • Client-side persistent storage - No data transfer involved.
  • Persistent & easy label management (Create, Modify & Delete).
  • Full screen display & Snapshot capture.

Straightforward image segmentation and labeling thanks to the Rectangle & Polygon built-in tool!

At PixLab, we really believe that annotate is a great fit for data scientists, developers or students looking for a straightforward, online image segmentation and labeling tool for their daily machine learning model training tasks and beyond...

Annotate Homepage

Feature & Support Requests