CHEAPUNO guide

How do I find valuable new app ideas amid the App Store surge?

Learn how to filter through the thousands of new app submissions to spot genuine market gaps and build tools that meet real user needs.

CHEAPUNO EditorialSources checked 8 10 月, 2026

Spotlight

PAGE: https://alison.com/course/building-machine-learning-app-interface

PROMISE: Map Keras models to iOS apps with Core ML for handwriting OCR features.

WHO: iOS developers who need to add on-device handwriting recognition features

WHYNOW: On-device inference with Core ML is now standard for privacy-focused mobile apps

DESC: This free computer programming course shows you how to use Keras, TensorFlow and Core ML to develop and build an optical character recognition app for iOS.

BEFORE: Stuck between Python ML concepts and iOS deployment requirements

BEFORE: Lacking a clear path from Keras training to Core ML integration

BEFORE: Unsure how to handle the OCR logic for handwritten input

AFTER: Connect Python ML concepts to specific iOS deployment requirements

AFTER: Follow a clear path from Keras training to Core ML integration

AFTER: Implement OCR logic for handwritten input within the app

GAIN: Ability to distinguish and choose between Keras and TensorFlow tools

GAIN: Knowledge of integrating machine learning specifically into iOS apps

GAIN: Understanding of the OCR process using OpenCV and Keras

STEP: You need to choose between a high-level API and a core framework for the project. || Distinguish between Keras and TensorFlow || You select the right tool for building the model architecture before starting code.

STEP: The environment setup for training the model feels uncertain or broken. || Classify images and how to install Anaconda with Keras || You have a working Python environment ready to process image data for training.

STEP: You must connect the trained model to an iOS app interface. || Explain the process of integrating machine learning into iOS apps || You deploy the model using Core ML within the iOS development workflow.

NOTFOR: Developers who already have a completed production-grade ML pipeline

NOTFOR: Users looking for backend API integration rather than on-device iOS ML

FACT: Average duration: 4-5 Hours (Contains Video)

FACT: CPD-accredited certificate available

FACT: Advanced level

COVERS: Distinguish between Keras and TensorFlow

COVERS: Explain how a convolutional neural network works

COVERS: Classify images and how to install Anaconda with Keras

COVERS: Describe the method to build an app that can recognize handwriting using Core ML

COVERS: Summarize how to perform OCR (optical character recognition) handwriting recognition using OpenCV, Keras, and TensorFlow

COVERS: Apply and set up the environment that enables you to draw on your screen

COVERS: Explain machine learning, and how it works

COVERS: Outline the uses of machine learning

COVERS: Explain the process of integrating machine learning into iOS apps

COVERS: Machine Learning

COVERS: Deep Learning

MODULE: Building A Convolutional Neutral Network || Use Of Keras And CNN Dataset | CNN Virtualization | Machine Learning Accuracy

MODULE: Building A Handwriting Recognition App || Writing Interface | Reading Metadata | ML Predictions

MODULE: Core Machine Learning Basics || Core Machine Learning Analysis | Interface Building | Image Cell and Helper File | Machine Learning Layout | ML Prediction And Photo Analysis

What you are probably seeing

You open your app research tools and see the submission volume skyrocketing, making it hard to distinguish established categories from noise. You spend hours scrolling through release notes but struggle to find patterns that indicate where actual user demand is growing versus where it is just saturated with new entrants.

Why it happens

The barrier to entry for mobile development has dropped significantly, leading to a flood of low-differentiation submissions that drown out high-quality concepts. This volume creates a signal-to-noise problem where trends are hard to track manually and emerging niches are often missed because they are buried under hundreds of similar entries.

What changed recently

2026-10-07 · best new apps for 2026
Appfigures Explorer is now tracking this surge for that gap:
• 557K new app submissions hit the App Store in 2025.
• New iOS app submissions increased by 24 percent from 2024.
• A developer built a human-like assistant for structure and motivation.
Source: autocomplete

Step by step

1. Use an analytics platform to filter new submissions by category and review their keywords to identify rising terms before they become mainstream. Focus your search on specific user problems rather than broad feature sets to find narrower market gaps. Read recent user reviews for top competing apps to uncover complaints that indicate unmet needs or friction points. Watch for developers launching companion tools or AI agents that solve structural or motivation problems for existing users. Set up alerts for specific search terms to monitor when new apps start ranking for those keywords.

2. Use an analytics platform to filter new submissions by category and review their keywords to identify rising terms before they become mainstream. Focus your search on specific user problems rather than broad feature sets to find narrower market gaps. Read recent user reviews for top competing apps to uncover complaints that indicate unmet needs or friction points. Watch for developers launching companion tools or AI agents that solve structural or motivation problems for existing users. Set up alerts for specific search terms to monitor when new apps start ranking for those keywords.

Ways to go further

Start here: Building a Machine Learning App Interface For when you want to add machine learning features to an app. Open the free course on Alison ↗

Better fit: Basics of iOS App Development For when you want to learn the basics of building an iOS app. Open the free course on Alison ↗

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Follow the evidence.

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