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Not once or twice have I heard things like, “Why should I follow this? It works just fine for me,” or, “I know what’s best for this project, why should I follow their conventions?”
It’s easy to ignore coding standards and conventions as unnecessary, especially when everything seems to be working well for you. But here’s the thing: a codebase with consistent styles and patterns isn’t just about personal preferences - it’s about creating an environment that’s easier to work with, understand, and maintain.
So, what’s the difference between coding standards and coding conventions? In simple terms, standards are formal rules that ensure code quality & consistency across projects, while conventions are agreed-upon practices for style and structure of a language or a project.
For example, a language convention in JavaScript or TypeScript is to use camelCase for variables, so you wouldn’t use kebab-case. On the other hand, a project convention might be to use camelCase for folder names, even though folder naming doesn't have a universal standard, unlike variables or functions. If the project adopts this convention, you should follow it.
An example for coding standards in action is using simple tools like ESLint (a linting tool that analyzes your code and provides feedback as you type) and Prettier (a formatter for consistent style). While these might seem trivial to you, they enforce coding standards and help catching common errors early, and it's much easier to review and maintain code that is formatted and well-organized.
But why does this matter? consistent code isn’t just about aesthetics - it makes the code easier to read & review. Imagine working on a large team where everyone uses their own styles. Reviewing code would become a nightmare, and onboarding new developers would take longer. Tools like linters catch issues early, and formatted code saves precious time during code reviews.
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In the end, following coding standards and conventions isn’t about limiting creativity, it’s about making collaboration easier and creating software that’s simpler to maintain and grow. Think of it as a shared language - one that makes sure everyone on the team is speaking the same "dialect". That’s how great software gets built.
- Artificial Intelligence (AI) is categorized into Narrow AI, General AI, and Super AI. Narrow AI specializes in one task like language translation. General AI is versatile and can learn and perform various tasks. Super AI conceptually outperforms human intelligence in all aspects. - AI models include Reactive machines (which don't form memories), Limited Memory models (that can 'remember' and utilize 'experience'), and Theory of Mind models (will understand emotions and thoughts; still under development). - AI applications span various sectors. In everyday life, we use AI via digital assistants like chatbots. In healthcare, AI aids early disease detection and resource management. In finance, AI helps detect fraud and guide investments. In robotics, AI enables robots to learn and adapt. - AI trends include self-learning technologies and deep learning, promising quicker, more reliable complex tasks. AI is forecasted to revolutionize search-engine technology, providing more accurate and personalized results. - The future of AI studies anticipates the exploration of General AI and Super AI.
- Android app development begins with Android Studio - a tool specifically tailored to help in code writing and debugging. - Create a project using templates to lay down foundational building blocks for your Android app. - Project files (activities, layouts, manifest files) serve different app structure purposes. - Modify the app user interface through Android Studio's Layout Editor and Attributes Panel for changes such as background color or padding. - Designing interface involves XML to structure your app’s view, and the combination of graphics and colors for final UI/UX. - Advanced topics include building apps for multiple screens, camera integration, and cloud services integration in apps. - Essential tools for Android app development include Android Studio, Android SDK for functions like GPS, data storage, and Firebase for aspects like data storage and user authentication. - Certifications in Android app development include Associate Android Developer Certification (tests ability to code android apps in Java or Kotlin), Google Play Store Listing Certificate (grants proficiency in managing your app in the Play Store), and Android Kotlin Developer Nanodegree (certifies proficiency in Kotlin for Android application development).
- Machine learning is a type of artificial intelligence that learns from data, whereas deep learning, a subset of machine learning, sorts data in layers for comprehensive analysis. - AI is technology that mimics human cognition, machine learning lets computer models learn from a data set, and deep learning uses neural networks to learn from large amounts of data. - Convolutional Neural Networks (CNNs) are crucial in both machine learning and deep learning. They enable image recognition in machine learning and help deep learning algorithms understand complex features in data. - Machine learning offers quick learning from limited data, like Spotify's music recommendations. Deep learning, utilized in complex tasks like self-driving cars, uses artificial neural networks to analyze large data sets. - The future of machine learning and deep learning is promising, with machine learning predicted to become more superior in deciphering complex data patterns and deep learning providing possibilities for processing large volumes of unstructured data.
