#Boilerplate code

[intelliatech]

Top 10 ChatGPT Prompts For Software Developers

ChatGPT can do a lot more than just code creation and this blog post is going to be all about that. We have curated a list of ChatGPT prompts that will help software developers with their everyday tasks. ChatGPT can respond to questions and can compose codes making it a very helpful tool for software engineers.

While this AI tool can help developers with the entire SDLC (Software Development Lifecycle), it is important to understand how to use the prompts effectively for different needs.

Prompt engineering gives users accurate results. Since ChatGPT accepts prompts, we receive more precise answers. But a lot depends on how these prompts are formulated. 

To Get The Best Out Of ChatGPT, Your Prompts Should Be:

Clear and well-defined. The more detailed your prompts, the better suggestions you will receive from ChatGPT.

Specify the functionality and programming language. Not specifying what you exactly need might not give you the desired results.

Phrase your prompts in a natural language, as if asking someone for help. This will make ChatGPT understand your problem better and give more relevant outputs.

Avoid unnecessary information and ambiguity. Keep it not only to the point but also inclusive of all important details.

Top ChatGPT Prompts For Software Developers

Let’s quickly have a look at some of the best ChatGPT prompts to assist you with various stages of your Software development lifecycle.

1. For Practicing SQL Commands;

2. For Becoming A Programming Language Interpreter;

3. For Creating Regular Expressions Since They Help In Managing, Locating, And Matching Text.

4. For Generating Architectural Diagrams For Your Software Requirements.

Prompt Examples: I want you to act as a Graphviz DOT generator, an expert to create meaningful diagrams. The diagram should have at least n nodes (I specify n in my input by writing [n], 10 being the default value) and to be an accurate and complex representation of the given input. Each node is indexed by a number to reduce the size of the output, should not include any styling, and with layout=neato, overlap=false, node [shape=rectangle] as parameters. The code should be valid, bugless and returned on a single line, without any explanation. Provide a clear and organized diagram, the relationships between the nodes have to make sense for an expert of that input. My first diagram is: “The water cycle [8]”.  

5. For Solving Git Problems And Getting Guidance On Overcoming Them.

Prompt Examples: “Explain how to resolve this Git merge conflict: [conflict details].” 6. For Code generation- ChatGPT can help generate a code based on descriptions given by you. It can write pieces of codes based on the requirements given in the input. Prompt Examples: -Write a program/function to {explain functionality} in {programming language} -Create a code snippet for checking if a file exists in Python. -Create a function that merges two lists into a dictionary in JavaScript.  

7. For Code Review And Debugging: ChatGPT Can Review Your Code Snippet And Also Share Bugs.

Prompt Examples: -Here’s a C# code snippet. The function is supposed to return the maximum value from the given list, but it’s not returning the expected output. Can you identify the problem? [Enter your code here] -Can you help me debug this error message from my C# program: [error message] -Help me debug this Python script that processes a list of objects and suggests possible fixes. [Enter your code here]

8. For Knowing The Coding Best Practices And Principles: It Is Very Important To Be Updated With Industry’s Best Practices In Coding. This Helps To Maintain The Codebase When The Organization Grows.

Prompt Examples: -What are some common mistakes to avoid when writing code? -What are the best practices for security testing? -Show me best practices for writing {concept or function} in {programming language}.  

9. For Code Optimization: ChatGPT Can Help Optimize The Code And Enhance Its Readability And Performance To Make It Look More Efficient.

Prompt Examples: -Optimize the following {programming language} code which {explain the functioning}: {code snippet} -Suggest improvements to optimize this C# function: [code snippet] -What are some strategies for reducing memory usage and optimizing data structures? 

10. For Creating Boilerplate Code: ChatGPT Can Help In Boilerplate Code Generation.

Prompt Examples: -Create a basic Java Spring Boot application boilerplate code. -Create a basic Python class boilerplate code

11. For Bug Fixes: Using ChatGPT Helps Fixing The Bugs Thus Saving A Large Chunk Of Time In Software Development And Also Increasing Productivity.

Prompt Examples: -How do I fix the following {programming language} code which {explain the functioning}? {code snippet} -Can you generate a bug report? -Find bugs in the following JavaScript code: (enter code)  

12. Code Refactoring- ChatGPt Can Refactor The Code And Reduce Errors To Enhance Code Efficiency, Thus Making It Easier To Modify In The Future.

Prompt Examples –What are some techniques for refactoring code to improve code reuse and promote the use of design patterns? -I have duplicate code in my project. How can I refactor it to eliminate redundancy?  

13. For Choosing Deployment Strategies- ChatGPT Can Suggest Deployment Strategies Best Suited For A Particular Project And To Ensure That It Runs Smoothly.

Prompt Examples -What are the best deployment strategies for this software project? {explain the project} -What are the best practices for version control and release management?  

14. For Creating Unit Tests- ChatGPT Can Write Test Cases For You

Prompt Examples: -How does test-driven development help improve code quality? -What are some best practices for implementing test-driven development in a project? These were some prompt examples for you that we sourced on the basis of different requirements a developer can have. So whether you have to generate a code or understand a concept, ChatGPT can really make a developer’s life by doing a lot of tasks. However, it certainly comes with its own set of challenges and cannot always be completely correct. So it is advisable to cross-check the responses. Hope this helps. Visit us- Intelliatech

[nightmareslug]

you would think, of all possible usages of AI, coding would be like... the least offensive/most ethical. but github copilot does not recommend me useful code. it's not giving me code at all. it just auto filled my dialog with lines from undertale and tried to name the protag rose lalonde

[coratatum]

Fam, this is so important. Please please please send the copyright people a polite comment explaining your (hopefully pro-artist) stance.

Seriously. This matters. To a lot of people. And if you don't think your work will be impacted, it will. I've already seen companies in software allow use of AI to help generate code, and potentially using it as justification to not backfill positions when an engineer quits.

I promise, artists are the beginning. Anyone who produces anything that could one day conceivably be filtered through AI will want this in place.

The US Copyright Office is opening a public comment period around AI

American friends! The US Copyright Office (which we know exerts huuuge influence in how these things are treated elsewhere) wants to hear opinions on copyright and AI.

"The US Copyright Office is opening a public comment period around AI and copyright issues beginning August 30th as the agency figures out how to approach the subject."

We can assume that the opposing side will definitely be using all of their lobbying power towards widespread AI use, so this is a very good chance to let them know your thoughts on AI and how art and creative content of all kinds should be protected.

[pritivora26]

Efficiency and productivity are paramount in software development. Boilerplate code, which is pre-written code snippets that offer a skeleton structure for developers, can streamline such issues in the process. Boilerplate code is the pillar of scalability, maintainability, and collaboration. 

The boilerplate code helps in setting up the dependencies and performs routine tasks. In this blog, we discuss 13 benefits of using Boilerplate code in software development. read this blog.

[paper-mario-wiki]

I'd like it to be known that I was going to use Tumblr as the platform of choice for creating a boilerplate landing page website for my job, as my boss tasked me with creating a basic address to represent the company. We've already got a domain that we've been using for an email address for almost a year, and I know tumblr theme HTML coding pretty well, so I set up an account, but was unable to forward my custom domain. That's when I learned that custom domains MUST NOW BE BOUGHT DIRECTLY FROM TUMBLR.

YOU CAN NO LONGER CONNECT YOUR OWN DOMAIN THAT YOU ALREADY OWN TO YOUR BLOG.

Additionally, if you HAVE a custom domain and it lapses for any amount of time, forcing you to turn it off, you will NOT BE ABLE TO RECONNECT IT.

I will be using a different website instead.

@staff this was a terrible decision. nobody likes this.

I personally advise every one of my 132k followers, as well as anyone who sees and reblogs this post, to NOT purchase a domain from tumblr, as they are more expensive than purchasing from any other registrar, and you are restricted to using it on a single platform.

Change it back.

[waywardmartian]

<p><i>Harbourmaster</i> <a href="http://www.waywardmartian.com/harbourmaster13/048-001.html"><i>Hell Is Empty</i> page 001</a> is up.</p>

<p><a href="http://www.waywardmartian.com/harbourmaster13/048-001.html">Start at the beginning of the arc.</a><br>

<a href="http://www.waywardmartian.com/harbourmaster1/001-001.html">Start at the beginning of the series.</a></p>

<p><a href="http://www.waywardmartian.com/faq.html">I'd like to know what this series is supposed to be about before I start reading, please.</a></p>

<p><a href="https://www.patreon.com/waywardmartian?ty=h">Patreon, because what else do you expect from a tourist town?</a></p>

( Oh, come on, first DevArt, now tumblr doesn't let me use code? I have to use buttons like someone who doesn't know basic HTML? It takes longer with buttons when you've got a standard boilerplate! )

[playstationvii]

Jest: A Concept for a New Programming Language

Summary: "Jest" could be envisioned as a novel computer programming language with a focus on humor, playfulness, or efficiency in a specific domain. Its design might embrace creativity in syntax, a unique philosophy, or a purpose-driven ecosystem for developers. It could potentially bridge accessibility with functionality, making coding intuitive and enjoyable.

Definition: Jest: A hypothetical computer language designed with a balance of simplicity, expressiveness, and potentially humor. The name suggests it might include unconventional features, playful interactions, or focus on lightweight scripting with a minimalist approach to problem-solving.

Expansion: If Jest were to exist, it might embody these features:

Playful Syntax: Commands and expressions that use conversational, quirky, or approachable language. Example:

joke "Why did the loop break? It couldn't handle the pressure!"; if (laughs > 0) { clap(); }

Efficiency-Focused: Ideal for scripting, rapid prototyping, or teaching, with shortcuts that reduce boilerplate code.

Modular Philosophy: Encourages user-created modules or libraries, reflecting its playful tone with practical use cases.

Integrated Humor or Personality: Built-in error messages or prompts might be witty or personalized.

Flexibility: Multi-paradigm support, including functional, procedural, and object-oriented programming.

Transcription: An example code snippet for a Jest-like language:

// Hello World in Jest greet = "Hello, World!"; print(greet); laugh();

A Jest program that calculates Fibonacci numbers might look like this:

// Fibonacci in Jest fib = (n) => n < 2 ? n : fib(n-1) + fib(n-2);

joke "What's the Fibonacci sequence? You'll love it, it grows on you!"; n = 10; print("The Fibonacci number at", n, "is:", fib(n));

Potential Domains:

Gamified education

Creative industries

AI-driven storytelling

Interactive debugging

Would you like me to refine or explore additional aspects?

Certainly! If we were to imagine Jest as the brainchild of a creative coder or team, their portfolio would likely include other innovative or experimental programming languages. Let’s expand on this concept and invent some plausible complementary languages the same inventor might have designed.

Related Languages by the Inventor of Jest

Pantomime

Description: A visual programming language inspired by gesture and movement, where users "drag and drop" symbols or create flowcharts to express logic. Designed for non-coders or children to learn programming through interaction.

Key Features:

Icon-based syntax: Conditional loops, variables, and functions represented visually.

Works seamlessly with Jest for creating visual representations of Jest scripts.

Sample Code (Visual Representation): Flowchart blocks: Input → Decision → Output.

Facet

Description: A declarative programming language focusing on creativity and modularity, aimed at artists, designers, and 3D modelers. Facet could integrate well with game engines and creative suites like Blender or Unity.

Key Features:

Embedded visual tools for shaders, animations, and simulations.

Simplified expressions for creative coding (e.g., animations and generative art).

Sample Code:

shape = circle(radius: 10, color: "blue"); animation = wave(shape, amplitude: 5, frequency: 2); render(animation, duration: 10s);

Quip

Description: A lightweight scripting language built for humor, similar to Jest, but more focused on natural language processing, chatbots, and interactive storytelling.

Key Features:

Syntax mirrors conversational English.

Designed for AI integration, especially in storytelling apps.

Sample Code:

character "Alice" says "Hello! What's your name?"; user_input -> name; character "Alice" says "Nice to meet you, {name}!";

Cryptic

Description: A language for designing cryptography algorithms and secure communications. Aimed at developers interested in blockchain, encryption, or cybersecurity.

Key Features:

High-level abstractions for cryptographic primitives.

Secure-by-design philosophy.

Sample Code:

key = generate_key(algorithm: "AES", length: 256); encrypted_message = encrypt("Hello, World!", key); print(encrypted_message);

Harmony

Description: A reactive programming language for building interactive user interfaces, designed to be highly interoperable with Jest and other tools in the suite. Perfect for web developers and app creators.

Key Features:

Two-way data binding with minimal boilerplate.

Component-based architecture.

Sample Code:

component Button { state: clicked = false; onClick() { clicked = !clicked; alert("Button clicked!"); } } render(Button);

Inventor’s Vision

The coder or team behind Jest and these complementary languages would likely be passionate about:

Accessibility: Making programming intuitive and fun for people of all skill levels.

Playfulness: Infusing coding with creativity, humor, and expressiveness.

Interoperability: Ensuring all their languages work well together for larger projects.

Would you like me to dive deeper into one of these languages, conceptualize others, or expand Jest further?

Expanded Concepts and Bridge Languages

Pantomime

Expansion: Adds support for AR/VR interfaces where users use gestures to program logic in 3D space. For example, drawing a loop physically creates a repetitive cycle.

Bridge Language: GestureScript: Converts AR/VR gestures from Pantomime into traditional code for broader applications.

Facet

Expansion: Enhanced for generative AI tools, allowing users to define complex artistic styles and AI-driven procedural designs.

Bridge Language: PixelForge: Focused on exporting Facet code into game engines, enabling real-time rendering pipelines for interactive environments.

Quip

Expansion: Includes pre-trained AI models for dynamic storytelling, allowing Quip scripts to evolve based on user interaction.

Bridge Language: Narrate: Combines Quip scripts with voice-to-text technologies for live storytelling in games or educational tools.

Cryptic

Expansion: Introduces quantum-safe cryptography primitives and allows simulation of blockchain-like ecosystems for testing purposes.

Bridge Language: CipherBridge: Translates Cryptic code into various blockchain frameworks like Ethereum or Solana.

Harmony

Expansion: Gains support for cross-platform development, allowing users to write code for web, mobile, and IoT devices simultaneously.

Bridge Language: Symphony: Converts Harmony components into platform-native scripts for seamless integration.

Additional Bridge Languages

Echo: Bridges Quip and Jest, enabling storytelling code to trigger functional scripts for immersive experiences.

Morph: A universal language that compiles any Jest family code into executable binaries for specific operating systems.

Thread: Integrates Cryptic and Harmony to build secure, reactive user interfaces for fintech applications.

Canvas: Bridges Facet and Pantomime to create interactive art in AR/VR, blending visual coding with gesture inputs.

Let me know if you'd like a deeper dive into any specific language or bridge concept!

Here's a detailed exploration of Echo, Morph, Thread, Canvas, and all previously mentioned languages, emphasizing their unique applications and advanced functionalities.

Expanded Bridge Languages

Echo

Purpose: Links storytelling elements in Quip with functional programming in Jest, enabling dynamic, immersive interactions between narrative and logic.

Key Features:

Story-driven logic triggers: e.g., a character’s dialogue prompts a database query or API call.

Integration with AI tools for real-time responses.

Use Case: AI-driven chatbots that incorporate both storytelling and complex backend workflows.

Sample Code:

story_event "hero_arrives" triggers fetch_data("weather"); response = "The hero enters amidst a storm: {weather}.";

Morph

Purpose: Acts as a meta-compiler, translating any language in the Jest ecosystem into optimized, platform-specific binaries.

Key Features:

Universal compatibility across operating systems and architectures.

Performance tuning during compilation.

Use Case: Porting a Jest-based application to embedded systems or gaming consoles.

Sample Code:

input: Facet script; target_platform: "PS7"; compile_to_binary();

Thread

Purpose: Combines Cryptic's security features with Harmony's reactive architecture to create secure, interactive user interfaces.

Key Features:

Secure data binding for fintech or healthcare applications.

Integration with blockchain for smart contracts.

Use Case: Decentralized finance (DeFi) apps with intuitive, safe user interfaces.

Sample Code:

bind secure_input("account_number") to blockchain_check("balance"); render UI_component(balance_display);

Canvas

Purpose: Fuses Facet's generative design tools with Pantomime's gesture-based coding for AR/VR art creation.

Key Features:

Real-time 3D design with hand gestures.

Multi-modal export to AR/VR platforms or 3D printers.

Use Case: Collaborative VR environments for designers and architects.

Sample Code:

gesture: "draw_circle" → create_3D_shape("sphere"); gesture: "scale_up" → modify_shape("sphere", scale: 2x); render(scene);

Deep Dive into Main Languages

Jest

Philosophy: A playful, expressive scripting language with versatile syntax tailored for humor, experimentation, and creativity.

Core Application: Writing scripts that blend functional programming with a whimsical, human-readable style.

Pantomime

Expanded Features: Includes AI-driven gesture recognition for predictive modeling and natural motion capture.

Advanced Use: Developing AR apps for rehabilitation therapy where exercises are coded as gestures.

Facet

Advanced Use: Procedural generation in games, defining terrains, weather systems, and NPC behaviors with artistic flair.

Quip

Advanced Features: Integrates pre-trained language models for nuanced, character-driven dialogue trees.

Advanced Use: Creating adaptive narratives in games where user decisions alter the story arc.

Cryptic

Advanced Features: Quantum-safe algorithms and AI-enhanced key generation for unbreakable security.

Advanced Use: Designing multi-layered blockchain ecosystems with Cryptic’s high-level abstractions.

Harmony

Advanced Features: Adaptive UIs that respond to user emotion, tracked via AI-enhanced cameras.

Advanced Use: Apps for mental health, where interfaces adjust to user stress levels.

Proposed New Bridge Languages

Chord

Bridges Harmony and Facet for designing interactive music-based visual art.

Use Case: Music videos where the visuals respond to real-time sound input.

Path

Links Cryptic and Canvas for secure AR/VR environments.

Use Case: Virtual meetings in secure, customized 3D spaces.

Fable

Connects Quip and Pantomime for storytelling via interactive gestures.

Use Case: VR education apps where lessons are taught through immersive narratives.

Would you like a specific implementation plan for any of these languages or applications?

[blake447]

Dragon Curve in C

So I wanted to challenge myself to a bit of code golf (kinda). Its probably not very good, but this is my code in c to draw a dragon curve. I wanted to include the boilerplate code as part of the challenge, so the method of drawing the curve i use is my GPU implementation. As we're printing char's in a loop we can simply do some math to test if the current char is on the curve. Essentially we treat the char as a fragment like in a shader, calculating a uv coordinate and performing a parallelized algorithm for drawing the curve (developed by yours truly)

Like I said, not very good code golf but at this point I'm just glad its working lol. It also helped me refine the math a little bit and discover a way to transform it from a vector algebra problem to a number theory one! Boy am I happy for that. I hope to get a similar implementation in assembly as well

[machine-unlearning]

Sure, we can call Don Quixote the origin of replication of Spanish literature. Paradise Lost is the photo negative of English literature. The Iliad and the Odyssey are the boilerplate code of Western literature.

[nikjag]

timing

i am only able to write code at 3 am when pointers suddenly make sense and i can move on from boilerplate

coincidentally the meds also seem to wear off around then

[szhmidty]

Sometimes the way a language is designed baffles me in the way it doesn't support what feel like very obvious use cases.

I'm figuring out Asymptote because I hate tikz[1] and the diagrams I want to make for this paper are beyond what I can easily whip up in matplotlib or matlab or whatever else. Asymptote has a path data structure that is essentially a collection of nodes + info on how to connect them. So, for example,

draw((0,0)..(1,1)..(2,4))

Would draw an approximation of a quadratic via some bezier curve that connects those nodes. A nice feature is that I can connect one path to another path, so I can build the above in a for loop:

path curve; for(int x = 0; x<3; x++) curve = curve..(x,x*x); draw(curve);

Which works nicely, I can neatly calculate path nodes and concatenate them onto the path. This often screws up, though, since the default bezier curve for those points may not match well the actual curve I'm trying to draw. I can calculate a lot more points to get a better approximation, but I can also tell Asymptote what the tangent at those points is. Doing the whole curve at once, I can write:

draw((0,0){(1,0)}..(1,1){(1,2)}..{(1,4)}(2,4))

But that gets irritating fast so I'd definitely want to just loop it:

path curve; for(int x = 0; x<3; x++){ curve = curve..(x,x*x){(x,2*x)}; } draw(curve);

Except this fails! It fails for two reasons: the first is that you cannot specify the right-side derivative of the final point. Because it doesn't connect to anything in that direction, specifying the derivative on that side does nothing. That's why I have to specify the tangent on the left side of the final point in the non-looped example.

This is whatever, it makes sense, I have to treat end points in a special way, not unheard of. I can work with that, but it'd be nice if I didn't have to.

The part I find baffling is actually in the first loop iteration, which is equivalent to

curve = (0,0){(1,0)}

At this point there is only one node on the path, and the tangent specified to the right of rightmost node does nothing. What's baffling is that the path is not evaluated in a lazy fashion i.e. it's not evaluated when it comes time to draw the curve. It seems to be evaluated immediately, it notices that the tangent does nothing, and simply removes it.

But why? Why would you design it that way? All that does is create more boilerplate for me, since now I can't start my curves as empty or even as a single node: I have to start every curve with two points if I want to specify the derivative for each point. That sucks! That's ugly! Add that to the fact that I have to specify the left side tangent on the right endpoint and the code just becomes irritatingly ugly if I want it to behave as it should.

That also means I can't compute the path segments individually and store them in a single list because if I do that then every tangent gets erased. This is what happens if I try doing that with sqrt(x) (in red is the true function):

I computed the proper tangent for each point! But the path segments were immediately evaluated, and the tangents discarded. If I specify everything properly:

This isn't insurmountable by any means, but I'm irritated that it took me 15 minutes to figure out why my tangent at 0 is always wrong, and doubly irritated that, though doable, every other way of doing this is just uglier.

1: I don't really hate tikz, I just find it deeply unwieldy for anything but simple diagrams.

[build-apps]

React Native Auto Code Application Development: The Smart Way to Build Mobile Apps

Introduction to React Native Auto Code Application Development

React Native auto code application development is changing the way mobile apps are built. Developers now use intelligent automation tools to generate code quickly and accurately. This speeds up development, reduces errors, and improves the overall quality of applications.

Auto code development with React Native is ideal for startups, agencies, and enterprises aiming to deliver high-quality apps fast.

Benefits of React Native Auto Code Application Development

1. Speed Up Development Cycles Automation tools handle repetitive tasks instantly. Developers can focus on building unique features rather than wasting time on boilerplate code.

2. Maintain Consistent Code Quality Auto-generated code follows consistent standards. This consistency improves code readability and eases future maintenance.

3. Reduce Human Errors By automating setup and structure, auto code tools minimize the chances of introducing bugs during the initial stages.

4. Enhance Developer Productivity Developers spend more time solving real problems and less time writing repetitive components.

5. Cost-Effective App Creation Faster development with fewer mistakes leads to lower project costs. This allows businesses to scale faster without ballooning budgets.

Popular Tools for React Native Auto Code Development

Ignite CLI Ignite offers pre-configured templates and plugins, making it easy to set up scalable projects instantly.

Hygen Hygen is a simple yet powerful code generator that helps teams maintain consistent coding standards with minimal setup.

Plop.js Plop.js allows developers to create custom generators for components, screens, and more, saving hours on manual coding.

Draftbit Draftbit provides a visual builder for React Native apps. Developers can create screens visually and export production-ready code.

Steps to Implement Auto Code in Your React Native Project

Step 1: Select the right auto code tools based on your project size and complexity. Step 2: Install and configure the tools within your development environment. Step 3: Create templates for commonly used components and screens. Step 4: Generate code structures automatically with simple commands. Step 5: Customize and enhance the generated code to meet unique business needs.

Following a structured approach ensures that automation becomes an asset, not a liability.

Challenges of Auto Code Application Development

1. Initial Learning Curve Some auto code tools require initial time investment for setup and training.

2. Over-Template Dependency Too much reliance on templates can sometimes limit creativity and flexibility.

3. Need for Regular Updates Auto code tools need constant updates to stay compatible with the latest React Native versions.

Developers can overcome these challenges with proper training and proactive tool management.

Best Practices for React Native Auto Code Development

- Use Automation for Repetitive Tasks Only Rely on auto code tools for repetitive elements but custom-build critical business logic manually.

- Keep Templates Updated Ensure all code generation templates are updated regularly to match new coding practices.

- Conduct Regular Code Reviews Even auto-generated code should undergo strict code reviews to maintain quality standards.

- Train Developers Continually Keep teams updated with the latest automation practices and tool updates for maximum efficiency.

Future of React Native Auto Code Application Development

The future points toward more AI-driven automation. Smart assistants will suggest code, build UI components, and even test functionalities automatically. React Native developers who embrace automation today will gain a competitive edge tomorrow.

Innovation combined with automation will define the next era of mobile app development.

Conclusion

React Native auto code application development offers a smarter, faster, and more efficient path to building world-class mobile apps. It helps developers minimize repetitive work, deliver projects quicker, and ensure consistent quality across platforms.

By integrating automation thoughtfully, businesses can create better apps and reach the market faster. React Native auto code development isn't just the future—it's the present.

Read More:

Ai Auto code

AI Wave maker

Rapid low code application development platform

Low code platform Enterprise software for application development

Low code application development platform or Low code platform for application development

What is Low code app development platforms

Composable low code isvs

Java-based low code platform

Composable isvs

RAD studio-Rapid application development software platform

APAAS-application platform as a service

Cloud Low code application development platform

Legacy application modernization solutions

React-native cross-platform mobile application development platform

Compare Wavemaker vs Outsystems vs mendix vs power apps — low code alternatives and its pricing

New and fast application development platform

Rapid application development model or RAD  model

Low-code for consumable Banking and financial Low-code platform solutions

Internal api vs external apis

Rapid application development vs SDLC Platform

Custom Enterprise low code application development platform

Legacy enterprise application Modernization Platform

Embedded banking and Finance, Low-Code and the Emerging Face of Adaptability

BAAS- Low code Banking as a service

Composable Low code banking solutions

Telecom low code platform

Alternative to Xamarin and Cordova 

Wavemaker Low code

Legacy application modernization platform

Cross-Platform React Native Mobile App Development Platform

[triviallytrue]

Honest question, how are you so consistently optimistic about technological changes in recent years, especially in AI and its connection to creative fields? I admire your writing and respect your opinion, but I just do not see anything to be optimistic about, as you've probably noticed.

I wouldn't call myself an optimist, and I would say some of the reason I'm not a pessimist is because I don't really buy the hype.

There's a lot of people who are doing the math, going "LLMs can produce human-looking output in this field, so they're probably very close to consistently producing human-quality output as well." If this is true, it will probably be pretty bad for human society, and your pessimism will be fully justified.

And for sure, this could be true! LLMs have blown past where I would naively have assumed they would be at this point in time. But what you're scared of is LLMs replacing writers, and I'm just not that worried about that at the moment. Maybe I should be, maybe GPT-6 will be able to write coherent novels. But it feels a bit too early to be so pessimistic. I need to see an effective application first.

My experience with LLMs in practice is as a better form of code autocomplete, and that's very nice to have - last week I saved maybe half an hour using a LLM to write a bunch of boilerplate functions that I would have needed to do by hand.

But I'm not scared of them taking my job - around 5% of autocompletions involved some form of basic hallucination (usually a misused API). It's possible to get it to write more complex functions, but only using a fair amount of prodding and prompting, usually sufficiently time consuming that I don't bother and just write the function myself.

They will undoubtedly get better, but for the forseeable future, anyone who tries to use them to replace humans will crash and burn. I expect the same is true of most forms of writing, especially longer form writing.

The people who are actually the most threatened have to be the visual artists. AI art looks pretty good and is already seeing some use cases. This sucks for people who make their living doing visual art. I think people will adapt and the need for human artists will continue, but it's going to take a bite out of their market share, there's no doubt about it.

We're on the edge of a precipice and we don't know if it'll be a 3ft drop or a 300ft drop, but I don't see the point of freaking out about it until we see more actual applications, instead of just hype.

[exeggcute]

it's also kind of funny (but not surprising) how of the different professions who would potentially be impacted by generative AI, programmers are by far the least concerned even though writing okay-ish code is the area where generative AI is probably the strongest right now. reactions there seem to range anywhere from enthusiasm to apathy but very rarely fear. which like, you know, getting people to Write Code is not realy the main bottleneck in software development. let alone shitty boilerplate.

[danielweasly]

Copilot Use Cases For Developers

Microsoft Copilot is revolutionizing the way developers interact with development tools, providing AI-powered assistance across a range of Microsoft products like Visual Studio, Azure, and GitHub. With Copilot integrated into these environments, developers can streamline workflows, automate routine tasks, and leverage advanced features for improved productivity. For example, in Visual Studio, Copilot helps write code faster by suggesting context-aware completions, refactoring code, and even generating entire functions based on brief descriptions. This drastically reduces the time spent on boilerplate code and increases efficiency, allowing developers to focus on more creative and complex aspects of their projects.

Furthermore, Microsoft Copilot empowers developers to work smarter by integrating with cloud-based services like Azure. By leveraging machine learning and AI, it can suggest optimized solutions, recommend cloud resources, and assist in troubleshooting infrastructure issues. This level of automation and intelligent support enables developers to make better decisions quickly, while reducing the cognitive load and manual effort needed to manage cloud-based applications. As Microsoft Copilot continues to evolve, it will increasingly become an indispensable tool in the developer toolkit, transforming how software is built and deployed in today’s fast-paced technological landscape.

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