How to Create an Application Using Neural Networks: A Complete Guide

When you think of a neural network, think of it as a highly intelligent assistant who has seen millions of lines of code, designs and architecture in one way or another over the years. This means that they understand how code is structured, and can generate code based on your description alone, as if you were to say "Create a web application with a registration form and an admin portal". It is not far-fetched that within a couple of minutes you would have a prototype application created with HTML/CSS/JavaScript and even a database. Pretty cool, huh?

There have been many use cases where GitHub Copilot can help you develop code at almost 1.5 times the speed and Replit Ghostwriter can generate web applications directly in the browser. Also, v0.dev allows you to take your text description and generate fully functional React components. What is different about these types of neural networks (vs. traditional template generators) is that neural networks are able to understand context and provide a solution according to your task rather than just placing a piece of static code into a location.

Moreover, there is a rapidly growing market for AI tools, and it is estimated to be worth billions of dollars, with a very optimistic growth trend. Therefore, these are not just gadgets, they are the future of development.

Traditional Development vs. AI-Assisted App Development

I have attempted to create an MVP with a small team before. It took months of work to come up with an idea, find out what was needed to make it happen (requirements), design it, code it, test it, then release it. It was expensive and lengthy, just like many others in the same situation.

As far as I know, now we have a much quicker way to do this: We write out what we need an AI to do — tell it what you want it to do, how to get there and ask it to write the code for us — then we can revise it and check that it is doing what we want it to. We used to have 3-5 people on the MVP project; now it can be done by 1-2 and can be released in a matter of days (or a few hours). This really changes the game!

To put this in perspective:

There are definitely nuances related to the AI approach. First, both speed and accessibility will give you an edge. Second, if you require something complicated, like, for example, implementing complex business logic or adding security features to your app, you will still need some human assistance to complete these tasks satisfactorily. Nonetheless, this methodology is ideal for virtually all projects, especially MVPs or things being used by an employee during the day.

Some of the types of applications that can be created using neural networks through the web include:

Online web applications can be found in various forms and offer both free and paid options. Presently, there are a few available to developers or those wanting to create their own web-based app.

The three common types of neural network services and platforms available are:

1. No-code platforms utilizing AI that allows an individual to create a web app without having to learn to code. Examples include ASCN.AI, Bubble, and Adalo.
2. Code Generators or AI program assistants provide coding help to coders and speed up a coder's program development with autocompletion and suggestions. Examples include GitHub Copilot and Tabnine.
3. AI Interface Generators provide a textual description or image to create ready-to-use UI code from the description or image; this example includes v0.dev and Uizard.

Here's a brief list describing a few of the various no-code platforms and tools to create web apps using neural networks:

ASCN.AI is a no-code platform with AI agents that starts at $29 per month. It is used to create automations, bots, and workflows, particularly for Web3. Bubble is a no-code platform with a range of pricing, from free to $500 through the development of web-based applications that will often require manual modification to accommodate complex backends. GitHub Copilot is an AI-based program assistant that costs $10 to $19 per month and can help with code autocompletion. It does require an individual to understand how to code. Replit Ghostwriter is an online IDE based on AI with a free option and is a useful resource for learning to program or creating prototypes. v0.dev is a free UI generator that creates React component code and is not restricted to any programming language such as JavaScript or HTML. Adalo is a no-code tool to build mobile and web apps starting at $36 per month, however, has limited options for customization of design.

A step-by-step guide on how to utilize application generation services is provided below. For example, let's say you wanted to explore ASCN.AI — one of the easiest no-code services using A.I. agents. Here are the next steps:

1. Register: You can register using your email or sign in with Google or GitHub. Once you successfully logged into ASCN.AI's main menu you will see three sections (workflow, secrets & documentation).
2. Configure Secrets: Secrets is the vault for any passwords or API keys you will use. To hide your data used in your scenarios, you will use a special format to include two curly braces and a secrets prefix around the value (i.e. {{secrets.your_value}}).
3. Choose an application type: The type of application you are creating (e.g. Telegram bot, CRM or web form) will determine which elements/nodes will be used in your scenario.
4. Create Workflow: Your workflow will have at least one trigger (e.g. new Telegram message, filter, Google Sheets Integration, notification) and the logic assembled in the order of the conditions of each Node in the Workflow.
5. Test: The last step is to test your scenarios by sending new test messages and/or check to see which errors need to be fixed.
6. Enhance: You can continue developing your scenario, adding new functionality, integrating with CRM(s), and integrating with analytic(s) and AI agents specific to your business needs.

According to the user experience, it has been reported that a prototype will usually take 30 minutes – a couple of hours to create, which is significantly quicker than conventional methods of developing.

Characteristics of Building Web-Based Applications using Neural Networks

Web-based applications consist of 2 layers: the front-end (or interface) & the back-end (or server logic). Both layers are built using neural networks; however, you must still pay attention when designing your web-based applications.

Frontend Development

With the help of some no-code tools such as v0.dev or Uizard you can generate interface code with either a written description or an image of what you want. However, you will always need to check how that code looks and functions across all your devices and browsers due to the importance of accessibility and responsiveness.

Server Side Logic and APIs

When it comes to data processing, user authentication, or business logic, there are many no-code platforms that provide you with the ability to build that logic from blocks or nodes (for example when creating a task list management app, the front-end provides the task list, while the backend processes create the tasks in a database using HTTP requests to various services like Supabase or Firebase).

Database Integration

You can create data queries and actions to use data from an external database (for example, either MongoDB, PostgreSQL or Google Sheets!) through the use of neural networks, and you can also create SQL queries based on your request descriptions.

Deployment and Hosting

With many no-code platforms you have built-in hosting for your generated code. If you create your generated code using no-code tools, you can deploy it to either Vercel or Netlify using their automated process.

Several real-world examples include: ASCN.AI's crypto analytical dashboard, which was developed using a web interface from v0.dev and AI agents working in concert to analyze data from the blockchain in only five days as opposed to the traditional 60-day timeframe. Another example is a booking system, developed in Bubble, that was launched in a few weeks, and a CRM developed in Retool/OpenAI API, which saved over $50,000 in development costs.

In general, while most complex bespoke systems will require a traditional development approach, there has been a huge increase in the ability of AI automation to provide for the completion of approximately 80% of business functions.

How Neural Network Code Generators Work — Description

Technical Mechanisms and Algorithms for Code Generation

In the background, transformer models are used as the engine driving the code generation process; transformer models are intelligent systems that learn what a character/word should be by looking at prior characters/words in the same sequence. The types of models that serve as the basis for many code generator platforms are these GPT-based models, which are trained on billions of lines of actual code from both GitHub and Stack Overflow.

In order for these models (code generators) to produce code, they go through three stages of training:

1. Pre-training: Where the model learns what code looks like and what the syntax and structure of code is (using billions of lines of actual code).
2. Fine-tuning: Where the model is specifically adapted to a certain type of task and/or field (such as software development).
3. Reinforcement Learning: Where the model receives feedback based on the user's current implementation of code.

Important algorithms that are used in the code generation process are the Attention Algorithm, Beam Search Algorithm and the Tokenization Algorithm. Tokenization refers to breaking an entire piece of text into smaller parts or tokens for easier processing. As an example, when requesting a function to check for palindromes, the code generator will produce basic palindrome code. However, if the request specifies that spaces and capitalization should not be in consideration for determining whether a palindromic function exists, then the code generator will be able to adjust the requested function accordingly. However, please note that the accuracy of the response is primarily dependent on the quality of the request. Therefore, prompt engineering skills are a good benefit.

Potential issues with the produced code are that sometimes the code generator produces code that appears to be correct but has logical flaws, and the code generators may produce code that utilizes outdated libraries and/or approaches. A third source of concern is that the model may produce code that is vulnerable and poses security risks. Research has demonstrated that the code generation success rate on standard tasks is approximately 70–85%.

Functions and Limits of AI for Programming

What AI tends to do better than humans:

• Producing standard boilerplate code (CRUD, REST APIs, database structure).
• Code autocompletion and code refactoring.
• Prototyping and executing Minimum Viable Products (MVP).
• Assisting with writing documentation and commentaries for code.

What AI struggles to do right now:

• Complex business logic and application-specific algorithms.
• High volume applications (millions of requests).
• Compliance with security and regulatory standards such as GDPR or PCI DSS.
• The subtleties of design and user experience, where a human touch is much needed.

The risks of using AI to generate code include:

• Generating unverified code can incur technical debt.
• Dependence on specific no-code platforms to execute and maintain solutions.
• Unclear legal implications of rights to generated code.

The best approach is to utilize routine tasks with AI while leveraging developers to customize, modify, and control quality. Doing so ideally affords speed and reliability.

Helpful Hints and Recommendations for Creating Applications Using Neural Networks

How to Determine the Best Service for Your Needs

The answer will be contingent upon who you are and what your goals are.

• If you are not a developer and need to quickly build a Minimum Viable Product (MVP), consider a no-code platform with a visual interface such as ASCN.AI, Bubble, or Adalo; verify that they offer templates and integrations with supporting services that you use (such as Google Sheets or Stripe), and evaluate their limitations with respect to free accounts and costs when creating a scaled solution.
• If you are a developer and want to increase the pace at which you write code, explore options such as GitHub Copilot, Tabnine, and Replit Ghostwriter and ensure that they are compatible with your desired development environment; additionally, for security purposes, prefer offline products for your basecode if you will not want other people to access it.

When you're looking for automation and integrated services, ASCN.AI, Zapier, and Make are great choices to start with. Just look at how many different services each supports and how flexible each service is in terms of creating different types of automations. You should also take the time to calculate how much it will cost to run multiple automations at once.

I started off with a trial version of these three services and then explored their respective communities to get a better understanding of them. I personally switched from using Zapier to ASCN.AI for my automation needs in the cryptocurrency industry and found that the speed of the integrations increased significantly while also reducing my costs.

Suggestions for Improving the Security and Quality of Your Generated Application

When it comes to code generated automatically, you have to be sure to do a code review before you trust it; make sure the code makes sense and can be read as it is intended.

In terms of security, be aware of the risks of SQL injection attacks and cross-site scripting (XSS); configure access levels appropriately, and hide API keys in your secrets.

In terms of testing, provide unit tests, integration tests, and load tests; there are many tools available to automate this process.

When considering your standards, make sure to keep in mind any relevant regulations such as GDPR or copyright issues.

To help you monitor your applications, establish tools like Sentry, Google Analytics, and alert systems to identify potential problems.

I had a case of an AI-generated integration for payments that saved credit card numbers in the logs by mistake, but thankfully we caught it before we released the application and ended up receiving a fine.

Free AI and Neural Network Tools for Creating Your Applications

Here are three examples of free AI tools and their corresponding features:

1. Replit Ghostwriter — a web-based Integrated Development Environment (IDE) with a limit of one hundred requests per month, making it a good way to learn how to write and design code while also providing tools for rapid prototyping. Please note that any projects created using this service are publicly available.
2. GitHub Copilot for Students — you will be given unlimited access to this application as long as you confirm your student status.
3. v0.dev by Vercel — allows you to generate ten UI designs per month for free, making it an excellent tool for developing prototypes.

How can I minimise costs while maximising my value received through the use of free options?

Try combining multiple applications by using v0.dev (F/E), Replit (B/E), ASCN.AI (A), and Vercel/Netlify (Hosting) together. Look for open-source alternatives such as LLaMA 2 or StarCoder. Use referral programs to invite others using your current account so that you may receive bonuses or increased limits. Educational programs like GitHub Education/AWS Educate will provide free access to all of their tools.

On free applications/platforms there are already working projects available (example: Bubble Marketplace & Crypto Bot using Replit + ASCN.AI; approximately $5,000.00 per month generated).

Frequently Asked Questions

May I create a complete functional app with a NN?

Yes, typically, it may generate 80–90% of anticipated functional capabilities required to deliver an application. A developer will be needed to polish any final versions and add any unique features. Simple CRMs, Marketplaces and Bots built using no-code will work appropriately.

In order to build DeFi applications, what skills do I need besides knowing how to write JavaScript?

You will need to be logical and able to define tasks through prompt engineering to succeed on applications that leverage AI. In short, you do not need programming experience to use no-code platforms but having some familiarity with coding will allow you to be more successful at working with AI assistants.

What types of applications cannot be built using AI?

Automation tools and internal tools (e.g., MVPs and content platforms) are areas where AI works very well. However, games, complex (& high-volume) systems, and critical fintech solutions are better suited to traditional development.

How do I keep my code secure and confidential?

You must carefully evaluate the tools you select for your coding process. Local tools (e.g., Tabnine) do not send data to a central server, while cloud services have various levels of privacy and security policy. You should always store your authentication keys in a secret and should not pass personally identifiable information (PII) in your requests. In the end, you are responsible for the security of your code.

This information is general, please consult with an expert for anything serious.

Conclusion: The Future of Application Development with Neural Networks

Application development is changing right before our eyes. Neural networks will be ubiquitous causing the entire process of developing applications to be democratized (allowing every individual, regardless of their technology abilities, to create mobile and web applications). As AI takes over repetitious tasks, humans will be allowed to focus on architecture and creative design. As a result, the emergence of micro SaaS products will allow individuals to launch multiple micro SaaS applications at the same time through automation. The opportunity for new career fields exists such as prompt engineers, no-code automation specialists, and AI auditors.

However, with all of these advancements come risks including technical debt, establishing platform dependency, and copyright infringement. Gartner predicts that by 2027, 70 percent of all new applications will be created with AI. Therefore, it is time to learn about no-code and AI services.

I have personally experienced the ASCN.AI platform, which offers fast automated task generation, as well as AI solution development without having to hire developers. It will absolutely make your life easier!