The Rise of Vibe Coding: AI's Impact on Software Development

Feeling the Vibes in Coding? Learn How AI is Revolutionizing Software Development

Getting AI assistants to write code based on a vague idea, called vibe coding, is picking up steam. That was my reaction to Andrej Karpathy's viral post describing his new "vibe coding" workflow. On 2 February, Karpathy -- a computer scientist and founding member of OpenAI -- defined it as a style of programming where software engineers "give in to the vibes, embrace exponentials, and forget that the code even exists." I understood what he meant, because I'd already practiced it. In the fall of 2024, I decided to see for myself what large language models could do and used them to revamp my personal website. I've since used AI to code several personal projects, including a graphical user interface (GUI) for AI speech-to-text transcription on my Windows laptop and a JavaScript app for tracking initiative in tabletop games. Though I've played with HTML and Python in the distant past, my knowledge of programming is limited, and I can't explain in detail how the AI-generated code in these programs functions. But they work, and the ease with which I created them has implications for software engineering. What Is Vibe Coding? Prasad Naik, a licensed professional mechanical engineer, is feeling the vibes. Naik works at Gripple, a company that builds wire joining and tension systems used to secure a variety of infrastructure, such as plumbing and gas lines in commercial buildings. Naik isn't a software engineer, but he recently developed two software tools for internal use. They help teams outside of engineering, such as the sales team, understand which product is right for a use case. He began by revisiting an iPad app he developed for the company over a decade ago. He programmed it in C for use on an iPad, but Naik wanted to convert it to a modern JavaScript web app. It was an intimidating project, as he'd never worked with JavaScript before. "I remember that [the original app] took me almost a month, because I had to study a lot of things I didn't understand," says Naik. "To my surprise, I managed to [convert it to JavaScript] in just two hours, using step-by-step directions from ChatGPT." Naik didn't use the code exactly as generated, but estimates that more than 90 percent of the code he used was generated by ChatGPT. After that success, Naik built a more complex app that connects to a server. The app accesses a database of spreadsheets containing information about hardware products, such as brackets and support systems used to hang pipes from ceilings in buildings. The entire development, including deployment, took about a week and a half, but Naik is doubtful he could have pulled it off without AI tools. "It was fascinating. I never in my wildest imagination thought I would end up developing an app this complex," Naik says. Vibe Coding for Proof-of-Concepts Jason Touleyrou, a data engineering lead who currently works for Corewell Health, uses AI-assisted coding to rapidly test new ideas. "Speed to ideation is critical," he says. "If I have an idea, and my team isn't on board, can I put together a scrappy project that proves its merit without wasting anyone's time?" Touleyrou has used AI to build with tools and programming languages he has little or no prior experience using. That, in turn, has helped him experiment with ideas that previously took too much time to explore. As an example, he pointed to a coffee tracking tool he built for his personal website. The tool uses several Google Cloud services, such as BigQuery and Pub/Sub, which he'd never touched in his professional career. The key to vibe coding, Touleyrou says, is the workflow. He believes projects should start with a well-defined idea before moving to ask an AI tool to generate code. Once the code is generated, the AI can move into an advisory role to assist with bugs. Developers should also continue to use tools that ensure code quality, such as linting tools that can identify obvious bugs. "It's not an alternative for knowing the basics. But I just can't look back knowing that I can build my initial proof-of-concept in 30 minutes, and then also have four additional iterations in four hours. They might even add new features," says Touleyrou. The Limits of Vibes Naik and Touleyrou have used AI to iterate on software more quickly and easily than before. But are they really vibe coding? Taher Vohra, a software engineer with 25 years of experience in software engineering, contends that they're not. "I took the vibe coding definition by heart, that I will not look at code," he says. And, for him, sticking to the literal interpretation of vibe coding quickly pointed out the idea's shortcomings. "As I went deeper, I found that specifying what I want the AI to do is turning out to be a harder problem than doing it myself." That's not to say Vohra is against using AI for programming, however. On the contrary, he uses multiple AI assistants, such as Cursor, to provide recommendations as he works. Still, he stresses that developers programming production-ready code need to keep their eyes on the code to ensure they have a proper understanding of how it functions and can identify subtle problems an AI assistant may miss. While Vohra's take on vibe coding is more critical than Naik and Touleyrou's perspective, they're not entirely in disagreement. They all agree on a particular point: Vibe coding can help engineers gain skills in programming languages and tech stacks they don't yet understand. "My advice to junior engineers is, don't just think of vibe coding a speed improvement for a skill you already have. Think of it as an accelerator for gaining new skills," says Vohra. "If you're a front-end engineer and want to become a full stack engineer, it's an excellent way to become a full stack engineer in a few months, as opposed to two years."

The machines are rising -- but developers still hold the keys

The decision the industry makes could have significant long-term consequences. Increasing complacency around AI-generated code and a shift to what has been termed "vibe coding" -- where code is generated through natural language prompts until the results seem to work -- will lead to code that's more error-strewn, more expensive to run and harder to change in the future. And, if the devaluation of software development skills continues, we may even lack a workforce with the skills and knowledge to fix things down the line. This means software developers are going to become more important to how the world builds and maintains software. Yes, there are many ways their practices will evolve thanks to AI coding assistance, but in a world of proliferating machine-generated code, developer judgment and experience will be vital. The risks of AI-generated code aren't science fiction: they're with us today. Research done by GitClear earlier this year indicates that with AI coding assistants (like GitHub Copilot) going mainstream, code churn -- which GitClear defines as "changes that were either incomplete or erroneous when the author initially wrote, committed, and pushed them to the company's git repo" -- has significantly increased. GitClear also found there was a marked decrease in the number of lines of code that have been moved, a signal for refactored code (essentially the care and feeding to make it more effective). In other words, from the time coding assistants were introduced there's been a pronounced increase in lines of code without a commensurate increase in lines deleted, updated, or replaced. Simultaneously, there's been a decrease in lines moved -- indicating a lot of code has been written but not refactored. More code isn't necessarily a good thing (sometimes quite the opposite); GitClear's findings ultimately point to complacency and a lack of rigor about code quality. However, AI doesn't have to be removed from software development and delivery. On the contrary, there's plenty to be excited about. As noted in the latest volume of the Technology Radar -- Thoughtworks' report on technologies and practices from work with hundreds of clients all over the world -- the coding assistance space is full of opportunities. Specifically, the report noted tools like Cursor, Cline and Windsurf can enable software engineering agents. What this looks like in practice is an agent-like feature inside developer environments that developers can ask specific sets of coding tasks to be performed in the form of a natural language prompt. This enables the human/machine partnership. That being said, to only focus on code generation is to miss the variety of ways AI can help software developers. For example, Thoughtworks has been interested in how generative AI can be used to understand legacy codebases, and we see a lot of promise in tools like Unblocked, which is an AI team assistant that helps teams do just that. In fact, Anthropic's Claude Code helped us add support for new languages in an internal tool, CodeConcise. We use CodeConcise to understand legacy systems; and while our success was mixed, we do think there's real promise here. It's important to remember much of the work developers do isn't developing something new from scratch. A large proportion of their work is evolving and adapting existing (and sometimes legacy) software. Sprawling and janky code bases that have taken on technical debt are, unfortunately, the norm. Simply applying AI will likely make things worse, not better, especially with approaches like vibe. This is why developer judgment will become more critical than ever. In the latest edition of the Technology Radar report, AI-friendly code design is highlighted, based on our experience that AI coding assistants perform best with well-structured codebases.

Vibe coding isn't here to take developer jobs. It's here to transform them into AI architects

Vibe coding -- creating and editing software simply by giving instructions to AI -- enables businesses and individuals to unleash their creativity without requiring a developer. Some worry that vibe coding will replace developers, but that's not the case. This trend proves that programming is evolving, and those who adapt will find more opportunities, not fewer. AI-powered coding assistants have been around for a while. They started as "autocomplete" tools, similar to how your phone predicts the next word when you text. GitHub Copilot, Cursor, and similar tools boosted developer productivity by helping them finish lines of code, detect mistakes, and suggest improvements. In fact, software engineering is the field where AI is most commonly applied. To put things in perspective, over 37% of all queries sent to Claude cover tasks like software modification, code debugging, and network troubleshooting. Even major tech companies, such as Google, now rely on AI for coding -- more than a quarter of their new code is AI-generated. But vibe coding takes this concept even further. Instead of merely assisting developers, tools like Hostinger Horizons, Lovable, or Bolt.new can now generate complete applications based on user prompts. People with no coding knowledge are creating personal web apps, automating tasks, and even building successful SaaS businesses. This marks a fundamental shift: AI is no longer just assisting developers -- it's taking on entire development tasks. While vibe coding is impressive, it comes with notable limitations that make human oversight essential. However, with research advancing rapidly, AI's coding capabilities will continue to improve. AI already outperforms humans in creative tasks, decision-making, and some other tasks, so similar progress in software development is inevitable. Basically, current large language models (LLMs) are advanced text predictors. They analyze patterns and generate outputs based on training data, but unlike humans, they can't comprehend the meaning of what they generate. AI's lack of true understanding causes several significant limitations: In other words, current vibe coding tools are similar to junior software developers. They help non-developers bring their software ideas to life with minimal effort. That said, complex projects still require more time, deeper expertise, and often the involvement of a professional developer. Vibe coding tools are evolving rapidly. In just a few months, we could see significant improvements -- bringing them closer to the expertise of mid-level or even senior developers. Nevertheless, AI will need humans to guide it, test, and refine the outputs. This is where the future developers will step in, ensuring AI-generated code is reliable and secure, managing and integrating different AI solutions, and solving complex and unique challenges. AI isn't a threat but rather a tool to solve tedious, repetitive tasks, allowing developers to focus on new, unique challenges, and create innovations. That's what we see in our business today. AI fully solves more than half of all client queries, allowing our Customer Success experts to focus more on advanced technical issues. As AI becomes an integral part of software development, the skill set required for developers is evolving. Future-proof developers will need to master AI literacy, prompt engineering, code analysis, debugging, and problem-solving to stay ahead in the job market. This shift also necessitates changes in education. Schools and universities must integrate AI-powered tools into their curricula to ensure graduates are relevant and competitive in the job market. Yet, this transformation is not limited to programming. With 400 million weekly ChatGPT users, it's hard to imagine any industry without AI, at least in terms of intellectual work. However, rather than replacing human ingenuity, AI helps people be more productive and efficient. Businesses will always need smart people for strategic thinking, decisive action, and driving innovation. The future of coding isn't about choosing between AI and human developers -- it's about collaboration. The best developers will be those who know how to harness AI effectively.

Vibe coding at enterprise scale: AI tools now tackle the full development lifecycle

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The vibe coding phenomenon -- where developers increasingly rely on AI to generate and assist with code -- has rapidly evolved from a niche concept to a mainstream development approach. With tools like GitHub Copilot normalizing AI-assisted coding, the next battleground has shifted from code generation to end-to-end development workflows. In this increasingly crowded landscape, players like Cursor, Lovable, Bolt and Windsurf (formerly codeium) have each staked their claim with various approaches to AI-assisted development. The term vibe coding itself represents a cultural shift in which developers focus more on intent and outcome than manual implementation details -- a trend that has both enthusiastic advocates and skeptical critics. Vibe coding is all about using AI-powered tools to help with basic code completion tasks and generate entire applications with just a few prompts. Vibe coding diverges from low-code/no-code platforms by going beyond visual tools for simple business applications. According to some advocates, vibe coding promises to augment or even potentially replace real software developers. In this competitive field, Windsurf's latest Wave 6 release which debuted on April 2 addresses a gap that some tools have often ignored: deployment. While code generation has become increasingly sophisticated across platforms, the journey from locally generated code to production deployment has remained stubbornly manual. "We've really removed a lot of the friction involved with iterating and deploying applications," Anshul Ramachandran, head of product and strategy at Windsurf told VentureBeat. "The promise of AI and all these agentic systems is that the activation energy, the barrier to building, is so much lower." Windsurf Wave 6 feature breakdown: What enterprises need to know Looking specifically at the new features in Windsurf Wave 6, several enterprise capabilities address workflow bottlenecks: Conversation management: Technical innovation that matters The Conversation Table of Contents feature in Wave 6 is also particularly interesting. It addresses a technical challenge that some competitors have overlooked: efficiently managing extended interactions with AI assistants when errors or misunderstandings occur. "AI is not perfect. It will occasionally make mistakes," Ramachandran acknowledges. "You'd often find yourself in this kind of loop where people try to prompt the AI to get out of a bad state. In reality, instead of doing that, you should probably just revert the state of your conversation to the last point where things were going well, and then try a different prompt or direction." The technical implementation creates a structured navigation system that changes how developers interact with AI assistants: Getting the 'vibe' of the vibe coding landscape The Windsurf Wave 6 release has got some positive feedback in the short time it has been out. It's a very active space, though, with fierce competition. Just last week, Replit Agent v2 became generally available. Replit Agent v2 benefits from Anthropic's Claude 3.7 Sonnet, arguably the most powerful LLM for coding tasks. The new Replit Agent also integrates: Cursor is also highly active and offers a steady pace of incremental updates. Recent additions include chat tabs, which enable developers to have multiple conversations with the AI tool at the same time. On March 28, Cursor added support for the new Google Gemini 2.5 Pro model as an option for its users. Bolt also released a new update on March 28, along with a new mobile release in beta. At the end of February, Bolt AI v1.33 was released, adding full support for Claude 3.7 and prompt caching capabilities. Though not always included in the vibe coding spectrum, Cognition Labs released Devin 2.0 this week. Much like the tabbed feature in Windsurf Wave, Devin now has the ability to run multiple AI agents simultaneously on different tasks. It also now integrates interactive planning that helps scope and plan tasks from broad ideas. Devin 2.0 also integrates a novel search tool to navigate better and understand codebases The evolution of developer roles, not their replacement The vibe coding movement has sparked debates about whether traditional programming skills remain relevant. Windsurf takes a distinctly pragmatic position that should reassure enterprise leaders concerned about the implications for their development teams. "Vibe coding has been used to refer to the new class of developers that are being created," Ramachandran explains. "People separating the 'vibe coders' and the 'non-vibe coders' -- it's just a new class of people that can now write code, who might not have been able to before, which is great," Ramachandran said. "This is how software has expanded over time, we make it easier to write software so more people can write software." What vibe coding tools mean for enterprises Much like how low-code and no-code tools never fully replaced enterprise application developers in the pre-AI era, it's not likely that vibe coding will entirely replace all developers. Vibe coding is fundamentally more powerful than low-code and no-code tools. Users can build all manner of applications without almost any restrictions. Perhaps more importantly, across many of the modern vibe coding tools is the ability to integrate with existing processes and even code bases in some cases. It's unclear which tool will be the winner in the space, and trying to pick a winning tool is probably not the right choice anyway, given how fast development is happening. Much like how enterprise developers have always had a choice of tools for any developer, the same will be true in the vibe coding era. Enterprise will be well advised to try out different tools and see what works best for their particular style and workflow. For technical leaders evaluating their approach to AI-assisted development, several considerations should inform strategic planning: The vibe coding movement offers genuine opportunities to accelerate development and expand who can contribute, but realizing these benefits in enterprise contexts requires tools designed with enterprise realities in mind.

Are Developers Becoming Obsolete? These AI Tools Say Yes

CTOs and AI experts predict that AI will soon handle 95% of coding, rendering all junior developers obsolete. For many years, software development has merged logic, creativity, and expertise. While developers have established the foundation of today's digital infrastructure, AI coding tools are now stepping into the spotlight in what is referred to as "vibe coding. " CTOs and AI experts predict that AI will soon handle 95% of coding, rendering all junior developers obsolete. However, with AI assistants writing, debugging, and even optimising code, a significant question arises: Are human developers heading towards obsolescence? These tools are not just assisting developers -- they are automating tasks that once required years of experience. Here's a look at some of the top AI-powered coding tools that are reshaping the industry. GitHub Copilot, developed by Microsoft and OpenAI, is arguably the most popular AI coding assistant today. Integrated with VS Code, Copilot suggests entire lines and functions in real-time. By analysing context and developer intent, it generates code snippets that can significantly accelerate the development process. It's like having a supercharged pair programmer who never sleeps. Cursor is an AI-powered code editor built as a fork of VS Code by the company Anysphere, is the one that started the 'vibe coding' phase of AI with Andrej Karpathy praising it for its capabilities. It provides real-time AI-driven code suggestions, intelligent debugging, and even automated refactoring supporting models from Anthropic, Google, and OpenAI. Codeium's Windsurf is an AI-powered coding assistant that offers autocomplete, refactoring suggestions, and documentation support. Developers are transitioning from Cursor to Codeium's Windsurf faster than ever, as they find that the platform is not only very similar to Cursor but also quicker and more precise. Replit's Ghostwriter is an AI-assisted coding tool integrated into Replit's cloud-based development environment. It helps developers by generating and completing code snippets, providing documentation support, and enabling real-time collaboration, even running on your smartphone. Tabnine is an AI-powered code completion tool focused on enterprise use cases. Unlike Copilot, which relies on public repositories, Tabnine allows organisations to train models on their private codebases, ensuring secure and context-aware assistance. AI coding tools are undoubtedly changing the development landscape, but full developer obsolescence is unlikely. While AI can generate code, it lacks human intuition, problem-solving abilities, and the deep understanding of business logic required to build complex software systems. Instead, these tools will likely transform the role of developers, making them more focused on architecture, security, and creative problem-solving rather than repetitive coding tasks. Rather than replacing developers, AI is enhancing their efficiency. The future of programming will likely feature a hybrid approach in which AI manages mundane tasks, allowing human developers to concentrate on innovation. Those who adapt and learn to leverage these AI tools will gain a competitive edge in the industry. So, are developers becoming obsolete? Not yet. But the smartest developers are learning to work alongside AI, using it as an ally rather than fearing it as a replacement.

I Don't Like the Term 'Vibe Coding', Says Replit CEO Amjad Masad

"When you're using Replit Agent, you don't have the luxury to look at the code." Amjad Masad, CEO of Replit, has expressed his discomfort with the popular term 'vibe coding', arguing that it oversimplifies the deeper capabilities of generative AI tools and undervalues the creative process behind building software. On the recent Sequoia's Training Data podcast, Masad remarked, "I don't like this term...It just cheapens the possibilities." Coined by Andrej Karpathy, co-founder of OpenAI, vibe coding refers to using AI tools like Cursor and Windsurf, and inputting ideas to create apps and software without writing a single line of code. While acknowledging that vibe coding might make sense for seasoned developers like Karpathy, who can casually interact with code while relying on AI to fill in the blanks, Masad emphasised that Replit is designed for a fundamentally different workflow. "If you're starting with Replit, you're actually not starting from a position of code -- you're starting from an idea," he said. Masad explained that Replit's AI agent helps users build that idea into code without requiring them to dive into technical details. "When you're using Replit Agent, you don't have the luxury to look at the code," he added. He drew a distinction between the company's main product and its Assistant tool, which caters to more advanced users and supports a back-and-forth coding workflow that might be more in line with vibe coding. However, Masad pushed back on the label itself. "If I were to explain Replit, it's just vibe. Like, don't code, vibe. Not vibe coding, just vibe." Beyond terminology, Masad also offered a contrarian take on AGI. While much of Silicon Valley anticipates a future where software engineers are obsolete, Masad believes AI will empower more people to build, not replace them. "We're all going to have jobs -- they're just going to be very powerful." He believes that AGI will become "functional", excelling at economically useful tasks with existing training data, like coding or math, but falling short in creative or novel domains. "Creativity in the sense of coming up with really novel things...I think it will still be the domain of the human," Masad noted. Reid Hoffman, co-founder of LinkedIn, recently ran an experiment with Replit to "clone LinkedIn" using a single prompt. The result was a surprisingly functional prototype, showcasing the potential of today's AI to turn ideas into working software. Masad recently said that learning to code may no longer be necessary. He shared a video clip on X which shows him agreeing with Anthropic's Dario Amodei, who predicted that most of the future code would be AI-generated. Amodei predicted that AI could be writing up to 90% of all code within the next six months. Replit is currently in talks to raise fresh capital at a valuation nearing $3 billion -- almost triple its previous worth -- reflecting strong investor confidence in AI-driven software development.

Replit Convinces a Billion Developers that There's No Need to Learn to Code

No fiddling with IDEs, managing imports, or dependencies. The agent just needs to be told what is required, and it'll get to work. The debate over which AI coding tool is the best might as well have been over before it started. For the longest time, Replit has been the most loved developer tool, consistently ranking above Cursor, Windsurf, Lovable, and others. Amjad Masad, the founder of Replit, is on a mission to convince a billion people that writing software doesn't need to start with years of tutorials, syntax struggles, or late nights trying to fix semicolon errors. Instead, all one needs is an idea -- and the ability to describe it in plain English. Yet, he doesn't call this process 'vibe coding' like others do. Notably, in a recent episode of Sequoia's Training Data podcast, Masad said he doesn't like the term. "It just cheapens the possibilities," he said. "You can build agents and not just generate things. It can actually reason. Vibe coding makes sense if you're starting from a position of coder and you're Andrej Karpathy. You don't want to worry too much about the code and keep hitting enter." Masad, however, believes that using Replit is different. "If you're starting with Replit, you're actually not starting from a position of code...You're starting from an idea you're iterating on. Then you go in, and the agent unfolds this code in front of you. Actually, when you're using Replit Agent, you don't have the luxury to look at the code," he said. He believes the most transformative outcome of AI won't be AGI, but everyday people -- from 12-year-olds in India to office workers in the US -- building software. Moreover, with AI coding tools lowering the barrier to entry, that future might be closer than we think. No fiddling with IDEs, managing imports, or dependencies. The AI just needs to be told what is required, and it'll get to work. In September last year, Replit launched an AI agent that not only writes code but also deploys software, making it one of the first platforms to offer this capability. Later, in February this year, it launched the second version of the agent with even more capabilities. The results are phenomenal. Reid Hoffman, co-founder of LinkedIn, recently ran an experiment on Replit. He asked the agent to "clone LinkedIn" using a single prompt. The result was a surprisingly functional prototype, showcasing AI's potential today to turn ideas into working software. This isn't a mere tool for experimentation. Replit has already powered some impressive projects. At the National University of Singapore, a small team used Replit to build a full-fledged app for over 700 students. The app provided laundry booking, crowd tracking, and events management -- despite most team members lacking backend experience. Startups are also jumping on the bandwagon. YC-backed companies like Fig, Amplemarket, and BerriAI, as well as unicorns like Deel, are using Replit in production. Replit's origin story is just as fascinating. Growing up in Jordan, Masad didn't even own a computer. Instead, he coded from internet cafés, repeatedly setting up his development environment every time he logged in. "Can you imagine writing code with no internet? It's kind of crazy!" he said in a podcast. Masad was always fascinated by the idea of the internet and how it links to a vast database of knowledge to help solve problems. In 10th grade, he found himself frustrated by the constant need to walk to a cafe, pay for the internet, and then find solutions for his problem under tight deadlines. So he decided to fix it -- for everyone. What started as a side project, turned into a "personal computer in the cloud". Today, Replit supports over 50 programming languages and has powered more than 240 million projects. Replit is also currently in talks to raise fresh capital at a valuation nearing $3 billion -- almost triple its previous worth -- reflecting strong investor confidence in AI-driven software development. Masad had just recently made a bold statement. "I no longer think you should learn to code," he said in a recent post on X, adding to a growing discourse around the role of human programmers in an AI-first future. He urged people instead to focus on creativity and problem-solving skills that he believes will hold value even as AI takes over the mechanics of programming. His perspective echoes a recent prediction by Anthropic CEO Dario Amodei, who suggested that AI could soon be writing up to 90% of all code. Masad agrees with Amodei as well. "In the upcase, like what Dario just said recently, all code will be AI generated," Masad explained. However, Masad acknowledged that coding isn't entirely obsolete -- at least not yet. He encouraged a foundational understanding of programming as a way to build more universal skills. "I would say learn a bit of coding...Learn how to think, how to break down problems...Learn how to communicate clearly, as you would with humans, but also with machines," he added. If Replit and Masad get their way, the next billion software creators won't come from CS classrooms; they'll come from artists, entrepreneurs, students, and dreamers who don't know a single line of Python but will still say, "I want an app that does this."

How Vibe Coding is Making App Creation Effortless for Everyone

Vibe coding, a concept introduced by Andrej Karpathy, is transforming the way software is developed by using the capabilities of artificial intelligence (AI). This innovative approach reduces technical barriers, allowing users to create functional applications with minimal effort. Abacus AI has adopted this paradigm through its tools, App LLM and Code LLM, which cater to both non-coders and experienced developers. These tools simplify the processes of app creation, debugging, and deployment, making software development more accessible, efficient, and inclusive for a wide range of users. At its core, vibe coding is about shifting the focus from technical know-how to the essence of your idea. Imagine telling an AI what you want -- whether it's a budgeting app, a photo editor, or a sleek landing page -- and watching it generate functional code to bring it to life. It's not just about simplifying the process; it's about empowering anyone, regardless of their technical background, to innovate and create. In this guide, World of AI explore how Abacus AI's App LLM and Code LLM are transforming the way we approach software development, offering tools that are as intuitive as they are powerful. At its essence, vibe coding uses AI to autonomously generate software based on user-defined "vibes" or prompts. Instead of requiring you to manage every detail of the coding process, this method allows you to focus on describing the desired outcome. The AI interprets your input and generates functional code, allowing you to prioritize creativity and problem-solving over technical complexities. This approach is particularly appealing to individuals without coding expertise, as it eliminates the steep learning curve traditionally associated with software development. For developers, it offers a way to streamline workflows and focus on higher-level tasks. App LLM is specifically designed for users with little to no programming experience, making it a powerful tool for those who want to bring their ideas to life without needing technical skills. By providing simple prompts, you can create fully functional applications, websites, or tools. For instance, you might describe an app for managing personal finances or a tool for editing photos, and App LLM will generate the necessary code to bring your concept to reality. Key features of App LLM include: This tool is ideal for creative thinkers, entrepreneurs, and anyone eager to prototype or launch ideas without the need for programming knowledge. Take a look at other insightful guides from our broad collection that might capture your interest in Vibe Coding. For developers, Code LLM offers advanced features that streamline workflows and enhance productivity. It is designed to assist with both routine and complex coding tasks, allowing developers to focus on innovation and efficiency. Key capabilities of Code LLM include: With an integrated code editor, Code LLM ensures that you maintain control over your projects while benefiting from AI-driven efficiency. Both App LLM and Code LLM are versatile tools that can be applied across various scenarios. Their flexibility makes them suitable for a wide range of users and industries. Examples of practical applications include: These tools are particularly valuable for small businesses, startups, and individuals who need to develop software quickly and cost-effectively. Abacus AI offers access to App LLM and Code LLM through a subscription plan priced at $10 per month. This package also includes Chat LLM, an AI-powered assistant capable of supporting various tasks across different domains. The subscription model ensures affordability while providing users with access to innovative AI tools. This pricing structure makes advanced software development capabilities accessible to a broader audience, including individuals and small businesses. Both App LLM and Code LLM are equipped with features that enhance collaboration and sharing, making them suitable for team-based projects and client interactions. You can refine your projects alongside the AI, download the generated code for further customization, and share your applications with others. These capabilities allow you to involve team members, showcase your work to clients, or gather feedback from stakeholders efficiently. The tools are designed to foster collaboration while maintaining ease of use. The tools provided by Abacus AI cater to a diverse range of users, offering benefits tailored to different needs and skill levels. These include: By addressing the needs of such a broad audience, these tools demonstrate the potential of AI to provide widespread access to software development and foster innovation across industries.