Growth Ritual #62
📋 IN THIS ISSUE: How "Vibe Coding" is Reshaping Entrepreneurship and Business
🎙️ AUDIO DEEP DIVE OF THIS ISSUE:
Sammy & Mila offer in-depth analysis on each newsletter issue. Subscribe to their podcast.
How "Vibe Coding" is Reshaping Entrepreneurship and Business
Imagine sketching out your next big app idea not with complex lines of code, but with plain English, letting an AI a few moments later bring it to life.
This isn't a far-fetched dream; it's the burgeoning reality of "vibe coding", a revolutionary approach that's sending ripples—or rather, waves—through the worlds of software development, entrepreneurship, and business.
What is Vibe Coding?
At its core, vibe coding is a new approach where users "fully give in to the vibes, embrace exponentials, and forget that the code even exists".
It’s about expressing intentions using plain speech, which artificial intelligence tools then transform into executable code.
This philosophy prioritizes rapid experimentation and iteration over meticulous upfront planning.
Vibe coding is a term coined by AI researcher Andrej Karpathy in February 2025, describing a software development approach where developers, or even non-programmers, use artificial intelligence (AI), specifically large language models (LLMs), to generate code through natural language prompts or voice commands.
Instead of manually writing code line by line, users describe what they want in plain English (or other natural languages), and the AI generates the corresponding code. This method prioritizes the "vibe" or high-level intent of the project over precise coding details, often bypassing traditional programming rigor.
The Mechanics: How Does "The Vibe" Translate to Code?
Vibe coding embraces a "code first, refine later" mindset.
This aligns perfectly with agile principles of fast-prototyping, iterative development, and cyclical feedback loops – all supercharged by AI.
Popular tools for implementing vibe coding include Replit, Cursor, and Microsoft GitHub Copilot. Some users also leverage powerful models like Claude 3.5 and the reasoning models O1, O1 Pro, and O3.
When debugging queries arise, developers might even turn to models like ChatGPT for their advanced reasoning capabilities.
Natural Language Input:
Description: The process begins with the user providing a natural language prompt to an AI tool, such as "Create a React app that takes a photo of a restaurant menu, translates it, and lists the food items with images" or simpler commands like "decrease the padding on the sidebar by half." These prompts can be typed into a chat interface or spoken using voice dictation tools like SuperWhisper.
Tools Involved: Common tools include AI-powered code editors like Cursor (with its Composer feature), Replit Agent, Bolt, Lovable, or general-purpose LLMs like ChatGPT, Claude, or DeepSeek. These tools are designed to interpret plain language and translate it into functional code.
Example: A user might say, “Build a website with a blog, contact page, and bio” and the AI generates the necessary HTML, CSS, JavaScript, and backend components.
AI Code Generation:
How It Works: The LLM processes the prompt using its training data, which includes vast repositories of code and documentation. It generates code by predicting the most likely sequence of instructions that match the user’s intent. For example, if asked to create a web app, the AI might produce a full project structure with frontend (e.g., React), backend (e.g., Node.js), and database (e.g., Supabase) components.
Autonomous Features: Advanced tools like Cursor Composer can iterate, lint (check for errors), and run tests autonomously, refining the code without constant user input. For instance, if the generated code has a bug, the AI might suggest fixes or restructure the code automatically.
Output: The AI delivers code files, snippets, or entire applications. For complex requests, it may generate multiple files (e.g., a React project with components, styles, and APIs) or integrate with cloud services like databases or authentication systems.
Iterative Refinement:
User Guidance: Vibe coding is an iterative dialogue. If the AI’s output isn’t perfect (e.g., the app runs but has a messy structure), the user provides follow-up prompts like “Simplify this code” or “Fix the navigation bar.” The AI refines the code based on these instructions.
Error Handling: When errors occur, users can copy-paste error messages into the AI tool, which often diagnoses and resolves them. For example, Andrej Karpathy noted that he pastes error messages without comments, and the AI typically fixes them. If the AI can’t resolve a bug, users might work around it by requesting unrelated changes until the issue disappears.
Testing: Some tools, like Amazon Q CLI, can generate and run tests (e.g., using Vitest) to ensure the code works. The AI may self-correct by iterating until tests pass, as seen in a case where Q CLI fixed failing tests multiple times without additional prompting.
Minimal Code Review:
Core Principle: A defining feature of vibe coding is the user’s willingness to accept AI-generated code without fully understanding or reviewing it. Karpathy described this as “forgetting the code even exists” and “accepting all” changes without reading diffs (code changes). This contrasts with traditional software development, where developers meticulously review code for correctness, performance, and security.
Implication: The generated code may grow beyond the user’s comprehension, requiring significant effort to understand if manual debugging is needed later. This trade-off prioritizes speed and creativity over precision.
Deployment and Iteration:
Running the Code: Users can test the generated app immediately, often within platforms like Replit, which provide integrated cloud services (e.g., databases, storage) to simplify deployment. For example, Replit Agent can set up and deploy an app in minutes from a plain-language description.
Further Tweaks: As users interact with the app, they can request changes via prompts, such as “Make the layout responsive” or “Add a comment section.” The AI updates the codebase, and the cycle of testing and refining continues.
Key Components and Tools
AI-Powered Tools:
Cursor: An AI-enhanced code editor based on Visual Studio Code, with a Composer sidebar for natural language instructions. It’s popular for vibe coding due to its ability to edit files directly and suggest fixes.
Replit Agent: A platform that builds entire apps from plain-language prompts, handling databases, storage, and deployment. It’s beginner-friendly and supports non-technical users.
Lovable and Bolt: Tools aimed at non-coders, integrating with Supabase for database and authentication features. They focus on simple, functional apps.
Amazon Q CLI: A command-line tool that generates and tests code, suitable for users comfortable with terminal interfaces.
General LLMs: ChatGPT, Claude, or DeepSeek can generate code snippets but may require more manual integration compared to specialized tools.
Voice Integration:
Tools like SuperWhisper allow users to dictate prompts, reducing the need to type. Karpathy highlighted using voice commands to instruct Cursor Composer, making vibe coding feel conversational.
Cloud Integration:
Platforms like Replit and Supabase provide built-in databases, authentication, and storage, removing the need for users to configure complex backend systems. This simplifies vibe coding for non-technical users.
Strengths of Vibe Coding
Accessibility:
Vibe coding lowers the barrier to software development, enabling non-programmers to create functional apps. For example, creators can build tools for their audiences without learning programming languages.
It’s ideal for hobbyists, entrepreneurs, or startups prototyping ideas quickly.
Speed:
Development time is drastically reduced. A livestream by Lovable and Supabase built an event management app in just over an hour using natural language prompts, compared to days or weeks for traditional coding.
Y Combinator reported that 25% of its Winter 2025 startup batch had codebases that were 95% AI-generated, reflecting rapid adoption in fast-paced environments.
Prototyping and Learning:
Vibe coding excels for “throwaway weekend projects” or proofs of concept, as Karpathy noted. It allows users to test ideas without significant investment.
Non-technical users can learn programming concepts by tweaking AI-generated code, while experienced developers can explore new languages or frameworks.
Productivity for Developers:
Experienced developers use vibe coding to automate repetitive tasks (e.g., generating boilerplate code) and focus on high-level design. For example, AI can set up authentication or database schemas quickly.
Practical Example
Imagine a non-technical creator wanting to build a personal website with a blog, contact form, and bio page:
Prompt: They open Replit and type, “Create a website with a blog, contact form, and bio page using HTML, CSS, and JavaScript”.
AI Response: Replit Agent generates a project with:
HTML files for each page.
CSS for styling (e.g., Tailwind CSS).
JavaScript for form submission.
A Supabase database for storing blog posts and form responses.
Testing: The creator runs the app in Replit’s Webview, notices the contact form doesn’t send emails, and prompts, “Add email functionality to the contact form”.
Refinement: The AI integrates an email API (e.g., SendGrid), and the creator tests again. If an error occurs (e.g., “API key not found”), they paste the error message, and the AI suggests adding the key to a configuration file.
Deployment: Replit deploys the site to a public URL, and the creator shares it with their audience.
This process, completed in hours, contrasts with traditional development, which might take weeks for a beginner to learn and implement.
Best Practices for Vibe Coding
To mitigate risks and maximize effectiveness, consider these tips from sources like Zapier and creatoreconomy.so:
Be Specific with Prompts: Instead of “Build a website,” say, “Build a website with a blue header, three blog posts, and a contact form using React and Supabase.”
Test Thoroughly: Manually test user-facing features (e.g., click through the UI, try odd inputs) and ask the AI to generate automated tests for critical paths.
Use Version Control: Save projects in GitHub to revert to working versions if AI changes break something.
Start Simple: Begin with basic features and add complexity incrementally to avoid overwhelming the AI.
Learn from Output: Ask the AI to explain code in simple terms or add comments to build technical knowledge over time.
Know the Limits: Use vibe coding for prototypes or small apps, not mission-critical systems, unless you can review and secure the code.
Revolutionizing Entrepreneurship: Speed, Agility, and Accessibility
The impact of vibe coding on the startup landscape is already palpable.
Founders are reporting staggering increases in development speed, with some claiming to work 10x or even 100x faster than traditional methods.
This newfound velocity means that if an idea isn't working or a feature isn't landing, developers can scrap and rewrite code with far less pain than if they had painstakingly crafted every line manually. It’s about failing faster, learning quicker, and iterating towards success with unprecedented agility.
This shift is also democratizing the very act of creation.
Non-technical founders, previously reliant on finding and funding technical co-founders or expensive development teams, are now gaining the ability to build and ship their own products.
This empowers them to validate their ideas directly in the market, potentially reaching that coveted product-market fit (PMF) more quickly and with significantly lower initial investment.
Vibe coding supports quick prototyping and rapid iteration, enabling businesses to progress quickly with a minimum viable product (MVP). This lowers sunk costs as resources are only committed to validated concepts, allowing for agile pivoting based on crucial market feedback.
The Shifting Landscape: From Coder to "Product Engineer"
Interestingly, it's not just novices embracing this new paradigm.
A striking statistic from a recent Y Combinator batch (as of early 2024/late 2023, depending on the specific cohort reference in the original data) revealed that a quarter of the startups estimated that over 95% of their codebase was AI-generated.
This is particularly noteworthy given that these founders were often highly technical individuals, capable of building from the ground up.
It signals that experienced minds are leveraging AI to achieve new heights of productivity.
The very definition of a software engineer is evolving. As AI takes on more of the heavy lifting in code generation, the emphasis shifts towards becoming a "product engineer".
In this role, qualities like good taste, a deep understanding of user needs, and the ability to guide the AI effectively become paramount, sometimes outweighing pure manual coding prowess.
Challenges and Caveats of Vibe Coding
However, the vibe isn't always smooth sailing.
Vibe coding faces its share of challenges. Novel or highly complex technical requirements can still stump AI, leading to technical complexity.
There are also legitimate concerns about the quality and performance of AI-generated code, particularly for distributed applications that demand structured architecture and sophisticated optimization.
Security is another critical hurdle.
AI-generated code, if not subjected to proper review, can harbor unseen vulnerabilities, creating significant risks for businesses and users.
Furthermore, the ability to debug remains an indispensable skill. AI models, for all their advancements, can still produce buggy or incorrect code.
Experienced developers are crucial for recognizing flawed outputs and guiding the AI towards more robust solutions.
Code Quality and Maintainability:
AI-generated code can be buggy, poorly optimized, or overly complex. Simon Willison, an AI researcher, noted that vibe coding is risky for production codebases where quality and understandability are crucial.
The “house of cards” phenomenon, described by some developers, refers to code that works superficially but collapses under real-world pressure due to lack of error handling or brittle logic.
Security and Privacy:
Vibe-coded apps may have undetected vulnerabilities, especially if users don’t review the code. A Cybernews study found many App Store apps (even human-coded ones) mishandle secrets like passwords, and vibe coding could exacerbate this.
For apps involving user data or large databases, experts like Sergey Tselovalnikov warn that vibe coding is dangerous without rigorous testing.
Limited Debugging:
If the AI can’t fix a bug, users without coding knowledge may struggle. Karpathy admitted to experimenting with random changes to resolve issues, which is inefficient and risky for complex projects.
Rachel Wolan, Webflow’s chief product officer, noted that AI sometimes overwrote her changes, causing frustration and lost work.
Not Suitable for Complex Systems:
Vibe coding works best for simple apps (e.g., games, static websites) or prototypes. Complex systems with intricate logic or edge cases (e.g., a full-stack blog with rich-text editors) often require human expertise.
A DEV Community post argued that vibe coding isn’t a substitute for real developers in complicated projects, as LLMs may fail to handle advanced features robustly.
Scaling the Summit: From MVP to Market Leader
And what happens when a product built on vibes achieves product-market fit and needs to scale?
This can present a significant challenge, as underlying code issues, glossed over in the rush to iterate, may surface.
The good news is that reaching PMF typically unlocks the resources —financial and human— to address these scaling pains.
This is where the classically trained systems thinker and architect proves invaluable, bringing the expertise needed to build robust, scalable infrastructure.
Technical founders with a deep understanding of systems can also play a vital role in identifying when AI agents or even human team members are providing incorrect or suboptimal information.
A Comparative Analysis of AI-Assisted Software Development Tools in 2025
The software development landscape is undergoing a significant transformation, driven by the rapid proliferation and increasing sophistication of Artificial Intelligence (AI) tools.
These tools are no longer confined to simple code completion or syntax highlighting; they are evolving into sophisticated, often agentic, partners capable of assisting with, and in some cases automating, complex stages of the software development lifecycle (SDLC).
This evolution marks a pivotal shift in how software is conceptualized, built, and maintained, promising substantial enhancements in productivity and innovation.
Key Comparative Highlights
A significant number of tools are centered around the Visual Studio Code (VS Code) ecosystem, either as extensions or as forked, AI-native IDEs.
There is a clear trajectory towards more "agentic" capabilities, where AI tools can undertake multi-step tasks, understand broader project contexts, and operate with a greater degree of autonomy.
Furthermore, the tools exhibit diverse focus areas: some are tailored for general-purpose code generation and assistance, others specialize in areas like automated testing and quality assurance, user interface (UI) development, or enabling non-technical users to build applications.
This diversity underscores the multifaceted ways AI is being integrated into software creation.
Top-Level Recommendations (Teaser)
Finding is that the "best" AI tool is not a one-size-fits-all determination.
The optimal choice is highly contingent upon the specific needs of the user or organization, the nature of the projects, the existing technical environment, and the strategic goals for AI adoption.
Subsequent sections will delve into the specifics that allow for such tailored decision-making.
Introduction to AI-Assisted Software Development
The integration of Artificial Intelligence into software development has progressed dramatically from its early days.
Initially, AI's role was largely supportive, offering features like intelligent syntax highlighting, basic code completion, and linting.
However, the advent of powerful large language models (LLMs) and sophisticated machine learning algorithms has propelled AI into a more active and collaborative role.
Today, AI tools are capable of suggesting entire blocks of code, refactoring existing codebases for clarity or performance, assisting in complex debugging processes, generating comprehensive test suites, and even scaffolding or fully generating entire applications from natural language prompts or design mockups.
This evolution is not merely an incremental improvement; it represents a fundamental shift in the software creation paradigm.
AI is transitioning from a passive assistant to an active collaborator, capable of understanding intent, managing context, and performing tasks that previously required significant human effort.
As noted, AI has become an important tool for developers when they write new code, check pull requests, create test cases, or fix bugs, with agentic AI being increasingly utilized for AI code reviews, AI code testing, and AI code generation, often initiated through simple text prompts.
This expanding role signifies a future where AI handles a larger proportion of the coding workload, allowing human developers to focus on higher-level architectural decisions, problem-solving, and innovation.
Categorization of AI Tools in the Development Lifecycle
The burgeoning field of AI-assisted software development tools can be categorized based on their primary functionalities and the stage of the development lifecycle they target. This categorization helps in understanding their core value propositions:
AI-Powered Code Editors & IDEs: These are development environments with deeply integrated AI capabilities, often aiming to provide a seamless AI-native coding experience. Examples include Windsurf, Trae IDE, and Cursor.
AI Coding Assistants & Extensions: These tools typically function as plugins or extensions within existing popular IDEs (like VS Code or JetBrains), providing features like code completion, chat-based assistance, and refactoring. GitHub Copilot and Cline are prominent in this category.
AI-Driven Testing & Quality Assurance Platforms: These platforms leverage AI to automate and enhance software testing processes, including test case generation, bug detection, and code quality analysis. Qodo is a key example.
AI-Powered No-Code/Low-Code Development Platforms: Aimed at enabling users with limited or no traditional coding skills (and also developers seeking rapid application development), these platforms use AI to generate applications from natural language descriptions or visual modeling. Lovable.dev, DataButton, and Bubble fall into this group.
Specialized AI Development Toolkits: These are collections of tools and resources designed to help developers build, train, evaluate, and deploy their own AI models or AI-powered applications. The AI Toolkit for VS Code is an example.
AI-Driven Data & Workflow Automation: This category includes tools that use AI to automate data integration, transformation, and business process workflows, which can be part of a broader development or operational ecosystem. Windsor.ai, Flowable, and Wisor (for supply chain) fit here.
This framework aligns with observations that AI-powered coding tools can be classified by functionality, such as AI code completion tools, AI code generators, AI debugging and error detection tools, AI test automation tools, AI code optimization tools, AI security and compliance tools, and AI documentation generators.
Such categorization is crucial for users to navigate the diverse market and identify tools that address their specific pain points and objectives.
Benefits and Challenges of AI Adoption in Development
The adoption of AI tools in software development offers a compelling array of benefits. Chief among these is a significant increase in developer productivity and an acceleration of development cycles.
GitHub Copilot, for instance, has been shown to increase developer productivity and speed up software development, and Windsurf claims potential productivity increases of 40-200%.
AI can assist with complex tasks, improve code quality through intelligent suggestions and automated checks, and make it easier for new developers to onboard by providing explanations and guidance.
Furthermore, AI-powered no-code/low-code platforms are democratizing development, enabling a wider range of individuals to create software.
However, the integration of AI also presents challenges. A learning curve is often associated with effectively utilizing these tools, particularly in mastering prompt engineering and understanding the nuances of different AI models.
There's a risk of over-reliance on AI, potentially leading to a decline in fundamental coding skills or critical thinking if developers accept AI suggestions without scrutiny.
AI-generated code is not infallible; it can sometimes be suboptimal, contain subtle bugs, or even introduce security vulnerabilities.
Integrating AI tools into existing complex workflows can also pose technical challenges. Cost is another consideration, with some advanced tools or extensive usage incurring significant expenses, especially with token-based models.
Data privacy and security are paramount concerns, particularly when proprietary code is processed by cloud-based AI models.
Finally, the "black box" nature of some AI models can make it difficult to understand their reasoning, posing challenges for transparency and explainability.
Flowable's documentation explicitly lists several AI risks, including privacy, security/accuracy, fairness, transparency/explainability, safety/performance, and costs, highlighting the need for careful consideration and governance.
A nuanced understanding of these benefits and challenges is essential for organizations to develop realistic expectations and implement effective strategies for AI adoption.
This involves recognizing that AI tools, while powerful, are best viewed as collaborators that augment human capabilities rather than complete replacements. This leads to a critical consideration: the dual trajectory of AI in software development.
On one hand, tools like Lovable.dev and DataButton are designed to empower non-developers or those with limited coding skills, effectively expanding the pool of software creators. This "democratization" of development can unlock innovation from individuals previously excluded from the app creation process.
On the other hand, tools like GitHub Copilot and Windsurf are focused on enhancing the capabilities of professional developers, enabling them to tackle more complex problems with greater efficiency.
This suggests that AI is not leading to a single future for software development, but rather a multifaceted one where different tools cater to varying levels of technical expertise and different goals.
This could redefine what it means to be a "developer", potentially creating a spectrum of roles with AI serving as a common enabling technology.
New collaborative models might emerge, where "citizen developers" using no-code AI tools work alongside "pro-code" developers leveraging advanced AI assistants, with AI itself orchestrating parts of this interaction.
Another important aspect arising from the capabilities and limitations of current AI tools is the growing imperative to "trust and verify".
While AI can generate impressive amounts of code, tests, and documentation, the potential for errors, security flaws, or suboptimal solutions remains a significant concern.
The ease with which code can be generated might lead to a rapid accumulation of software that has not been deeply understood or rigorously vetted by human experts.
This situation necessitates a heightened focus on robust review processes, comprehensive testing strategies, and thorough validation of AI-generated outputs.
For instance, Tempo AI's Agent+ service explicitly mentions that its quality is "guaranteed by Human Engineers and Designers", and Qodo positions itself as a "quality-first" platform emphasizing "code integrity".
This implies a shift in developer responsibilities: less time might be spent on writing boilerplate code, but more time and critical judgment will be required to evaluate, refine, and integrate AI-generated artifacts.
The role of senior developers could evolve further towards architectural oversight, sophisticated AI output validation, and the establishment of stringent quality gates.
Consequently, tools that facilitate this "trust and verify" paradigm —such as AI-assisted code review features, advanced automated testing suites, and AI systems that can explain their suggestions— will become increasingly vital.
Developer education must also adapt to include skills in effective prompting, interpreting AI outputs, and debugging AI-generated code.
It is important to acknowledge that a direct, one-to-one feature comparison across all tools is inherently challenging due to their varied nature.
Some are direct coding assistants, others are full-fledged IDEs, and some cater to no-code development or specialized automation tasks.
Therefore, the comparison aims to highlight the distinct characteristics and value propositions of each tool within its respective category, enabling users to identify solutions that align with their specific contexts and requirements.
This table serves as a central reference point, offering an at-a-glance overview that facilitates a more objective initial assessment and helps users quickly filter and identify tools relevant to their needs by standardizing information across a disparate set of solutions.
The Future is Vibey: An Accelerating Trend
Despite these hurdles, vibe coding is more than just a fleeting trend; it's seen by many as a dominant and accelerating force that's here to stay.
While still in its early stages, its evolution is rapid, and its popularity is only expected to grow.
It represents a significant shift in how we interact with technology and build the future, one vibe at a time.