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The Software Development World Is Undergoing Dramatic Change
The software development world is undergoing dramatic change alongside the evolution of AI. The concept of "AI writing code" — once the stuff of science fiction — is now materializing as a new movement called "Vibe Coding." This is not merely an automation tool. It is a new development style in which developers and AI collaborate to bring ideas to life at speeds and efficiencies previously unthinkable. Tom, a Y Combinator (YC) partner, is paying close attention to the possibilities of Vibe Coding and experimenting with it himself. His findings suggest that Vibe Coding is not only surprisingly powerful — it is also a practical technique that anyone can improve at through iteration and the accumulation of best practices. Like prompt engineering a few years ago, new discoveries are made every week, and the development community is actively sharing what it learns.
This article draws on Tom's insights and first-hand perspectives from founders in the latest YC batch to explain in detail the specific methodologies and practical tips for maximizing Vibe Coding and dramatically improving software development productivity.
- What Is AI Coding "Vibe Coding"? The Latest Techniques YC Founders Are Using
- Practical Strategies for Successful Vibe Coding: Planning, Testing, and Version Control
- From Bug Fixes to Advanced Applications: Tips for Getting the Most Out of AI Coding
- Summary
What Is AI Coding "Vibe Coding"? The Latest Techniques YC Founders Are Using
Vibe Coding refers to a new approach to software development that leverages AI tools including large language models (LLMs). It goes beyond simply auto-generating code — the goal is to bring ideas to life through conversation with AI and to accelerate the entire development process: design, implementation, and debugging. As Tom points out, the optimal techniques for Vibe Coding overlap significantly with the principles and practices that experienced software engineers already use every day. Some might ask, "Isn't that just software engineering?" — but the point is how to leverage these powerful AI tools to produce the best results. The field is evolving rapidly, and like prompt engineering before it, new techniques and best practices in Vibe Coding are being discovered and shared daily.
The founders in YC's latest batch are working at the cutting edge of AI tools, and the practical tips they've shared are highly instructive for getting the most out of Vibe Coding. For example, one tip: if an AI-integrated IDE gets stuck in a loop on a specific implementation or debugging task, copy that code and paste it directly into the UI of an LLM like ChatGPT and ask the same question — you may find a solution the IDE couldn't provide. This suggests that environmental constraints and differences between AI models play a role, and it can be an effective workaround when you're stuck.
Using multiple AI coding tools in combination is also a viable strategy. Using Cursor and Windsurf as examples: Cursor is relatively fast and better suited to fine-tuned frontend adjustments and full-stack work connecting frontend and backend, while Windsurf thinks more deeply and is better for building complex agents and refining prompts. Time that used to be spent just staring at a phone while waiting for an AI response can now be used to work on frontend revisions in Cursor while Windsurf handles a complex process in parallel — dramatically reducing development downtime. You can also give both tools the same context and prompt simultaneously, then pick the best of the generated variants. This lets you compare diverse approaches in a short time.
One founder says AI should be thought of not as a mere tool but as "a new kind of programming language." Traditional programming is coding in code; Vibe Coding is programming in natural language. To get good results, it's essential to convey necessary context, background information, and intent in detail and clearly — just as you would when giving instructions to another developer. Vague instructions produce vague results from AI.
Some founders are incorporating a test-driven development (TDD) approach. They begin by manually writing detailed test cases without using an LLM. These test cases become the strict "guardrails" the LLM must respect when generating code. As long as those guardrails are maintained, the LLM is free to generate code however it likes. If all tests pass (go green), there's no need to micromanage the code implementation — you can focus on the big picture: the overall modularity and structure of the codebase.
Some founders also emphasized the importance of thoroughly working through the scope and architecture of the feature being developed in conversation with a pure LLM (like ChatGPT) before handing off any coding to AI. Skip this step and you risk the AI haphazardly adding features to the codebase, producing code that either doesn't work well or is difficult to maintain. Having humans clearly understand "what" and "how" to build, then handing that blueprint to the AI, is the key to success.
Finally, there was shared advice on what to do when an AI starts "going astray" during code generation or debugging — falling into an inefficient loop. If it keeps regenerating similar code, if what's being generated just seems wrong, or if you find yourself repeatedly copy-pasting the same error messages, something is off. Stop, and prompt the LLM to "step back and think about why this is failing." Analyze whether there's insufficient context or whether you're just unlucky and facing a genuinely hard problem, then correct your approach. The practical wisdom these founders share makes clear that Vibe Coding is not a fantasy — it is a real technique that can be incorporated into daily development work.
Practical Strategies for Successful Vibe Coding: Planning, Testing, and Version Control
Where to start with Vibe Coding depends on your experience level. If you have no programming experience at all, starting with tools like Replit or Lovable is a good idea. These provide visually intuitive interfaces and are particularly well-suited for rapidly prototyping user interfaces. In fact, many product managers and designers have begun using these tools to implement ideas directly in code instead of creating mockups in Figma. The speed is remarkable. That said, based on Tom's experience, tools like Lovable are strong on UI changes but show limitations when you try to precisely modify backend logic — sometimes changing things you didn't intend to touch.
If you have even a little programming experience (even if rusty), you can go straight to more serious AI coding tools like Windsurf, Cursor, or Claude Code. These integrate with existing codebases and handle more complex development tasks.
Once you've chosen a tool, rather than jumping straight into coding, it is extremely important to first collaborate with an LLM to create a comprehensive development plan. Save this plan as a markdown file in your project folder and refer to it consistently throughout the development process. This is a roadmap for making progress with AI step by step — the antithesis of trying to build everything at once in a single shot.
Concrete steps for planning and incremental implementation:
Create an initial draft of the plan: Have a conversation with the LLM to produce an initial draft covering the features you want to implement, technical requirements, and rough architecture.
Review and refine: Carefully review the plan, removing unnecessary items and setting priorities. Complex features can be explicitly marked "Won't Do" for now, and future ideas kept in a separate "Future Considerations" section. This communicates the current scope clearly to the LLM.
Implement section by section: Break the plan into logical sections and give the LLM clear instructions such as "Let's just implement Section 2 first."
Verify and commit: Confirm that the implemented code works as expected, run your tests. If there are no problems, commit the changes to Git.
Update the plan: Have the AI mark the completed section (in this case, Section 2) as "done" in the plan file.
At this point in time, having AI build an entire complex product all at once is likely not realistic. By implementing incrementally based on a plan, verifying and committing at each step, you stabilize the development process and make it easy to revert to a previous state if something goes wrong. Keep in mind that AI models are evolving so fast that this "incremental implementation" advice may look different in a few months.
Version control is absolutely essential. Git plays an extremely important role in Vibe Coding. AI coding tools sometimes have their own "undo" functionality, but it cannot yet be fully relied on. Before starting any new feature implementation, it is recommended to always start from a clean state in Git (no uncommitted changes). This way, if the AI does something unexpected or breaks the codebase, you can instantly revert to the last known-good version with a command like git reset --hard HEAD.
If AI is not producing the expected results, endlessly tweaking prompts and retrying usually leads to bad outcomes. The AI tends to layer on ad hoc fixes rather than understanding the root cause, accumulating low-quality code. If you've iterated many times and finally found a solution, note just that solution, reset everything in Git back to a clean state, then re-prompt the AI to implement just that solution cleanly. This approach maintains a clean codebase in the long run.
Creating tests is also indispensable. LLMs are good at generating test code, but by default they tend to generate low-level unit tests targeting individual functions. In Vibe Coding, it is better to write higher-level integration tests and end-to-end tests — tests that simulate scenarios where a user actually clicks through a site or app and verify that features work together as intended. What matters is guaranteeing that the functional value is being delivered, not the correctness of individual functions.
In particular, LLMs have a bad habit of unexpectedly modifying unrelated logic outside the area they were instructed to change. Having a high-level test suite lets you catch these unintended changes (regressions) early and Git-reset before the problem grows.
AI is also a powerful assistant for non-coding tasks. Tom described handing over DNS server configuration — something he found difficult — and Heroku deployment setup to Claude Sonnet 3.7, and feeling like it had acted as a DevOps engineer and accelerated development tenfold. He also described having ChatGPT generate a favicon image, then having Claude write a simple script to resize it into the six different sizes and formats required by various platforms. In this way, AI can take on the roles of designer and infrastructure engineer. Planning, version control, testing, and applying AI to non-coding tasks — being mindful of these will let you unlock Vibe Coding's full potential.
From Bug Fixes to Advanced Applications: Tips for Getting the Most Out of AI Coding
Bugs are inevitable in software development, and efficient bug-fixing techniques exist in Vibe Coding too. The most basic first thing to try: copy the error message you've encountered and paste it directly into an LLM. Server log output or browser JavaScript console errors alone are often enough for AI to identify the cause and suggest a fix. You don't even need to explain what the problem is or how to fix it. Tom predicts that in the future, even the copy-paste step will be unnecessary — AI coding tools will automatically monitor log files, launch headless browsers to detect JavaScript errors, and make fix suggestions on their own.
When facing a more complex bug, rather than immediately trying to fix the code, it can be more effective to first prompt the LLM to "list three or four possible causes" and have it form hypotheses. Then try fixes based on each hypothesis — but critically, if each attempt fails, Git-reset back to a clean state before trying the next one. Skip this and, as mentioned, ad hoc fix code accumulates and the problem becomes more complex. "If it fails, reset and try again" — and don't forget to add debug logging code where needed ("Logging is your friend").
If one AI model isn't working, actively try others. Claude Sonnet 3.7, OpenAI's GPT series, Google's Gemini — models have different strengths and weaknesses. A problem one model couldn't solve may be resolved easily by another. And if you've identified the cause of a tricky bug, it's best to reset all the changes you made during the trial-and-error, then give the LLM very specific instructions targeting just that specific fix against a clean codebase. This prevents "junk code" accumulation and maintains a healthy codebase.
To get more control over LLM behavior and consistently get expected results, clearly writing "Instructions" is essential. Whether they're called "Rules" in Cursor, "Rules" in Windsurf, or a markdown file passed to Claude, each tool has a way to specify the LLM's basic operating principles, coding conventions, and project-specific rules. Some founders write hundreds of lines of detailed instructions, dramatically improving the efficiency and accuracy of AI coding agents. There's plenty of information online about what to include — worth looking up.
When referencing external library or API documentation, current AI agents sometimes struggle to directly reference web documentation. Using MCP servers is one advanced approach, but Tom recommends a simpler method: download the necessary documentation and put it in a subdirectory of your project folder. Then include in your LLM instructions: "Before implementing this feature, please read the documentation in the local docs directory." This lets you generate code based on more accurate information.
LLMs can be used not just to write code, but as "teachers." When learning a new programming language or framework, you can ask the AI to explain the code it generated line by line. It's a far more efficient and interactive learning experience than scrolling through Stack Overflow.
When tackling a new feature that would normally be considered too complex to hand off to AI, rather than directly integrating it into a large existing project, it's recommended to first create a small standalone reference implementation in a completely isolated, clean codebase. Create it yourself, or find a similar implementation on GitHub and download it. Then show that reference implementation to the LLM and say: "Please re-implement this in the main project." This approach is remarkably effective.
In code organization, "small files" and "modularity" are more important than ever. This is good practice for human developers and equally true for LLMs. A massive monolithic repository with tightly intertwined internal dependencies is difficult for both humans and LLMs to understand — changes in one place risk unexpectedly affecting others. A shift toward modular or service-based architecture with clear API boundaries is likely to accelerate. When external interfaces (APIs) are stable, internal implementations can be freely changed (as long as tests pass), making AI-driven modification easier.
The technology stack you choose for development can also affect AI coding efficiency. Tom described being surprised at the AI's high performance when building a project in Ruby on Rails, which he had used years ago. He attributed this to Rails being a framework with 20 years of history and well-established conventions — many Rails projects have similar structures and it's relatively clear where to implement a feature and what the "Rails way" is. As a result, there's a large volume of consistent, high-quality Rails training data online that AI could learn from effectively. By contrast, a friend told him that with relatively newer languages like Rust or Elixir, AI performance wasn't what he'd hoped — though this too is likely to change rapidly.
New interaction modes like screenshots and voice input are also worth leveraging. When visually showing a UI bug or referencing another site's design, pasting a screenshot directly to an AI agent is very convenient. With a voice input tool like Aqua, you can give instructions at around 140 words per minute — faster than typing. AI is forgiving of minor grammatical errors or punctuation issues, so a perfect transcription isn't necessary.
Finally, don't overlook the importance of frequent refactoring. With code that works and tests implemented, you can refactor with confidence — the tests will catch any regressions. You can even ask the LLM to "identify areas of repetition or refactoring candidates in the codebase." This is a habit any professional software developer should maintain, and keeping files small and modular makes the code easier to understand for both humans and LLMs. Above all, always maintain an experimental mindset. The cutting edge of AI coding changes on a weekly basis. Try every new model as it's released and see which excels at debugging, planning, implementation, and refactoring. As of the time the source video was published, Tom felt Gemini excels at indexing entire codebases and drafting implementation plans, while Claude Sonnet 3.7 was a strong performer for actually implementing code changes. Always try new tools and models and keep searching for your optimal workflow — that is the key to mastering Vibe Coding.
Vibe Coding is an exciting new frontier with the potential to transform the landscape of software development. As YC partners and founders demonstrate, AI coding tools are already delivering remarkable capabilities — and by incorporating best practices like a planned approach, rigorous version control, a focus on testing, and continuous experimentation, you can extract their maximum value. By treating AI not merely as a code generator but as a design consultant, debugging assistant, DevOps engineer, and even teacher, it's possible to dramatically improve productivity across the entire development process.
The key is not to think of AI as a magic wand, but to understand it as a powerful new "tool" or "programming language" and to sharpen your skills for leveraging its characteristics. The principles of great software engineering — detailed instructions, shared context, incremental work, and constant verification — remain just as important in the Vibe Coding era.
AI model evolution shows no signs of stopping, and today's cutting-edge technique may be obsolete tomorrow. That is precisely why a spirit of continuous inquiry — gathering new information, trying different tools and models, and constantly optimizing your own workflow — is required. We hope the tips and strategies in this article serve as a foundation for your AI coding efforts, helping you achieve more efficient and creative software development. Let's explore the future of development together, with AI as our partner.
Reference: https://www.youtube.com/watch?v=BJjsfNO5JTo
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