Vibe Coding: My Adventures in Agentic Coding! 😄

In this blog, I'm excited to share my experiences with vibe coding, also known as agentic coding. This innovative approach to software development employs an AI agent to tackle coding tasks in a revolutionary way. You may have heard folks say, “there’s virtually no actual coding” involved in this method, and while that’s quite the claim, there’s a lot of truth to it!

Context 🌍

So, what’s the deal with vibe coding? The concept revolves around using AI agents integrated into IDEs like Cursor, Copilot, or Windsurf. Instead of the old school method of pressing Tab to auto-complete a line, the aim here is to let the AI compose entire applications from scratch to finish. No more rote coding! Isn’t that a futuristic dream?

I've been having a blast exploring Cursor and Repo Prompt, and recently began to tinker with Google Jules. Let’s delve into what I’ve learned!

Learnings 📚

I’m not here to regurgitate a how-to guide—there’s a treasure trove of YouTube videos out there for that. Instead, let's chat about my personal learnings from vibe coding adventures. Here are some essential insights to maximize your experience:

Detail the Specifications 🗂️

First things first, you’ve got to write down your specifications. Like, really spell them out! You can even lean on AI tools like ChatGPT or Grock3 to transform your ideas into structured user stories. Your spec should detail technical specs, app functions, database schema, and API endpoints. Clear direction leads to cleaner code. Trust me, your AI will thank you!

Define Rules ⚙️

Once your detailed specs are in place, it’s time to roll them into the AI panel in your IDE of choice. Before doing that, an important lesson learned, especially for larger codebases, is to set rules. Both Cursor and Windsurf support defining rules, guiding the AI on how you want to code, which technologies to use, and your preferred workflows. These rules are instrumental in keeping your AI in check and minimizing unexpected tech-switch-a-roos.

Pro Tip: Using rules may save you from random tech decisions that could derail your entire project. Nobody wants that!

Understanding the Need for Rules 🔍

Grasp why certain rules are necessary—it reflects on how the AI behaves. Here’s a collection of key insights:

• Simple Solutions: Ensure the AI generates simple, effective code. Sometimes, our clever coding buddies can overthink things and become too complex.
• Code Duplication: AI can inadvertently duplicate code when adding features, so instruct the agent to review the codebase for similar functionalities before jumping in.
• Separate Environments: Clearly state your development, testing, and production environments; otherwise, your AI might mix things up (not good!).
• Define Your Stack: Specify tech stacks to prevent tech mismatches (one wrong library can throw the whole thing off!).
• Change Request Focus: When requesting fixes, guide the AI to focus strictly on your specified change without altering unrelated parts of the code.
• Keep It Clean: Address disorganization early on to avoid daunting messes down the road.
• Limit File Size: Aim for a maximum of 300 lines of code in a file to keep your code manageable; larger files are daunting for any AI (and developers alike!).
• Configuration Care: No overwriting key configuration files! Keep your environment intact and running smoothly.

One Step at a Time — Strict Configuration Management ⏳

Aim for a focused approach: handle one feature or fix at a time. Test, stage your code, commit, and then move to the next task. It’s a surefire way to maintain order in your programming universe!

Managing the Context — Not Too Much, Not Too Little 🔄

The context you give the AI matters. Too much or too little can hamper its performance. Test and experiment to find the sweet spot for starting new chats. And if you lose context, tools like RepoPrompt can help you reintroduce essential files, ensuring optimal code generation.

Embrace Iterative Testing 🧪

Agentic testing is as crucial as agentic coding. Instead of focusing solely on unit testing, leverage end-to-end testing where the AI simulates user actions. Trust me, it’s the most reliable method we've explored so far.

Stick to Popular Stacks & Avoid YOLO Mode ⚡

Most large language models are trained on popular tech stacks, so using common technologies like Python, HTML, and SQL yields better results. Also, avoid auto-apply features (a.k.a. YOLO mode)—always review changes before they go live.

Attention is All You Need 👀

Pay close attention to agent conversations. Sometimes it’s easy to overlook minor decisions, so carefully read through prompts before accepting. Changes can be tricky to roll back!

Avoid Addiction — Be THE MASTER, Not a Slave 🙅‍♂️

It's easy to fall into the trap of relying too heavily on these AI tools. Sometimes, my coding skills easily beat the AI’s output for simpler tasks. Maintain control of your coding journey!

Summary 📝

Agentic coding can turbocharge your development process, but it’s vital to use it wisely. Stay in control, keep learning, and ensure the AI never replaces your precious problem-solving skills.

It's been an exhilarating adventure with vibe coding, and I look forward to sharing more insights as I continue my journey. Don't forget to share yours as well—let's vibe together! 😄