I’ve been automating my code creation with AI for a while now, long before the recent surge of interest in tools like Cursor. Back when Cline was still known as Claude Code, I was already practicing what’s now called “vibe coding” – describing the desired outcome and letting AI generate the corresponding code.
If you’re skeptical, just browse through my earlier LinkedIn posts. I’ve diligently documented this evolving process, and while the achievements have been remarkable, they’ve also presented their own set of unique challenges.
My Current Toolkit: The Best AI Coding Tools
After substantial experimentation across a range of platforms, here’s what I’ve concluded regarding the current state of available AI coding options:
- Cline: My top recommendation for beginners. It builds upon Claude’s robust reasoning capabilities while providing a straightforward user interface. It streamlines the coding process and is easy to grasp, especially for those just starting out.
- Roo Code: This is a more sophisticated derivative of Cline, packed with many experimental configuration settings. It’s primarily suited for developers who enjoy tinkering and fine-tuning their AI-assisted coding process to a very high degree.
- Cursor: While it’s quite popular, it hasn’t performed as well as Cline in my personal tests. Cursor is definitely capable, but it lacks some of the refinements and ease of use that Cline offers.
The underlying models have a substantial impact, and Claude (whether it’s the 3.7 Sonnet or 3.5 Sonnet version) consistently yields impressive results. That being said, Gemini 2.5 Pro has recently edged out Claude in certain applications, but there’s a valid reason why Claude is so well-regarded within the developer community. It’s reliable and powerful.
Key Takeaways: Lessons I’ve Learned from Countless Hours of AI Coding
Through my significant hands-on experience with AI-driven development, I’ve boiled down some fundamental principles that reliably deliver better outcomes:
1. Rigorous Testing: Always Verify AI-Generated Code
AI models can produce code that seems legitimate but has subtle errors or can be easily exploited through security vulnerabilities. It’s imperative to write tests for all code generated with AI. This isn’t simply sound practice — it’s critical when using AI to code. If you don’t know how to test, you shouldn’t be using AI to code.
2. Modular Design: Break Down Complex Projects
Deconstruct your projects into smaller, isolated files instead of putting all code into a single, large file. There are two clear advantages to this method:
- It enables AI models to better understand and properly modify more manageable pieces of code.
- It encourages stronger architecture, making your codebase simpler to maintain as a whole.
3. Prioritize Security: Always Conduct Security Audits
Always perform a security assessment before releasing your project to the public. AI models can sometimes make significant security mistakes. This includes leaving API keys visible or implementing weak authentication methods. Never place API keys directly within code. Instead, use secure vaults or environment variables.
This is notably relevant for individuals who use AI to code without sufficient technical expertise. This is a huge issue, and the security consequences can be catastrophic.
4. Version Control: An Absolute Requirement
Be absolutely certain to use Git or another version control system if you’re using a tool such as Cursor that lacks integrated version control. Cline has this functionality built in, which is a primary reason I favor it over other options.
5. The Art of Prompting: An Essential Skill
Have a clear concept of what you want before creating any prompts. Understand your objectives, but avoid overwhelming the AI by giving it the complete task prematurely. Begin only with the basic framework and incrementally incorporate features and functionalities. This strategy helps the model digest and accurately execute your vision.
6. Selecting the Right Language: Stay Mainstream
Whenever possible, use commonly used programming languages and frameworks. Even if a more obscure language technically performs better for your application, AI will serve you best if you stay within languages supported by extensive training data. As an example, React will likely generate superior results compared to using less-known libraries with fewer examples to learn from.
7. Manage Context: When To Reset and How To Remember
If the model starts deviating from the desired course, don’t hesitate to reset the context. AI can sometimes become fixated on counterproductive patterns. Restarting with a clean slate will usually improve the situation.
To reduce the disruption caused by context resets, maintain detailed documentation and project memory files. This enables you to quickly reinstate context after performing a reset by simply re-introducing the AI model to those files.
My Typical AI Coding Workflow
Below is a simplified outline of my strategy for taking on a new project using AI:
- Define the Project Scope. I begin by writing a detailed account of the project’s objectives, required features, technologies being used, as well as any limitations imposed.
- Establish the Architecture. I then ask the AI to propose a fundamental structure for the project, including how files should be organized.
- Design the Core Framework. In partnership with the model, I develop the foundational structure that will support all core functionality.
- Implement Features Incrementally. We add functionalities step-by-step, along with appropriate tests, to ensure everything functions as expected.
- Document Extensively. I continually create memory files that comprehensively describe how each element of the project operates.
- Perform Security Reviews. Nearing completion, the AI is tasked to review the entire project for security concerns, identifying potential vulnerabilities.
Future Outlook: The Trajectory of AI Coding
The substantial improvements in models like Gemini 2.5 Pro suggest that AI’s coding skills will only continue increasing quickly. We’re rapidly progressing toward a point where the primary obstacle is not the AI’s capacity to generate code, but instead it is our ability to articulate and define our requirements clearly. The Athena AI agent is a powerful tool for helping with that.
As models get better at managing more extensive contexts and preserving consistency in complex projects, I expect that niche-specific AI coding tools will emerge that can deeply comprehend specific domains, whether it’s embedded systems, data engineering, or front-end software development.
Tool Selection: When and Where to Use Each
Based on my direct experiences, the following guidelines apply in selecting AI coding tools for different scenarios:
- Cline: Choose this for general development jobs, particularly if you’re a beginner. Its integration with version control is a big plus.
- Roo Code: Best suited for expert users wanting very granular control over every element of their AI-assisted coding activities.
- Cursor: Use if you are already working within a distinct version control framework and simply desire a more lightweight coding tool.
- Claude Directly: Optimal for simpler tasks or those instances when you need to explain fairly complex business logic that relies substantially on Claude’s underlying reasoning engine. Do not use directly for writing complex code.
Final Thoughts
AI-enhanced coding is not about substituting programming expertise with artificial intelligence. Instead, it’s about making expertise more potent and scalable. The most effective use of AI coding tools occurs when you possess a solid awareness of their strengths and weaknesses.
For me, adopting AI in coding has considerably accelerated the development cycle. Yet at the same time, I’ve also learned that the need to plan carefully, test thoroughly, and be vigilant regarding security aspects is more important than ever.
As AI technology advances, the traditional divisions between “programmers” and “non-programmers” will become more indistinct. The key factor will be your capacity to express your vision to AI and validate its outputs rather than the number of programming languages you’re familiar with.
How have you experimented with AI coding tools? I welcome you to express your opinions and provide feedback on which workflows have worked for you.
