10 things i learned from burning myself After extensive experimentation with AI coding agents, a developer reflects on the challenges and insights gained from the experience.
10 things i learned from burning myself
The Allure of AI Coding Agents
For many tech enthusiasts, the introduction of AI coding agents has been nothing short of revolutionary. Much like the excitement of using a 3D printer for the first time, where a simple button press transforms a digital file into a tangible object, AI coding agents promise to simplify and enhance the software development process. However, just as a 3D-printed object often requires further refinement and expertise to be production-ready, the output from AI coding agents necessitates a level of skill and understanding that goes beyond mere interaction.
Since November, I have been using Claude Code and Claude Opus 4.5 through a personal Claude Max account, diving deep into the world of AI-assisted software development. My journey has also included some experimentation with OpenAI’s Codex, although my usage of that platform has been less frequent. After completing fifty projects, I can confidently say that this experience has reignited a passion for programming that I haven’t felt since my early days coding in BASIC on an Apple II Plus at the age of nine. However, this enthusiasm comes from personal experience rather than an endorsement of the technology. I voluntarily undertook this project and financed both OpenAI and Anthropic’s premium AI plans out of my own pocket.
My Programming Background
To provide context for my journey, it’s essential to understand my background in programming. Throughout my life, I have engaged with coding primarily as a utilitarian endeavor. I have written small tools and scripts as needed, but my primary focus has been on modifying existing code to suit my requirements. My programming journey began in 1990, and over the years, I have dabbled in various languages, including BASIC, C, Visual Basic, PHP, ASP, Perl, Python, Ruby, and MUSHcode. While I am not an expert in any of these languages, I have acquired just enough knowledge to accomplish my goals.
In addition to my utilitarian coding, I have developed hobby games using BASIC, Torque Game Engine, and Godot. This experience has given me a foundational understanding of what constitutes good software architecture, particularly for modular programs that can be expanded over time. However, my coding skills have always been limited by my lack of formal training, which has often led to frustration when attempting to tackle more complex projects.
The Initial Excitement
The initial thrill of using AI coding agents was palpable. The ability to generate code snippets, automate repetitive tasks, and even brainstorm project ideas felt like a significant leap forward in productivity. I found myself rapidly prototyping applications that would have taken much longer to develop manually. The AI’s ability to understand natural language queries and translate them into functional code was a game-changer, allowing me to focus on higher-level design and problem-solving rather than getting bogged down in syntax.
However, as I delved deeper into this new realm, I began to encounter challenges that I had not anticipated. The AI’s outputs, while impressive, often required significant refinement. This led to a realization: while AI coding agents can assist in the development process, they are not a substitute for foundational programming knowledge.
Key Lessons Learned
1. AI is a Tool, Not a Replacement
One of the most critical lessons I learned is that AI coding agents should be viewed as tools rather than replacements for human developers. While they can generate code and automate tasks, they lack the nuanced understanding of context that a human programmer possesses. This means that the responsibility for quality assurance, debugging, and overall project architecture still falls on the developer.
2. The Importance of Context
AI coding agents often struggle with context. They can generate code based on prompts, but they may not fully grasp the specific requirements of a project. This can lead to outputs that are technically correct but misaligned with the project’s goals. As a result, I found myself spending considerable time refining and adjusting the AI-generated code to fit my needs.
3. Learning Curve and Adaptation
While AI coding agents can accelerate the development process, they also come with a learning curve. Understanding how to effectively communicate with the AI and frame prompts to yield the best results took time and experimentation. I had to adapt my approach to coding, learning to think in terms of how the AI interprets language and code.
4. Collaboration with AI
Rather than viewing the AI as a competitor, I learned to see it as a collaborator. This shift in mindset allowed me to leverage the AI’s strengths while compensating for its weaknesses. By treating the AI as a partner in the development process, I could focus on higher-level design and problem-solving while the AI handled more routine tasks.
5. The Need for Human Oversight
Despite the impressive capabilities of AI coding agents, human oversight is essential. The AI can generate code, but it cannot assess the quality or security of that code. I encountered several instances where the AI produced code that was functional but had security vulnerabilities or performance issues. This reinforced the importance of conducting thorough code reviews and testing, even when using AI-generated outputs.
6. Balancing Creativity and Structure
AI coding agents can assist in brainstorming and generating creative solutions, but they also tend to favor established patterns and structures. This can be beneficial for certain projects, but it may stifle creativity in others. I found that while the AI could generate code quickly, I often had to push back against its suggestions to explore more innovative solutions.
7. The Role of Documentation
Documentation became even more critical in the context of AI-assisted development. As I worked with AI-generated code, I realized that clear documentation was essential for understanding the rationale behind certain decisions and ensuring that future modifications could be made with ease. This is particularly important when collaborating with others or when revisiting a project after some time.
8. Ethical Considerations
The use of AI in coding raises ethical questions that developers must consider. Issues such as copyright, data privacy, and the potential for bias in AI-generated outputs are critical to address. As I navigated my projects, I became increasingly aware of the need to approach AI coding with a sense of responsibility and ethical consideration.
9. The Future of Development
AI coding agents are likely to play an increasingly prominent role in software development. As technology continues to evolve, it is essential for developers to adapt and embrace these tools while also recognizing their limitations. The future of development will likely involve a hybrid approach, where human creativity and intuition are complemented by AI’s efficiency and speed.
10. Personal Growth and Reflection
Finally, my experience with AI coding agents has prompted significant personal growth and reflection. I have gained a deeper understanding of my own coding abilities and limitations, as well as a renewed appreciation for the art of programming. This journey has not only enhanced my technical skills but has also encouraged me to think critically about the role of technology in our lives.
Conclusion
In conclusion, my exploration of AI coding agents has been a rewarding yet challenging experience. While these tools offer incredible potential to enhance productivity and streamline the development process, they also require a thoughtful approach and a solid foundation in programming principles. By viewing AI as a collaborator rather than a replacement, developers can harness its strengths while mitigating its weaknesses. As we move forward in this rapidly evolving technological landscape, it is crucial to remain vigilant, ethical, and adaptable in our approach to AI-assisted development.
Source: Original report
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Last Modified: January 20, 2026 at 2:41 am
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