To accelerate development and improve the overall user experience, I experimented with Cline through its VSCode plugin as my primary AI coding assistant. Here’s a detailed breakdown of my experience. (View Highlight)
As someone who primarily focuses on backend development, UI/UX has always been a challenge. My limited knowledge of modern web frameworks and general aversion to CSS made frontend work particularly daunting. However, leveraging AI tools transformed this weakness into an opportunity for rapid improvement. (View Highlight)
The impact was immediate and substantial. I tasked the LLMs with redesigning every page of the website:
• The landing page underwent a complete transformation
• The management interface (where users configure their services) received significant upgrades
• The overall design evolved from basic to professional-grade (View Highlight)
Working with Cline proved intuitive thanks to its ability to analyze open files and understand repository context. (View Highlight)
• Established Context: Clearly identified the technology stack and file structure
• Defined Scope: Provided specific implementation requirements
• Set Boundaries: Prevented unnecessary JavaScript modifications
• Specified Requirements: Detailed the exact structure needed for the wizard implementation (View Highlight)
• Expertise Matters:
• With strong domain knowledge, AI became a powerful accelerator
• Code refinement and optimization suggestions were particularly valuable
• Repetitive tasks were handled efficiently
• Pitfalls for Non-Experts:
• Limited domain knowledge led to accepting suboptimal AI suggestions
• Understanding the underlying technology proved crucial for effective AI collaboration
• Code review skills remained essential for quality assurance (View Highlight)
In particular, I asked ChatGPT to write a function by knowing precisely how I would have implemented it. This is crucial since without knowing the expected result and what every line does, I might end up with a wrong implementation. The function I asked to implement is a simple similarity score among strings. Since the bot is posting deals by searching for keywords on Amazon, the PA-API is likely to return on the same request multiple version of the same product (e.g. a blue camera vs a black camera of the very same model). (View Highlight)
With a very precise prompt, that must contain also a valid example, the generated function is very similar to the one I had in mind to solve this problem. (View Highlight)
The generation time is just a couple of seconds, plus one minute to read and understand if the function does what I requested. The boost in productivity is huge since I would have spent at least 3 or 4 times this time to write by myself the function. (View Highlight)
While the overall experience was positive, several challenges emerged:
• Context Management: Claude Sonnet 3.5’s window limits required careful task segmentation
• Performance Trade-offs: Balancing speed (Sonnet) versus capacity (Gemini)
• Expertise Requirements: Backend development highlighted the importance of human expertise
• Quality Assurance: Continuous review remained essential for maintaining code standards (View Highlight)
AI-assisted development has fundamentally changed my approach to coding. For frontend tasks, tools like Cline with models such as Claude Sonnet 3.5 have proven invaluable, offering rapid solutions to design challenges. In backend development, these tools excel when guided by experienced developers who can effectively validate and integrate AI suggestions. (View Highlight)
The key to success lies in understanding that AI tools are powerful amplifiers of existing skills rather than replacements for fundamental knowledge. They excel at accelerating development cycles, improving designs, and streamlining workflows, particularly in areas outside one’s core expertise. (View Highlight)
