How AI Can Enhance Your Learning in New Technologies - My Experience with Solana Development
Introduction
When facing a new technology like Solana, I encountered an intense learning curve that involved mastering both Solana development and the Rust programming language. These studies revealed a series of technical challenges, and artificial intelligence proved to be a strategic ally in optimizing the learning process. In this article, I share how I utilized different AI tools to understand the intricacies of Solana and Rust, reflecting on how these resources can facilitate the study of any new technology.
My Background
I have been a full-stack developer since 2007, with extensive and diverse experience in blockchain, accumulated since 2018. Over the years, I have worked extensively with Ethereum, Neo, and Flow blockchains, applying each to different types of projects and decentralized solutions. This background has provided me with a solid foundation in blockchain systems, enabling a quick adaptation to the fundamentals of the Solana environment, while AI was essential for navigating its specific technicalities.
AI as a Resource for Technical Studies
Leveraging Courses and Documentation with AI Support
My initial approach was to explore the introductory courses and the official documentation of Solana. The courses on the Solana website have a very practical and well-structured methodology, with progressive exercises that allowed me to consolidate my initial knowledge efficiently and clearly. This foundation was essential, but as I delved into more advanced topics, specific doubts arose, and to resolve them, AI, particularly Claude, became an indispensable ally.
Asking Claude was advantageous for several reasons. Unlike other AIs I tested, Claude offers more up-to-date and detailed answers, especially in areas where blockchain frameworks evolve rapidly. Additionally, Claude tends to be more assertive on technical topics, providing precise explanations, while other AIs, such as ChatGPT and Gemini, struggled to handle specific themes related to the Rust language and the Anchor framework. This complementary resource to the course methodology allowed me to maintain an accelerated learning pace, dynamically aligning theory and practice.
AI as a Partner in Creating a Practical Project
After completing the introductory courses, I felt ready to create a complete application in Solana, and it was at this point that AI became even more relevant. To structure my project, I began by defining the main concepts and laid out an initial plan with topics and functionalities I wanted to implement. In the development phase, I used the Cursor IDE, an AI tool that helps write and refine code in real-time.
The process began with generating the base code for an Anchor contract, which I used as a starting point. For each generated snippet, I carefully read the code and adjusted parts that did not exactly fit my vision. My strategy was to ask for explanations to understand the rationale behind each decision, which helped me tailor the code to my needs, with several interactions to arrive at functional code that met the project's initial vision.
However, using AI at this level brought some challenges:
Submissiveness: At many points, when questioning the AI, it seemed to respond submissively, inventing functions of Anchor or Solana that didn't exist just to satisfy my requests. This led to confusion and required additional time to filter out the valid information from the incorrect suggestions.
Limitation of Reasoning: The AI often showed limitations in its reasoning capabilities. In many instances, when I sought more creative or complex solutions, it provided only standard responses. This necessitated me to conduct independent research without the AI to find the right approaches and techniques needed for my project.
Outdated Information: The Anchor framework is a constantly evolving tool, and in some instances, the AI suggested commands and practices that were already obsolete, resulting in errors and requiring extra manual review to ensure the code aligned with the latest versions.
Despite these difficulties, having a solid study base was crucial to discern what made sense and what needed correction. Without that discernment, it would be easy to adopt incorrect answers from the AI without realizing it, potentially compromising the project. In summary, the experience with AI was valuable, but my accumulated knowledge was decisive in making assertive decisions in development.
Enhancing Contract Performance with AI Insights
During the development of the contract, I suspected that a particular solution might be costly in terms of performance. To address this, I asked the AI to evaluate the performance based on a number of interactions. The AI presented potential problems along with possible solutions, guiding me in optimizing the contract’s efficiency. This interaction significantly enhanced my understanding of performance considerations, and I was able to apply what I learned to refactor other parts of the code, ultimately achieving a highly performant application.
Exploring and Addressing Security Issues
As I delved deeper into security for Solana, especially concerning the use of PDAs (Program Derived Addresses) and transaction inclusion in blocks, I developed an exploration method that allowed me to identify vulnerabilities in the contract. To do this, I created a testing script that simulated an attack, which helped validate and enhance the security of the code. These simulations revealed a potential flaw, and I had already conceived an alternative solution to address the security issue.
This process demonstrated that while AI was useful for writing and adapting code, the analysis of security and complex implementation decisions still required critical thinking and in-depth experience in understanding how the network operates.
Conclusion: How AI Can Complement Your Learning
My experience using AI to learn Solana and Rust showed that, despite its limitations, it is a powerful tool when combined with a structured study strategy. For new developers, I recommend:
Using AI to obtain quick answers and alternatives to conventional learning methods.
Being critical of AI responses and validating information to ensure accuracy.
Leveraging AI as a partner while also committing to manual and experimental practices.
AI is extremely useful for generating code quickly, streamlining the writing process and allowing developers to focus more on the logic and architecture of the project.
I hope this narrative inspires other developers to explore AI in their studies of new technologies. Learning with AI is not only efficient; it is a pathway to self-sufficiency and technical mastery in less time.