Hackathons Should be A Nationwide Movement, Not Just the Domain of Developers: Reflections on A New Paradigm for Hackathons in the AI Era
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Author：Marshal Orange @ Contributor of PermaDAO
Translator：少华 王 @ Contributor of PermaDAO
Reviewer： Saiee @ Contributor of PermaDAO
Hackathons Should be A Nationwide Movement, Not Just the Domain of Developers: Reflections on A New Paradigm for Hackathons in the AI Era
In this era of "everyone is a product manager," everyone has endless inspiration and creativity. However, they are often limited by any threshold in the technical or product implementation path, which leads to a lack of the ability and courage to participate. This is also the situation I inquired about and learned from many people in Arweave's PermaDAO during their hackathon. But a hackathon is not the exclusive domain of a few; it is a nationwide event.
I believe that the process of a hackathon competition is more like a User-Generated Content (UGC) activity centered around technology and innovation. The participants in the competition are clearly "content creators" for the blockchain ecosystem. So, we can break down the complete workflow of hackathon projects into five stages that developers are familiar with:
Problem Definition and Planning Phase
Requirements Analysis Phase
Product Prototype and Software Design Phase
Software Development Phase
Software Testing Phase
The real challenge lies in how to reduce the barriers that participants face in these five stages of product development. Humans can leverage AI as a productivity tool to participate in this nationwide event by integrating the existing UGC elements of the hackathon. This enables every creative idea to be maximized, just as EverID abstracts accounts to reduce the barriers for users to use Web3 wallets. Clearly, these efforts to reduce barriers can greatly propel the development of the blockchain industry.
Problem Definition and Planning Phase: Using data from ecological user feedback, AI large models can be employed to generate issues that are currently receiving attention or have market pain points, which are more valuable and meaningful for the ecosystem. Adot uses AI to generate sentiment analysis labels for each parsed top DApp, and users can search for data and filter results based on these labels. By analyzing existing on-chain user behavior and demands, AI can help users identify potential issues and provide suggestions on how to address these issues, thereby better defining the project scope.
Requirements Analysis Phase
Data Mining: Blockchain hackathons typically involve the storage and analysis of a large amount of data. Using real-time querying and manipulation of permanent data on the Arweave chain implemented with GraphQL, combined with AI to accelerate data processing and analysis, can help identify patterns, trends, and potential issues, providing participants with suggestions on how to optimize blockchain applications. AI can analyze data such as historical case matches and user behavior to identify trends and patterns in future demand, assisting participating teams in better understanding user requirements.
Automated Requirement Validation: AI can automatically verify whether requirements are complete, consistent, and verifiable (though rarely used in the intense schedule of hackathons). It can identify potential conflicts in requirements, thereby reducing the workload for later fixes.
Product Prototyping and Software Design Phase
Auto-generating Prototypes: Utilizing image-generating AI like Midjourney to create initial interface prototypes, saving a significant amount of time for product managers. This can help the team validate concepts more quickly and facilitate rapid iteration.
Providing Design Suggestions: AI can offer recommendations on user interface design, layout, and usability to enhance the product's user experience.
Software Development Phase: AI Can Fill the Gaps for Non-Technical Personnel; AI can generate partial or complete code, expediting the development process. This is especially valuable for the development of specific modules involving inheritance, overriding, and overloading, significantly improving program efficiency. AI can also detect potential errors, vulnerabilities, and security issues in the code, enhancing code quality.
Software Testing Phase: Utilizing AI to analyze the application's code and functionality, AI-driven automated testing tools can conduct large-scale testing, cover various use cases, and automatically detect and report issues.
In addition, blockchain hackathon projects typically require documentation and presentations. AI can automatically generate technical documents, PowerPoint presentations, and test reports, alleviating the burden of document creation for participants. Furthermore, in situations where hackathon teams need to form freely, AI can analyze the skills, interests, and project experiences of team members and provide recommendations for suitable teammates, thus promoting better teamwork.
AI allows anyone to become a product development team, even if they have no development experience. From problem formulation to solution development and documentation writing, AI can provide various forms of support to participating teams in blockchain hackathons. For Web3, AI is not only a technological change but also a change in production relations. At the same time, Web3 has profound implications for the field of AI. The privacy, trustlessness, decentralization features of Web3, as well as data and asset ownership and the token economy, can bring significant changes to AI infrastructure, allowing the three most important elements of AI infrastructure - data, models, and computing power - to be optimized accordingly.
In the era of large models, data determines the height of AI. Taking decentralized storage as an example, the natural advantage of blockchain is that most on-chain transactions are structured data. Arweave has also achieved permanent data storage. Therefore, blockchain provides high-quality and massive data for AI, helping large AI models process data more efficiently and providing a solution to the bottleneck of multi-modal large models. At the same time, decentralized computing platforms greatly reduce costs on the user side (compared to centralized computing platforms) and avoid the monopoly of computing power by centralized companies. Web3 and AI are intertwined, shaping the future of the digital economy together.
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