作者：Marshal Orange， PermaDAO
In this era of "everyone is a product manager", everyone has endless inspiration and creativity, but they are often limited by any threshold in the technology or product implementation path and do not have the ability and courage to participate in it, which is also the situation of many people I asked and learned in PermaDAO when Arweave held the hackathon, but the hackathon is not the patent of a few people, but a national event.
I think the process of the hackathon competition is more like a UGC (user-generated content) event around technology and innovation, and the contestants are obviously the "content creators" of blockchain ecological applications, so we can disassemble the complete workflow of the hackathon project is nothing more than the five stages of product development that developers are familiar with:
- Problem definition and planning phase
- Needs analysis phase
- Product prototype and software design stage
- Software development stage
- Software testing phase
In fact, the pain point is how to lower the threshold encountered by the participants in the above five stages of product development, human beings can use AI as a productivity tool to participate in the blockchain hackathon by combining the existing UGC elements of the hackathon, so that any creativity can be played as much as possible, just like EverID uses account abstraction to lower the threshold for users to use Web3 wallets, these things that lower the threshold can obviously greatly promote the development of the blockchain industry.
Five Phases
01 Problem Definition and Planning
Through the data on the feedback of ecological users, the AI model can be used to generate questions that have attracted much attention or have market pain points, which are more valuable and meaningful to the ecosystem. Adot uses AI to generate tags for each parsed top DApp sentiment analysis, based on which users can search for data and filter results. By analyzing existing on-chain user behaviors and needs, AI can help users identify potential issues and provide recommendations on how to address them, allowing them to better define the scope of the project.
02 Demand Analysis
Data mining: Blockchain hackathons typically involve the storage and analysis of large amounts of data, using Arweave-based GraphQL to enable real-time querying and manipulation of permanent data on the Arweave chain, combined with AI to accelerate the data processing and analysis process, can help identify patterns, trends, and potential issues, and provide participants with recommendations on how to optimize blockchain applications. AI can analyze data and user behavior, such as historical case matching, and can identify trends and patterns in future demand to help participating teams better understand user needs.
Automated Requirements Validation: AI can automatically verify that requirements are complete, consistent, and verifiable (but rarely used in a time-scorching hackathon). It can identify potential conflicts in requirements, which reduces the effort of later repairs.
03 Product Prototype & Software Design
Auto-generate prototypes: Use image-generative AI like Midjourney to generate initial interface prototypes, saving product managers a lot of time. This can help teams validate concepts faster and iterate quickly.
Provide design recommendations: AI can provide recommendations on user interface design, layout, and usability to improve the user experience of your product.
04 Software Development
Non-technical people can make up for development shortcomings with AI, which can generate partial or complete code to speed up the development process. It is especially useful for the development of some specific modules such as inheritance, overwriting, overloading, etc., which will greatly improve the efficiency of program operation. AI can also detect potential bugs, vulnerabilities, and security issues in your code, improving code quality.
05 Software Testing
Leveraging AI to analyze an application's code and functionality, AI-driven automated testing tools can perform testing at scale, cover a variety of use cases, and automatically detect and report issues.
In addition to this, blockchain hackathon projects usually require documentation and presentations, and AI can automatically generate technical documents, PPTs, and test reports, reducing the burden of document writing for participants. And sometimes hackathon teams need to form teams freely, and AI can be used to analyze the skills, interests, and project experiences of each team member, and provide them with suggestions for recommending teammates to promote better teamwork.
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AI allows anyone to become a product development team, even if they don't have development experience. AI can support participating teams in a variety of ways, from problem formulation to solution development and documentation, and AI is not only a change in technology for Web3, but also a change in production relations, and at the same time, Web3 has also had a profound impact on the AI field. The privacy, trustlessness, and decentralization of Web3, as well as the confirmation of data and asset ownership, and the token economy, can bring great changes to the AI infrastructure, so that the three most important elements of the AI infrastructure, data, models, and computing power, can 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 on-chain transactions are basically structured data, and Arweave also realizes the permanent storage of data, so the blockchain provides AI with high-quality and massive data, helps its AI large model to process data more efficiently, and provides a solution for the breakthrough of the bottleneck of multi-modal large models. At the same time, the decentralized computing platform can greatly reduce the cost of computing power on the user side (compared with the centralized computing platform), and also avoid the monopoly of computing power by centralized companies. Web3 and AI are intertwined to shape the future of the digital economy.
