How important is a reliable Dev?
When they woke up, SKYAI holders were stunned. What was once a bright future for AI Alpha suddenly saw its market value plummet by 99%, with no possibility of recovery.
The reason for this situation is that SKYAI's developer Bob (@futuristfrog) stated on January 5th that his experiment had failed, "Bad news. I have failed." Following this, SKYAI's value plummeted, leading many investors who were previously optimistic about the project to express their desire to find reliable project teams, stating that the founder plays a decisive role in a project.
Bob was uncovered to have been a high school student in 2022, and at the launch of SKYAI, he was labeled as a genius Dev with a practical token. In his final statement, he wrote, "So far, I have only created a very narrow, overfitted computer application AI agent, and even so, these agents have hardly produced any results." Therefore, he decided not to make any updates to SkyAI before the release of the new generation of large models.
Would this behavior be considered a responsible Dev behavior? From an investor's perspective, it clearly can't be called a good Dev.
On January 5th, the native token BUZZ of the DeFi AI Agent The Hive, which participated in the Solana AI hackathon, saw its market value exceed $80 million in just two days. This FOMO was because the community uncovered that its Dev Jason Hedman's track record showed he had nearly achieved first place in every project he participated in. He is already a nine-time hackathon winner, and his contributions on GitHub are also dense, with a complete resume and clear past development records.
Related reading: (BUZZ Market Cap Quick Report 40M, "DeFi Agent" fires the first shot?)
Jason's GitHub homepage
Moreover, Jason holds 5% of the BUZZ token supply, but to enhance project transparency and market trust, he decided to lock up this portion of tokens for one year, which is also said to be part of the upward potential of BUZZ.
From the failure of SKYAI to the explosion of BUZZ, it is not difficult to see the importance of a reliable Dev to a project—technical strength, sense of responsibility, and the developer's personal reputation are often closely linked to the project's future value appreciation space.
How to find good AI Devs or projects on GitHub?
Once we recognize the importance of developers to a project, the next question is how to efficiently identify potential "Bobs" and "Jasons" among numerous AI+Crypto projects, and find the technical teams that are truly worth tracking and investing in?
GitHub is undoubtedly the best "transfer station." As the world's largest open-source code collaboration and version management platform, it not only showcases developers' technical levels and continuous output abilities, but also provides valuable data on community interaction and version iteration. BlockBeats has outlined several key tips for "treasure hunting" on GitHub, helping everyone better control project quality and Dev levels.
Skillfully using search and the Trending page
GitHub's Trending page showcases recently popular projects, usually sorted by language, time span, and Star increment. By selecting AI and related languages (such as Python, C++, Go, etc.), one can quickly discover which projects have high popularity and broad application scenarios. Projects with a high number of Stars, Forks, and Watches often indicate high community recognition, as well as more abundant resources and discussions.
In addition to the Trending page, you can also use GitHub's advanced search function to filter by Star count, programming language, last updated time, etc. For example, entering stars:>100 language:Python topic:AI in the search bar will find projects related to "AI" that have more than 100 Stars and use Python, laying the foundation for accurately identifying suitable Devs.
In addition to viewing the contribution levels of AI Devs on GitHub, you can also visit their personal pages to see which projects they have participated in and whether they have submitted quality code to well-known AI frameworks. You can also check their personal page's "Pinned Repositories" section to understand their key projects.
ai16z founder Shaw's GitHub homepage
In addition, third-party services like CodersRank and Sourcegraph score or analyze developers based on their public code. If you want a more comprehensive understanding of a developer's programming habits and tech stack, you can also pay attention to the data provided by these tools.
Review README, code, and commit history
A project's README file typically includes the project's goals, feature overview, usage instructions, and dependencies. A detailed and logically clear README not only helps you quickly get started with the project but also reflects the developer's professionalism and friendliness towards community users. If the README contains architecture diagrams, performance comparison tests, links to related papers, etc., it adds to the project's professionalism.
Carefully reading the project's code structure, module division, and naming conventions can provide an initial assessment of the developer's maturity in software engineering. If you frequently see them uploading files via drag-and-drop (Add files via upload), it likely means that the project does not have the capability for continuous development management and lacks real technical support. Conversely, if the commit messages are concise and the function divisions are clear, it indicates a more professional and traceable development process.
Image source: @onlyzhynx
In AI projects, whether unit tests and continuous integration (Travis CI, GitHub Actions, etc.) are set up is also a key indicator of whether the developer values quality. A high-level AI Dev usually combines automated testing methods to ensure the correctness and stability of project functions.
Issues, Pull Requests, and Contributors interface
In the project's Issues (issue tracking) and Pull Requests (merge requests), we can intuitively understand the developer's working style and the level of community interaction. For example, whether Issues are replied to promptly, whether the PR review process is rigorous, and whether there is sufficient Review and testing when merging code. Excellent AI Devs usually guide new contributors through Code Review or provide more technical background and implementation logic in the PR description, helping the project community quickly understand and verify new features.
If a project has a large and evenly distributed number of Contributors, it indicates that the project has relatively broad community support and sustained development motivation. If only one or two people are frequently submitting code, then it is necessary to delve into their backgrounds and the project's scalability.
Some high-quality projects may link to external forums, Slack, Discord channels, etc., in their README or Issues section to discuss the evolution of project functions, bug fixes, etc. If user communication in the forum is very active, it indicates that the project has a certain community foundation, which is also one of the criteria for evaluating the team.
Lastly, I want to say that while it's important for developers to have reliable technical skills, emotional intelligence is also a necessary factor for a project to go far. In this bubble of AI+Crypto, if we can't control the assets in hand due to outrageous actions by the Dev, resulting in a total loss overnight, perhaps we can consider another approach. By conducting medium to long-term tracking of projects by reliable Devs, profiting from token price fluctuations can also be a more stable operation method.