How Important is a Reliable Dev?

Upon waking up, SKYAI holders were stunned. What was once a promising AI Alpha saw its market value plummet by 99% in an instant, with no chance of recovery.

The reason for this situation was that SKYAI's developer Bob (@futuristfrog) announced on January 5 that his experiment had failed, stating, "Bad news. I have failed." Consequently, SKYAI's value crashed, leading many investors who were previously optimistic about the project to express their desire to find reliable project teams, emphasizing that the founder plays a decisive role in a project.

Bob was revealed to have been a high school student in 2022, and at the launch of SKYAI, he was labeled a genius Dev with a utility token. In his final statement, he wrote, "So far, I have only created a very narrow, overfitted computer application AI agent, and even then, these agents have had almost no effect." Therefore, he decided not to make any updates to SkyAI before the release of the next generation of large models.

Would this behavior be considered responsible for a Dev? From the perspective of investor returns, it clearly cannot be called a good Dev.

On the same day, January 5, 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 within two days. The FOMO was driven by the community uncovering that its Dev, Jason Hedman, had a track record of winning first place in almost every project he participated in, being a nine-time hackathon winner, with a dense contribution record on GitHub and a complete resume showcasing clear past development records.

Related Article: "BUZZ Market Value Surges to 40M, Is 'DeFi Agent' the First Shot?"

Jason's GitHub Profile

Moreover, Jason holds 5% of the BUZZ token supply, but to enhance project transparency and market trust, he decided to lock this portion of tokens for a year, which he referred to as part of BUZZ's upward momentum.

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 future appreciation potential of the project.

How to Find Good AI Devs or Projects on GitHub?

Once we recognize the importance of developers to projects, the next question is how to efficiently identify potential "Bobs" and "Jasons" among the many AI+Crypto projects and find the truly worthy technical teams to track and invest in?

GitHub is undoubtedly the best "transit station." As the world's largest open-source code collaboration and version management platform, it not only showcases developers' technical levels and continuous output capabilities but also provides valuable data on community interaction and version iteration. BlockBeats has outlined several key tips for "treasure hunting" on GitHub to help everyone better control project quality and Dev levels.

Smart Use of Search and Trending Pages

GitHub's Trending page showcases recently popular projects, usually sorted by language, time span, and Star increment. By selecting AI and its 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 and 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, update 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 groundwork for accurately identifying suitable Devs.

Besides checking the contribution levels of AI Devs on GitHub, you can visit their personal profiles to see which projects they have participated in and whether they have submitted quality code on well-known AI frameworks. You can also check their "Pinned Repositories" to understand their core projects.

GitHub Profile of Shaw, Founder of ai16z

Additionally, third-party services like CodersRank and Sourcegraph score or analyze developers' public code, providing a more comprehensive understanding of their programming habits and tech stacks. You can also pay attention to the data provided by these tools for a fuller picture.

Review README, Code, and Commit History

A project's README file typically includes the project's goals, feature overview, usage methods, and dependent libraries. 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, and links to related papers, it adds to the project's professionalism.

Carefully reading the project's code structure, module division, and naming conventions can provide an initial judgment on the developer's maturity in software engineering. If you frequently see them uploading files via drag-and-drop (Add files via upload), it likely indicates that the project lacks sustainable development management capabilities and real technical support. Conversely, if the commit messages are concise and the functional divisions are clear, it suggests 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 typically 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 responded to promptly, whether the PR review process is rigorous, and whether there is sufficient review and testing during code merging. Excellent AI Devs often guide new contributors through Code Review or provide more technical background and implementation logic in PR descriptions, helping the project community quickly understand and validate new features.

If a project has a large number of Contributors distributed evenly, it indicates that the project has broad community support and ongoing development momentum. If only one or two people are frequently submitting code, it is necessary to delve deeper 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 for discussing project feature evolution, bug fixes, etc. If user interactions in the forum are very active, it indicates that the project has a certain community foundation, which is also one of the criteria for evaluating the team.

Finally, it is worth mentioning that while having reliable technical developers is important, emotional intelligence is also a necessary factor for a project to go far. In this round of AI+Crypto bubble brewing, if we cannot manage our holdings due to outrageous actions by the Dev leading to a total loss overnight, perhaps we can consider a different approach—tracking projects with reliable Devs for the medium to long term and profiting from token fluctuations is also a relatively stable strategy.

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