There are AI Agent projects everywhere. How to judge whether Dev is reliable through GitHub?

How important is a reliable Dev?

When they woke up, SKYAI holders were dumbfounded. AI Alpha, which had a bright future, saw its market value plummet by 99% in an instant, and there was no possibility of recovery.

The reason for this is that Bob (@futuristfrog), the developer of SKYAI, said on January 5 that his experiment had failed, Bad news. I have failed. SKYAI then collapsed like a waterfall, causing many investors who were very optimistic about this project to say that they would look for reliable project owners, saying that the founder plays a decisive role in a project.

Bob was revealed to be a high school student in 2022, and SKYAI was also dubbed as a utility token for genius Dev when it was first launched. In Bobs final speech, he wrote, So far, I have only made very narrow, overfitting computer application AI agents, and even then, these agents have had little effect. Therefore, he decided not to make any updates to SkyAI until the next generation of large models are released.

Would this behavior be considered a responsible Dev behavior? From the perspective of investor returns, it is obviously not a good Dev.

Also on January 5, the market value of BUZZ, the native token of DeFi AI Agent The Hive, which participated in the Solana AI hackathon, exceeded 80 million US dollars in two days. The reason for such FOMO is that the community has dug up the record of its Dev Jason Hedman, who has won the first place in almost every project he participated in. He is already the winner of 9 hackathons. His contributions on Github are also very dense, with a complete resume and clear development records in the past.

संबंधित पठन: BUZZs market value quickly reaches 40 M, is DeFi Agent the first to fire?

Jsaon GitHub homepage

And Jason owns 5% of BUZZs token supply, but in order to enhance the transparency of the project and market trust, he decided to lock up these tokens for one year, which is also called part of BUZZs rising potential.

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 personal reputation of the developer are often closely linked to the future value-added space of the project.

How to find good AI Dev or projects on Github?

When we recognize the importance of developers to a project, the next question is how to efficiently identify potential Bob and Jason among many AI+Crypto projects and find the technical teams that are truly worth tracking and investing in?

GitHub is undoubtedly the best transit station. As the worlds largest open source code collaboration and version management platform, it not only demonstrates the technical level and continuous output capabilities of developers, but also provides valuable data such as community interaction and version iteration. BlockBeats sorted out several key skills of GitHub treasure hunting to help everyone better control project quality and Dev level.

Smart use of search and trending pages

GitHubs Trending page shows recent popular projects, usually sorted by language, time span, and Star increment. Select AI and its related languages (such as Python, C++, Go, etc.) to quickly find out which projects have high popularity and wider application scenarios. Projects with more Stars, Forks, and Watches often mean that the community has a high degree of recognition for them and have more sufficient resources and discussions.

In addition to the Trending page, you can also use GitHubs advanced search function to filter by number of stars, programming language, update time, etc. For example, if you enter stars:>100 language:Python topic:AI in the search bar, you can find projects with more than 100 stars, using Python, and related to AI, laying the foundation for accurately locking in the right Dev in the future.

In addition to checking the contribution of AI Dev on GitHub, you can also visit their personal homepage to see what projects they have participated in and whether they have submitted high-quality code on well-known AI frameworks. You can also learn about their core projects that they can really show off on their personal homepage Pinned Repositories.

ai16z founder Shaws GitHub homepage

In addition, third-party services such as CodersRank and Sourcegraph will score or analyze skill graphs based on developers public code. If you want to have a more comprehensive understanding of developers programming habits and technology stacks, you can also pay attention to the data provided by these tools.

View README, code and commit records

A projects README file usually contains the projects goals, functional overview, usage methods, and dependent libraries. A detailed and logically clear README can not only help you quickly get started with the project, but also reflect the developers professionalism and friendliness to community users. If the README contains an architecture diagram, performance comparison test, and links to related papers, it is a plus for the projects professionalism.

Carefully reading the code structure, module division and naming conventions of the project can help you preliminarily judge the maturity of the developers in software engineering. If you frequently see them uploading non-Git regular submissions such as dragging and dropping files (Add files via upload), it is very likely that the project does not have the ability to manage continuous development and lacks real technical support. On the contrary, if the commit message is concise and the functional division is clear, it means that the development process is more professional and traceable.

Image source: @onlyzhynx

In AI projects, whether unit testing and continuous integration (Travis CI, GitHub Actions, etc.) are set up is also a key indicator of whether developers pay attention to 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 projects Issues and Pull Requests, we can intuitively understand how developers work and the level of community interaction. For example, whether issues are responded to in a timely manner, whether the PR review process is rigorous, and whether there are enough reviews and tests when the code is merged. Excellent AI Devs usually मार्गदर्शक new contributors through code reviews, or provide more technical background and implementation logic in the PR description, so as to help the project community quickly understand and verify new features.

If a project has a large number of Contributors and they are evenly distributed, it means that the project has broad community support and continuous development momentum. If there are only one or two people who frequently submit code, you need to have a deep understanding of their background and the scalability of the project.

Some high-quality projects may link to external forums, Slack, Discord channels, etc. in the README or Issues area to discuss the projects functional evolution, bug fixes, etc. If user communication in the forum is very active, it means that the project has a certain community fundamentals, which is also one of the criteria for testing the team.

Finally, I want to say that although it is important for developers to have reliable technology, emotional intelligence is also a necessary factor for a project to go far. In this round of AI+Crypto bubble, if we cannot grasp the target in our hands and it goes to zero overnight due to the outrageous operation of the Dev, perhaps we can also change our thinking and track the projects of reliable Dev in the medium and long term. It is also one of the more stable operation methods to make profits with the rise of tokens.

This article is sourced from the internet: There are AI Agent projects everywhere. How to judge whether Dev is reliable through GitHub?

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