Piefed contributor and part of the piefed.social admin team.

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Joined 2 years ago
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Cake day: November 20th, 2024

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  • I run a personal lemmy instance and two personal piefed instances, so I was just doing some comparisons. My instances are mainly used for development and testing, so they are only subscribed to a handful of communities and just have one active user.

    You are correct that when it comes to performance, like snappiness and responsiveness, the database is probably going to be the bottleneck. Unless you are scaling up to a huge degree, I would be surprised to see meaningful differences in the number of requests that could be handled due to language differences between rust and python. Yes, python is an interpreted language, but most of the libraries you are using are basically calling other system libraries written in a language like C, and the program can execute way faster than your database I/O can give it data to process anyway.

    Here is my usage summary. The lemmy instance has been running for about 1.5 years while the piefed instance has been running for just shy of a year now. I have only included the memory usage and disk since I don’t think either is really CPU hungry or bound in my use case.

    Software Memory consumption Disk Usage
    Lemmy ~1.5 GB ~800 MB
    Piefed ~1 GB ~200 MB


  • The theory that the lead maintainer had (he is an actual software developer, I just dabble), is that it might be a type of reinforcement learning:

    • Get your LLM to create what it thinks are valid bug reports/issues
    • Monitor the outcome of those issues (closed immediately, discussion, eventual pull request)
    • Use those outcomes to assign how “good” or “bad” that generated issue was
    • Use that scoring as a way to feed back into the model to influence it to create more “good” issues

    If this is what’s happening, then it’s essentially offloading your LLM’s reinforcement learning scoring to open source maintainers.


  • Really great piece. We have recently seen many popular lemmy instances struggle under recent scraping waves, and that is hardly the first time its happened. I have some firsthand experience with the second part of this article that talks about AI-generated bug reports/vulnerabilities for open source projects.

    I help maintain a python library and got a bug report a couple weeks back of a user getting a type-checking issue and a bit of additional information. It didn’t strictly follow the bug report template we use, but it was well organized enough, so I spent some time digging into it and came up with no way to reproduce this at all. Thankfully, the lead maintainer was able to spot the report for what it was and just closed it and saved me from further efforts to diagnose the issue (after an hour or two were burned already).