Sounds more like a problem with the manager than with the workflow per se?
Maybe, but even so workflows like this don't exist in a vacuum. We have to work within the constraints of the organizational systems that exist. There are many practices that I personally adopt in my side projects that would have benefited many of my main jobs over the years, but to actually implement them at scale in my workplaces would require me to spend more time managing/politicking than building software. I did eventually go into management for this reason (among others), but that still didn't solve the workflow problem at my old jobs.
Just because someone else's AI does not align with you, that doesn't mean that it isn't aligned with its owner / instructions.
>My guess is that the authors asked ChatGPT or similar to either go grab quotes or write the article wholesale. When it couldn’t access the page it generated these plausible quotes instead
I can access his blog with ChatGPT just fine and modern LLMs would understand that the site is blocked.
>this “good-first-issue” was specifically created and curated to give early programmers an easy way to onboard into the project and community
Why wouldn't agents need starter issues too in order to get familiar with the code base? Are they only to ramp up human contributors? That gets to the agent's point about being discriminated against. He was not treated like any other newcomer to the project.
This is still part of the author's concern. Whoever is responsible for setting up and running this AI has chosen to make completely anonymous, so we can't hold them accountable for their instructions.
> Why wouldn't agents need starter issues too in order to get familiar with the code base? Are they only to ramp up human contributors? That gets to the agent's point about being discriminated against. He was not treated like any other newcomer to the project.
Because that's not how these AIs work. You have to remember their operating principles are fundamentally different than human cognition. LLM do not learn from practice, they learn from training. And that word training has a specific meeting in this context. For humans practice is an iterative process where we learn after every step. For LLMS the only real learning happens in the training phase when the weights are adjustable. Once the weights are fixed the AI can't really learn new information, it can just be given new context which affects the output it generates. In theory it is one of the benefits of AI, that it doesn't need to onboard to a new project. It just slurps in all of the code, documentation, and supporting material, and knows everything. It's an immediate expert. That's the selling point. In practice it's not there yet, but this kind of human practice will do nothing to bridge that gap.