I was "volunteered" by a colleague to help the program committee for PyCon 2018. I rarely think of myself as qualified for this kind of thing. Yes. I have six books on Python (with a seventh on the way) but the PSF folks are brilliant and dedicated and hard-working, and I'm just a slob.
Yes, I do get to help the community by reviewing almost 700 individual proposals. Some good. Some really good. Some which we must hear.
The collateral benefit?
Side reading.
My browser history is filled with things I hadn't known existed.
Next time, I need to get started *before* the deadline so I can have a little more interaction with the authors. There were a few outlines where we could only discuss the possibility of making a change if the proposal was accepted.
In particular, there seemed to be a lot of Machine Learning-Bayesian-Deep Learning-Recommender-Data Science pitches that had abbreviated outlines. They tend to all look alike to someone who's not an expert. Five bullet points: the author's background, the problem domain, ML (or modeling or whatever), a Jupyter notebook showing the results, and a conclusion. Providing some distinct angle to the pitch (other than the problem domain) might help me understand them more fully. It seemed best to defer to the consensus on these.
I've been learning to live with my personal bias against meta-talks about building community. A presentation on community building at a community event seems redundant to me. But that doesn't mean they're not thorough, articulate talks that will be useful to others. Since I have a seat at the table, I'm biased. The Python tie-in feels weak, but our code of conduct (Open, Considerate, Respectful) means PyCon really is the place for more of this. Most importantly, they're objectively solid talks. (And -- as a member of the the over-represented old male nerd class, I do need to listen more.) It's been enlightening. And the conference will rock.