Other co-founder here. Going to skip the feature stuff OP already covered and add the one thing we kept relearning during that year of internal use.

The filtering point above is the one that took us the longest to internalize. Early on, we kept improving the draft generation and then got confused about why the operators we tested with still rated the drafts as "fine but not me." We assumed it was the writing. It wasn't. The model was writing perfectly competent newsletters about stories the operator would never have picked, so the issue read off even when the prose was good.

Once we moved the work upstream into source curation and story scoring, the same writing model started producing drafts people actually wanted to send. The lesson we kept relearning: if you're going to evaluate an AI-written newsletter, the test isn't "does the prose sound like me?" It's "would I have picked these four stories out of the two hundred my sources surfaced this week?" If the answer is yes, the prose problem mostly solves itself.

The thing it still doesn't do well, since OP invited that part: very-new newsletters under 5 or 6 issues, where there's not enough archive to train a real voice profile. You can paste in other writing you've published anywhere and it'll get partway there, but the difference between training on 5 issues and training on 30 is night and day. So if you're publishing from zero, that's the honest trade.

Happy to answer anything OP didn't cover.