I've been looking at ProseMirror (mostly through tiptap2, which seems to wrangle it effectively) but taking the jump is daunting. I'd be interested in others' experiences.

> Leadership is a concept largely foreign to the software industry

Maybe in the companies you’ve worked for, but I haven’t found this to be true at all

> An excellent software developer tends to score high in agreeability, but a strong leader knows how to turn that down to 0 for maximum confrontation and/or defiance

Hard disagree. “Maximum confrontation and/or defiance” has never been a goal of good leaders who are trying to build a team that works together.

Encouraging people to speak their mind is good. Encouraging “maximum confrontation” is just going to create chaos. The goal is to work together to ship, not to argue and defy all the time. I can’t think of anyone who would want to work on a team where everyone had agreeableness dialed down to 0 where leaders encouraged confrontation all the time, except maybe for people who just like to argue a lot.

> Most people in software are deathly afraid to abandon conventions of comfort whether in business or in product/process innovation.

Another strong generalization that I can’t agree with. Most people I’ve worked with in software have been so aggressive about bucking trends and trying new things that we’ve had to dial it back a notch. A lot of the debates I’ve had with teams have been about choosing boring, stable technologies over the newest cutting edge technology that’s popular on Twitter. Same goes for business strategies, where I’ve had to deal with everyone from product managers to sales people trying to do things their own creative way when the standard, boring practices are what finally got the job done.

Morality and legality aside, there's a substantive difference between use of content and use of a model. Pretraining a GPT 4-class model from raw data requires trillions of tokens and millions of dollars in compute, whereas distilling a model using GPT 4's output requires orders of magnitude less data. Add to that the fact that OpenAI is probably subsidizing compute at their current per-token cost, and it's clearly unsustainable.

The morality of training on internet-scale text data is another discussion, but I would point out that this has been standard practice since the advent of the internet, both for training smaller models and for fueling large tech companies such as Google. Broadly speaking, there is nothing wrong with mere consumption. What gets both morally and legally more complex is production - how much are you allowed to synthesize from the training data? And that is a fair question.

How is a company able to register a generic name like "Texts" as a brand? how was marketing ok with such a name that is impossible to search on google?

This is a commonly overlooked factor in gardening and agriculture especially in beginners.

Yeah, that would be a fantastic resource. Also implementations in various languages, and trade-offs between different designs.