ktrnka

I saw it decline over the last year at my employer. In 2021, the recruiting team and I were all on the same page about letting candidates know the decision within a day either way. In 2022 there was some desire to do that, but the recruiting team lost their most senior people and became very understaffed for a while, so it often took a while to get notice sent.

In 2021 my recruiting partners were cool with me moving candidates through stages, which triggered automated notification emails for the candidates. In 2022 I didn't have the permissions for that. Not sure how that all happened, but there was definitely a lack of an ATS admin of some sort.

It depends. In some cases I've implemented the core concept and tested it on my own work. In other cases I wait to see if it's replicated or used often.

When I don't have those options I read carefully, particularly any evaluation methods. If it's machine learning I also know to look for mistakes in hyperparameter tuning.

That said, an alternate approach is to just read the best papers from well respected conferences and venues in your area, rather than cherry picking particular papers.

The big pain points have been...

People that feel strongly about the solution but don't have much evidence it's right for the user or business. I've seen that from both ux and engineers. Getting both groups more exposure to user feedback and customer feedback helps. Dogfooding also works great when applicable.

Focusing on details over the big picture. For ux I mean the emphasis on pixels over interaction design. For engineers I've seen it too. Exposure to user feedback helps. PMs help. A calm discussion of tradeoffs involving delivery dates help too, or budgeting post launch time to improve the feature helps smooth out the initial debates over scope. Doing rapid iterations with limited groups of users also helps.

Some people feel that they've had unfair compromises so many times that they're no longer willing to compromise at all. I've seen that from both ux and engineers.

If the company is otherwise pretty good, it's good to be candid that you don't understand the context or goal and could use some background. Usually I find that there are other people equally confused but also not speaking up

My experience is that it's about practicing the ability to control my attention. I think of it like exercise for my attention. When I've been doing it, even if my mind gets pulled in many directions I can choose how much time and energy to focus on each thing. When I haven't been doing it, I'm more susceptible to having my attention controlled by fears or anxieties more than hopes and goals.

I worked at Swype and then Nuance after the acquisition.

My experience with Google keyboard has been very good. The main difference I've noticed over the years is that phones got bigger. Swype was magical on a 3 inch screen but not nearly as good on a 5 inch. If I remember right the core would take the exact touch points less seriously on a small screen but on a big screen usage might go back and forth between very accurate and sloppy.

If I had to speculate somewhat, it's possible that newer keyboards take a very principled approach to the code for input recognition. I didn't work on the input matching part at Swype but it was often discussed with great fear, because it was a couple giant functions with tons of conditionals. It had been built up over I think a few years early on but without much documentation.

The other thing is that there was a race to the bottom on price, which later led to much smaller teams working on keyboards. There just isn't enough financial incentive to put many resources towards it, compared to something like speech recognition.