I use my commuter benefit debit card to pay for public transit. Notably this does not include rideshares like Waymo. In order to
claim the transit reward I could use a personal card to pay for both transit and Waymo, but that leads to considerable friction where I need to collect receipts and submit claims to use my commuter benefit.
Most companies in SF must offer a commuter benefit so it doesn't seem like many people will be getting this Waymo transit reward.
I don't live in California, so maybe I'm misunderstanding something. Uber and Lyft were ostensibly originally meant for people already travelling from point A to point B to share the ride with someone and make some money - and still could be used that way. But Waymo had always been a car-for-hire service with no pretension. So why is it grouped together with "rideshare" and not taxis?
At least in the US, the traditional taxis were terrible - only way to order is via phone call, you have no idea when your car will arrive, nor how much your trip will cost, there is no easy way to send feedback to driver, and there was a good chance for an generally unpleasant experience...
Uber/Lyft changed this all and actually made the car-for-hire services usable. In my circles, this was the real reason why everyone simply stopped using taxis completely the moment any alternatives appeared.
And Waymo has a nice app and clean experience, and won't take you long way so it can charge you more, hence people group it with rideshares, despite it technically being closer to taxi.
(I understand that in other countries, taxis were much better. Before Lyft, I always felt envious while reading about their taxi systems)
waymo, uber, lyft, etc. are akin to a classic black cab service where you call a number to make a sort of ride appointment. these never needed taxi licenses, as taxis are specifically hailable from the street at will rather than prebooked.
There's a lot of current research around using machine learning to optimize software. One paper I'm thinking of is: https://dl.acm.org/doi/10.1145/3533028.3533308 where they use ML to optimize latency and hardware usage of Apache Flink dataflow programs.
Zig is great. The editor tooling could use some more work but I think after their new stage2 compiler is more ready, it sounds like they’ll invest more into that.
Cherry MX browns are the second product for r/mechanicalkeyboards, but I can't say all the talk about them is positive. Most people meme on how bad they are. So in this case popularity in discussion doesn't really correlate to a product you should buy.
The purpose of a title is both to summarize content and grab the readers attention. It's up the author which one they put for emphasis on.
You clicked so it worked, even if you don't like it.
The user only saw the headline and closed, isn't the author aiming visitors to read the content or clicks?
The same analogy could be made to a baker luring customers in their bakery with an attractive facade but the very same "customers" just give a quick glimpse and leave.
In fairness, I did in fact read more or less the whole thing. I walked away with a fairly low opinion of the article and the author, but I did read it :)
