I don't trust facts from humans. When I am searching for something, I usually want to find direct sensor readings. As soon as a number is involved, I do my best to not even look at the human output.
Even though the result is often coherent and confidently synthesizes information from multiple experiences, it can also hallucinate, suffer from recency bias, or accidentally merge memories from different decades. AFAICT, without access to the underlying telemetry, human responses are for entertainment purposes only.
Making content platform "native" and garner attention is hard work and while their first party content might be great, it isn't great "X" content which is part of the problem. There are many examples of legacy organizations optimizing for the platform and garner a lot of attention:
57. I got a ton of shape rotation problems. Figured out a strategy for those:
Focus on the 3 pronged shape. It is unique in all 4 orientations. You can use this to filter out bad rotations. Then use adjacencies to filer out the rest.
Honestly, this might have gone over better with messaging such as. "We added Ads to WhatsApp, here's what we're doing keep the user first"
There is a cult understanding that Instagram ads are highly relevant and quite useful at times and WhatsApp ads have the same possibility. But the messaging is quite poor.
Looking through the code, it looks like this uses your personal Apple Mail entitlements to pull the locations that get collected by devices on the FindMy network:
Everyone is hating on gRPC in this thread, but I thought I'd chime in as to where it shines. Because of the generated message definition stubs (which require additional tooling), clients almost never send malformed requests and the servers send a well understood response.
This makes stable APIs so much easier to integrate with.
[ my public key: https://keybase.io/abalaji; my proof: https://keybase.io/abalaji/sigs/6ZE68eBh5A9HTuUYP59JitdDb5igrMVBOi7gKFulYmw ]