> I’m a little ashamed remembering myself talking about failed relationships, family struggles

It sucks what happened, but, yeah, you need to establish filters for yourself. No matter what they ask you, it's an absolutely terrible idea to bring up your failed relationships in an interview. Something tells me they did not ask for that private information specifically and you just decided it would be a good idea to volunteer it, otherwise the story would have said so.

It does not matter what you think they asked. You are the one in control of the words that come out of your mouth. This was poor judgement all around.

In the US any employer who asks you about personal relationships during an interview is opening themselves up to an illegal discrimination lawsuit.

Wealth transfer.

> providing for children from a first marriage if you get married and predecease your second spouse

Wealth transfer.

> protecting assets if you are professional who faces high chance of being sued

Dodging responsibility for professional malpractice?

That's an irrelevant diversion though, because the measure that matters when discussing the fairness of taxes is how much people are left with at the end after paying whatever taxes they pay, including sales tax, income tax, and any other kind of tax. And for those particular people you're talking about the answer is very little, next to none, and for the people for whom a wealth tax would even apply the answer is unimaginable amounts.

So is getting rid of intergenerational wealth transfer. So since we're already dreaming about a new system that seems irrelevant.

> legitimate use cases

Intergenerational wealth transfer also has "legitimate use cases" if one gets to define "legitimate". I'm curious what legitimate cases you have in mind.

Uh. We're not talking about knowing what good is, which is completely irrelevant to anything in this thread. You made a claim without qualification about what it is more likely for PMs to do. I can't tell if you've lost the chain or are engaging in some kind of motte and bailey fallacy. Either way it's a bad sign for this conversation.

I'm going to summarize the threads so far. I hope it highlights why what you've said sounds so silly:

Someone: "I see X failing to do Y."

You: "X definitely do Y. Why would you think that X aren't doing Y? Doing Y is the obvious thing for X to do."

Someone: "I literally am seeing it happen right now."

You: "Well then those X are bad."

Someone: "Yeah, no shit. They just said as much."

You: "But most X would do Y."

Someone: "In my experience that is false."

Someone else: "Mine too."

Someone else: "Mine as well."

Someone else: "Same."

You: "The bad ones shouldn't have their jobs."

Someone: "They do though."

You: "But we can tell which ones are the bad ones."

Someone: "Bartender, another drink please."

Nobody "deserves" anything. They do have the jobs though. Thinking that the world isn't full of people doing what they need to do to get by who don't give a shit about fitting a fantasy ideal is wild.

I feel compelled to point out to you that this is a completely unsustainable, unsupportable, unsubstantiable claim. You have met ~0% of PMs, and of the ones you've met maybe you've experienced a non-zero percentage of their work, but statistically that's also very unlikely.

If you think you can say what most PMs do or what PMs are likely to do, then, I'm sorry, but you are not even thinking like an engineer. You're thinking, actually, a lot more like a PM to many of us.

> just like good devs

I'm so sorry, my sides just can't handle the starry-eyed nature of these takes. This is just too much for me.

To many of us this reads like you've never met people before. But who knows, maybe you live in Lake Wobegon, where all the women are strong, all the men are good-looking, and all the children are above average! If so then we're jealous, but you still should be more careful about how unrigorous your mental model is because it will make you a worse engineer.

Experience with different PMs and developers aside, the older you get in the profession the more you will hopefully realize that none of your quality effort fantasy matters. Sales happen and money rolls in independently of whether you think the PMs or the people who call themselves engineers do a "good job". Businesses thrive on sales and marketing, not engineering.

Hahahahahaha. Sorry, I couldn't help myself; this reads like satire. The answer is "real life experience says otherwise".

Sorry but the HomePod wasn't "killed" just because they upgraded from gen 1 to gen 2. Gen 1 HomePod literally just got a software update a month ago with another on the way. The iPhone X wasn't "killed" just because they released the iPhone 11. This list is egregiously version-centric for things where it makes no sense.

Speaking as a person who understands it extremely well and who has an advanced degree in computer vision, I'm sure that internet randos did, but I promise the people who actually know about the failure modes of the different modalities did not. I don't really expect you to take my word for it, but maybe this will spark an interest in investigating the failure scenarios of 3D reconstruction using cameras in computer vision. Just know that Google is an absolute top tier juggernaut in the CV/ML/AI research world, and they don't use lidar out of ignorance.

> less sensor collision

This isn't a real thing for anyone doing a good job. A sensor can be good for a scenario or it can be bad for a scenario. More sensors feeding input only gives you gradations of accuracy instead of binary accuracy. Having gradations of accuracy is an unambiguously good thing. When you only have one sensor, you have no way to know whether in the moment it is feeding you an optical illusion. That's what it means for something to be an optical illusion. But when you have multiple sensors of different modalities, then you have meaningful information about whether local disagreement between the different modalities means that one is better or worse than the other, because you can contextually characterize the failure scenarios of each.

> It's not magic, it performs poorly in inclination weather and can have issues with resolution over range and data processing (although lidar does do a lot of things well).

Inclement, not inclination. And I hate to be the bearer of bad news, but cameras also do poorly in inclement weather and have issues with resolution over range, and the solutions are identical for both (superresolution, temporal blending, alternate wavelengths, stereo correspondence, etc).

Tesla people always say (said?) things like "Well humans only drive with their eyes, so cars should be able to as well," but that's not a true statement about what humans have in relation to what Teslas have. Humans have many more different sensor modalities than what Tesla's cameras give. Teslas have single-view fixed-focus cameras that, for much of the FOV, can only reconstruct structure from shape assumptions (object detection and classification) and inter-frame changes (optical flow) coupled with sensation of the vehicle's motion. That's all they get. It's not bad at all, especially coupled with advanced machine learning, but you do have more than that coupled with even more advanced machine learning. When you as a human drive, in addition to what Teslas have (you do also have them), you also have binocular stereopsis cues, autofocus lens convergence cues, vehicle-independent motion parallax cues, and the ability to manipulate shade cover so you don't get blinded. Are all those extra cues necessary for every scenario? No, obviously not. Do they help though? Yes. Try driving with only one eye open and without moving your body or head at all. You can absolutely do it, but you won't be as good as you would with both eyes open and free movement.

Three things can be simultaneously true:

* Tesla's cameras are sufficient for some scenarios.

* Tesla's cameras are insufficient for other scenarios.

* A system with good data and bad algorithmic processing is still going to be bad. The Chinese vehicles almost always fail the tests because they see the obstacle but drive into it anyway.