100% agree with this and it's extremely frustrating to deal with that. I've got the feeling that our managers look to the US and see people like Musk and Trump (no judgement here!) making .. let's say "bold claims" - aka bullshitting aka lying aka marketing, call it whatever you want - and think they can just copy that. But Europeans just suck at marketing / bullshitting, it's just not our strength. Now we're stuck with bullshitting "leaders" who cannot deliver results. And this creates a vicious circle where actually capable people refuse to take on any kind of leadership roles as they'll be forced to play that game.
Unfortunately I don't know why we didn't choose HackerRank in the end. LeetCode was also an option but it didn't really fit what we wanted to test for.
This doesn't correspond to my experience. In my experience McKinsey consultants prefer not to talk to lower-level minions - on the contrary a major incentive for going into (strategy) consulting appears to be to able to deal with C-suite executives directly and not having to care about understanding any of the day-to-day business.
It usually goes like this: The CEO, a former McKinsey partner, hires McKinsey to formulate a strategy for the company: Often on a trending topic like digitalization, AI, etc. The details are usually confidential so there's no involvement of lower-level employees (maybe providing some data but without any context as to why). After a few months of utter secrecy management presents some transformation strategy.
I have yet to meet a normal employee to respect what comes from McKinsey. The average strategy consultant will look down on lowly employees and prefer to have as little interaction with them as possible.
"I have a PhD in computer science from a T1 university"
Why is the "tier" of your university important here? Not trying to offend you, but that kind of elitism is part of the reason consultants have a bad reputation. And unfortunately it is reflected in their work.
> they're not trying to win over users; they're trying to extract money from them.
Fair enough. But was there ever an attempt to extract money and/or data from the users through third-party apps? For example by forcing third-party to serve ads?
I'm actually questioning if it's in the long-term monetary interest of big tech to force users into subpar experiences. My guess is that current big tech apps don't even serve their purpose of maximizing profits & data extraction optimally. I'd expect a talented indie dev to be able to provide a better UX & monetizing users at the same time.
I mean if we want to go the evil route, we might even incentivize 3rd party devs. Let everyone build a Twitter or Reddit client, but they must serve ads and might get a fixed or percentage share. If someone can build an app where users actually like and click on the served ads, great.
Bugs (e.g. the YouTube Shorts one) or performance (GitHub) are the dev's responsibility, aren't they? (correct me if I'm wrong, I have no idea how big tech companies organise internally)
> 3. Devs shouldn't be the ones creating UI/UX.
Why not? Some of the best apps I've used come from devs who do both. E.g. Christian Selig of Apollo. And bugs / performance greatly influence UX.
> The title seems to be unrelated to the description
Sorry, English isn't my mother tongue so I might have chosen a bad title. Wasn't intentional.
> and the final paragraphs about 3rd party clients is again another topic.
Let me explain: My observation is that many first party clients (frontends) are often (sometimes far) inferior to third party clients while the first party clients have the best people in the industry working on them whereas third party clients are often developed by some indie devs without any big tech in their CV.
Well where do we draw the line here? I'm not even talking about diffs etc.: Just browsing through a repo and looking at single files is noticeable slow. As an end user I don't really care if it's the network request latency or because inefficient DOM rendering, it's not a great UX. For example loading this site here: https://github.com/GoogleCloudPlatform/gsutil/blob/master/te... took 1.26 seconds. And it's like that for any file or folder. Gitlab seems to have a similar initial load time however only once, after that it's faster.
This is somewhat provoking title, but I can't get my head around it: Amazon, Apple, Google, Microsoft et. al. claim to hire only tech's top talent. They often still put some mediocre to terrible frontends out there.- Apple TV+ on the web: https://tv.apple.com/ is not a great experience. I get that Apple wants to people to use an Apple TV, iPad, iPhone to watch Apple TV+. So it might be an...
Locking you customers into your ecosystem? Fine, whatever. But even within the ecosystem restricting usage in such a way!?
It's been said for years but the iPad could be so much more than a mere media consumption device if it weren't for short-term-profit driven design decisions.
Maybe they do better with the Vision Pro.
I looked into Flutter & Dart as it seems thatr everybody loves it. But then apparently even for Flutter you need another framework for state management. I thought state management is the core issues these frameworks want to solve?
I would've thought game engines have solved the problem of complex state management though. There are games with reactive in-game browsers/websites which work better than most real websites!
So far the easiest for me to grasp and getting started was actually react / nextjs:
- Extremely well documented
- Little boilerplate (especially compared to Flutter - I find that StatefulWidget really ugly)
- Combined with a component library it's incredible easy to get started
- When sample size grows, frequentist and bayesian (if the prior is not too restrictive) point estimates seem to converge to each other anyway
- The distribution of your point estimate (frequentist) vs. the estimated distribution (bayesian) also don't seem to differ too much either
- When the sample size is small the Bayesian prior dominates
- Interestingly, when I see Bayesians simulate random data (to introduce the concepts on this data) they usually assume a true parameter value. E.g. when sampling from Y = a + b * X + e, they'll assume fixed, true values of a and b and not random variables - which is a frequentist assumption! So far I've never seen e.g. b being sampled from Normal(mu=2, sigma=1) instead of just setting b=2.
- The frequentist assumption of a true population value which we try to estimate just makes sense to me. For example there is a true mean income over the working population. It's not a random variable but a fixed value which can be computed if we just asked every single working person for their income and then compute the mean over all values.
I tried getting into Bayesian stats but honestly it just seems overkill for most cases. For a simple regression computing b_hat = inv(XX')Y is just faster and easier than numerically sampling traces. Bayesian forces you to think about the data generating process - I appreciate that, but you need to the same when it comes to frequentist stats, it's just a little less obvious.
By modelling skill and perceived skill as uniform draws between 0 and 100, the unskilled (e.g. skill=0) will over-estimate their skills (estimated skill = 50, the mean on the uniform random variable) and the skilled (e.g. skill=100) will underestimate it (as 50 as well, again the mean of the same random variable). The only ones who will be correct (on average) are the average skilled ones (skill=50).
I think Google Cloud needs someone like Jeff Bezos as their head: Look what your customers actually want and need and understand their requirements. And they usually want good customer support and want a competent key account manager as well.
When we were looking to migrate our analytics database from on-premise to a cloud alternative we were looking at BigQuery and Snowflake. BigQuery is a great product and we were already deeply invested in GCP as well. However the GCP sales team just couldn't sell BigQuery - they just don't know what old corporations want to hear in a sales pitch. So we went with Snowflake in the end. Not because it's the better product but because their sales team is better.
I'm not sure if the cloud business is actually a priority at Google. If it is then I think they don't understand the mistrust Google is facing when it comes to stable long term support of their products.