912 karma · joined April 6, 2017
There are in betweens here, with the major one being threads in slack. Everyone gets notified about a single message at the start of the thread, but does not get notified for any subsequent discussion. Any interested party can read more and participate as needed. For someone like me (a leader on paper but not really in practice), I'd read all the message and look for dependency or similar problems, but for others they may not need to.
(I think I've been in the SF tech scene too long-- I've literally had every one of those things pitched to me before)
See jasonhealth.com (for Quest) or ownyourlabs.com (for Labcorp). The main issue is that you can't bill insurance without a provider's order, but a lot of tests are cheaper when not billed through insurance.
I'm not saying this is the case necessarily for planes, but I'm just trying to provide context for how proxy measures of air quality may not tell the full story.
Unless I missed something, you have yet to share the science that supports your view. I'm not bowing out of discussing this because we have different opinions, I'm bowing out because I shared a study (and could provide more) and you responded by shifting goal posts and standing by your claim, not by responding in kind with similar studies or different interpretations of the data. There's not much discussion to be had if we're not working from a shared understanding of data and facts, and those data and facts aren't driving our opinions and beliefs. Anyway, best of luck out there!
> More often than not that's what people mean when they're saying that politicians have given up on fighting Covid.
I know a lot of people in public health and disability spaces, and every person I know that talks about the failure of public health around the covid pandemic is referring to the dismantling of surveillance (e.g., testing), the lack of investment in next generation vaccines and treatments, the failure to upgrade ventilation and filtration, and removing mask mandates in targeted places (like emergency rooms). I haven't heard anybody in the US discuss lockdowns in years. I haven't even heard people talk about broad (i.e., outside healthcare) mask mandates in over a year. You need to get "mitigations = lockdowns" out of your head, that's not what people are "implying" when they discuss fighting covid.
That's a completely different argument, and one that really can't be objectively measured (e.g., how do you value a life saved?). But lockdowns were effective at saving lives in 2020, and since we knew nothing about SAR-CoV-2 and had limited treatment options there was basically no other option. Nobody (other than you) has mentioned reinstating lockdowns.
Had all states had restrictions/"lockdowns" in 2020-2022 that were as strict as the strictest state's mitigation measures, we would have saved ~360k more lives[1].
1: https://jamanetwork.com/journals/jama-health-forum/fullartic...
Bold of you to assume those were people and not also AI
The three biggest parts that I learned and got good at, which have come in really handy are: reflective listening, non-directive communication, and not asking "why" questions.
Reflective listening is repeating back what the person is saying to them. It's definitely more art than science, since if you do it too overtly it just feels cheesy and not genuine. But done subtly and well, people open up in really noticeable ways.
Non-directive communication means not telling people what to do. That doesn't mean you don't have opinions about the situation or that you don't push them towards a decision... it just means that your role is to be a sounding board. If someone comes to you saying "My partner cheated on me", the response many people have is "dump them!" -- but you're not living their life, you don't know all the complexities of the situation even if this is your best friend. So, instead of "dump them", a series of questions can often help someone through the situation and leave everyone feeling more seen and heard ("What happened? How are you feeling about that? What do you think this means for your relationship? What would have to happen for you to trust them again? How would you feel staying with them? breaking up with them?" etc)
Not asking why questions is difficult, but once you get the hang of it it's really easy. "Why" questions are almost impossible to say without there being some tone or judgement. "Why did you do that? Why did that thing break? Why didn't you tell me that?" You can often rephrase the question to a more neutrally worded question (sometimes they also sound judgmental, so tone and context matter a lot): "What were you hoping would happen? How did you see this playing out? What do you think led to the system breaking? What can I do differently to allow you to feel comfortable sharing?"
Ultimately it all takes practice, and fundamentally you need to have the time and space to be a good listener. If you only have 2 minutes between meetings, then you're probably not going to have the capacity to listen to your friend talk about their divorce or health scare. But ultimately people like talking about themselves while feeling safe-- so anything you can do to create and encourage that environment will cause people to feel like you're a good listener.
The getting the interview part is difficult: some places will more liberally interview candidates, and others more heavily screen them. The three things that you can change to improve your odds here are tailoring your resume to use more words and phrases from the job description (trying to game any AI or human resume screener), networking to try to bypass the screening stage altogether, and casting as wide of a net as possible. Networking can be anything from having a social media presence, going to various forms of dev events, talking to friends about open roles they've heard about, or even cold emailing people you're interested in (although, if you do this, you'll probably have more luck asking to zoom/coffee for career advice than asking if they have a job available for you). And then, regarding the role you're targeting, I moved into increasingly technical roles starting as a data analyst-- I know not everyone would agree with this approach, but it worked well for me. I was a really technical analyst, who became a data scientist, who worked up the ranks, and then started moving between DE/MLE/DS roles. But this was also back in the days when "data scientist" was a new term, and before it got so watered down-- so maybe with title inflation, my original "data analyst" jobs might be "data scientist" jobs today? Anyway, my point is that I think it's easier to slowly slide in to your ideal role than it is to try to hop directly into it.
The passing the interview part ends up being so much about how you communicate and frame work that you've done. It sucks because this ends up inadvertently screening out really smart/good people that struggle with this kind of thinking (and screening in people who are good at talking but suck at doing e.g., many MBAs). But once you're talking to a live person, I think emphasizing how your degrees (both grad and ugrad) have really prepared you for exactly the role in front of you. You can also often take non-technical experience as evidence of certain components of the technical job requirements. Like, I worked in a restaurant when I was a teenager, and you better believe that prepared me to deal with many concurrent demands from many different sources, and required me to think on my feet about the priority/order of operations. So, when I was earlier in my career, I got really good at answering questions along the lines of "you know, I haven't done this work in a single role, but I have experience doing everything you're asking for over multiple roles..."
But it sounds like a lot of the issue you're running into is just getting in the door to begin with? Unfortunately, I think so much of it comes down to luck-- just keep applying to as large of a variety of jobs as possible, and network as much as you can.
I'm just saying that I seem to have more luck with people coming out of those traditional programs. But, also, as you add more jobs to your resume, the specifics of your education matters less and less.
So, some of the "working on the same problems" was intentional-- it would require effort from both teams. But the dividing line was nebulous. The DS team would have preferred to do all of the stakeholder work through building a model, and then hand a pickled model to the ML team to implement in production. The ML team would have preferred to have the DS team scope the problem and hand off to them to do anything involving any form of modeling. It was a total mess.
But I have never worked at an organization where this has gone well, so I don't think it was an issue specific to that org. If you're involved in data things, you want to do interesting work and there's only so much interesting work to go around. And, ultimately, the vast majority of organizations don't have a need for tons of people to be doing the really technical aspects of ML/AI/etc. SO much of the work is scoping problems, cleaning data, worrying about pipelines, etc... and so if OP or whomever is thinking they're going to waltz into a job and make the next version of ChatGPT, that's really unlikely with anything less than a PhD. Personally, I've found a pretty good home being able to interact with leadership to define nebulous problems and solve those problems with whatever tool is appropriate-- and my success has way more to do with communication/project management/scoping skills than with technical skills (although both are necessary)... and I think those skills are better fostered through the more traditional programs.
Those programs tend to show me that you're interested in and have done the more boring but foundational coursework that is often cut to make the sexy degree programs. That means that hopefully you won't be upset that 100% of your job isn't deep learning, and that you'll be better suited to pick the right tool for the job.
At one of my last jobs, there was a machine learning engineering team (all boys) and a data science team (all girls and gays) who had the same ML chops. The DS team ended up getting more models into production and more research published than the ML team because they had more "soft" skills to navigate the problems the org was facing. When someone in leadership would say "we're having issues booking appointments", the ML team would set off building some fancy deep learning model while the DS team would generate hypotheses with stakeholders, do some exploratory analysis, run a few prospective studies, and then use those results to inform some regression models that would end up in production. It wasn't as sexy as some deep learning model, but the leadership team wanted full interpretability of their model so deep learning was never going to be acceptable. I generally think of these kinds of skills being taught more the stats, applied math, or epi programs than in the designer ML programs. ymmv
Your comment suggests that the driver of your (and your peers) anhedonia is some lingering threat of some future lockdown by governments? Is that what you're saying? And, is that actually the case or is it just the easiest cause to put your finger on?
I understand that period was really rough for so many reasons. But a lot of the angst I see among friends/family/coworkers today isn't from the lockdowns per se, but it's more from having to slow down and consider some heavy, almost-existential questions surrounding their relationships, life, fulfillment, social supports, and purpose. And, at least for me, struggling with how many of the things I thought I knew about myself turned out to not really be true. Lockdowns may have forced me to see and acknowledge these issues, but they were always there.
Phantom braking has always been a bit of an issue -- when I first got the car, I just wouldn't use cruise control in heavy traffic, but felt ok using it once out of a city. In the past year or so, it's been so bad I'm scared to use it at all, and I've filed multiple complaints with the NHTSA. I'll be driving on I-80 through Nevada or Utah and every ~10 minutes the car will slam on its brakes. This is dry, consistently lit, consistent colors, minimal bridges casting shadows, etc.
I wish there was a way to make my cruise control "dumb"/non-adaptive just to stop the random abrupt braking in the middle of nowhere.
But as the internet became more popular, dating sites became more mainstream, and then the location-based ones matured, it almost became a race to the bottom (so to speak).
If someone is horny right now, why chat with person A (with a text-based profile) when person B has photos? Why chat with person B when person C has shirtless photos? Why chat with person C when person D sends dick pics right away? Why chat with person D when person E sends dick pics and will drive to your house in 10 minutes? So a subset of users start pushing this towards being hyper efficiency, but that comes at the expense of the other subset of users who don't necessarily want that.
My experience has been you can't ever escape that. That mentality has permeated the system, and now we're conditioned to "meet up within 3 messages", "send pics in first message", "no fats, no fems, no flakes", etc. And if you don't like that and want something slower then you get told "it's just grindr, what do you expect?" (which eventually morphs into "it's just tinder what do you expect?", "it's just hinge, what do you expect?"). But even the people saying "it's just grindr" also complain that after they have sex, they just feel lonely again and that they feel trapped or addicted to grindr.
Obviously I'm painting with really broad strokes. Some people do find relationships on grindr. Some people are satisfied with their interactions. But, I think like the original article describes, it feels soul destroying. And by the time you're in your 30s, I think a lot of gay men realize that easy sex doesn't necessarily mean good sex and it often doesn't mean feeling satisfied or content afterwards. But it's difficult when you have a heterogenous population, with a vocal faction of the population that keeps pushing the limits of efficiency, and the rest of the population is just sorta dragged along.
From "CEO-Founder": "First team in biotech software that actually knows what they are doing when it comes to biotech software", "Advice to Management: Keep doing what you are doing."
From "Contractor": "Rude and overconfident management who make it really undesirable to work there. If they are not successful it will mainly be because Alfredo and Aidan are mean people with a big ego who drive away good people from considering joining the company."
One of the difficulties is that in many systems medical billing is done by coding specialists based off the provider's note. They may recommend CPT codes, but that may not be what's actually billed. In addition, most providers are too swamped to do things like put together an estimate or drag and drop CPT codes. Hell, many providers will literally count the clicks they have to make in an EHR and will LOUDLY let you know if your proposal will increase their number of clicks by even one.
I don't mean to be a downer on this, and I do think there are solutions... but I think 90% of the problems in healthcare aren't technological ones but are navigating large, entrenched systems that have very little incentive to change.
But antidepressants is defined as benzodiazepines, which aren't antidepressants, they're anxiolytics. Normally when someone talks about "antidepressants", they're referring to SSRIs, SNRIs, NDRIs, and some similar meds. They have a category for SSRIs, but their example medications include non-SSRIs. Regardless, all of the medications they list in the categories other than SSRIs ARE controlled substances. So, it should be at least 45% consume controlled substances.
Anyway, the odd math and misclassification of medications makes me question the results of this study. I don't doubt that tech execs are stressed, drink a lot, use illicit drugs, and prescription drugs... but I'm not sure I'd believe the specific percents given here.
Mr. Beast doesn't do _philanthropy_, he makes entertainment videos. His niche is giving money away in exchange for challenges or stunts. This gets him views, and has created a profitable business for him. That's not altruistic at all, it's just a business guy who is quite good at knowing his audience and executing his trade. "thousands of people regaining their sight" is a glorified marketing campaign.
Yes, money goes to causes; but the fact that money wouldn't otherwise go there is a policy decision that we've collectively made. We could tax billionaires and use the revenue to fund health, education, housing, infrastructure, research, or whatever. I'd much prefer to see us do that than wait around until we see Mr. Beast's Children's Hospital pop up somewhere.
I've been to a bunch of events, including the Indy 500, Times Square for NYE, and Mardi Gras in New Orleans, and by far people were more sloppy drunk at the Kentucky Derby.
Also, this isn't strictly a Christian concept, but similar ideas exist in
Judaism: https://www.chabad.org/library/article_cdo/aid/256321/jewish...
Buddhism: https://www.learnreligions.com/perfection-of-giving-449724
Islam: https://www.quranexplorer.com/blog/Education-In-The-Light-Of...
Hinduism: https://www.hindupedia.com/en/Ideals_and_Values/Charity_and_...?