Why wouldn't stem PhDs follow some bell curve of quality? I'm sure many PhDs that are leaving don't contribute but some of them do. I personally don't see a reason for it to be skewed for only PhDs which don't contribute to leave.
I agree with this. I guess you already believe they follow a bell curve.Then from your former comment you also believe it's worth it to lose many PhDs that don't contribute to also lose the few that do.
I guess the conflict is my value judgement that it's good to keep PhDs that don't contribute if it allows US to keep the ones that do contribute. I believe so for 2 reasons.
- Distinguishing between contributors and non contributors at scale is difficult.
- the value of research can be large from a few contributors.
It's essentially an inline function with only 1 client. Can be a preference for inline readability and automatically enforces there are no other clients of the "function".
As someone who works in this space. A large org like Google often separates the feature work and counter abuse teams. The org structure leads to unintended feature consequences. It sucks when your trying to provide value to people and it's taken advantage of by bad actors.
For some games like dota 2 cheats only make them marginally better at the game but much more frustrating to play against. The most common cheats are map hacks and instant action scripts both of which can be useless without the game knowledge for correct play. But both of these cheats make playing against them frustrating but they wouldn't rise to the top.
Again I think there’s a better way if we push through the concept of a fair game, and just focus on fun. It should be possible and accepted to block (and never match with going forward) players who are just… unfun. Annoying, poor sports, or cheaters. Heck, maybe player-curated and shared matching and blocking lists could become a thing.
Games are a social thing we do to have fun, there’s no obligation to spend your limited social free time hanging out with annoying people.
I think there are a couple of underlooked points for weightlifting's contribution to sustainable weight loss.
- the weight that you are gaining with a surplus diet turns into muscle instead of fat. You can take diet breaks and just gain muscle faster which will help when returning to a deficit.
- the increased 100-200 calories from lifting can make a 100 calorie deficit easier to adhere to as it's a smaller proportion of your total.
- weightlifting reduces stress which is a common cause for over eating.
Overall I agree with the point that people don't take the effort to change themselves and connect with another human being.
> The only way to establish relationships is to be real
Personally, I found emotional dissonance when people tell me this phrase. For a long time, acting like myself has ostracized me from other people and built shallow relationships. It's only when I didn't act like myself and faked it until it became a habit did I build deeper friendships.
It's emotionally difficult when your natural way of acting is not accepted.
I think the article creates a false dichotomy between top down and bottoms up approach to understanding a topic. In the end, looking at a project from multiple perspectives will give you clarity.
A quick top-down view provides scaffolding to latch onto. A follow up deep dive will poke holes in your assumptions or reinforce your current understanding.
As the article mentioned, always staying at a high level can provide a fake understanding but I think just doing a deep dive slows understanding higher level abstractions and purpose. Constantly undulating between the two or having reports who can provide the right details are the best approaches I've seen from mentors.
I noticed it in general observationally and later found a generic explanation for it.
I used to do behavioral analytics and noticed that programmers tend to have a distinct clustering of behaviors.
I’m pretty sure it’s the TNXB gene, it’s been overlooked because it’s traditionally hard to sequence but modern sequencing 30x or long reads can find SNPs here. A colleague who has the same rare set of behaviors and interest as me and is also hyper-mobile had found that he has a TNXB SNP combination that should by random chance only occur in 1/10K people, I subsequently did a DNA test and found that I too had the same SNPs. I have since collected more samples from friends and colleagues who are world class in their fields and so far 5 of 5 have had the same TNXB SNPs. It also appears that in reality this combination is much more common than random chance due to sexual selection which should underscore how behaviorally impactful it is.
Hyper mobility is considered a spectrum and a theory that I’m yet to prove is that the spectrum is almost entirely down to the number and type of TNXB SNPs. And it appears that people on this spectrum are more likely to be programmers.
Consider the over representation of transgenderism in programming. That same over representation exists in hyper-mobility and I would suggest this is due to a common cause.
That is true, I tend to use transgenderism instead of autism because the data is cleaner and that more than makes up for the smaller sample sizes. At least for the things I am interested in.
But for internet discussions I should have used the autism cluster instead.
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