rfeather

I have decent luck with Microcenter as a Newegg substitution. + if you live in some cities, they have brick and mortar

I work in a company in the climate tech space, and this all rings very true. Our customer is utilities and I have stayed from fairly early stage through acquisition. It's frustratingly slow moving at times, but we've also been able to have a meaningful impact. Each person in the company can honestly say that they've contributed to emissions reductions to the point that really nothing they do could ever make them carbon positive again. Most of this is due to the scalability of software.

It's also been really interesting to see how regulations play into this. Getting utilities to value reducing their topline revenue through user energy efficiency requires regulation, and our customer base mostly reflects which utilities are under such regulations, though we also have a customer experience play for when that's not a primary driver.

Like layoric, I would also recommend working in this space for similar reasons. I would also add my experience is that the people are great. It's not get rich quick, so the people here are driven mostly by mission and interesting problems, which leads to a generally high level of positivity.

Thanks for the warning. I'll go with my normal approach to non-entertaining non-fiction and just read a couple of online summaries.

E.g. https://www.nateliason.com/notes/antifragile

Where did the OP say that they have a dog?

I grew up around hunting and farms and also don't find it surprising that animals are fairly intelligent.

While your statement about pasturing being good for the environment is partially true in that there are some forms that do some good, by and large meat production is actually one of the largest causes of GHG emissions.[1] A lot of this land would actually be used more efficiently for production of non-animal proteins like legumes, especially considering the land that is used to supplement pasturing through production of animal feed in most modern agriculture.

http://www.fao.org/news/story/en/item/197623/icode/

Agree. It's naive to assume your use case is the basic one when using these libraries or that you know the underlying implementation and all of the parameters, since in ML the implementations vary enough to affect the outcome and will have different controls.

To the point of exploratory analysis in some of the parents ,I prefer statsmodels for that purpose. It's not quite up to where the similarly purposed tools are in other languages, but for most of my work where I care about interpretation, it hits the right spot between usability and providing the standard statistical outputs.

I know this is meant to show some level of not knowing stuff being somehow correlated to a rise in level, but couldn't it be an example of successful use of abstraction. For example, if a manager leads a group of 5 people with diverse expertise, you would need that manager to know 5x as many things as each employee if they're really expected to be as deep as each. It seems that it would be hard to find such people. A more accomplishable strategy would be to learn who knows what and what are the top level fundamentals of each field so that the right questions can be asked.

My biggest concern right now with China trade, aside from their abysmal environmental standards is that they are actively committing ethnic cleansing[1]. It baffles me how little attention this gets.

To the point of exploitation, why not use tariffs as a way to enforce standards AND protect economic interests? Instead of targeting specific countries, it could be a points based system where "Long hours without overtime?" +5%, "Poor safety standards" +10%, and so on. Not to say the US is necessarily perfect here, but as far as I can tell, it's a long way ahead of the typical places it outsources production to. Enforcement might be hard, but any resistance to inspection etc. could be met with automatic application of the suspected tariff.

This seems like it would satisfy the goal of advancing human rights, but also give a more incentive to just hire workers in the developed countries.

[1] https://en.wikipedia.org/wiki/Xinjiang_re-education_camps

No doubt the first 4 hours are intense and draining, which I definitely experience. But curious if any on this thread notice any correlation with what you put into your body? If I heavily caffeinate, I'll usually want to collapse right after work, which isn't helpful since I really would rather spend at least some time on personal projects each night (personal preference, not prescribing or judging to those that don't). Also, a bigger/carbohydrate heavy lunch usually knocks me out whereas something lighter or more fat+protein based won't as much.

[edit, cleared up who this is targeted at]

Maybe, maybe not. And if there is, they may or may not have enough bandwidth/power to really flesh out the full product vision. Part of the attraction to startups for some, myself included, is the agency that technical people can gain with respect to what they ultimately work on in an environment where anyone with an idea might get "airtime".

I'm not sure there implementation of inverse in stock strategies, but assuming the basis is returns proportional to market rate x, that logic doesn't really follow for all cases I think. If the random strategy has gains of 1x and the non-random strategy has a loss of -0.5x, then wouldn't the inverse just be a gain of 0.5x?

Funneling money to the shareholders of companies involved in the production of weapons, of course.

No. You cite an article that falsely compares sales and delivery numbers. It's deliveries on already sold cars that slowed.

I'm glad you're adding new info here, but I'm not sure why it has to be couched it divisive left-right rhetoric. I'm leftish and didn't know this. I am however, for certain kinds of consumption taxes.

My partner got me back into lifting via the her membership to NerdFitness Academy. Helpful for those who might benefit from some gamification and gives a decent set of "levels" to progress through to get into it without going the full on coaching route. That said, I agree with others that you probably would want to get some coaching, or at least do a lot of self study when progressing to heavier lifts (like the dead lift). Source: lots of thankfully minor injuries before I started really learning form.

Because it can be.

I like the idea and genuinely hope that fact checking becomes more mainstream. Seriously, kudos to the author for building something. But what I'm not getting is why a news platform would use this, unless this is intending on becoming a news platform itself?

I think there is an underlying assumption that the general world cares about things like "primary sources" and "logical fallacies" enough to bother hovering over the text or viewing the content, and that there isn't an intentional manipulation of these things by media organizations to fit a narrative. Maybe if this was a browser plugin that came with <major browser> by default and automatically highlighted fact checked statements in exiting articles. That way folks wouldn't have to opt in, but as it is, whats the incentive for either the populace or the media to participate? Think of it this way- how did the fact checking in the examples get done? Someone spent 2 minutes on their favorite search engine. People willing to do that will do it if they have the time, and people not willing probably aren't interested in information that would contradict their opinions anyway - unless it comes from a source they already trust, like someone in their bubble. My own experience with this is that the folks who will care exist but are rare. Others, you can literally watch their eyes glaze over the moment you introduce a little cognitive dissonance.

That's the thing. P values don't prove that anything must be. They simply say that if rerunning the experiment again, it would be surprising to get a different result. Conversely, if you don't find "statistical significance" it definitely doesn't mean there isn't a difference. In practice, it might (often) mean the study didn't have enough samples to find a relatively small effect, but the layperson making decisions (do I allow right turn on red or is that dangerous?) may not get that nuance. A book that really helped clarify my thinking on this is _Statistics Done Wrong_ by Alex Reinhart.

Edit: remove "interpret" from last sentence to clarify

I think it depends on if there is a distinction between digits/letters and words, otherwise "26" would be a good starting answer too (since each letter is it's own word).

If you look at the referenced map, the affordable states aren't exactly hotbeds of opportunity. Anecdotally,I had to move from one of those to one of the not affordable ones to have a decent career (by my standards). On a less anecdotal note, they are also less populous states,so they don't represent the condition experienced by most Americans. If trading off "quality of life" for the ability to participate in a high productivity industry like tech is a common experience, what is the aggregate effect on those industries?

Absolutely. Though as someone who has eaten squirrel (WV native. Yes it tastes like chicken.), I am amazed that someone took the time to get the brains out and eat them. It takes several squirrels to make a decent meal. I can't imagine how many it would take to have a substantial amount of what must be very small brains.

The article doesn't talk about greenhouse gas emissions, so the power grid and fuel source isn't really relevant to the point, especially since oil is an extremely small part of the energy mixture in most places.[1]

If they were talking about greenhouse gases, then the answer seems to be that it depends on where you live and how/what you drive, but an electric vehicle almost always produces less emissions than an equivalent gas powered one. [2] does a decent job explaining though I feel he leaves a few parts out for the sake of clarity. For example, driving a P100D isn't the equivalent of driving the average car. It's more like driving a sports sedan (like a BMW M5), so those sorts of vehicles would probably be a more fair comparison. In the same vane, the whole US car fleet includes lots of SUVs and other trucks, which if there were equivalent electric models, would obviously not go as far as a Leaf. It would be great if there was a US calculator that worked like [3] does for the UK so that we could do the same sort of comparison based on the mix where we live.

[1] https://en.wikipedia.org/wiki/Electricity_generation#Product... [2] https://www.youtube.com/watch?v=6RhtiPefVzM [3]https://www.nextgreencar.com/tools/emissions-calculator/

Not the poster's job. Facebook is a business. If they want a solution from the poster, pay for it.

Are you aware of one for the kind of "Architect" you are? I'd definitely like to read that.

Off topic, but I'm looking at similar data. Are you talking about a public source (eg NHANES) or something else?

The point about hacking away at the code needs to be couched heavily. It's too easy to conclude you've got negative or positive results when what you really have is a silly little bug. The lack of focus on implementation skills in data (or even "real" science) is frightful. The one take away anyone trained in software engineering could share is that if you aren't very sure if it is working as intended, it's very likely not. Code review is very applicable here when making major pivots, even if unit or other testing is decidedly too time consuming for the train test improve loop.

Edit: typo "of" to "if". Somewhat serendipitous if you think about it.

The boss could very easily at least make it known which engineer did the good work to fix it. If that isn't standard practice, something is seriously wrong. I make it a goal to be clear about who did the work when talking about something my team accomplished, even if I had the idea.

All of the bang commands are probably a great way for them to get feedback on what they're not giving good results from. Very targeted training data.

Given that this consequence seems to be unintended, it seems surprising that two companies this large couldn't/didn't find a way to test the effect ahead of time.

The unfortunate thing is that no matter whether something is illegal or not, it can still be a victim of bycatch when fishing for legal species, and bycatch mortality is quite high. Bycatch is actually higher than actual catch by weight for some fisheries+methods.[1] It's not just what you eat, but where it came from.

[1] http://www.fao.org/docrep/003/t4890e/t4890e03.htm

Edit: A couple of extra spaces removed

Could you elaborate more on the advantages of a functional language?