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.
> 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.
This is a great point and I've found this as well, even if the mission drive is not there, others are just glad to be working on something that isn't one way or another selling ads and grounded in the real world (sun/wind/energy) so makes for a great group of people.
You can't cheat with reading. Whatever you think of NNT he carefully picks metaphors to simplify underlying fabric. When I read a book it feels like a conversation with the writer, it takes me through a new door and unlocks another paradigm. Try to read the whole Skin in the Game book. It's worth the investment. Summaries are like a hand job it doesn't compare with the real deal.
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.
I just clicked his blog and searched "dog" to see if he had a dog.
> by and large meat production is actually one of the largest causes of GHG emissions.
Your link puts it at 14.5% for the entire life-cycle, which makes it one of the... smallest causes for GHG emissions. It's the smallest versus any other sector in the graphs I can find[1] (vs Commercial and residential, Transportation, Industry, Electricity are all larger slices of pie). Why say its one of the largest?
It really depends on the land and the crops. Lots of land cannot support anything but pasture, and lots of places would be ecologically improved with more pasture. Killing (say) a cow is worse than the killing all animals + ecosystem of the field that row crops require (obviously complete habitat destruction for any field critters at harvest time, but also pesticides and herbicides and such side effects).
If you are worried about GHG issues, why not talk about food footprint or animal deaths per calories? Should you really be trucking in lettuce from Mexico and California if you live in New Hampshire, where I live? Should you be buying avocados if what grows here are animals? Should lettuce be grown at all?
It seems very... speculative that so many are confident that the calculus tips in favor of "not the cow" here. If you eat pastured cow, you are supporting the ecosystem of cow (for its life or partial life if it EOLs in a feedlot) but also possums, raccoons, ducks, field mice, butterflies, snakes, birds, bees, and thousands of other insects that populate the fields of pasture. Ideally, for your entire meat consumption for the entire year, you eat a grand total of 1/2 cow. If you eat only row crops you support the destruction of these creatures, necessarily, unless at the same time you are carving out land for them elsewhere. But if there is reduced insect biomass in the world, or colony collapse disorder among pollinators, then I say this is blood on the hands of the vegetarians. Row crops (corn/soy/etc) have other destructive effects in the nature of their planting and harvesting, and good pasturing (which isn't all of it) builds soil and restores land. (If you want better pasturing everywhere, you're making an economic argument for something else, just like people who wish row crops didn't require mass pesticide/herbicide dousing.)
It gets even more nuanced and weird if you look into the ecological destruction required to keep other crops going. And it gets further nuanced still if you get into what GHG emissions for livestock are. In the 1800's Americans destroyed an absolutely massive biomass of bison, to near extinction[2], and replaced them with an almost equivalent biomass of cows, unintentionally making the methane impact... pretty neutral, on the lifetime scale of things. But maybe if the bison still roamed free today, environmentalists would advocate for destroying them, I don't know.
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.
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.
Had to switch from coffee to black tea for this very reason. Cannot subsist without some caffeine for mental health reasons, and absolutely love coffee, but it just does not love me back.
When I drink too much coffee, it makes alcohol that much more necessary to offset the extra stress and anxiety, but black tea does not have this issue, it just kinda fades away for me. Then a little cannabis at night for a bit of pleasure and sleep promotion.
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?
If the non-random strategy was “buy stocks A, B, and C”, the inverse might be “the broad market minus the stocks A, B, and C” rather than “short stocks A, B, and C”.
Hmm, let's call "buy nothing" a neutral strategy. Then a combination of “buy stocks A, B, and C” and “short stocks A, B, and C” has the same effect as the neutral strategy. But the combination of “buy stocks A, B, and C” and “the broad market minus the stocks A, B, and C” is equal to "buy everything" and has not the same effect as the neutral strategy.
You seem to assume "buy everything" to be the neutral strategy. Then everything plays out as you say.
That's true but still doesn't map here. Or at least, to my interpretation of it.
Stock-picking is specifically referring to where you should allocate a given amount if you are investing. There's obviously more to stocks than simply picking them, but as for picking specifically, there's no analog in poker because you don't divide your bet that way. (Aside from raising on a bluff, but that's stretching it).
Whether, when, and how much you should invest is a separate strategy that's related to your confidence and expected return of the picking strategy—but not the same thing.
Just like whether and when you agree to a game of chess vs tic-tac-toe might depend on your confidence in your skills; but that decision is _not_ relevant to chess strategy, which assumes you're already playing the game.
This still doesn't make sense to me. If the return of inverse strategy S' were greater the loss of strategy S, couldn't you then guarantee a positive return by using both strategies simultaneously?
