Weird that people find Obsidian before Joplin
438 karma · joined June 25, 2018
I can understand the struggle at an older age, but age matters a lot here.
IT infrastructure, cloud, cyber security, privacy and risk, customer success, QA, SRE.
Add a dash of doing stuff that not everyone else wants to do and that gets me or has kept me with a high paying job in IT.
Not true, many companies give incentives to employees if they hire their referral. Why would I need that employee need to know them.
In a recent story, D3f4ult discussed that this happened to him for a year and he was given 24/7 isolation for one year. [2]
[1]: https://solitarywatch.org/facts/faq/ [2]: https://journalistsresource.org/home/solitary-confinement-re... [3]: https://darknetdiaries.com/transcript/139/
That would be a bit much and not accomplish improvements.
MAMAA
I use Joplin for personal and Obsidian for work.
> The issue with this is that the amount of vitamin D someone might take is correlated with how much sun exposure is available. And the amount of available sun exposure can impact what is considered a little / a lot to each person.
That could be case but not necessarily in this study.
I think that is the point, we should be doing better by the citizens.
Plus Economics, Law, and Political Science are strictly separate sciences just like chemistry and physics are not strictly separated sciences. I think the figuring out what a mathematician does is more difficult, but also still understandable and important. [1]: https://www.cambridge.org/core/journals/political-science-re...
The idea of an engineer that researches, designs, tests, and measures and a programmer that implements seemed to cost too much (not just monetarily) for the industry that employees them and there isn’t the sufficient need to regulate all sun-groups of the industries that employ software programmers / engineers.
“We report racial and socioeconomic disparities without attempting to control for other covariates. First, if New York City residents of different races face different levels of police deployments, that disparate impact is itself important; it can also bias algorithms trained on downstream policing data irrespective of the true causal mechanism. Second, controlling for other factors in policing data can be difficult to interpret, introducing concerns about omitted variable bias and model misspecification which make it difficult to identify which factor is truly the “cause” of higher police deployments. For the sake of transparency and simplicity, therefore, we report results by stratifying each variable separately, noting that these disparities are themselves important but that multiple causal mechanisms may underlie them.”