It is unfortunate that the only way to get through the regulatory process is copious $ - but it does work. If this project were better funded, it would likely have gotten through.
There is an entire political party representing something like half the population of the US dedicated to shrinking the regulatory apparatus, including the FDA. That doesn't sound like career security to me.
My perception is that the parent commenter is referring at least partly to "subcriminal" or nearly subcriminal behaviour. There is/was nowhere to "put" this kind of individual when they come from these settings, for a whole host of reasons, ranging from: something like "it's your neighbour's mentally ill kid, you really gonna call the cops for THAT?" to "what cops?".
School shooters and "crazy people acting violently" are probably incredibly rare by comparison. Sub/urban environments probably reduce the impact of the mentally ill on their surrounding neighbours in a whole host of ways, in fact -- in no small part because treatment is much less available in rural areas. (There's more of them in a smaller area and they're more visible and increasingly less criminalized, so people get the idea that poverty and health issues of that variety are modern, urban problems.)
Keywords in the literature around this include "community impact" or "community health", but it's not my area.
This is cool. I hooked up Llama to an open-source TTS model for a recent project and there was lots of fun engineering that went into it.
On a different note:
I think the most useful coding copilot tools for me reduce "manual overhead" without attempting to do any hard thinking/problem solving for me (such as generating arguments and types from docstrings or vice-versa, etc.). For more complicated tasks you really have to give copilot a pretty good "starting point".
I often talk to myself while coding. It would be extremely, extremely futuristic (and potentially useful) if a tool like this embedded my speech into a context vector and used it to as an additional copilot input so the model has a better "starting point".
I'm a late adopter of copilot and don't use it all the time but if anyone is aware of anything like this I'd be curious to hear about it.
Prior evidence suggests that sedentary behaviour increases blood biomarkers of cardiometabolic dysfunction.
They studied a hunter-gatherer tribe in Tanzania and found that despite having similar patterns of sedentary behaviour, their blood biomarkers of cardiometabolic dysfunction were much lower than those in industrialized nations. The authors posit that one reason for this is that sedentary behaviour in this group of individuals does not involve furniture -- rather, it usually involves a "deep squat", and the authors show that in this position the muscles are much more engaged than when someone sits in a chair.
This is consistent with evidence that breaking up periods of sitting with movement is good for you.
Their open-access paper talks about some evolutionary context for this hypothesis [1].
Eh, with one confirmed injury, I'm comfortable calling it "conceivable". The testimony of the injured worker in that case even reflects the fact that a spiked tree does not guarantee injury or death to the operator -- indeed, first there seems to be substantial damage to the machinery, and only then, a risk to the operator [1]:
> Alexander later filed a lawsuit against Louisiana-Pacific claiming that the band saw had been weakened from previous strikes with nails, but that he was forced to work with the saw or face dismissal.
Tree spiking is one example of this. It's so common as to be illegal in the United States [1]. Unfortunately, without strict worker protections, it's conceivable that workers would be the ones to pay for their employers' (or their upstream suppliers') illegal actions.
I wrote some Python code recently that uses a similar data structure (Futures instead of Promises, without knowing necessarily about the data structure's use in JavaScript). It wasn't really for caching.
I modeled mine on the Django ASGI reference library's server implementation, which uses the data structure for maintaining references to stateful event consumers. Exception handling is done with a pre-scheduled long-running coroutine that looks at the map.
I'm curious about your second point -- why exactly do things get bad with high tail latency? Is it only a weakness of the data structure when used for caching? I'm having trouble picturing that.
There is an entire political party representing something like half the population of the US dedicated to shrinking the regulatory apparatus, including the FDA. That doesn't sound like career security to me.