GC also protects you against forget-to-free, which in most non-GC languages is a common form of memory leak.
4,381 karma · joined January 14, 2011
steve at snewman dot net
https://amistrongeryet.substack.com/ – AI blog https://climateer.substack.com/ – climate blog
Edit: per comments downthread, perhaps they're actually not quite there yet, but still I think the principle holds if internal generation is exclusively renewable and net imports are negative.
See also: https://muq.org/~cynbe/muq/muf3_22.html
And in any case, the key point is that the primary process can lead to a candidate taking office who is well off-center even within their district.
A district tilts heavily toward one of the two main parties -- it is a "safe" district for that party. The candidate nominated by that party in the primary election is nearly certain to win the general election. If the candidate is highly extremist / flawed, they might lose, but party affiliation is sufficiently strong nowadays (aka the electorate is sufficiently polarized) that a candidate can be pretty far to one extreme -- farther out than the bulk of the electorate -- and still win.
Meanwhile, there is a tendency for the more centrist voters in both parties to skip the primary. Thus, the candidate who survives the primary is often relatively extreme.
The result is that the victor of the general election is often to the extreme side of not only the electorate as a whole, but the membership of their party.
To oversimplify, imagine that political views fall on a one-dimensional spectrum ranging from 0 to 1, and the electorate consists of:
- 40% at 0.4 (center-left)
- 30% at 0.6 (center-right)
- 30% at 0.8 (heavy right)
1. How likely is it that a collision like this (two large satellites, high enough for the fragments to have a long orbital lifetime) occurs in any given year?
2. This announcement popped up on HN a couple of days before the event. Are we not able to project potential collisions farther ahead? I would think objects of this size are well tracked.
I'm wondering how difficult it would be to detect these far enough in advance to send something up to nudge one of the dead satellites out of the way.
https://caseyhandmer.wordpress.com/2019/08/27/there-are-no-k...
2. What is your source for death rates not fluctuating with cases? In general, deaths have reliably tracked cases, with a delay of a few weeks (because it takes time to progress from diagnosis to death).
3. Shuttering the economy is not binary. With an intelligent approach, and general compliance with measures such as mask wearing, we ought to be able to control the virus without a complete shutdown. Unfortunately we keep making unintelligent tradeoffs and executing inconsistently.
I would like to write a test to verify that my code properly handles the case where one of the MySQL instances has experienced a hardware failure. The point is that I can't do this as a strict black-box test where I merely issue calls to my service's public API.
[edit] And of course "testing the externalities" doesn't help here. I can test the MySQL instances and verify that they are all running, but that doesn't remove the need for my code to handle the possibility that at some point one of them goes down.
(Source: worked at Google 2006 - 2010, was very slightly involved in helping the Jotspot team integrate)
Where do you get this figure? Your first link shows 14 deaths per 100,000 in that age range, or 0.014%, but that is for the entire population, not just people who have had the virus. The same data set reports 1705 cases in that age range. 14 deaths out of 1705 cases is an 0.82% fatality rate.
Of course, that 0.82% figure, reflecting only confirmed cases, is inflated. But conversely, some of those 1705 afflicted 18-45 year olds may still succumb to the virus. The true IFR in this age range in this region _might_ be closer to 0.1% than 0.82%. But 0.01% is just wrong.
You can find a more general overview at https://scalyr.com/product.
This is a very nice way of framing the question.
I think there may be at least two scenarios to consider. For some products, cost is primarily driven by R&D; for others, cost of operating the service (COGS) dominates. Of course it's a continuum, but I suspect there are more services near the ends than the middle.
For an R&D-dominated service, if your needs are nontrivial and there is an off-the-shelf product that is a decent fit, you probably want to go off-the-shelf. A commercial provider can amortize the R&D over a large customer base.
For a COGS-dominated service, the commercial provider may still be more efficient but it is less of a slam dunk. Log management (e.g. Splunk) involves a substantial operational component, because you're dealing with large volumes of data.
Disclaimer: I am the founder of Scalyr, a Splunk competitor. Early on we realized that, even though log management is COGS-heavy, we could achieve huge economies of scale, because query workloads are highly bursty. By carefully managing a central pool of resources shared by many clients, we're able to run much more efficiently than a single-tenant homegrown solution [1]. This is one of the key points that we highlight when having build vs. buy discussions with potential customers.
[1] https://www.scalyr.com/blog/searching-1tb-sec-systems-engine...
https://fivethirtyeight.com/features/infectious-disease-expe...
Responding on March 16 and 17 to the question, "how many cases will the US report by March 29", their median prediction was 19,000. As of this moment, early on the 21st, that figure has ALREADY been exceeded (per Worldometer). Based on current growth rates, it appears virtually certain that the actual figure on the 29th will exceed the "best-guess" prediction of every single expert surveyed, and the "high-end" prediction of all but one or two. They've completely whiffed a prediction just a few days out.
This is a novel (no pun intended) situation, even for epidemiologists. To understand it, critical thinking skills are at least as important as subject matter expertise. My read is that the Medium article represents some of the best critical thinking on the subject so far.
Giving up and attempting to resume normal activity is also unacceptable, full stop.
The best/latest explanation I've seen is https://medium.com/@tomaspueyo/coronavirus-the-hammer-and-th.... Please read it (everyone, not just parent) if you haven't already.
Then if you're frustrated that your political leaders aren't moving to make best use of your sacrifice... let them know!
This doesn't seem like a very good alternative. Even with adequate hospital care, the true fatality rate seems to be in the neighborhood of 0.5% to 1%, which would be quite harsh if allowed to spread throughout the population.
I am hoping that improvements in treatment can make the difference. A vaccine may not be available for quite some time, but drugs that reduce the fatality rate might be ready sooner.
Of course the actual course of action must include some mix of all-of-the-above: containment, new hospitals, new treatments.
EDIT: as voidmain notes, we might also hope to quickly reduce the number of infections to the point where we can contain them with testing and contact tracing, rather than blanket shutdowns.
Imagine 100 people contract the virus, 10 develop serious symptoms, and one dies.
In country A, with widespread testing, 40 of the 100 are identified. The visible statistics are 40 cases, one death, death rate of 2.5%.
In country B, with poor testing, only the 10 with serious symptoms are identified. The visible statistics are 10 cases, one death, death rate of 10%.
Again, this is about the reported statistics, not the underlying truth of the disease.
As many have pointed out, this was not actually the postmortem, just an "update". I still find it pretty weak. There has been plenty of time to assemble basic details, such as a rough timeline. Yes they are busy with the actual response and cleanup, but this is a big professional team. The update feels less like "we're on it, here's what we have so far, more to come", and more like a bland PR minimization exercise.
I'll note that they've found time to determine that "For most Google users there was little or no visible change to their services", "YouTube measured a 10% drop in global views during the incident", and "approximately 1% of active Gmail users had problems", and yet they don't mention the time at which the incident was fully resolved or even when it started! Did the impact last for a few minutes, an hour, or multiple hours? Reading this, I have no idea. But I do learn that "the Google teams were keenly aware that every minute which passed represented another minute of user impact", they "brought on additional help to parallelize restoration efforts", "networking systems correctly triaged the traffic overload", and "low-bandwidth services like Google Search recorded only a short-lived increase in latency". In other words, this update on what seems to have been a very major incident consists mostly of vague, positive statements. It does say "we take it very seriously", but it doesn't make me believe it.
(I would add that I worked at Google from 2006 to 2010, and based on that experience I'm sure they are taking this very seriously and that there will be an excellent internal postmortem. But man, reading this sure makes it hard to remember that.)