From the historical point of view, this heavy decoupling is associated with microkernels which fell out of favor with the rise of the Linux kernel. I think in this day and age, modern programming languages could make building a microkernel that doesn't fall prey to the shortcomings of MINIX a possibility.
P.S. I specifically underline references and research experience because that's where it appears you'd be weakest at the moment. Getting a 4.0 GPA in your grad school coursework (which is online, I presume) says a lot more about your organizational skills (which must be really good!) than your ability to research. Getting someone with a PhD to write "This person is capable of doing good research and I've seen it with my own two eyes" is probably one of the best things you can get on a recommendation letter to a PhD program, which is far more valuable than "this person did well in my class" (see: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&c...)
I would say, with your background, consider applying to DeepMind[1], or a national research lab. What you'd be looking for would be "entry level" research work. This would allow you to easily stand out in your applicant pool, and make it to the interview, where'd you'd have a better chance to properly explain your non-traditional academic background. If you're a US Citizen, maybe consider applying to positions at the MIT Lincoln Lab, Los Alamos, or Argonne National Lab. A lot of these guys aren't doing that much with ML/RL/AI, but that's okay! If you're serious about research you'll probably want to do a PhD. To get into a good PhD program, you'll need good references.
The audio quality of these interviews made me grimace. Audio quality aside, this looks like it would be a great resource for anyone trying to go through a "FAANG-style" multi-round interview process.
Posts like these really continue drive home the message of "Silicon Valley is just a shell of what it used to be. Only go there for the sake of relocation or with a job offer in hand." There's nothing left for the dreamers or hopefuls in SV who have nothing to run on but just that--hopes and dreams. It just makes so much more sense to take a pay cut and swing for the fences in Atlanta, Houston, Austin, or Phoenix. Hell, I'll still throw in Boston and Seattle because they're still THAT much cheaper than anywhere in SV.
This really drives home a sentiment I've acquired during the course of my college education: CS/Math is often considered "hard", but I feel that's just because we've struggled with getting good visual/verbal communicators to dedicate their lives to CS/Math education. I really feel that when explained properly (and the definition of "properly" sometimes need to be adapted from person to person), topics like Gibbs Sampling or Fourier Transforms or Backpropagation aren't topics that should take entire weeks of self-study to grasp in 2018. Yes, they require some math background, but there's some strong intuition behind them. Maybe I'm just slow or thick in the skull.
FWIW, after realizing I never directly answered your question, I figured I should give you a full response.
Rospy aside, writing and implementing your own SLAM/path-planning tools in ROS is quite simple once you get familiar with the architecture of ROS. All the sensor data can be collected by subscribing to the right channel, and then you can return driving instructions back to a controller by publishing them on another channel. Since all this channel business operates on the network layer, you can even offload the computational workload of the algorithms to a remote system and have the robot get its moving instructions over Wi-Fi.
This is some really cool information! I implemented a map-parsing algorithm that used a data structure that enabled neighboring cell access in constant time. It's biggest weakness is that very high-resolution occupancy maps take quite some time to render. I'm very inexperienced when it comes to sharing my coding projects with the world, so any feedback on the readability/accessibility of my repository would be greatly appreciated! https://github.com/dwrodri/LQTLD3
I have not used real robotic simulations, but I have used RViz and ROS for testing my own path-finding algorithms. Performance on my Late 2015 MacBook Pro was stuttery (about 15-20 fps), but still good enough to be somewhat usable.
Overall, I had more issues with finding good resources on the rospy module than anything else. It seems almost all robotics development with ROS is done using C++ instead of Python (in my little experience), so there is little I could find in terms of code samples that would allow to get past a project like this (https://github.com/gandalf15/CS3027--CS5059--Robotics-Univer...).
I think the best takeaways from the SO survey come from year-over-year analyses. You can watch JS explode in popularity over time, and see how quickly languages and libraries get adopted.
It would also be really nice if the "popularity" metrics that OP mentions (i.e. preferred language, least preferred language, language most used at work, etc.) could easily be grouped by "Developer Type". After some quick googling, it has become apparent that the data from previous years is available to download, but it would be nice for SO to show that data in the blog post.
I think this is the first game I've seen posted to HN that wasn't a "Show HN" post. That being said, I haven't been a member of HN for that long. I'd be very interested to see what else has been posted here.
I'm kinda surprised the Nvidia was totally cool with this presentation. Maybe the engineers think this info should be commonplace but are trapped in NDA hell?
Why would Oracle do this to their language? It seems like a surefire way to make sure a business will never choose Java ever again. When it comes to languages, I think there's definitely a right and a wrong way to implement changes. It's been four years since I tried to do any Java programming, but I know there are many business who are trying to build codebases that last even longer than that. Are we transitioning into a workplace where language stability is a thing of the past? Is it already a thing of the past?
Thank you for sharing! I did some quick Googling and found this BBC article that seems to back up what you're saying (http://www.bbc.co.uk/news/uk-39176110). I really do wonder why they wouldn't talk more about it? I think it would be a fair compromise to say "hey we stopped X amount of terrorist attacks, but we can't tell you everything about how we stopped them because that's classified."
While this is definitely a personal opinion, my takeaway is that the image of terrorism that is commonly painted on the nightly news in the US is vastly different from what terrorism is in the rest of the world.
Reading this article made me reflect on my understanding on the goals of counter-terrorist measures of the United States and how they (somewhat hypocritically) revolve entirely around a consequentialist point of view.
Maybe I have my tin foil hat a little to snug on my head, but I can't seem to find a way in which the mentality of the U.S. government's perception of "terrorism" has evolved from the days of COINTELPRO. To the US government, the only difference between MLK and Kim Jong Un is that only one of those was a threat to the state in 60's while the other is a threat now.
While I'm admittedly not really well versed in stoicism, I believe that an individual's oikeiôsis (https://en.wikipedia.org/wiki/Oikei%C3%B4sis) of something or someone else is the guiding factor of how they act or behave towards something. However, is it really fair to hold people to the same standard as a government?
I don't think there's any question as to whether or not mainstream media outlets across the Western world have been capitilizing on the "War on Terror" for decades, but one that has remained unanswered is how effective have the Western world's anti-terrorist measures been?
There are some measures, like TSA in America, that appear to be more focused around the illusion of security than actually preventing acts of terrorism. But what about some of the surveillance efforts conducted by intelligence organizations within the Five Eyes?
Maybe this merely a display of my own ignorance, but I don't understand why the efficacy of surveillance measures (both domestic and non-domestic) isn't discussed more often. If governments were more open about all the terrorist attacks they were able to prevent ahead of time, maybe the general public wouldn't have such a negative opinion of them when one terrorist slips through the cracks.