Not just Mars, but yes. Biodegradability isn’t even a factor since there’s no biosphere to speak of, which also raises philosophical questions like: “what is pollution, exactly?”

What will really bake your noodle is to imagine a future where we settle the Moon and Mars. Do old space program artifacts become monuments and parks (debris and all), or are they trash to be removed from the environment?

Now that you mention it, that’s probably why.

What I’m curious about is the “engineering model.”

All NASA missions have duplicate probes, satellites, rovers, here on earth. They’re essential for testing various scenarios like training astronauts (in the case of the Hubble repair missions), or testing the limits of the systems in question. I wonder if the engineering model for Curiosity has one of its wheels cut away in the same pattern, to simulate difficulties in navigation and traction?

without any real mental analysis or realization of the manipulation that’s going on.

I see it as zero introspection. That might also explain the projection, hate, and bigotry that run in those circles; it’s hard to keep that up once you see where it’s coming from. An inability to introspect is also an aspect (among many) of narcissistic personality disorder, and helps keep that pain-train rolling.

For me, it’s all the suck brought on by a pathological fear (trauma) of authority figures abusing their power. In this case: TSA, and their ability to completely screw with your travel plans. To be clear, this is not rational and 100% nothing bad happens.

Honestly, I’m not 100% sure. I would bet that a modern compiler would just “do the right thing” but I’ve never written code in such a high performance fashion before.

Especially these days. Current-gen x86 architecture has all kinds of insane optimizations and special instruction sets that the Pentium I never had (e.g. SSE). You really do need a higher-level compiler at your back to make the most of it these days. And even then, there are cases where you have to resort to inline ASM or processor-specific intrinsics to optimize to the level that Roller Coaster Tycoon is/was. (original system specs)

Too bad.

…

Hello there!

I never played this game; is it really so good that fans were clamoring for a re-release? What am I missing if anything?

Also, I may be misremembering when this was launched but isn’t it a bit early for a re-master on this one?

Java itself is kind of blissful in how restricted and straightforward it is.

Java programs, however, tend to be very large and sprawling code-bases built on even bigger mountains of shared libraries. This is a product of the language’s simplicity, the design decisions present in the standard library, and how the Java community chooses to solve problems as a group (e.g. “dependency injection”). This presents a big learning challenge to people encountering Java projects on the job: there’s a huge amount of stuff to take in. Were Java a spoken language it would be as if everyone talked in a highly formal and elaborate prose all the time.

People tend to conflate these two learning tasks (language vs practice), lumping it all together as “Java is complicated.”

$0.02: Java is the only technology stack where I have encountered a logging plugin designed to filter out common libraries in stack traces. The call depth on J2EE architecture is so incredibly deep at times, this is almost essential to make sense of errors in any reasonable amount of time. JavaScript, Python, PHP, Go, Rust, ASP, C++, C#, every other language and framework I have used professionally has had a much shallower call stack by comparison. IMO, this is a direct consequence of the sheer volume of code present in professional Java solutions, and the complexity that Java engineers must learn to handle.

Some articles showing the knock-on effects of this phenomenon:

• https://stackoverflow.com/questions/11865307/how-to-expand-size-of-java-stack-trace-to-see-bottom-of-stack-triggering-a-sta
• https://www.reddit.com/r/java/comments/16g30jx/can_java_errors_stack_traces_be_longer/
• https://www.quora.com/Why-does-the-Java-Spring-Framework-produce-gigantic-unreadable-stack-traces
• https://community.splunk.com/t5/Splunk-Dev/What-are-the-best-extraction-methods-for-Java-Stacktrace-Errors/m-p/380397
• https://stackoverflow.com/questions/65436457/springboot-stack-trace-logging-filter-only-from-my-packages

Yes, but “Proto Indo-European” doesn’t exactly roll off the tongue. /s

Put any distro in front of me and provided I don’t need to master it, I’m good. Ubuntu is fine. Debian is fine. RedHat is fine. Fedora is fine. I even have a tiny low-end system that is using Bohdi. Whatever. We’re all using mostly the same kernel anyway.

90% of what I do is in a container anyway so it almost doesn’t matter; half the time that means Alpine, but not really. That includes both consuming products from upstream as well as software development. I also practically live in the terminal, so I couldn’t care less what GUI subsystem is in play, even while I’m using it.

The only time I’ve encountered people that care a little too much about what distro is being used, is right after having transitioned to Linux; the sheer liberating potential of the thing can make you lose your head.

I’ve come across a lot of professional bias about Linux distros, but that’s usually due to real-world experience with tough or bad projects. Some times, decisions are made that make a given distro the villain or even the hero of the story. In the end, you’ll hear a lot of praise and hate, but context absolutely matters.

There’s also the very natural tendency to seek external validation for your actions/decisions. But some people just can’t self-actualize in a way that’s healthy. Sprinkle a little personal insecurity into the mix and presto: “someone is getting on great with that other Linux I don’t use, so Imma get big mad.”

Thank you!

I firmly believe this is how we wound up with tabs as a feature in the first place.

Honestly, this is why I tell developers that work with/for me to build in logging, day one. Not only will you always have clarity in every environment, but you won’t run into cases where adding logging later makes races/deadlocks “go away mysteriously.” A lot of the time, attaching a debugger to stuff in production isn’t going to fly, so “printf debugging” like this is truly your best bet.

To do this right, look into logging modules/libraries that support filtering, lazy evaluation, contexts, and JSON output for perfect SEIM compatibility (enterprise stuff like Splunk or ELK).

Heisenbugs are the worst. My condolences for being tasked with diagnosing one.

It absolutely is.

As a kid, everyone’s parents (boomers) called NES cartridges “tapes”. Considering their generation had a lot of experience with 8-track, cassette, and VHS/Betamax, it kind of makes sense. I guess every generation has this.

Last time I did anything on the job with C++ was about 8 years ago. Here’s what I learned. It may still be relevant.

• C++14 was alright, but still wasn’t everything you need. The language has improved a lot since, so take this with a grain of salt. We had to use Boost to really make the most of things and avoid stupid memory management problems through use of smart (ref-counted) pointers. The overhead was worth it.
• C++ relies heavily on idioms for good code quality that can only be learned from a book and/or the community. “RAII” is a good example here. The language itself is simply too flexible and low-level to force that kind of behavior on you. To make matters worse, idiomatic practices wind up adding substantial weight to manual code review, since there’s no other way to enforce them or check for their absence.
• I wound up writing a post-processor to make sense of template errors since it had a habit of completely exploding any template use to the fullest possible expression expansion; it was like typedefs didn’t exist. My tool replaced common patterns with expressions that more closely resembled our sourcecode¹. This helped a lot with understanding what was actually going wrong. At the same time, it was ridiculous that was even necessary.
• A team style guide is a hard must with C++. The language spec is so mindbogglingly huge that no two “C++ programmers” possess the same experience with the language. Yes, their skillsets will overlap, but the non-overlapping areas can be quite large and have profound ramifications on coding preferences. This is why my team got into serious disagreements with style and approach without one: there was no tie-breaker to end disagreement. We eventually adopted one after a lot of lost effort and hurt feelings.
• Coding C++ is less like having a conversation with the target CPU and more like a conversation with the compiler. Templates, const, constexpr, inline, volatile, are all about steering the compiler to generate the code you want. As a consequence, you spend a lot more of your time troubleshooting code generation and compilation errors than with other languages.
• At some point you will need valgrind or at least a really good IDE that’s dialed in for your process and target platform. Letting the rest of the team get away without these tools will negatively impact the team’s ability to fix serious problems.
• C++ assumes that CPU performance and memory management are your biggest problems. You absolutely have to be aware of stack allocation, heap allocation, copies, copy-free, references, pointers, and v-tables, which are needed to navigate the nuances of code generation and how it impacts run-time and memory.
• Multithreading in C++14 was made approachable through Boost and some primitives built on top of pthreads. Deadlocks and races were a programmer problem; the language has nothing to help you here. My recommendation: take a page from Go’s book. Use a really good threadsafe mutable queue, copy (no references/pointers) everything into it, and use it for moving mutable state between threads until performance benchmarks tell you to do otherwise.
• Test-driven design and DevOps best-practice is needed to make any C++ project of scale manageable. I cannot stress this enough. Use every automated quality gate you can to catch errors before live/integration testing, as using valgrind and other in-situ tools can be painful (if not impossible).

1 - I borrowed this idea from working on J2EE apps, of all places, where stack traces get so huge/deep that there are plugins designed to filter out method calls (sometimes, entire libraries) that are just noise. The idea of post-processing errors just kind of stuck after that - it’s just more data, after all.