Normalize not naming new languages with a single letter.

Provision forgejo on k8s with c4k

This title belongs in a “statements made up by the deranged” meme

This is the same article that was posted yesterday with the title “Kill DNS”

This is a neat trick but it works best on Linux and maybe macOS.

Implementing it on Windows requires some luck as you can’t just map adjacent pages, you have to just request two of them and hope the OS gives you two contiguous ones. For example, see this abandoned Rust crate: https://github.com/gnzlbg/slice_deque/blob/045fb28701d3b674b5da413266ca84b3e5a70190/src/mirrored/winapi.rs#L57

Will AI steal their jobs? 70% of professional programmers don’t see artificial intelligence as a threat to their work.

If your job can be replaced with GPT, you had a bullshit job to begin with.

What so many people don’t understand is that writing code is only a small part of the job. Figuring out what code to write is where most of the effort goes. That, and massaging the egos of management/the C-suite if you’re a senior.

It’s an ideal that’s only achievable when you’re able to set your own priorities.

Managers and executives generally don’t give two shits about yak shaving.

Tip #1: if you’re gonna post your programming blog to social media, make sure it can handle the traffic…?

I feel like if your body follows the Unix filesystem structure, you have a real problem.

It’s a glob pattern (edit: tried to find a source that actually showed ** in use).

That’s why you have backups.

sudo rm /heart/arteries/**/clot

I wonder why I haven’t seen a standard open-source license for this.

This would be a lot more readable with some paragraph breaks.

Any highlights from those in the know?

It was $5k worth of training, and well worth it, since you still remember the lesson.

Yep.

That’s also not the most money I’ve ever unintentionally cost an employer.

The applications I’ve built weren’t designed for serverless deployment so I wouldn’t know. It seems like you pay a premium for the convenience though.

I ran up like a $5k bill over a couple weeks by having an application log in a hot loop when it got disconnected from another service in the same cluster. When I wrote that code, I expected the warnings to eventually get hooked up to page us to let us know that something was broken.

Turns out, disconnections happen regularly because ingress connections have like a 30 minute timeout by default. So it would time out, emit like 5 GB of logs before Kubernetes noticed the container was unhealthy and restarted it, rinse and repeat.

I know $5k is chump change at enterprise scale, but this was at a small scale startup during the initial development phase, so it was definitely noticed. Fortunately, the only thing that happened to me was some good-natured ribbing.

It executes on a native thread in the background. That way it doesn’t stall the Javascript execution loop, even if you give it a gigabyte of data to hash.

I once had a spambot @ me and like 100 other people I didn’t know on a repo I’d never touched before.

I reported it for spam and got an email from Github Support within an hour notifying me that they had taken action.

Actually, Android doesn’t really use Dalvik anymore. They still use the bytecode format, but built a new runtime. The architecture of that runtime is detailed on the page I linked. IIRC, Dalvik didn’t cache JIT compilation results and had to redo it every time the application was run.

FWIW, I’ve heard libgcc-jit doesn’t generate particularly high quality code. If the AOT compiled code was compiled with aggressive optimizations and a specific CPU in mind, of course it’ll be faster. JIT compiled code can meet or exceed native performance, but it depends on a lot of variables.

As for mawk vs JAWK vs go-awk, a JIT is not going to fix bad code. If it were a true apples to apples comparison, I’d expect a difference of maybe 30-50%, not ~2 orders of magnitude. A performance gap that wide suggests fundamental differences between the different implementations, maybe bad cache locality or inefficient use of syscalls in the latter two.

On top of that, you’re not really comparing the languages or runtimes so much as their regular expression engines. Java’s isn’t particularly fast, and neither is Go’s. Compare that to Javascript and Perl, both languages with heavyweight runtimes, but which perform extraordinarily well on this benchmark thanks to their heavily optimized regex engines.

It looks like mawk uses its own bespoke regex engine, which is honestly quite impressive in that it performs that well. However, it only supports POSIX regular expressions, and doesn’t even implement braces, at least in the latest release listed on the site: https://github.com/ThomasDickey/mawk-20140914

(The author creates a new Github repo to mirror each release, which shows just how much they refuse to learn to use Git. That’s a respectable level of contempt right there.)

Meanwhile, Java’s regex engine is a lot more complex with more features, such as lookahead/behind and backreferences, but that complexity comes at a cost. Similarly, if go-awk is using Go’s regexp package, it’s using a much more complex regex engine than is strictly necessary. And Golang admits in their own FAQ that it’s not nearly as optimized as other engines like PCRE.

Thus, it’s really not an apples to apples comparison. I suspect that’s where most of the performance difference arises.

Go has reference counting and heap etc, basically a ‘compiled VM’.

This statement is completely wrong. Like, to a baffling degree. It kinda makes me wonder if you’re trolling.

Go doesn’t use any kind of VM, and has never used reference counting for memory management as far as I can tell. It compiles directly to native machine code which is executed directly by the processor, but the binary comes with a runtime baked in. This runtime includes a tracing garbage collector and manages the execution of goroutines and related things like non-blocking sockets.

Additionally, heap management is a core function of any program compiled for a modern operating system. Programs written in C and C++ use heap allocations constantly unless they’re specifically written to avoid them. And depending on what you’re doing and what you need, a C or C++ program could end up with a more heavyweight collective of runtime dependencies than the JVM itself.

At the end of the day, trying to write the fastest code possible isn’t usually the most productive approach. When you have a job to do, you’re going to welcome any tool that makes that job easier.