I had a lengthy argument about this in our architecture forum. I argued that "re-use" shouldn't be included as an Enterprise (keyword here) Architecture principle because they are clear use-cases where duplication is preferable to alternatives. e.g. deployment and testing decoupling etc etc. I had a lot of resistance, and eventually we just ended up with an EA principle with a ton of needless caveats.
It's unfortunate that so many people end up parroting fanciful ideas without fully appreciating the different contexts around software development.
> It's unfortunate that so many people end up parroting fanciful ideas without fully appreciating the different contexts around software development.
Of course that's true of both sides of this discussion too.
I really value DRY, but of course I have seen cases where a little duplication is preferable. Lately I've seen a steady stream of these "duplication is ok" posts, and I worry that newer programmers will use it to justify copy-paste-modifying 20-30-line blocks of code without even trying to create an appropriate abstraction.
The reality of software is, as you suggest, that there are many good rules of thumb, but also lots of exceptions, and judgment is required in applying them.
Heh. I've been saying that since I was on Mandrake in the early 2000s. This is just what the Linux landscape is like.
That said, I'm generally not easily impressed, especially by random *nix distro 347, but CachyOS is surprisingly good. I've finally switched full time from Windows. I don't even need VS anymore because Rider is x-platform.
After I read that emotive response I couldn't help but wondering if this wasn't part of a scheme to help someone cover up a crime. This is how I would have responded:
"Hi,
These do appear to be quite serious crimes. I've sent all the URLs, your email address, emails and responses to the relevant law agencies.
These guys are quite well-known in China and have recently started uploading tto Youtube as well. Their videos are quite entertaining and have extremely high production value compared to many other creators.
I switched to iPhone from Android for a few months earlier this year. I don't think I qualify as an elderly person yet (I'm 47) but even I had trouble figuring things out. I don't think it was super-hard to use, but I often found myself asking "Why would they do it like this? Who uses a smart-phone like this?". I just found some things very unintuitive. Take for example re-arranging icons. I don't know if I would ever have figured out this technique without looking it up:
I went through many iPhones, my first iPhone 3G is still lying around. But nowadays I need to look for howto vidoes and blogs for rather simple things. The simplicity is gone, there are too many settings and bugs. Looking for used Google Pixel 9 or 10 Pro Fold to install GrapheneOS and give it a try. It can’t be worse than my iPhone 14 pro max with broken mail.
I definitely agree that Apple have buried the complexity of some features, but moving icons on the Home Screen is press + hold and then drag by default, same as android.
It used to be simpler. I remember it taking years before Apple finally added this ability to move more than one icon at a time, and it’s a change for the better in my opinion. Using it as a reordering technique is a knock-on effect I’d never really thought about, but I think using this to move multiple icons to another page or into a folder makes a lot of sense. I’m not sure how you’d do it better (though I’m sure I’m lacking in UI imagination).
I would recommend watching Curt Jaimungal's series of talks with Jacob Barandes. He gives a nice background history of various aspects of QM, including the formulation of Matrix and Wave mechanics (and loads of other ideas). Barandes is excellent at clearly articulating complex ideas in very simple, concise terms. He also has his own formulation of QM based on "Indivisible non-Markovian Stochastic Processes". Even if you disagree with his ideas, the interviews are quite fascinating.
In this interview he goes over pretty much exactly what you mentioned (and a lot more):
I don't really have that much of an issue with vibe coding as an appropriate tool in experienced hands. I think the worst ideas in 2025 are probably related to IT execs pushing AI in the wrong ways, or people espousing vibe coding as some sort of software development panacea.
Pre-print of a paper which studied 1950 "transients" which - in tl;dr terms - might be evidence of artificial objects in orbit before the satellite era.
> The structured data gets written to our Postgres database by tens of thousands of simultaneous writers. Each of these writers is a “meeting bot”, which joins a video call and captures the data in real-time.
Maybe I missed it in some folded up embedded content, or some graph (or maybe I'm probably just blind...), but is it mentioned at which point they started running into issues? The quoted bit about "10s of thousands of simultaneous writers" is all I can find.
What is the qualitative and quantitative nature of relevant workloads? Depending on the answers, some people may not care.
I asked ChatGPT to research it and this is the executive summary:
For PostgreSQL’s LISTEN/NOTIFY, a realistic safe throughput is:
Up to ~100–500 notifications/sec: Handles well on most systems with minimal tuning. Low risk of contention.
~500–2,000 notifications/sec: Reasonable with good tuning (short transactions, fast listeners, few concurrent writers). May start to see lock contention.
~2,000–5,000 notifications/sec: Pushing the upper bounds. Requires careful batching, dedicated listeners, possibly separate Postgres instances for pub/sub.
>5,000 notifications/sec: Not recommended for sustained load. You’ll likely hit serialization bottlenecks due to the global commit lock held during NOTIFY.
What is wrong with you? Why would you even bother posting a comment like this?
Maybe you also don't know what ChatGPT Research is (the Enterprise version, if you really need to know), or what Executive Summary implies, but here's a snippet of the 28 sources used:
In that snippet are links to Postgres docs and two blog posts, one being the blog post under discussion. None of those contain the information needed to make the presented claims about throughput.
To make those claims it's necessary to know what work is being done while the lock is held. This includes a bunch of various resource cleanup, which should be cheap, and RecordTransactionCommit() which will grab a lock to insert a WAL record, wait for it to get flushed to disk and potentially also for it to get acknowledged by a synchronous replica. So the expected throughput is somewhere between hundreds and tens of thousands of notifies per second. But as far as I can tell this conclusion is only available from PostgreSQL source code and some assumptions about typical storage and network performance.
> In that snippet are links to Postgres docs and two blog posts
Yes, that's what a snippet generally is. The generated document from my very basic research prompt is over 300k in length. There are also sources from the official mailing lists, graphile, and various community discussions.
I'm not going to post the entire outout because it is completely beside the point. In my original post, I expliclity asked "What is the qualitative and quantitative nature of relevant workloads?" exactly because it's not clear from the blog post. If, for example, they only started hitting these issues with 10k simultaneous reads/writes, then it's reasonable to assume that many people who don't have such high work loads won't really care.
The ChatGPT snippet was included to show that that's what ChatGPT research told me. Nothing more. I basically typed a 2-line prompt and asked it to include the original article. Anyone who thinks that what I posted is authoritative in any way shouldn't be considering doing this type of work.
I'd like to see stats on how many people are getting arrested for petty crimes e.g. marijuana (which isn't even a crime in some contexts any more) back then vs now.
It's unfortunate that so many people end up parroting fanciful ideas without fully appreciating the different contexts around software development.