I hit the flag button on the comment and suggest others do too.

I was not actually sure this one was a bot, despite LLM-isms and, sadly, being new. But you can look at the comment history and see.

The C and assembly programs we still use are the ones that were good enough to last. The thousands that weren't are gone.

Nobody counts the programs that were never finished because the language made them too hard to write in the first place.

Crazy how many stories like this I’ve heard of how doing performance work helped people uncover bugs and/or hidden assumptions about their systems.

They found that they had fewer bugs in Python so they continued with it.

Meanwhile my experience has been that whenever there has been a performance issue severe enough to actually matter, it's often been the result of some kind of performance bug, not so much language, runtime, or even algorithm choices for that matter.

Hence whenever the topic of how to improve performance comes up, I always, always insist that we profile first.

But, of course, profiling is always step one.

Pure speculation, but I would guess this has something to do with a copy constructor getting invoked in a place you wouldn't guess, that ends up in a critical path.

This was particularly true for one of the projects I've worked with in the past, where Python was chosen as the main language for a monitoring service.

In short, it proved itself to be a disaster: just the Python process collecting and parsing the metrics of all programs consumed 30-40% of the processing power of the lower end boxes.

In the end, the project went ahead for a while more, and we had to do all sorts of mitigations to get the performance impact to be less of an issue.

We did consider replacing it all by a few open source tools written in C and some glue code, the initial prototype used few MBs instead of dozens (or even hundreds) of MBs of memory, while barely registering any CPU load, but in the end it was deemed a waste of time when the whole project was terminated.

The main lesson of the story. Just pick Python and move fast, kids. It doesn’t matter how fast your software is if nobody uses it.

One of the reasons the project was killed was that we couldn't port it to our line of low powered devices without a full rewrite in C.

Please note this was more than a decade ago, way before Rust was the language it was today. I wouldn't chose anything else besides Rust today since it gives the best of both worlds: a truly high level language with low level resource controls.

Almost every mainstream languages (except Go, Swift, Kotlin and Rust) are more than 30 years old, by the way.

The mentality was "the language is fast, so as long as it compiles we're good"... Yeah that worked out about as well as you'd expect.

If you're writing FastAPI (and you should be if you're doing a greenfield REST API project in Python in 2026), just s/copy/steal/ what those guys are doing and you'll be fine.

The reason nobody uses your software could be that it is too slow. As an example, if you write a video encoder or decoder, using pure Python might work for postage-stamp sized video because today’s hardware is insanely fast, but even, it likely will be easier to get the same speed in a language that’s better suited to the task.

I'd rather not use python. The ick gets me every time.

    if input() == "dynamic scope?":
        defined = "happyhappy"
    print(defined)

Most the the business I do is rewriting old working python prototypes to C++. Python sucks, is slow and leaks. The new C++ code does not leak, meets our performance requirements, processes items instead of 36 hour in 8 hours, and such.

We are also rewriting all the old python UI in typescript. That went not so easy yet.

And when there are still old simple python helpers, I rewrite them into perl, because this will continue to run in the next years, unlike python.

Turns out the metrics just rounded to the nearest 5MB

Just write the parsing loop in something faster like C or Rust, instead of the whole thing.

You are not the same.

Not because they are brilliant, but because they are pretty good at throwing pretty much all known techniques at a problem. And they also don't tire of profiling and running experiments.

Recently I tried Codex/GPT5 with updating a bluetooth library for batteries and it was able to start capturing bluetooth packets and comparing them with the libraries other models. It was indefatigable. I didn't even know if was so easy to capture BLE packets.

Flakey internet connection: most of current 'soy devs' would be useless. Even more with boosted up chatbots.

We used to make the same jokes about Googling Stackoverflow since before many users on this site were born.

When the blackout the only protocols which worked fine where IRC, Gopher and Gemini. I could resort to using IRC->Bitlbee to chat against different people of the world, read news and proxy web sites over Gemini (the proto, not the shitty AI). But, for the rest, the average folk? half an our to fetch a non-working page.

That with a newspaper, go figure with the rest. And today tons of projects use sites with tons of JS and unnecesary trackers and data. In case of a small BGP attack, most projects done with LLM's will be damned. Because they won't even have experience on coding without LLM's. Without docs it's game over.

Also tons of languages pull dependencies. Linux distros with tons of DVD's can survive offline with Python, but good luck deploying NPM, Python and the rest projects to different OSes. If you are lucky you can resort to the bundled Go dependencies in Debian and cross compile, and the same with MinGW cross compiling against Windows with some Win32, SDL, DX support but that's it.

With QT Creator and MinGW, well, yes, you could build something reliable enough -being cross platform- and with Lazarus/Free Pascal, but forget about current projects downloading 20000 dependencies.

The BLE battery syncing was a nice-to-have for an IoT prototype. Not something I wanted to spend hours digging through wireshark to figure out but fine for some LLM hacking.

Eh? It's all about trade-offs. If our infrastructure is degraded enough that the internet goes down, I have more important things to do than work through a few more Jira tickets.

Especially since a lot of the work me and a lot of other folks are doing is delivered to customers via the internet anyway.

But that would be a good blog post and I got some travel coming up. But honestly it was just “oh here’s a BLE python library, see if we can get it running”. I prefer Codex because it seems to do well for guiding the LLMs for complete engineering changes.

If you have a comprehensive test suite or a realistic benchmark, saying "make tests pass" or "make benchmark go up" works wonders.

LLMs are really good at knowing patterns, we still need programmers to know which pattern to apply when. We'll soon reach a point where you'll be able to say "X is slow, do autoresearch on X" and X will just magically get faster.

The reason we can't yet isn't because LLMs are stupid, it's because autoresearch is a relatively new (last month or so) concept and hasn't yet entered into LLM pretraining corpora. LLMs can already do this, you just need to be a little bit more explicit in explaining exactly what you need them to do.

I'm not sure if it's particularly token hungry.

I'm not so sure. People have been doing stuff like (hyper) parameter search for ages. And profiling and trying out lots of things systematically has been the go-to approach for performance optimisation since forever; making an LLM instead of a human do that is the obvious thing to try?

The concept of 'autoresearch' might bring with it some interesting and useful new wrinkles, but on a fundamental level it's not rocket science.

That has not been my experience. JS/TS requires the most hand-holding, by far. LLMs are no doubt assumed to be good at JS due to the sheer amount of training data, but a lot of those inputs are of really poor quality, and even among the high quality inputs there isn't a whole lot of consistency in how they are written. That seems to trip up the LLMs. If anything, LLMs might finally be what breaks the JS camel's back. Although browser dominance still makes that unlikely.

> Very few people will then take the pain of optimizing it

Today's LLMs rarely take the initiative to write benchmarks, but if you ask it will and then will iterate on optimizing using the benchmark results as feedback. It works fairly well. There is a conceivable near future where LLMs or LLM tools will start doing this automatically.

But yes I see what you mean and I think people are trying to solve it with skills and harnesses at the application layer but its not there yet

Would be kind of cool if e. g. python or ruby could be as fast as C or C++.

I wonder if this could be possible, assuming we could modify both to achieve that as outcome. But without having a language that would be like C or C++. Right now there is a strange divide between "scripting" languages and compiled ones.

I suspect it’s more likely to be something like passing std::string by value not realising that would copy the string every time, especially with the statement that the mistake would be hard to express in Python.

It's true that writing code in C doesn't automatically make it faster.

For example, string manipulation. 0-terminated strings (the default in C) are, frankly, an abomination. String processing code is a tangle of strlen, strcpy, strncpy, strcat, all of which require repeated passes over the string looking for the 0. (Even worse, reloading the string into the cache just to find its length makes things even slower.)

Worse is the problem that, in order to slice a string, you have to malloc some memory and copy the string. And then carefully manage the lifetime of that slice.

The fix is simple - use length-delimited strings. D relies on them to great effect. You can do them in C, but you get no succor from the language. I've proposed a simple enhancement for C to make them work https://www.digitalmars.com/articles/C-biggest-mistake.html but nobody in the C world has any interest in it (which baffles me, it is so simple!).

Another source of slowdown in C is I've discovered over the years that C is not a plastic language, it is a brittle one. The first algorithm you select for a C project gets so welded into it that it cannot be changed without great difficulty. (And we all know that algorithms are the key to speed, not coding details.) Why isn't C plastic?

It's because one cannot switch back and forth between a reference type and a value type without extensively rewriting every use of it. For example:

    struct S { int a; }
    int foo(struct S s) { return s.a; }
    int bar(struct S *s) { return s->a; }

    struct S { int a; }
    int foo(S s) { return s.a; }
    int bar(S *s) { return s.a; }

The usual strategy is to write a script then if it's slow see how you could design a program that would

The usual strategy in the real world is to copy paste thousands of lines of C++ code until someone comes along and writes a proper direct solution to the problem.

Of course there are ideas on how to fix this, either writing your own scripting libraries (stb), packages (go/rust/ts), metaprogramming (lisp/jai). As for bugs those are a function of how you choose to write code, the standard way of writing shell it bug prone, the standard way of writing python is less so, not using overloading & going wider in c++ generally helps.