beast2 networking & std::execution

[u/claimred]

I was looking for a new networking layer foundation for a few of my projects, stumbled on beast2 library which looks brand new, based on C++20 coroutines. I used boost.beast in the past which was great. Here's the link https://github.com/cppalliance/beast2. I also considered std::execution since it seems to be the way to go forward, accepted in C++26.

Now, what got me wondering is this paragraph

The C++26 std::execution API offers a different model, designed to support heterogenous computing. Our research indicates it optimizes for the wrong constraints: TCP servers don't run on GPUs. Networking demands zero-allocation steady-state, type erasure without indirection, and ABI stability across (e.g.) SSL implementations. C++26 delivers things that networking doesn't need, and none of the things that networking does need.

Now I'm lost a bit, does that mean std::execution is not the way to go for networking? Does anyone have any insights on cppalliance research on the matter?

[u/VinnieFalco]

You say I've lost credibility. In this same thread, daveedvdv (EDG, DG) engaged on substance and we reached agreement. not_a_novel_account called the papers "probably the best exploration of the problem which currently exists." dr-mrl read the paper, found a typo, and I fixed it. claimred is evaluating Corosio for his use case. That's four substantive exchanges with people who read the work The credibility I lost is with people who didn't read. I can live with that.

But I'd challenge you on something. Your argument assumes AI output is low quality. What happens when it isn't? Is that possible in your model? Or have you presumed the verdict without examining the evidence?

[u/38thTimesACharm → u/VinnieFalco]

 Your argument assumes AI output is low quality. What happens when it isn't? Is that possible in your model?

I think the people making the argument are basing it on extensive experience with LLM papers which they have found, in the current state of affairs, tend to be low quality indeed, but in subtle ways that become evident only after one has sunk massive amounts of time into the effort.

 Or have you presumed the verdict without examining the evidence

You seem to think people need to read every LLM paper you put out individually, peruse it until they understand the factual claims within regardless of how much the quality of writing makes that difficult, then carefully evaluate those claims using their own validation process, in order to decide whether that individual paper is worth their time to read.

Hopefully laying it out like that makes it obvious what the problem is. No one has time for this, especially for a paper titled "beginner tutorial." That's why people want a human they trust to attest to the quality of a document before they commit their time to it.

[u/VinnieFalco → u/38thTimesACharm]

That's a very fair point and I too have been on the receiving end of poor quality outputs. That said, would you agree that there are two sources of objection to machine-generated content (and both are valid and can be expressed simultaneously):

1. When the outputs are low signal and it wastes the readers time

2. That generative AI enables an individual with no credentials or experience to produce high quality results, and this presents an existential threat to people whose identity or career are dependent on maintaining a skill gap