[doctorpangloss]

what i learned from making a webrtc+kubernetes game streaming product:

- openai is wrong. almost of the issues they described are issues with libwebrtc, not with webrtc, kubernetes, network architecture, etc. the clue was when they said "the conventional one-port-per-session WebRTC model."

- there are no alternatives worth trying. everything else open source in the ecosystem, like pion, coturn, stunner, are too immature.

- libwebrtc is the only game in town.

- they haven't discovered libwebrtc feature flags or how it works with candidates, which directly fix a bunch of latency issues they are discovering. a correct feature flag can instantly reduce latency for free, compared to pay for twilio network traversal style solutions

- 99% of low latency voice END USERS will be in a network situation that can eliminate relays, transceivers, etc. it is totally first class on kubernetes. but you have to know something :)

this is the first time i'm experiencing gell mann amnesia with openai! look those guys are brilliant, but there is hardly anyone in the world who is doing this stuff correctly.

[Sean-Der]

Did you use libwebrtc on the backend? When you say `libwebrtc` is the only game in town are you talking about clients or servers?

Even for clients you have things like libpeer that libwebrtc can't hit.

[doctorpangloss]

yes - i used libwebrtc on the backend and, pre-LLM, patched it to work around a lot of the things i discovered that were directly related to low latency AV streaming. pion didn't exist then.

i think the challenge is that pion is an excellent product today. it would benefit me if its innovations were subsumed into libwebrtc, because eventually those innovations will show up in the iOS stack, which is one of the customers that matter to me. it is subjective if it is the MOST important customer, that is my belief and it is probably true of openai, at least until they get their own device out the door.

there can be many, many use cases though! not everything has to be, try to make the thing for 1b people that has to interact with all the most powerful and meanest businesses on the planet.

[jiggawatts]

Something I noticed is that companies that are vibe-coding their products miss out on the intelligence that (still) only humans can bring to bear. Just the knowledge cutoff alone puts AI at a serious disadvantage in any rapidly changing field.

[fragmede]

GPT 5.5's knowledge cutoff is August 2025. Which aspect of WebRTC has meaningfully changed since then?

[jiggawatts]

There's a difference between some piece of information being "officially published" and the AIs gaining a sufficient understanding of it.

Take any popular technology problem that has been around for a few years such as... wrangling Kubernetes with YAML config files. There's probably hundreds of thousands of discussions, source code samples from GitHub, official docs, blogs, bug reports, pull requests, etc... all discussing the nuances, pitfalls, pros/cons, etc. During pre-training the AIs internalise this and can utilise it later.

Now compare this with anything recent and (relatively) obscure, such as new .NET 10 features which were first officially publishing in November 2025, a month before GPT 5.5 cutoff.

As a human developer, these new language capabilities are on the same "level" for me in my day-to-day work as the features from .NET 9 or .NET 8. Similarly, my IDE has native refactoring and code cleanup support that can take C# code from the previous years and bring it up to the idiomatic style of $currentyear.

The AIs just can't do this, because one single Microsoft release note and one learn.microsoft.com page is nowhere near enough training data! The AI hasn't seen millions of lines of code written with .NET 10, taking advantage of .NET 10 improvements, and hasn't seen thousands of discussions about it. Not yet.

This is a fundamental issue with how LLMs are (currently) trained! Simply moving the cutoff date is not enough.

Human learning is second-order. If I see even the tiniest bit of updated information that invalidates a huge pile of older information, my memory marks everything old as outdated and from that second onwards I use only the new approach.

AI learning is first-order. It has to be given the discussions/blogs/posts that say "Stop using the legacy way, it's terrible! Start using the new hotness"! That, it can learn, but it'll be perpetually behind the rest of us by at least a few years.

Not to mention that thanks to AI forums like StackOverflow are dying, so... where is it going to get this kind of training data from in the future!?

AI training needs to switch to "second order", but AFAIK this is an unsolved problem at this time.

[mschuster91]

The problem is the sheer amount of knowledge out there. Particularly when using niche technologies (which webrtc and web audio still is, when measured by how many people develop using it), it is not surprising that AI doesn't have everything available in its responses, unless you specifically ask it about something you already know it should know.

[tedsanders]

Dec 2025, actually: https://developers.openai.com/api/docs/models/gpt-5.5

(though knowledge cutoffs in practice can be bit fuzzy)

[fragmede]

That's what I get for trusting the output of the LLM instead of checking the docs!

[chevman]

When you have hard problems with unclear optimal solutions, taking this approach of a public show & tell will often (always?) solicit lots of interesting ideas the team may have not yet considered :)