Vibe Coding Meets Real-Time: What AI Assistants Are Really Like for Serious WebRTC Work

markdown formatted blog content There's a running joke in the WebRTC community that every few years, someone declares the protocol dead. And every few years, WebRTC proves them wrong by quietly powering the infrastructure behind video calls, game streaming, and voice AI applications at companies you definitely recognize.

The reality is that WebRTC isn't going anywhere—it's the plumbing that makes real-time communication work on the web. And the engineers maintaining that plumbing are starting to incorporate AI coding assistants into their daily workflow with surprisingly practical results.

The "Vibe" in Vibe Coding Is Actually Work

Let's be honest about what vibe coding actually means when you're working on something like libWebRTC. It's not about letting an AI loose on your codebase and shipping whatever comes out. It's about recognizing where AI assistants genuinely accelerate the tedious parts while human judgment remains irreplaceable.

For instance, fixing a background throttling issue in WebRTC stats collection required shifting a JavaScript polling mechanism into C++. The AI didn't just generate the code—it helped translate the problem across language boundaries while respecting the conventions and abstractions that Chromium reviewers expect. The human reviewed, tweaked, and took responsibility for the final result. That's the workflow that actually works.

Why Large Codebases Are Different

Here's what separates vibe coding on a personal project from vibe coding on something like Chromium or libWebRTC: the accumulated complexity of decades of decisions.

These codebases have unwritten rules. Conventions that evolved because someone ran into a problem five years ago. Internal abstractions that make sense in context but confuse anyone approaching cold. Traditional AI tools would cheerfully generate code that technically works but violates these soft constraints, earning swift rejection from reviewers.

The real progress came when the tooling got better AND the ecosystem invested in consistency. Adopting tools like Include What You Use (IWYU) and clang-tidy across libWebRTC wasn't just about satisfying AI assistants—it was about making the codebase genuinely more maintainable for everyone. The irony is that the same practices that help humans understand code also help AI generate better suggestions. The discipline benefits all tools that come after, AI included.

The Cryptography Rabbit Hole

Some of the most interesting work happening in WebRTC right now involves extending encryption to areas that historically went unencrypted. RFC 9335, for example, closes a gap in WebRTC's cryptography by protecting RTP header extensions that previously shipped in plaintext.

One practical example: the ssrc-audio-level extension, which Jitsi and other conferencing systems use to determine who's speaking, contains just one byte of information per packet. It's authenticated but not encrypted. No critical vulnerabilities exist, but the community has been uncomfortable with this for years.

Implementing RFC 9335 support required coordinated work across multiple codebases—libWebRTC on the browser side and libSRTP on the protocol library side. It meant engaging with IETF mailing lists when specifications weren't fully clear, taking lessons from one implementation to another, and improving the spec along the way.

The skill that transfers across all these venues isn't coding—it's knowing what should change, why it should change, and what "good" looks like in each specific context. AI assistants can help with the implementation once you know what you're building, but they can't replace the judgment call about whether this change makes sense.

What Actually Works

After watching AI tools evolve from "interesting but unreliable" to genuinely useful for serious engineering work, a few patterns emerge:

First, guardrails matter. Codebases with strong test discipline and cultural expectations around adding tests rather than just consuming them create an environment where AI assistance can thrive. You can verify that generated code does what it claims, and you catch regressions quickly.

Second, consistency compounds. Investments in tooling and code style pay dividends across multiple dimensions. Cleaner code is easier for humans to maintain, easier for AI to understand, and easier for new contributors to navigate. The work isn't glamorous, but it creates foundations that everything else builds on.

Third, the human remains responsible. Every patch that lands in Chromium went through human review. The AI is a collaborator, not a replacement for engineering judgment. When something breaks in production, you can't blame your assistant—you made the call to ship it.

The Real-Time Stack Keeps Evolving

WebRTC isn't just for video calls anymore. It's the backbone of voice AI applications, real-time collaboration tools, and increasingly, the infrastructure layer for AI interactions that need low latency.

The engineers working on this infrastructure are learning to work with AI tools without surrendering the standards that keep critical software running. It's a balancing act that requires understanding both the capabilities and limitations of the technology.

If you're building applications that depend on real-time communication—whether you're a startup building the next collaborative tool or an enterprise integrating voice capabilities—you're standing on foundations maintained by people doing exactly this kind of work. And increasingly, that work involves knowing when to delegate to AI and when human judgment remains essential.

The vibe in vibe coding isn't about letting AI do your job. It's about finding the rhythm where AI handles the scaffolding so you can focus on the architecture. In a stack as critical as WebRTC, that distinction matters.