Today, we’re releasing gpt-realtime — our most capable speech-to-speech model yet in the API and announcing the general availability of the Realtime API.

Speech-to-speech systems are essential for enabling voice as a core AI interface. The new release enhances robustness and usability, giving enterprises the confidence to deploy mission-critical voice agents at scale.

The new gpt-realtime model delivers stronger instruction following, more reliable tool calling, noticeably better voice quality, and an overall smoother feel. These gains make it practical to move from chained approaches to true realtime experiences, cutting latency and producing responses that sound more natural and expressive. — Read More

I’ve been staring at Jenkins configs for the better part of a decade. YAML indentation errors at 2 AM. Flaky integration tests that pass locally, fail in CI, pass again when you rerun them. The entire apparatus of modern continuous integration—the build servers, the artifact registries, the deployment scripts marching in lockstep—it works, mostly, until it doesn’t. And when it fails, you’re the one who has to figure out which of seventeen microservices decided to timeout during health checks this time.

So when someone tells me we’re entering the era of “agentic DevOps,” where AI agents will automate, optimize, and self-heal our delivery pipelines, my first instinct isn’t excitement. It’s pattern recognition. I’ve heard this song before—infrastructure-as-code would solve everything, GitOps would eliminate configuration drift, service mesh would make networking trivial. Each wave delivered genuine value. Each also brought new failure modes we hadn’t anticipated.

But this one feels different. Not because the marketing promises are more extravagant—they always are—but because the underlying mechanism has actually changed. We’re not just automating what humans already scripted. We’re delegating judgment. — Read More

Open source projects grow over time. They are a product of incremental development. A project starts lean, gains adoption, pivots to accommodate that adoption, and maintains backwards compatibility throughout this process.

These lean projects become large ships. Historically, this has been the great power of open source. But what inevitably happens is the infrastructure that you build on becomes outdated. You try to Theseus your way out of it, rebuilding layers of your project on more modern foundations, but it can be hard to reorient your ship in the wake of its own velocity.

This has resulted in two forms of change: forks and total rewrites. You take the foundation that someone else built and you diverge paths. Or you take their contracts (like an API surface), and rewrite it on more modern, stable ground. Examples of this are S3-compatible APIs which are now commonplace, or something like redpanda–a kafka-compatible total-rewrite. — Read More