Building a social media app means a single user action must propagate to potentially millions of other users in real time, while staying fast, safe, and cheap. Every feature touches every other feature. And the hard problems shift as you scale. At 100K users, it's the database. At 1M users, it's the fan-out strategies. At
How Stream's chat, feeds, and video infrastructure is built to hold up under trading-app load, from market open to a widely held stock's earnings release.
Voice assistants like Siri and Alexa are great for non-trivial everyday personal assistive tasks. However, they are limited in providing accurate answers to complex questions, real-time information, handling turns, and user interruptions.
You can vibe code almost anything right now. Describe an app, and a model will build a working interface in minutes. So why hasn't that produced a wave of new companies? We wanted to find out. So we picked one of the hardest products to fake, an AI support concierge, the kind of system that handles millions of customer conversations for companies valued in the billions. Then we tried to build one with a single prompt.
You might remember Moltbook, a vibe-coded social network for AI agents that fell apart within a week because messages weren't private and there was no moderation. This piece is about the gap between a vibe-coded demo and a production-ready real-time feature, and why AI tools can't close it for you.
Stream Chat now has experimental UI components for Jaspr, the Dart web framework that Google's own dart.dev and flutter.dev run on. One package gets you the components and the low-level client, under names you already know from the Flutter SDK. It is an experiment, and how far we take it depends on whether you want to build chat on the web in Dart.
Apps like Slack and WhatsApp made team chat easy. The right AI features can make it better. Here's how to build them into community, collaboration, and team platforms to increase user satisfaction and speed up task completion.
Understanding rarely arrives through a single sense at a time, and neither should an agent. This guide breaks down how to design multimodal AI agent architecture for voice and video apps that hold up in production, using the architectural choices, transport options, and model constraints that matter most.
What we learned building a prescription-intake voice agent with Stream's Vision Agents SDK and AssemblyAI: five lessons for safety-critical AI agents.
The fastest way to build with Stream. Start a new project or improve an existing one. Full CLI and documentation integration out of the box.
Ask your agent:
