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A dropped connection used to cost a typing indicator. With AI in the product, it costs an entire response, mid-generation. That single shift is enough to reopen a decision most teams made years ago and stopped thinking about: whether to keep building realtime infrastructure themselves, or buy it. Fin, the AI agent platform formerly known as Intercom, made the call to buy. It had run its own realtime system, Nexus, for years, at the scale of one of the biggest support platforms on the internet.

AI Transport v0.5.0 is now available. It adds first-class support for running an agent turn inside a durable execution framework, such as Temporal or Vercel's Workflow Development Kit (WDK), while every client watching the conversation still sees one clean, resumable stream. The last release, v0.4.0, let an agent hydrate its history from your own database. This one is about what happens when the process running the agent isn't around for the whole turn.

The pitch for AI coding was speed. Claude Code, Copilot, Cursor, whatever you’re running, they all generate business logic faster than you can review it. That part is real. But look at what happens after the code gets written and the numbers get ugly. CircleCI’s 2026 State of Software Delivery Report found AI drove a 59% increase in average throughput.

Thirteen vendors were formally evaluated. The market is projected to grow from $65 million in 2024 to **$1.434 trillion by 2030 **(in other words, this is a MAJOR emerging need)..

React Native debugging poses particular challenges due to the framework’s cross-platform codebase, which combines JavaScript and native platform components. However, an effective React debugging regime will enable you to proactively fix issues in both production and development. Today’s post will give you the fundamentals to build that regime, showing you: Want to jump to a specific bit of knowledge? Here’s the full list of contents.

HTTP works fine for a chatbot that responds in seconds. Add token streaming, and it mostly still works. But once an agent starts doing things that take real time, reasoning across multiple tool calls, spawning sub-agents, running for minutes instead of milliseconds, the UX starts to falter. The connection drops while the agent is mid-thought. The user switches tabs, comes back five minutes later, and the session is gone. The agent finishes its work, but the client has already moved on.

Your frontend can look flawless — fast search, clean filters, a map view that renders in under a second — and none of it will matter the day you connect your fifth MLS. Somewhere between the second and third regional feed, most PropTech teams hit the same wall: the code that worked beautifully for one market starts breaking in ways nobody predicted. A field that was always a number is suddenly a string. A boolean flag for “has pool” shows up as free text in the new feed.

AI coding agents have quickly become part of the daily workflow for Node.js developers. Whether you're using Claude Code, Codex CLI, OpenCode, Antigravity CLI, or Pi Agent, these tools are great at generating code, explaining implementations, and automating development tasks. But debugging production systems is a different challenge.