A developer measured one AI response: 1.2K output tokens, 0.8-second time to first token, 80 tokens/sec generation rate. But behind the numbers, what we should really note is the chain structure—it's not one long pipe, but three different channels collaborating; and when the AI was asked to explain its own host architecture, it got it wrong.
What this is
Streaming response—the experience of AI outputting word by word while you watch it generate. After DeepSeek open-sourced the Harness project (an open-source AI middleware layer), one developer traced the actual code, ran a request, and mapped out the complete chain.
The conclusion contradicts the common narrative: the browser submits the question via a one-shot HTTP POST, the model's response streams back to the Harness middleware layer via SSE (Server-Sent Events), and the browser ultimately receives the incremental output via WebSocket (bidirectional long connection). The middleware also writes each fragment to Session logs as the source of truth for recovery, trajectories, and statistics.
Even more noteworthy: the developer asked the model to explain this chain itself, and the model gave a wrong answer—it described the entire flow as SSE. Only real runtime logs and source code can serve as architectural evidence; this is a reminder to everyone who "uses AI to explain AI."
Industry view
The supportive view holds that pulling an open-source product down to a log-auditable granularity shows rare engineering sincerity in the Chinese AI community. Most streaming responses on the market only talk about "speed"—not about "how to recover if disconnected, how to sync across devices."
On the flip side, this breakdown is highly technical—RPC IDs, events.mux, Session event streams—discouraging reading for the vast majority of developers. What it truly affects is middleware and Agent framework designers, not end users. Additionally, over-focusing on underlying channels may obscure more upstream questions: what AI interaction rhythm do users actually need.
Impact on regular people
For enterprise IT: When selecting an AI middleware platform, "streaming response reliability, disconnection recovery, multi-device consistency" are more worth asking about than time to first token.
For individual professionals: When you encounter AI tool lag or dropped messages, understanding that "the middleware is writing logs" can help you judge whether it's a network issue or a product issue.
For the consumer market: Future AI assistant experiences will diverge—not on "whether it can answer," but on "whether you can pick up across devices mid-stream."