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Hacker News - Newest: "AI"

AI can't read an investor deck AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination Hacking MCP Servers in AI Systems – The Rug Pull: Tool Changes After Approval GitHub - MeepCastana/KubeezCut: Free Web based video editor Can AI judge journalism? A Thiel-backed startup says yes, even if it risks chilling whistleblowers Coming soon: 10 Things That Matter in AI Right Now DARPA built an AI to fact-check enemy weapons claims What explains heterogeneity in AI adoption? When AI Meets Muscle: Context-Aware Electrical Stimulation Promises a New Way to Guide Human Movements - Department of Computer Science AI Changed How We Build. It Did Not Change What Matters. Linux rules on using AI-generated code - Copilot is OK, but humans must take 'full responsibility for the… Meta spins up AI version of Mark Zuckerberg to engage with employees Code Mode: Let Your AI Write Programs, Not Just Call Tools | TanStack Blog GitHub - Delavalom/graft: Go framework for building AI agents. Type-safe tools, multi-provider (OpenAI, Anthropic, Gemini, Bedrock), zero vendor SDKs. India's TCS tops estimates, says new AI models did not dent services demand Gen Z's fading AI hype Strong feeling: we are in a folded AI reality GitHub - machinarii/total-recall-catalog: A reference catalog of latest knowledge retrieval, memory & RAG systems GitHub - mensfeld/code-on-incus: Give each AI agent its own isolated machine with root, Docker, and systemd. Active defense detects and stops threats automatically.. Quantization, LoRA, and the 8% Problem: Benchmarking Local LLMs for Production AI Iran war: We spoke to the man making Lego-style AI videos that experts say are powerful propaganda Powell, Bessent discussed Anthropic's Mythos AI cyber threat with major U.S. banks GitHub - immartian/bellamem: Persistent belief-graph memory for AI agents. Retrieves decisive context by importance — not recency, not RAG, not /compact. recursive-mode: The Repo-Native Operating System for AI Engineering After the attack on Sam Altman's home, will AI CEO's go on the offensive? The biggest advance in AI since the LLM Opus 4.6 vs GPT 5.4 One Prompt Unity World Generation Test “AI polls” are fake polls Client Challenge Can AI be a 'child of God'? Inside Anthropic's meeting with Christian leaders
HTTP streaming and AI
zknill · 2026-05-29 · via Hacker News - Newest: "AI"

Direct HTTP streaming is fine for one-off interactions and breaks down everywhere else. These are the four limitations that show up once an AI app is in production.

Most AI frameworks support a simple client-driven interaction: the client makes an HTTP request, an agent handles it, and the response streams back to the client over Server-Sent Events or a similar HTTP stream. The pattern is simple, surprisingly effective for one-shot interactions, and every framework supports it. The simplicity of the pattern is also the source of its limitations.

The limitations below arise from coupling the client-to-agent interaction to the transport that carries it. The connection, the request, and the streamed response are all the same lifetime: they exist for one interaction, between one client and one agent. Anything that requires the interaction to outlive the connection (or be visible to anything other than that one client) requires building new infrastructure on top.

Streams fail on disconnection

The operation of a response stream is tied to the health of the underlying connection. When the connection drops, the response stream fails.

This happens routinely. A phone switches from Wi-Fi to cellular. A user refreshes the page. A laptop lid closes mid-response. The LLM continues to generate tokens, and there is nowhere to deliver them.

SSE is the default streaming transport for most AI frameworks. The SSE protocol does include a mechanism for a reconnecting client to specify a position in the stream to resume from. In practice it is rarely supported, because supporting it adds significant backend complexity. To resume an SSE stream you assign sequence numbers to token events for ordering, buffer those events in an external store, and add a new HTTP endpoint to handle resume requests. That is a substantial departure from a stateless request handler. Even with the work done, resume only covers reconnection of an existing client; it does not cover continuity after a page refresh, because SSE has no built-in concept of session identity. Building that is yet another layer on top.

Sessions do not span devices

With HTTP streaming, the connection is exclusive to the requesting client and the agent that handled it. A second tab or a phone has no way into that stream. It only exists for the client that initiated the request.

In reality, users move between surfaces constantly. A second browser tab. The mobile app. Picking the conversation up later from a different device. Without shared access to the session, each surface is isolated. There is no way for a new client to see the in-progress stream. And sharing the conversation history, or its current state.

Clients cannot reach the agent

An SSE request initiated by the client is one-way: server to client. The client has no way to send a signal to the agent through the same connection once the initial request has been made. The only options the client has are to read the stream to completion or to cancel it by closing the connection.

Using cancellation as the sole upstream signal creates a fundamental conflict. Consider a stop button that cancels an in-progress stream. The implementation has to choose between two interpretations of a closed connection: either it is a cancel (in which case the LLM should stop), or it is a disconnect (in which case the LLM should keep going so the stream can resume). There is no way to disambiguate.

Even with a bidirectional transport like WebSocket, the connection is still an exclusive pipe between one client and one agent. Other devices have no upstream channel, so they cannot interrupt or steer from a second device.

Multi-agent architectures are complex

In multi-agent systems, an orchestrator handles the client's connection and delegates work to specialised sub-agents. When the connection between the client and the orchestrator is exclusive and point-to-point, every interaction with a sub-agent has to be proxied by the orchestrator. If users need to see intermediate progress or responses from sub-agents, every update is mediated by the orchestrator, adding complexity and coupling.

  • Why AI Transport: how a durable session layer solves each of these problems.
  • Sessions: the persistent, shared conversation state that replaces the ephemeral connection.