HTTP Streaming and AI
HTTP streaming is effective for one-off interactions but has significant limitations in production environments. Key issues include disconnection problems, lack of session continuity across devices, and the inability for clients to communicate back to agents during a stream. These challenges necessitate additional infrastructure to manage persistent sessions and multi-agent interactions.
- ▪HTTP streaming works well for single interactions but fails to support ongoing sessions.
- ▪Disconnections during a stream can lead to lost responses, complicating user experience.
- ▪Clients cannot communicate back to agents once a stream is initiated, limiting interactivity.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,008 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Ably |
| Canonical URL | https://ably.com/docs/ai-transport/why/http-streaming-and-ai |
| Publication time | Fri, 29 May 2026 15:34:31 +0000 |
| Retrieval time | 2026-05-29T15:40:02.061Z |
| Last seen | 2026-05-29T15:40:02.061Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
HTTP streaming and AIDirect 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.Open inMost 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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Ably.
Discussion
0 commentsMore from Ably
