Show HN: OpenTalking – Privately deployable real-time AI avatar
It covers the core path of a digital-human conversational product: frontend interaction, session state, LLM replies, STT, TTS and voice selection, interruption control, subtitle events, WebRTC audio/video playback, and calls into local or remote model services. OpenTalking is designed as a practical digital-human production stack. The WebUI, avatar and voice asset libraries, knowledge bases, memory, multi-session state, LLM / STT / TTS providers, WebRTC playback, and model backends are organized in one project.
- ▪It covers the core path of a digital-human conversational product: frontend interaction, session state, LLM replies, STT, TTS and voice selection, interruption control, subtitle events, WebRTC audio/video playback, and calls into local or r
- ▪OpenTalking is designed as a practical digital-human production stack.
- ▪The WebUI, avatar and voice asset libraries, knowledge bases, memory, multi-session state, LLM / STT / TTS providers, WebRTC playback, and model backends are organized in one project.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,301 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 | GitHub |
| Canonical URL | https://github.com/datascale-ai/opentalking |
| Publication time | Mon, 03 Aug 2026 01:50:44 +0000 |
| Retrieval time | 2026-08-03T01:55:40.819Z |
| Last seen | 2026-08-03T01:55:40.819Z |
| 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 | UB5KRRIMGPOc · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| 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
OpenTalking Open-source real-time digital-human pipeline: LLM, TTS, WebRTC, character voices, and pluggable model backends 中文 · Documentation · GitHub Demos · Deployment · Quickstart · Models · Roadmap · Docs & Community Overview OpenTalking is an open-source orchestration framework for real-time digital-human conversations. It covers the core path of a digital-human conversational product: frontend interaction, session state, LLM replies, STT, TTS and voice selection, interruption control, subtitle events, WebRTC audio/video playback, and calls into local or remote model services. OpenTalking is designed as a practical digital-human production stack.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.