Show HN: SyntaxCue – local-first AI interview copilot (whisper.cpp and BYOK)
SyntaxCue turns the audio of a live technical interview into a streamed answer suggestion, on-device, in a few hundred milliseconds — without ever recording the call or sending audio anywhere. This piece walks the whole pipeline: the OS audio API, voice-activity detection, local transcription, and the streamed model response, with the actual APIs and the actual measured numbers. It’s written for a reader who wants to know whether “local transcription, no backend” is a real architectural claim or a marketing one, so most of it is specifics — which CoreAudio call, why an aggregate device, and why Windows once took 25 seconds per transcription when macOS took a fraction of a second.
- ▪SyntaxCue turns the audio of a live technical interview into a streamed answer suggestion, on-device, in a few hundred milliseconds — without ever recording the call or sending audio anywhere.
- ▪This piece walks the whole pipeline: the OS audio API, voice-activity detection, local transcription, and the streamed model response, with the actual APIs and the actual measured numbers.
- ▪It’s written for a reader who wants to know whether “local transcription, no backend” is a real architectural claim or a marketing one, so most of it is specifics — which CoreAudio call, why an aggregate device, and why Windows once took 25
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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 | Syntaxcue |
| Canonical URL | https://syntaxcue.com/how-syntaxcue-captures-system-audio/ |
| Publication time | Thu, 17 Sep 2026 09:48:19 +0000 |
| Retrieval time | 2026-09-17T09:53:42.644Z |
| Last seen | 2026-09-17T09:53:42.644Z |
| 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 | rs_EMshvsa7a · 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
SyntaxCue turns the audio of a live technical interview into a streamed answer suggestion, on-device, in a few hundred milliseconds — without ever recording the call or sending audio anywhere. This piece walks the whole pipeline: the OS audio API, voice-activity detection, local transcription, and the streamed model response, with the actual APIs and the actual measured numbers. It’s written for a reader who wants to know whether “local transcription, no backend” is a real architectural claim or a marketing one, so most of it is specifics — which CoreAudio call, why an aggregate device, and why Windows once took 25 seconds per transcription when macOS took a fraction of a second.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Syntaxcue.
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