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Where the engine gets written up.

How the pipeline is built, what each layer is doing, and why the quality mechanisms sit alongside the stream rather than in front of it.

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On this page The first subject Nothing published yet The subjects Live demo
The first subject, already drawn

The mechanism exists. The write-up does not.

Context-aware linear streaming speech-to-text, drawn rather than described. This is the depth the first piece would be written at. The piece has not been written.

One turn, arriving in order
ApproachHow it is arrangedWhat each step seesWhat each step costs
Re-decodingDecode the whole turn again at every stepThe whole turn so farGrows with the turn
ChunkingDecode each chunk on its ownThe current chunk onlyFlat, and the context is gone
AlebexCarry the context, pay the chunk priceThe whole turn so farFlat, any turn length

How three approaches trade context against cost across one turn. Arrangement, not to scale. The blocks show whether the work grows with the turn, not how long it takes.

Empty

Nothing published yet.

There are no posts on this site yet, and none are listed here that do not exist. When the first piece is published it will appear on this page. There is no schedule to give you and nothing to sign up to.

Resources · Blog

The subjects the pieces come from

The engine’s four documented innovations. These are the subjects, not a publishing schedule — no piece exists until it is on this page.

01 Unwritten Context-aware linear streaming speech-to-text Full-context transcription at linear cost: re-decoding’s coherence at chunking’s price, any turn length.
02 Unwritten The parallel hallucination gate Latent-space check plus NLU confirmation riding the synthesis window, verdict in under 50 ms.
03 Unwritten Barge-in classification A learned classifier on raw audio (WavLM-based) that separates real interruptions from backchannels and line noise in milliseconds.
04 Unwritten Multimodal intent understanding An LLM decoder that consumes audio and text together and writes the intent label: open vocabulary, dynamic per deployment, capturing the hesitation and tone a transcript flattens.
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