Conduit Intelligence

[Lydia (in SF) @LydNot]

so @conduitpbc spent 6 months running a basement lab 20 hours/day to collect the largest neuro-language dataset ever built—~10,000 hours from thousands of people.

the wild part isn’t the model. it’s the operations.

turns out:

•⁠ ⁠noise matters < 4k hours; after that, scale eats it

•⁠ ⁠dynamic overbooking beats every recruitment strategy

•⁠ ⁠a 4-pound helmet is fine if you cut custom polygonal padding

•⁠ ⁠rewriting the backend cut marginal cost/hour by ~40%

•⁠ ⁠people will happily wear multimodal headsets if the LLM talks to them, not at them

•⁠ ⁠zero-shot decoding works on people the model has never seen:

thought → text:

“the room seemed colder” → “there was a gentle breeze”
“do you have a favorite app?” → “any favorite robot?”

the future of thought-to-text isn’t sci-fi.

it’s logistics, padding foam, airflow pipes, and a booking algorithm.

full post: https://t.co/Y8harECGU8