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