GoKawiilToday we release Needle 3: a foundation model for mobile, wearables, robots, smart home, automotive and microcontrollers. The whole model is a single 8-29 MB binary built on our Simple Attention Network , and we trade general chat capacity to beat models 10x its size on mobile tool calls and match 2-3x bigger models on extraction.
Text embedding The same model returns a vector for a sentence, so an app can search, match and route locally: find the note you mean, pick the tool closest to a request, collapse duplicate alerts.
Structured extraction Declare a shape, hand over messy text, get typed fields back: an invoice, a booking, a notification, a form. The decode grammar guarantees the output parses. Extraction generalised well to classification problems too.
Tool calls Given the functions your app exposes, Needle picks the right ones and fills every argument from what the user said. Ask for two things and you get two calls in order; ask for something no tool covers and you get an empty list, not a guess.
What that looks like in a product:
Smart home Go from pressing buttons to talking to the house. "Dim the bedroom and lock up" becomes two calls, executed offline, with no hub round trip.
Robots Give a vacuum or a small robot nuanced instructions: "clean the kitchen but leave the bedroom", "go back to the dock when you are done". Each becomes a sequence of moves it can execute.
Phones An assistant that acts on the device instead of answering: make an album from last weekend's photos, open a site, dim the screen, find the lease in your files.
Wearables Read a notification into structured data on the wrist: a card charge into merchant, amount and date; a message into a reply; a complaint into a sentiment flag.
AR glasses Navigation and nearby search from a short request, with no phone or network in the loop.
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