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Kinesis

Control your Mac with the Meta Neural Band

Details

External ID
49695408
Source
HN
Company
—
Product
kinesis
Website domain
github.com
Launched
Sept. 14, 2026
Cohort
—
Upvotes
121
Upvotes percentile
0.9393939393939394
Tags
—
Fetched at
Sept. 18, 2026, 5:02 p.m.
Updated at
Sept. 18, 2026, 5:02 p.m.

Description

a couple hours ago i had astra take on a 10 month old repo of mine https://github.com/callbacked/neural-band-poc/ that involved finding a way to use the meta neural band independently from the glasses to read the sEMG data off of it. i really think it is a cool piece of technology that largely gets overlooked by the glasses, and always thought it was a shame it couldn't be used outside of its main application.after making quick work of that, i wanted to use it to control my mac using gesture controls and that's how kinesis was born. it has been very cool being able to swipe from desktops using my fingers, swiping up to open up mission control or controlling a virtual knob to adjust my volume.i definitely havent scratched the surface with the neural band but it has been a really fun endeavor thus far! i really hope others can find creative uses for their neural bands too :)note: i really hope this works on other bands because the sample size has just been me so far.

Enrichment

Theme
interactive simulations and creative experiments
Vertical
Horizontal
Function
Dev tools
Audience
Prosumer
AI stance
Not AI
Project type
Hobby / open-source project
Normalized one-liner
mac control interface using the meta neural band
Manually corrected
False

Could you build this?

No Interfacing with proprietary hardware (Meta Neural Band) to capture raw surface electromyography (sEMG) data and decode biological motor unit signals into desktop input requires specialized signal processing, reverse engineering, and biosensing domain knowledge.

What it would actually take: The project requires reverse engineering undocumented Bluetooth LE GATT services or proprietary radio protocols used by Meta's neural band to intercept raw high-frequency sEMG data streams. It then requires digital signal processing pipelines (filtering 50/60Hz noise, rectifying, feature extraction) and lightweight ML classifiers running in real time to map muscle activation patterns to discrete OS mouse/keyboard actions. Expertise in RF reverse engineering, embedded protocols, and biosignal processing/neuromotor interfaces is required.

Discussion

20 comments analyzed.

Competitors mentioned: Kinesis, Tap Strap, Leap Motion, OCZ NIA

Concerns raised: High user inertia sticking to QWERTY/touchscreens, Privacy and security concerns with Meta hardware, Midas effect triggering unintentional actions, Limited gesture fidelity and robustness, Risk of hackability being patched out

Feature requests: Better-lit demo video showing hand gestures, Multi-touch AR support with two bands, Offline use without account login

Competitors

Other products that read as similar to this one — 91 launches clear the similarity bar, closest 8 shown.

Attention rank: #11 of 92 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 320 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a dev tools tool for Sales yet.