30 karma · joined January 12, 2012
Location: whatever works 4 you (onsite /eu)
Remote: yes
Technologies: im looking for advanced stuff in quantum computing, qiskit, quantum ai, or ai + physics intermediately
Résumé/CV: https://stillonearth.github.io/And yes, we built custom beacons on top of OpenWRT routers & USB dongles –> hence high frequency updates, high power & no battery dependance. Also we built a mechanism that allows us to update majors/minors/uuids so that no external party can use our beacons.
Battery beacons are only usable for a couple toy usecases in my opinion.
Think of Uber / AirBNB / etc with no fees and no government power over them.
Another huge opportunity in distributing revenue of these services to peers running the network. This may cause rapid growth & unparalleled fault tolerance.
Third thing that might be disrupting is ability of such services to issue their own coins/stocks, which price is linked to service's revenue. Revenue is then distributed among stock owners, and service can issue such stocks at will without government's approval.
1. Limits speed of information distribution
2. Can be used to filter out specific information
3. Promotes centralised points of view -> Fewer people decide what is information is valuable
But: 1. May lead to better quality of information
---3 facts vs 1 possibility
nodejs / backbone and lots of goodness
your posts are so entertaining, keep it up
As for pure JavaScript, dealing with callbacks is definitely not fun.
This technology might disrupt indoor advertisement.
We're building mall app with indoor positioning.
Triangulation (trilateration) the way it's used in GPS will not work indoors because it's impossible to build accurate signal propagation model – model would be extremely complicated.
On the other hand signal propagation model can be learned via sampling actual signals in different spots on site. There are two ways to sample signals: 1) direct 2) indirect (via statistical models like SLAM)
Positioning can be done by comparing signal strengths from model and actual device and feeding them to some statistical positioning algorithm like particle filter or LSE. These algorithms use not only signal strengths but accelerometer, gyro and magnetic sensors for movement model. Fingerprinting techniques can provide up to 30 cm accuracy.
Huge win of WiFi fingerprinting is that it works on existing consumer-level hardware. But the downside is that you cannot scan WiFi in iOS public API.
There is less accurate way to position user indoors using supersonic marks – and it works on any existing phone.
Companies who do indoor positioning: WiFiSlam, Qubulus, WalkBase, Google, Nokia and we (applying to yc :). Some provide APIs, some are in stealth mode and Google has indoor positioning in some US and Japanese malls and airports.
Personally I think that there is huge opportunity for location-aware apps for malls, airports, parking lots, hospitals, etc and it has not been tackled much by anyone yet.