The worldโs largest camera infrastructure for mapping, autonomous driving, and physical AI. Join 260k drivers and earn rewards. Powered by @Solana.
Joined April 2022
- Tweets 3,351
- Following 119
- Followers 140,118
- Likes 7,353
1,192 Photos and videos
Jun 11
One centralized data pipeline can't cover the world. ๐
NATIX does it differently: Decentralized collection, global scale, every road, every market, every edge case.
Because the gap between AV demos and AV deployment is a data gap.
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Jun 10
NATIX is at the center of some BIG moves in the autonomous driving world ๐
From World Models to VLMs and End-to-End models, data is the key that binds them together.
It's only a matter of time before Physical AI takes over, and the ones building it are using NATIX data ๐ช
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Jun 8
The city-by-city playbook made sense in 2016. โก๏ธ
It doesn't anymore.
Autonomous systems need edge cases from all over the world, not just one city at a time.
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Jun 7
The data collected for Physical AI training is important, but is it also important who collects that data?
0%
Expert drivers
50%
Day-to-day drivers
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A mix of both
12%
Doesn't matter
8 votes โข Final results
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Jun 5
Our May Progress Update dropped:
๐น>176K Hours of Multi-Camera Footage
๐น>6.78B $NATIX Staked
๐VX360 HODL Clubs launched
๐บHow video turns into intelligence
๐Physical AI's geography problem
Full recap๐
natix.network/blog/natix-netโฆ
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Jun 4
Raw footage is only the starting point.
NATIX turns real-world visual data into structured spatial intelligence by extracting road signs, lane markings, road geometry, and infrastructure assets at scale.
This is how roads become machine-readable. ๐
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Jun 3
If an AV can drive in New York, it doesn't necessarily know how to drive in Tokyo.
Different roads mean different driving behaviors. ๐ฃ๏ธ
The only way to bridge that gap before sending autonomous cars into the real world is to have enough training data.
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Jun 2
Heads up Drive& users, June 30th is coming fast ๐
That's your last day to withdraw and qualify for the staking campaign. Stake your $NATIX and earn up to 15% bonus on top of standard APY.
Make sure not to miss it ๐
natix.network/blog/earn-extrโฆ
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Jun 1
One view is never the full scene. VX360 captures the road from multiple angles at once.
More context means stronger real-world data for Physical AI. ๐
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May 28
Autonomous driving is not just an algorithm race.
It is a geography problem. ๐
Roads, signs, weather, and driving culture change everywhere.
That is why global data coverage matters ๐
natix.network/blog/autonomouโฆ
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May 28
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Nowadays, AV systems are deployed city-by-city, but this kind of autonomy does not scale.
Every new market means new roads, edge cases, maps, and tuning. ๐
The future needs a data layer that can teach a vehicle how to drive in a different geography before it gets there.
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May 28
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This is where NATIX comes in.
Our decentralized camera network captures real-world driving data across countries, climates, and driving cultures.
That is how Physical AI gets the geographic coverage it needs to scale. ๐
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May 27
World models need more than pixels.
They need motion, context, uncertainty, and all the edge cases that happen outside the lab.
That is why real-world video matters ๐น
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May 26
Some edge cases we can deal with, but what about the ones we have never encountered? ๐คก๐
Autonomy has a long way to go until it catches up to the unpredictability of the real world.
Luckily, NATIX is on it ๐
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May 25
Raw video isn't a dataset.
It has to be ingested, cleaned, and tagged before it can train anything meaningful.
That pipeline is what separates footage from fuel. โก๏ธ
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May 24
Where will the next billion driving miles come from? ๐
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AV company fleets
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OEM-equipped cars
60%
Decentralized network
10%
Synthetic data generation
10 votes โข Final results
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May 22
Cameras. HD map. Estimation module. Algorithms. AV system.
That's the traditional stack. Each box is hand-engineered, glued together, and maintained forever.
End-to-end collapses it all into one model trained on video. The bottleneck moved from code to data. ๐
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May 21
Autonomous driving is no longer a sensor problem. It is a data infrastructure problem. ๐
Raw video is not a dataset. What a self-driving system learns depends almost entirely on what happens after the recording ends ๐
natix.network/blog/dashcam-tโฆ
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May 21
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The harder problem is the long tail. The rare scenarios that decide whether a system is safe.
You cannot schedule edge cases. You catch them at scale, across regions and angles. VLMs surface them. Multi-camera footage shows the full scene. ๐
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May 21
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This is why NATIX is building an open-source multi-camera World Foundation Model with Valeo.
The footage is the starting point. The pipeline is what turns it into intelligence. โก๏ธ
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