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NATIX Network@NATIXNetworkA car cuts in. The driver slams the brakes 🚗 An end-to-end model learns that reaction from two things in sync: what all six cameras saw, and when and how hard the driver braked. Internet video only has the first. NATIX telemetry catches the second on its own.
NATIX Network@NATIXNetworkTesla's new robotaxi safety page counts 3.1M autonomous miles 🚕 The split says more than the total: 862K after dark, 113K in rain, 575K in rush hour. Miles only count for the conditions they cover. Night, rain and traffic are where driving data earns its keep 🌧️
Validator hotkey 5CAQAgXxoU3NPo1YG5VQKz4Ackh7goNx3TvPD9A6LoqA7FmJ announced new child keys on SN25, SN55, SN72, SN94, applying at block 9,450,479 (~24h). Stake delegated to it there routes through its children after that.
Between blocks 9,443,266 and 9,443,326
NATIX Network@NATIXNetworkKITTI came out in 2012 and gave self-driving research its first shared benchmark 🏁 BDD100K, nuScenes and Waymo Open followed. Today's world models need more than they were built for: recent multi-camera video from many places. What they taught us 👇 natix.network/blog/open-driving-datasets-limits
Linknatix.networknatix.network/blog/open-driving-datasets-limitsTaosis News: the week’s articles from the subnet teams, the press and community newsletters, every Friday. Taosis Weekly: the week’s movers, best yields and chain events, with dollar values, every Monday. Past issues
NATIX Network@NATIXNetworkDePIN taught crypto to crowdsource maps, WiFi and compute. DePAI points the same idea at physical AI: everyday people collect the real-world data robots and self-driving cars learn from, and get rewarded for it. NATIX drivers do it with the cameras built into their Teslas.
The Waymo Open Dataset brought scale and label quality to open driving research. See what its perception dataset and motion dataset hold and where limits begin.
SN72 StreetVision by NATIX blog (sitemap)source ↗
A vision language action model links what a car sees, how it reasons and what it does. Learn what VLA training data needs and why open datasets fall short.
SN72 StreetVision by NATIX blog (sitemap)source ↗
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Open driving datasets like the Waymo Open Dataset, nuScenes and KITTI shaped autonomy research. Here is what they taught us and where world models outgrow them.
SN72 StreetVision by NATIX blog (sitemap)source ↗
The nuScenes dataset brought full 360-degree sensor data to open research. See what nuScenes contains, how nuPlan extended it and where both fall short today.
SN72 StreetVision by NATIX blog (sitemap)source ↗
The KITTI dataset created the first shared benchmark for self-driving research. Learn what the KITTI benchmark measured, why it mattered and why it looks dated.
SN72 StreetVision by NATIX blog (sitemap)source ↗
BDD100K proved that everyday dashcam video can teach driving models real variety. See what this dashcam dataset contains and where single-camera data stops.
SN72 StreetVision by NATIX blog (sitemap)source ↗
NATIX Network@NATIXNetworkGoldman Sachs puts general-purpose robots about where self-driving was in 2017 or 2018 🦾 Its estimate: they’ll need around 10 million hours of high-quality physical interaction data. Self-driving learned this first. The models got good once the real-world data caught up.
Fake support agents, fake tickets and scam bots target every crypto community. Here is how NATIX moved to AI moderation to stay ahead of them, day and night.
SN72 StreetVision by NATIX blog (sitemap)source ↗
NATIX Network@NATIXNetworkOctober was the month demand showed up 📈 A 3,000 hours data sale to a world model lab, over 931M $NATIX burned in total, and the VX360 Desktop App open beta live in the U.S. Our October recap is here 👇 natix.network/blog/progress-update-natix-networ…
Linknatix.networknatix.network/blog/progress-update-natix-network-october-2026NATIX's October 2026 recap: a 3,000 hours data sale to a world model lab, over 931M $NATIX burned in total, and the VX360 Desktop App open beta in the U.S.
SN72 StreetVision by NATIX blog (sitemap)source ↗
Validator hotkey 5HbScNssaEfioJHXjcXdpyqo1AKnYjymidGF8opcF9rTFZdT announced new child keys on SN6, SN11, SN14, SN24, SN32, SN37, SN38, SN43, SN45, SN53, SN61, SN69 and 6 more, applying at block 9,406,042 (~24h). Stake delegated to it there routes through its children after that.
Between blocks 9,398,838 and 9,398,900
NATIX Network@NATIXNetworkThe real line between ADAS and L4 is not capability. It is who catches the mistake when the model is wrong. Answer that and the training data question mostly answers itself. Full breakdown on coverage, labels and edge cases 👇 natix.network/blog/adas-data-vs-full-autonomy-t…
Linknatix.networknatix.network/blog/adas-data-vs-full-autonomy-training-dataNATIX Network@NATIXNetworkMore world models, more demand for real driving data 📈 This week Rivian bought Third Dimension to turn real footage into 3D worlds, and Baidu is about to reveal its own world model. Whoever builds the model needs the footage. NATIX has over 200,000 hours of it.
NATIX Network@NATIXNetworkAutonomous driving shouldn’t be locked inside a few companies 🔓 The Autoware Foundation builds open-source self-driving software, backed by members like AMD, AWS and Arm. As a Premium member, NATIX supplies multi-camera data for Autoware’s open-source end-to-end model 🤝
Synthetic data vs real data in autonomous driving: where simulation is strongest, where real-world data cannot be replaced, and how teams combine both.
SN72 StreetVision by NATIX blog (sitemap)source ↗
The SAE levels of autonomous driving explained from Level 0 to 5, and what each level demands from training data in coverage, labels and rare edge cases.
SN72 StreetVision by NATIX blog (sitemap)source ↗
What the operational design domain (ODD) means in autonomous driving, what goes into one, and how to turn an ODD into clear, measurable data coverage targets.
SN72 StreetVision by NATIX blog (sitemap)source ↗
What edge case scenarios are in autonomous driving, how they differ from corner cases, and how teams find rare, safety-critical events in large video sets.
SN72 StreetVision by NATIX blog (sitemap)source ↗
Data annotation for autonomous vehicles explained: label types, auto labeling, driver actions as supervision, and how deep labels should go for ADAS or Level 4.
SN72 StreetVision by NATIX blog (sitemap)source ↗
ADAS data and full autonomy data differ in coverage, labeling depth and edge-case density. See what Level 2+ and Level 4 teams should prioritize in a dataset.
SN72 StreetVision by NATIX blog (sitemap)source ↗
NATIX Network@NATIXNetworkOver 200,000 hours of driving footage, and none of it came from a test fleet 🚗 It was recorded by drivers in the NATIX network, on their everyday routes. That’s the network behind every dataset, partnership and world model NATIX powers 🌍
Validator hotkey 5G3wMP3g3d775hauwmAZioYFVZYnvw6eY46wkFy8hEWD5KP3 announced new child keys on SN17, SN20, SN24, SN29, SN39, SN44, SN47, SN48, SN52, SN56, SN61, SN63 and 14 more, applying at block 9,374,755 (~24h). Stake delegated to it there routes through its children after that.
Between blocks 9,367,539 and 9,367,598
Validator hotkey 5EqYdsxW1EYfb7PdJrazCYd1JuunqhABtHSWKh34jPbQoaUf announced new child keys on SN72, applying at block 9,366,703 (~24h). Stake delegated to it there routes through its children after that.
Between blocks 9,359,483 and 9,359,544View subnet →