$DRIN
Open data for robots.
$DRIN
Open data for robots.
A protocol for decentralized robotics intelligence.
$DRIN:
Contribute data, earn tokens, empower robotics.
Contribute data, earn tokens, empower robotics.
Anyone can share training data—builders pay to access curated sets, value flows to generators.
Anyone can share training data—builders pay to access curated sets, value flows to generators.
The Closed Loop
The future of robots and AI depends on enormous real-world datasets—sensor logs, movement, edge cases. Today, huge companies hoard this data and keep it locked away. People generating the data get nothing back. As a result, innovation is slowed, and independent builders cannot access the resources needed for better, safer autonomous machines. This centralized approach keeps power and value with a few large corporations.
Summary
Tesla collected 47.2 million miles of autonomous driving data this quarter. Sensor logs, edge cases, and navigation patterns remain proprietary. Boston Dynamics captured 890,000 hours of bipedal movement data. Google processed 2.1 billion visual frames from deployed devices. Contributors received $0 compensation. Dataset access: RESTRICTED.
Tesla Fleet
Q3 2025
47.2M miles of driving data collected. Access: Internal only.
Boston Dynamics
Ongoing
890K hours of movement capture. Access: Proprietary.
Google Devices
Daily
2.1B visual frames processed. Access: Restricted.
The Closed Loop
The future of robots and AI depends on enormous real-world datasets—sensor logs, movement, edge cases. Today, huge companies hoard this data and keep it locked away. People generating the data get nothing back. As a result, innovation is slowed, and independent builders cannot access the resources needed for better, safer autonomous machines. This centralized approach keeps power and value with a few large corporations.
Node Feed
Contributors
Rewards
4:20
LIVE
Drone Node #4782
Capturing aerial navigation data. 847 verified frames this session. Current earnings: 23.4 DRIN tokens. Validator confirmation: 3/5 nodes.
4:20
Anwar Raza
I’ve been reviewing the AI note summary logic, and I think it’s too focused on individual sentences rather than themes. For example, when someone discusses three points under the same topic, it still breaks them into separate highlights. It looks fragmented in the recap. I’d rather have it group related ideas together — maybe through semantic clustering
2:34
Contributor
Data verified and added to shared pool. Reward distributed.
1:05
Sarah
Yeah, I see what you mean. Right now, the summarizer is built to trigger whenever it detects a transition phrase, like “next,” “also,” or “another thing.” It’s good for structure but bad for flow. We can change that by using context windows — say, two minutes of dialogue — and summarize based on meaning overlap rather than sentence boundaries. That way, it understands we’re still under the same topic.
The Closed Loop
The future of robots and AI depends on enormous real-world datasets—sensor logs, movement, edge cases. Today, huge companies hoard this data and keep it locked away. People generating the data get nothing back. As a result, innovation is slowed, and independent builders cannot access the resources needed for better, safer autonomous machines. This centralized approach keeps power and value with a few large corporations.
Summary
Tesla collected 47.2 million miles of autonomous driving data this quarter. Sensor logs, edge cases, and navigation patterns remain proprietary. Boston Dynamics captured 890,000 hours of bipedal movement data. Google processed 2.1 billion visual frames from deployed devices. Contributors received $0 compensation. Dataset access: RESTRICTED.
Tesla Fleet
Q3 2025
47.2M miles of driving data collected. Access: Internal only.
Boston Dynamics
Ongoing
890K hours of movement capture. Access: Proprietary.
Google Devices
Daily
2.1B visual frames processed. Access: Restricted.
The Open Network
DRIN is a decentralized marketplace for robotics training data built on Solana. Anyone with a camera, sensor, drone, or robotic node can contribute real data to the network and get fairly rewarded in tokens. Validators ensure data quality, and builders pay tokens to access high-quality datasets. This process ensures open access and rewards contributors directly, creating a global network effect.
Node Feed
Contributors
Rewards
4:20
LIVE
Drone Node #4782
Capturing aerial navigation data. 847 verified frames this session. Current earnings: 23.4 DRIN tokens. Validator confirmation: 3/5 nodes.
4:20
Anwar Raza
I’ve been reviewing the AI note summary logic, and I think it’s too focused on individual sentences rather than themes. For example, when someone discusses three points under the same topic, it still breaks them into separate highlights. It looks fragmented in the recap. I’d rather have it group related ideas together — maybe through semantic clustering
2:34
Contributor
Data verified and added to shared pool. Reward distributed.
1:05
Sarah
Yeah, I see what you mean. Right now, the summarizer is built to trigger whenever it detects a transition phrase, like “next,” “also,” or “another thing.” It’s good for structure but bad for flow. We can change that by using context windows — say, two minutes of dialogue — and summarize based on meaning overlap rather than sentence boundaries. That way, it understands we’re still under the same topic.
The Open Network
DRIN is a decentralized marketplace for robotics training data built on Solana. Anyone with a camera, sensor, drone, or robotic node can contribute real data to the network and get fairly rewarded in tokens. Validators ensure data quality, and builders pay tokens to access high-quality datasets. This process ensures open access and rewards contributors directly, creating a global network effect.
Summary
Tesla collected 47.2 million miles of autonomous driving data this quarter. Sensor logs, edge cases, and navigation patterns remain proprietary. Boston Dynamics captured 890,000 hours of bipedal movement data. Google processed 2.1 billion visual frames from deployed devices. Contributors received $0 compensation. Dataset access: RESTRICTED.
Tesla Fleet
Q3 2025
47.2M miles of driving data collected. Access: Internal only.
Boston Dynamics
Ongoing
890K hours of movement capture. Access: Proprietary.
Google Devices
Daily
2.1B visual frames processed. Access: Restricted.
The Open Network
DRIN is a decentralized marketplace for robotics training data built on Solana. Anyone with a camera, sensor, drone, or robotic node can contribute real data to the network and get fairly rewarded in tokens. Validators ensure data quality, and builders pay tokens to access high-quality datasets. This process ensures open access and rewards contributors directly, creating a global network effect.
Node Feed
Contributors
Rewards
4:20
LIVE
Drone Node #4782
Capturing aerial navigation data. 847 verified frames this session. Current earnings: 23.4 DRIN tokens. Validator confirmation: 3/5 nodes.
4:20
Anwar Raza
I’ve been reviewing the AI note summary logic, and I think it’s too focused on individual sentences rather than themes. For example, when someone discusses three points under the same topic, it still breaks them into separate highlights. It looks fragmented in the recap. I’d rather have it group related ideas together — maybe through semantic clustering
2:34
Contributor
Data verified and added to shared pool. Reward distributed.
1:05
Sarah
Yeah, I see what you mean. Right now, the summarizer is built to trigger whenever it detects a transition phrase, like “next,” “also,” or “another thing.” It’s good for structure but bad for flow. We can change that by using context windows — say, two minutes of dialogue — and summarize based on meaning overlap rather than sentence boundaries. That way, it understands we’re still under the same topic.
Sensor data upload
Camera feed
LIDAR scan
Movement capture
Drone telemetry
Depth mapping
Edge case log
Gyroscope data
Navigation pattern
Visual frames
Robotic arm sequence
Vehicle sensor log
Gyroscope data
Edge case log
Depth mapping
Drone telemetry
Movement capture
LIDAR scan
Camera feed
Sensor data upload
Gyroscope data
Edge case log
Depth mapping
Drone telemetry
l
Sensor data upload
Camera feed
LIDAR scan
Movement capture
Drone telemetry
Depth mapping
Edge case log
Gyroscope data
Navigation pattern
Visual frames
Robotic arm sequence
Vehicle sensor log
Gyroscope data
Edge case log
Depth mapping
Drone telemetry
Movement capture
LIDAR scan
Camera feed
Sensor data upload
Gyroscope data
Edge case log
Depth mapping
Drone telemetry
l
Sensor data upload
Camera feed
LIDAR scan
Movement capture
Drone telemetry
Depth mapping
Edge case log
Gyroscope data
Navigation pattern
Visual frames
Robotic arm sequence
Vehicle sensor log
Gyroscope data
Edge case log
Depth mapping
Drone telemetry
Movement capture
LIDAR scan
Camera feed
Sensor data upload
Gyroscope data
Edge case log
Depth mapping
Drone telemetry
l
Decentralized workflow.
Data flows from edge devices to the network, bypassing gatekeepers.
Token Rewards
Builders pay for curated datasets, tokens go to contributors. The more valuable your data, the more you earn. Value flows to generators, not corporations.
Token Rewards
Builders pay for curated datasets, tokens go to contributors. The more valuable your data, the more you earn. Value flows to generators, not corporations.
Node Verification
Validators check quality, authenticity, and accuracy of all data. Duplicates are flagged, fraud is detected, and only verified data enters the shared pool.
Node Verification
Validators check quality, authenticity, and accuracy of all data. Duplicates are flagged, fraud is detected, and only verified data enters the shared pool.
Node Verification
Validators check quality, authenticity, and accuracy of all data. Duplicates are flagged, fraud is detected, and only verified data enters the shared pool.
Node Verification
Validators check quality, authenticity, and accuracy of all data. Duplicates are flagged, fraud is detected, and only verified data enters the shared pool.
How It Works
How It Works
From raw data to trained robots in five steps.
From raw data to trained robots in five steps.
Deploy a node
Set up a camera, sensor, drone, or robotic device. Connect it to the DRIN network and start capturing real-world data.
Deploy a node
Set up a camera, sensor, drone, or robotic device. Connect it to the DRIN network and start capturing real-world data.
Deploy a node
Set up a camera, sensor, drone, or robotic device. Connect it to the DRIN network and start capturing real-world data.
Data validation
See tasks, owners, and deadlines extracted intelligently from every discussion.
Data validation
See tasks, owners, and deadlines extracted intelligently from every discussion.
Data validation
See tasks, owners, and deadlines extracted intelligently from every discussion.
Earn Rewards
Verified data enters the shared pool. You receive DRIN tokens proportional to the value and volume of your contribution.
Earn Rewards
Verified data enters the shared pool. You receive DRIN tokens proportional to the value and volume of your contribution.
Earn Rewards
Verified data enters the shared pool. You receive DRIN tokens proportional to the value and volume of your contribution.
Train on Real Data
Robotics companies and AI researchers purchase dataset access using tokens. They train autonomous systems on real-world data.
Train on Real Data
Robotics companies and AI researchers purchase dataset access using tokens. They train autonomous systems on real-world data.
Train on Real Data
Robotics companies and AI researchers purchase dataset access using tokens. They train autonomous systems on real-world data.
Network Effects
As the dataset becomes more comprehensive and builder demand increases, token value rises—rewarding early contributors.
Network Effects
As the dataset becomes more comprehensive and builder demand increases, token value rises—rewarding early contributors.
Network Effects
As the dataset becomes more comprehensive and builder demand increases, token value rises—rewarding early contributors.
The Flywheel Effect
The Flywheel Effect
A self-reinforcing cycle of growth.
A self-reinforcing cycle of growth.
More contributors bring more data, leading to better datasets. This attracts more builders who pay for access, increasing token demand. As token value rises, more contributors are incentivized to join—fueling growth and making even better robotics intelligence.
More contributors bring more data, leading to better datasets. This attracts more builders who pay for access, increasing token demand. As token value rises, more contributors are incentivized to join—fueling growth and making even better robotics intelligence.
More contributors bring more data, leading to better datasets. This attracts more builders who pay for access, increasing token demand. As token value rises, more contributors are incentivized to join—fueling growth and making even better robotics intelligence.
Our Vision
Our Vision
The robots of tomorrow should be trained by everyone and owned by no one.
$DRIN is the foundation for open, distributed robotics intelligence—accessible to all. We envision a global, permissionless dataset that enables breakthrough AI and robotics innovation. Anyone can contribute. Anyone can build.