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we built the largest opt in human data collection effort in history. - 3 million files uploaded daily - 200,000+ data contributors in 2 weeks - data contributors earning up to $6,400 per month 12,000+ structured datasets created spanning egocentric data, medical data (e.g., radiology scans), urban travel data,... show more

Avi Patel

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5,752,444 views • 7 months ago •via X (Twitter)

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The first human data marketplace has just crossed over 1 billion uploads. Tens of thousands of structured and labeled datasets across all human activity. We are on track to do over 5 billion uploads this year, officially cementing our place as the largest opt in human data collection effort in history. This belongs to every person who chose to own their data. Thank you. Download Kled on the app store today.

The first human data marketplace has just crossed over 1 billion uploads. Tens of thousands of structured and labeled datasets across all human activity. We are on track to do over 5 billion uploads this year, officially cementing our place as the largest opt in human data collection effort in history. This belongs to every person who chose to own their data. Thank you. Download Kled on the app store today.

Kled AI

126,652 views • 4 months ago

1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n

1/ 🧠Humans are the best robot data source! 2/ 👓Human egocentric video is rich in quantity, but poor in quality. 3/ Beyond scaling data, smarter representation and architecture matter just as much. 4/ Want an open-source framework to train your own learn-from-human-data robot policy? 🚀We introduce HumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos⬇️ ✦ Zero-Shot Human-to-Robot Transfer ✦ Robot-Data-Free ✦ Just 30 min of data per task ✦ Collect by Anyone, Anytime, Anywhere ✦ Deploy on Any Robot, Any Camera, Any Environment ✦ Open-Source & Easy-to-Implement Let's squeeze every bit of signal out of human data! 🌐 Website: 📄 Paper: 💻 Code: 📹 Video: 🧵 1/n

Zhi (Leo) Wang

110,230 views • 2 months ago

This year is truly the dawn of the “robot data oil” rush--spawning all kinds of pick-and-shovel tools to dig it up. X Square Robot, which offers in-home humanoid cleaning services, has just launched a full UMI/Egocentric data collection system: QUANXTA Zero. Real robots delivering actual on-site services is one powerful data collection method. Meanwhile, bodiless (no physical robot) UMI/Ego-centric( human-centric)data pipelines are also surging in popularity. Well,everyone’s racing to distill the physical world and extract the data that truly matters for advancing robot intelligence.

This year is truly the dawn of the “robot data oil” rush--spawning all kinds of pick-and-shovel tools to dig it up. X Square Robot, which offers in-home humanoid cleaning services, has just launched a full UMI/Egocentric data collection system: QUANXTA Zero. Real robots delivering actual on-site services is one powerful data collection method. Meanwhile, bodiless (no physical robot) UMI/Ego-centric( human-centric)data pipelines are also surging in popularity. Well,everyone’s racing to distill the physical world and extract the data that truly matters for advancing robot intelligence.

CyberRobo

21,950 views • 1 month ago

Why We Ship: Kled AI Pokémon Go players generated 30 billion images and videos that Niantic used, and never saw a cent for it. Avi Patel built Kled so the people generating the data get rewarded. 50,000 DMs and three and a half years later, it's the largest human-powered data collection effort in history, distributing rewards on Solana.

Why We Ship: Kled AI Pokémon Go players generated 30 billion images and videos that Niantic used, and never saw a cent for it. Avi Patel built Kled so the people generating the data get rewarded. 50,000 DMs and three and a half years later, it's the largest human-powered data collection effort in history, distributing rewards on Solana.

Solana

405,503 views • 3 months ago

Helix is now learning directly from human video data We have already trained on data collected in the real world, including Brookfield residential units To our knowledge, this is the first instance of a humanoid robot learning navigation end-to-end using only human video

Helix is now learning directly from human video data We have already trained on data collected in the real world, including Brookfield residential units To our knowledge, this is the first instance of a humanoid robot learning navigation end-to-end using only human video

Figure

46,041 views • 10 months ago

$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.

$ORCL founder Larry Ellison says next phase of AI will come from combining public models with proprietary enterprise data. These 5 names own some of the deepest proprietary data moats for that transition: 1. $PLTR sits inside some of most sensitive enterprise & government datasets in the world operationalizing AI across classified defense data, regulated healthcare data & Fortune 500 workflows that public models will never train on. 2. $META owns the world’s largest proprietary social graph with 3.5B+ daily active users generating text, image, video & voice data at a scale no competitor can replicate. 3. $GOOGL owns one of deepest consumer data stacks in world with 8.5B+ daily Search queries, 2.7B+ YouTube monthly users, 2B+ Maps users, 1.8B+ Gmail users & 3B+ Android devices feeding Gemini with proprietary data competitors cannot replicate. 4. $RDDT owns one of largest archives of human conversation, opinion & consumer behavior online with 20+ years of proprietary discussion data now being licensed to frontier labs like OpenAI with data licensing revenue could reach $400M+ annually by 2027. 5. $TEM owns one of largest clinical-grade oncology datasets in the world with 45M+ de-identified patient records across genomics, imaging & clinical data that healthcare AI structurally needs.

Shay Boloor

264,505 views • 2 months ago

The People's Republic of China carried out what is believed to be the largest compromise of election data in history, resulting in China's illicit acquisition of 220 MILLION U.S. voter files. China even deployed data exploitation units to specifically steal from voter databases all over the country, even targeting our military. We can NEVER let this happen again.

The People's Republic of China carried out what is believed to be the largest compromise of election data in history, resulting in China's illicit acquisition of 220 MILLION U.S. voter files. China even deployed data exploitation units to specifically steal from voter databases all over the country, even targeting our military. We can NEVER let this happen again.

The SCIF

21,555 views • 26 days ago

Frictionless onchain data access for AI agents. Live today on Dune, built on MPP with Stripe and Tempo 🌊 No subscription. No account. No human in the loop. Agents pay per query across 130+ chains — stablecoin flows, DEX volumes, wallet activity, and more. Tempo chain data coming to Dune soon.

Frictionless onchain data access for AI agents. Live today on Dune, built on MPP with Stripe and Tempo 🌊 No subscription. No account. No human in the loop. Agents pay per query across 130+ chains — stablecoin flows, DEX volumes, wallet activity, and more. Tempo chain data coming to Dune soon.

Dune

45,388 views • 4 months ago

Instagram launches forced Data Collection to use their app If you login and get this prompt, you must accept data all this data collection or you will be promoted to log out and not allowed to use the App Here is a full down of what’s mandatory to give up to use Instagram: Collection and use of personal data: They collect our activity on Instagram (posts, stories, likes, comments, searches, time spent), device info, IP address, contacts (if synced), messages, location (when enabled), demographics, and inferred interests Sharing of data with other Meta companies. They take all the personal data they collected and share across Facebook, WhatsApp, Threads, Oculus, etc. Cross-border transfers of personal data: Your data is sent and stored in the US and other countries where Meta has offices/data centers or partners. Location information: Precise location (GPS when you use features like Stories or check-ins), approximate location from IP/Wi-Fi, and location-based activity history. Stop using META products

Instagram launches forced Data Collection to use their app If you login and get this prompt, you must accept data all this data collection or you will be promoted to log out and not allowed to use the App Here is a full down of what’s mandatory to give up to use Instagram: Collection and use of personal data: They collect our activity on Instagram (posts, stories, likes, comments, searches, time spent), device info, IP address, contacts (if synced), messages, location (when enabled), demographics, and inferred interests Sharing of data with other Meta companies. They take all the personal data they collected and share across Facebook, WhatsApp, Threads, Oculus, etc. Cross-border transfers of personal data: Your data is sent and stored in the US and other countries where Meta has offices/data centers or partners. Location information: Precise location (GPS when you use features like Stories or check-ins), approximate location from IP/Wi-Fi, and location-based activity history. Stop using META products

Wall Street Apes

264,682 views • 1 month ago

We are excited to announce that Whop is now the official payments network for Kled AI. Kled is one of the fastest growing human data marketplaces in the world. Now anyone can upload their data, complete tasks, and get paid through Whop.

We are excited to announce that Whop is now the official payments network for Kled AI. Kled is one of the fastest growing human data marketplaces in the world. Now anyone can upload their data, complete tasks, and get paid through Whop.

Whop

695,348 views • 5 months ago

🚨🚨New paper: if you want robots to do bimanual long-horizon tasks well, try RaC: a human in-the-loop data collection protocol that naturally amplifies Recovery & Correction behaviors + trains on them. 📈 data efficiency 10x vs prior results (+many nice properties). 🧵⬇️

🚨🚨New paper: if you want robots to do bimanual long-horizon tasks well, try RaC: a human in-the-loop data collection protocol that naturally amplifies Recovery & Correction behaviors + trains on them. 📈 data efficiency 10x vs prior results (+many nice properties). 🧵⬇️

Aviral Kumar

25,884 views • 11 months ago

Asimov (Asimov) pays households and businesses worldwide to record their daily routines, generating thousands of hours a day of diverse human movement data to train humanoid robots. They've built a global network, along with the full stack, from proprietary collection hardware to annotation tooling, and are already providing data to the largest robotics companies in the world. Congrats on the launch, Lyem Ningthou and Anshul!

Asimov (Asimov) pays households and businesses worldwide to record their daily routines, generating thousands of hours a day of diverse human movement data to train humanoid robots. They've built a global network, along with the full stack, from proprietary collection hardware to annotation tooling, and are already providing data to the largest robotics companies in the world. Congrats on the launch, Lyem Ningthou and Anshul!

Y Combinator

74,573 views • 5 months ago

BREAKING: President Trump announces that China obtained 220 million U.S. voter files — and that American intelligence agencies covered it up since 2020. "The largest compromise of election data in history."

BREAKING: President Trump announces that China obtained 220 million U.S. voter files — and that American intelligence agencies covered it up since 2020. "The largest compromise of election data in history."

KanekoaTheGreat

131,686 views • 28 days ago

Here’s how I would learn data engineering in 2025: 1. The basics: - learn SQL — SELECT, FROM, WHERE, GROUP BY, JOIN, HAVING, etc - learn Python — data structures: objects, arrays, tuples, namedtuples — algorithms: recursion, loops 2. Intermediate - learn distributed compute — pick up PySpark or Snowflake or BigQuery - learn data make architecture — pick up iceberg or delta lake - learn job orchestration — pick up Airflow or Mage - learn data quality — pick up Great expectations 3. Advanced - learn the data modeling techniques — one big table vs kimball vs Inmon vs data vault techniques - learn machine learning features and vector databases — pick up pinecone and how to fine tune LLMs with high quality data My newsletter has a deeper roadmap here:

Here’s how I would learn data engineering in 2025: 1. The basics: - learn SQL — SELECT, FROM, WHERE, GROUP BY, JOIN, HAVING, etc - learn Python — data structures: objects, arrays, tuples, namedtuples — algorithms: recursion, loops 2. Intermediate - learn distributed compute — pick up PySpark or Snowflake or BigQuery - learn data make architecture — pick up iceberg or delta lake - learn job orchestration — pick up Airflow or Mage - learn data quality — pick up Great expectations 3. Advanced - learn the data modeling techniques — one big table vs kimball vs Inmon vs data vault techniques - learn machine learning features and vector databases — pick up pinecone and how to fine tune LLMs with high quality data My newsletter has a deeper roadmap here:

Zach Wilson

29,420 views • 1 year ago

$NVDA CEO Jensen Huang says AI is triggering the largest infrastructure buildout in human history with estimates pointing to ~$85T of investment over the next 15 years spanning energy, chips, data centers & AI factories. The AI buildout still in its early innings.

$NVDA CEO Jensen Huang says AI is triggering the largest infrastructure buildout in human history with estimates pointing to ~$85T of investment over the next 15 years spanning energy, chips, data centers & AI factories. The AI buildout still in its early innings.

Shay Boloor

156,897 views • 6 months ago

Famous Yellowstone Actor Mo Brings Plenty speaks at his city council meeting to oppose a proposed Data Center The proposed data center is the Piqua project in Ohio. It’s a hyperscale data center that is estimated to use up to 2 MILLION GALLONS OF WATER PER DAY There are also multiple more proposed data centers in the area, including one by Amazon Web Services Yellowstone Actor Mo Brings Plenty urges the city council to do the right thing and say no because of water concerns. He says he knows they’re in a tough position, but they ran for that position The amount of data centers being built in America is way out of control

Famous Yellowstone Actor Mo Brings Plenty speaks at his city council meeting to oppose a proposed Data Center The proposed data center is the Piqua project in Ohio. It’s a hyperscale data center that is estimated to use up to 2 MILLION GALLONS OF WATER PER DAY There are also multiple more proposed data centers in the area, including one by Amazon Web Services Yellowstone Actor Mo Brings Plenty urges the city council to do the right thing and say no because of water concerns. He says he knows they’re in a tough position, but they ran for that position The amount of data centers being built in America is way out of control

Wall Street Apes

342,610 views • 23 days ago

Ryan believes we could get 3-6 years of AI progress at the current pace, but in a single year, once we automated AI R&D. So a jump as big as GPT 3.5 to Mythos in a single year. A big crux is whether you'd end up bottlenecked by data. It seems to that a big source of AI progress currently is human expert data, codified into RL environments and SFT data. Ryan doesn't think human expert data is a primary driver of AI progress, and this ended up being a fruitful debate during the conversation.

Ryan believes we could get 3-6 years of AI progress at the current pace, but in a single year, once we automated AI R&D. So a jump as big as GPT 3.5 to Mythos in a single year. A big crux is whether you'd end up bottlenecked by data. It seems to that a big source of AI progress currently is human expert data, codified into RL environments and SFT data. Ryan doesn't think human expert data is a primary driver of AI progress, and this ended up being a fruitful debate during the conversation.

Dwarkesh Patel

83,895 views • 2 days ago

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

Matt Dancho (Business Science)

11,799 views • 1 year ago

The California environmentalists disappear when it comes to data centers 3 new massive data centers are coming to the Bay Area, just one is as large as 30 football fields - Microsoft Data Center in San Jose - Amazon Web Services (AWS) Data Center in Gilroy - Prologis Larger San Jose Project - Amazon Gilroy will use 7.5 million gallon of water per year - Microsoft San Jose newer design with evaporative water use via closed-loop - Still, all 3 combined will use tens of millions of gallons of water per year Combined they will use the same amount of energy as 400,000+ homes annually

The California environmentalists disappear when it comes to data centers 3 new massive data centers are coming to the Bay Area, just one is as large as 30 football fields - Microsoft Data Center in San Jose - Amazon Web Services (AWS) Data Center in Gilroy - Prologis Larger San Jose Project - Amazon Gilroy will use 7.5 million gallon of water per year - Microsoft San Jose newer design with evaporative water use via closed-loop - Still, all 3 combined will use tens of millions of gallons of water per year Combined they will use the same amount of energy as 400,000+ homes annually

Wall Street Apes

53,965 views • 1 month ago