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Model progress is no longer constrained by architecture, but by access to high-quality, human-generated data. Scraped internet data is finite, low-signal and increasingly synthetic. Kled AI pays real people to upload authentic, real world content task by task in their mobile app. We are super excited to back Avi... show more

Nichole Wischoff

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36,974 Aufrufe • vor 4 Monaten •via X (Twitter)

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Introducing $KLED: the first data marketplace. Thousands of users are already selling their content. Today, we’re extremely excited to launch our Enterprise Portal: AI labs, data companies, and Fortune 500s can now search, preview, and license datasets instantly. Making Kled the only two sided data marketplace on the planet. Our vision is now a reality. Full demo and signup link will be live on our completely revamped site in the next 48-72 hours→

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Most AI is still learning from the same recycled internet data and that is the real bottleneck no one likes to talk about. The internet is running out of high quality human signal, what remains now is noisy, repetitive, and heavily biased toward the same regions and behaviors. The truth is, physical AI and next generation LLMs need something better like data that comes from real people, real environments, real languages, and real actions. KGeN Foundation 🟩 as a verified human network is changing the game, as it spans more than 60 countries to collects multimodal data including voice, first person video, preferences, and everyday behaviors from actual humans rather than bots or synthetic sources, resulting to a better data from real people for better AI. 𝗘𝘃𝗲𝗿𝘆 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻𝘀 𝘁𝗵𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮. I'm sure you wouldn't want to be left behind, so to be part of this great movement, Go to: to start contributing today.

Most AI is still learning from the same recycled internet data and that is the real bottleneck no one likes to talk about. The internet is running out of high quality human signal, what remains now is noisy, repetitive, and heavily biased toward the same regions and behaviors. The truth is, physical AI and next generation LLMs need something better like data that comes from real people, real environments, real languages, and real actions. KGeN Foundation 🟩 as a verified human network is changing the game, as it spans more than 60 countries to collects multimodal data including voice, first person video, preferences, and everyday behaviors from actual humans rather than bots or synthetic sources, resulting to a better data from real people for better AI. 𝗘𝘃𝗲𝗿𝘆 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝗲𝗻𝘀 𝘁𝗵𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮. I'm sure you wouldn't want to be left behind, so to be part of this great movement, Go to: to start contributing today.

𝐓𝐡𝐞𝐎𝐧𝐥𝐲𝟏𝐇𝐞𝐫𝐨👑

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After 4 months of legal groundwork, engineering, and partnership development, we’re excited to introduce Kled Life. A fully new experience inside the Kled app that reimagines how user data is shared, and how users are compensated. Kled users can now link their Netflix, Uber, Gmail, Spotify, and over 100 more apps directly to their Kled profile. Every time you use those linked accounts, Kled securely captures your behavioral data, how you listen, watch, shop, move, and pays you for it. How many songs you played today. How many hours you streamed. Which games you love most. It’s data that’s usually extracted and sold behind your back. Now, you get a share of the game. All in under 60 seconds of setup. We’re excited to be rolling this out with our wonderful partners and collaborators who have made this possible. We’ve tasked over 300 select users to test out these features while we simultaneously organize this new data pipeline for enterprises. We expect Kled Life to triple the size of our user payouts and enterprise contract demand once rolled out to the public. Get paid for your data, get paid for using your phone, get paid for labeling data. One app. The ultimate data side hustle.

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“we’ll have to have some sort of Universal Basic Income (UBI)” Elon Musk But who is going to pay? We believe our data! HUDI = Human Data Income We are building a Universal Basic Income based on our data. Our data is our asset, the new oil, but we have no transparency nor profit from it. Help us build the Human Data Income: we made great progress, but it’s just the beginning!

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Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Tony Blair and Oracle co-founder Larry Ellison plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" their AI models. "We have to take all of this data... and move it into a single... unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

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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.

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476,586 Aufrufe • vor 1 Jahr

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:

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Larry Ellison—owner of Oracle, CBS, and now TikTok—tells Tony Blair about his plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" his AI models. "We have to take all of this data... and move it into a single, if you will, unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

Larry Ellison—owner of Oracle, CBS, and now TikTok—tells Tony Blair about his plan to use digital ID to "unify" all data on each country's citizens "so it can be consumed and used by" his AI models. "We have to take all of this data... and move it into a single, if you will, unified data platform." "When we want to ask a question, we've provided that AI model with all the data they need to understand our country." "We need to unify all of the national data, put it into a database where it's easily consumable by the AI model, and then ask whatever question you like."

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144,618 Aufrufe • vor 7 Monaten

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.

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126,934 Aufrufe • vor 4 Monaten

A new era in Data Science is here 🚀. Say hello to your Chief AI Chat Data Scientist! From this day forward, advanced analytics and automated data workflows are within anyone's reach. We like to call it "Freedom To Create".

A new era in Data Science is here 🚀. Say hello to your Chief AI Chat Data Scientist! From this day forward, advanced analytics and automated data workflows are within anyone's reach. We like to call it "Freedom To Create".

Carlos Mendez

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Finland President Alexander Stubb: Well, we have to be more independent in some cases. If you, for example, look at storage of personal data, obviously a lot of Europeans are angry that their personal data is stored outside of the EU. So we have to build our own data centers and our own AI factories so we can actually own the data of our own citizens. But in the end, there is no such thing as European full autonomy. You will always need to collaborate with our friends in Canada, in the US, in other partnerships around the world. ASML and other tech companies can play a crucial role both in the US and here.

Finland President Alexander Stubb: Well, we have to be more independent in some cases. If you, for example, look at storage of personal data, obviously a lot of Europeans are angry that their personal data is stored outside of the EU. So we have to build our own data centers and our own AI factories so we can actually own the data of our own citizens. But in the end, there is no such thing as European full autonomy. You will always need to collaborate with our friends in Canada, in the US, in other partnerships around the world. ASML and other tech companies can play a crucial role both in the US and here.

Clash Report

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DATA VAMPIRES is a four-part series exploring the costs of hyperscale data centers and why tech billionaires are desperate to build more. In Part 3, we explore how the AI boom is accelerating the build out and its massive climate impacts. Full ep:

DATA VAMPIRES is a four-part series exploring the costs of hyperscale data centers and why tech billionaires are desperate to build more. In Part 3, we explore how the AI boom is accelerating the build out and its massive climate impacts. Full ep:

Tech Won't Save Us

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China is our adversary. They are building more power than we are, and then using it to power data centers to advance both economic interests and of course military as well. We can’t let the Chinese beat us in AI. We’ve gotta build more data centers, we’ve gotta build more power. We’ve got a lot ahead of us here because they’re beating us on this. We cannot let this happen.

China is our adversary. They are building more power than we are, and then using it to power data centers to advance both economic interests and of course military as well. We can’t let the Chinese beat us in AI. We’ve gotta build more data centers, we’ve gotta build more power. We’ve got a lot ahead of us here because they’re beating us on this. We cannot let this happen.

Kevin O'Leary aka Mr. Wonderful

19,405 Aufrufe • vor 3 Monaten

At Axis, every trajectory submitted by our community undergoes a strict replay validation process. We run each submission through checker to verify whether the target task was successfully completed. To see how strict it is, check this demo (Task: Place The Toy Train On The Board Game Box). Real human data passes smoothly (Video 1). However, bots or manually altered data will fail (Video 2). Why? Faking numbers breaks the simulation's physics causality. Even tiny tweaks cause error accumulation, resulting in failed movements. This invalid data is automatically rejected. Because of this mechanism, data submitted via bots will ultimately fail our replay verification. Invalid data is strictly excluded from model training, and the task slot is reopened to the community to collect genuine, high-quality trajectories. Furthermore, we actively monitor for duplicated data. Trajectories that are identical lack the diversity required for robot learning and will not be credited by our scoring system. If we detect accounts submitting a massive volume of identical trajectories, all associated addresses will be permanently banned. For Axis, the quality and diversity of data are the only ways to solve the robotics generalization gap. They will always be our absolute top priorities.

At Axis, every trajectory submitted by our community undergoes a strict replay validation process. We run each submission through checker to verify whether the target task was successfully completed. To see how strict it is, check this demo (Task: Place The Toy Train On The Board Game Box). Real human data passes smoothly (Video 1). However, bots or manually altered data will fail (Video 2). Why? Faking numbers breaks the simulation's physics causality. Even tiny tweaks cause error accumulation, resulting in failed movements. This invalid data is automatically rejected. Because of this mechanism, data submitted via bots will ultimately fail our replay verification. Invalid data is strictly excluded from model training, and the task slot is reopened to the community to collect genuine, high-quality trajectories. Furthermore, we actively monitor for duplicated data. Trajectories that are identical lack the diversity required for robot learning and will not be credited by our scoring system. If we detect accounts submitting a massive volume of identical trajectories, all associated addresses will be permanently banned. For Axis, the quality and diversity of data are the only ways to solve the robotics generalization gap. They will always be our absolute top priorities.

Axis Robotics

30,876 Aufrufe • vor 4 Monaten

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Kled Version 3 is coming. Over $20M+ in rewards will be paid directly to users from leading AI labs across robotics, legal services, image and video generation, world modeling, and more. In the last seven days, we’ve received inbound data requests from several decacorn AI labs and enterprises for datasets our human data marketplace is uniquely positioned to provide. Since receiving the specs for these requests, we now have a much better picture and understanding of how to reshape the systems that collect this data, so here’s what’s coming: 1. A fully redesigned home experience: The home feed is being rebuilt to surface the highest-value, most relevant tasks for each user, similar to how Uber Eats surfaces top restaurants. The goal is to turn every user into their most effective version as a data contributor. 2. Automated quality enforcement at scale: New ML systems are being built to evaluate task-specific requirements in real time. For example, if a task requires “two hands visible on camera at all times,” any video that fails that spec will be automatically rejected. This logic will apply across thousands of tasks and specifications using a general ML. 3. Kled Shop: Some tasks require better capture hardware. We’re introducing Kled Shop, where users can redeem points or tokens for equipment like Meta glasses, drones, and other tools. Points and tokens can be converted directly from payouts. 4. Partner-run data labeling and evaluation work: Some of our partners operate high-paying data labeling and model evaluation programs. We’re integrating their workflows directly into Kled so qualified users can access these roles in one place. These jobs are owned and managed by our partners. Kled’s role is to route the right people to the right work. Some opportunities pay $50–$1,000 per hour depending on expertise. 5. Global payouts and localization: We’re partnering with a major payment processor to enable cashouts in users’ native currencies. This unlocks broader global participation. Multi-language support is also coming to accelerate user growth. This full suite of tools will be rolling out soon, directly to Kled users. Top earners are currently making ~$7,000 per month. With this update, we should see the first ~$10,000 per month earner.

Avi Patel

124,728 Aufrufe • vor 7 Monaten

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."

ANI

163,581 Aufrufe • vor 1 Jahr

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

Matrix AI Network

60,855 Aufrufe • vor 2 Monaten