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What we’re seeing: frontier robotics teams are rebuilding data pipelines from scratch because generic licensed datasets aren’t moving their models forward. The next wave won’t be about more data. It’ll be about precise, domain-specific data that actually improves performance. The bottleneck isn’t compute. It’s the right data. 🎥 Our... show more

Perle Labs

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30,562 views • 4 months ago •via X (Twitter)

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We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork

Pascal Bornet

186,223 views • 3 months ago

Everyone is talking about AI models, but almost no one is talking about data ownership. For years, centralized systems have dominated data pipelines, opaque processes, hidden biases, and zero contributor visibility. We call it innovation…, but in reality, it’s a black box. And that’s exactly how we ended up facing model degradation risks today. Because here’s the truth: AI is only as good as the data it learns from. That’s why Sovereign AI Data matters. To me, it means control, traceability, and accountability. Perle Labs is approaching this differently: ◈ On-chain provenance → Every data point is verifiable ◈ Expert-driven input → Quality over quantity ◈ Reputation systems → Trust becomes measurable This isn’t just a technical upgrade. It’s a paradigm shift. Instead of: Anonymous contributors ➠Unverified datasets ➠Centralized ownership ➠We move toward: Transparent data flows, Credible expertise and Distributed value creation If AI is the brain, then data is the memory. And right now most systems are learning from memory, which they can’t verify. Perle is solving that. While others focus on scaling models… This is about fixing the foundation they stand on. And foundations decide everything. #PerleAI #ToPerle participating in Perle Labs community campaign

Everyone is talking about AI models, but almost no one is talking about data ownership. For years, centralized systems have dominated data pipelines, opaque processes, hidden biases, and zero contributor visibility. We call it innovation…, but in reality, it’s a black box. And that’s exactly how we ended up facing model degradation risks today. Because here’s the truth: AI is only as good as the data it learns from. That’s why Sovereign AI Data matters. To me, it means control, traceability, and accountability. Perle Labs is approaching this differently: ◈ On-chain provenance → Every data point is verifiable ◈ Expert-driven input → Quality over quantity ◈ Reputation systems → Trust becomes measurable This isn’t just a technical upgrade. It’s a paradigm shift. Instead of: Anonymous contributors ➠Unverified datasets ➠Centralized ownership ➠We move toward: Transparent data flows, Credible expertise and Distributed value creation If AI is the brain, then data is the memory. And right now most systems are learning from memory, which they can’t verify. Perle is solving that. While others focus on scaling models… This is about fixing the foundation they stand on. And foundations decide everything. #PerleAI #ToPerle participating in Perle Labs community campaign

Big_Wealth❤️‍🔥

11,486 views • 4 months ago

Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning

Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning

Alex Ratner

495,851 views • 1 year ago

I don't worry about the actual firmware stack. I would want every country to be using American chips so that the programmers there are held at bay by our technology. The Chinese are actually trying to develop this on their own. They are beating us in developing a larger electrical grid in China very quickly, because they don't have any permitting problems. And the other area that people have been talking about directly concerning these chips is they have much more compute power. They're building data centers at a rate that's four times ours. We have more data centers, but they're catching up very quickly.

I don't worry about the actual firmware stack. I would want every country to be using American chips so that the programmers there are held at bay by our technology. The Chinese are actually trying to develop this on their own. They are beating us in developing a larger electrical grid in China very quickly, because they don't have any permitting problems. And the other area that people have been talking about directly concerning these chips is they have much more compute power. They're building data centers at a rate that's four times ours. We have more data centers, but they're catching up very quickly.

Kevin O'Leary aka Mr. Wonderful

23,276 views • 2 months ago

Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!

Training robot foundation models faces two key hurdles: how to get enough data to train an effective model, and how to make sure that new skills can be acquired quickly. The team at Rhoda AI believes that the answer is training Direct Video Action models from web data. Web data is plentiful, to the point where Rhoda can train their base model on hundreds of years of video data. And then, with the addition of robot data, they can quickly adapt it to new tasks with as little as 20 hours of in-domain data, performing complex, multi-step manipulation tasks with their purpose-built video foundation model. Tongzhou Mu 🤖🦾🦿 Eric Chan and Changan Chen joined us to talk more about their approach. Watch Episode #79 of RoboPapers, with Michael Cho - Rbt/Acc, Chris Paxton, and Jiafei Duan, to learn more!

RoboPapers

24,475 views • 2 months ago

There is a new extremely viral theory going around that many of the data centers being built in America aren’t really data centers They’re actually modern day panopticon. A digital surveillance state Even Tucker Carlson thinks this is the case and brings it up with Kevin O'Leary There are some real reasons to question what we’re being told, one of the reasons is the size and scale Think about this China’s largest reported single data center campus is only about 200 football fields in size Compare that to the US where a single data center is being planned at 33,000 football fields. Not to mention there are tons of them that are massive being planned and built It really doesn’t make sense when you compare the population of China being bigger than the US This is a conspiracy, but it’s something to think about because we know America has been moving towards a surveillance state, that’s every real Keep paying attention and questioning things

There is a new extremely viral theory going around that many of the data centers being built in America aren’t really data centers They’re actually modern day panopticon. A digital surveillance state Even Tucker Carlson thinks this is the case and brings it up with Kevin O'Leary There are some real reasons to question what we’re being told, one of the reasons is the size and scale Think about this China’s largest reported single data center campus is only about 200 football fields in size Compare that to the US where a single data center is being planned at 33,000 football fields. Not to mention there are tons of them that are massive being planned and built It really doesn’t make sense when you compare the population of China being bigger than the US This is a conspiracy, but it’s something to think about because we know America has been moving towards a surveillance state, that’s every real Keep paying attention and questioning things

Wall Street Apes

178,337 views • 2 months ago

We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

We recently crossed 10,000 GitHub stars on Rerun 😍 That's great and all, but something bigger is happening in robotics and Physical AI right now: The open-source ecosystem has seen real acceleration over the last few years. More teams are sharing real infrastructure: datasets, models, simulators, tooling, and the unglamorous plumbing that makes iteration possible. Jan Florian Maas, Physical Intelligence, Hugging Face are just some of the names driving this forward. Visualization and data infra belong in that category. If your entire data loop depends on being able to query and inspect what your robots are doing, that layer can’t be fragile or vendor-controlled. It needs to be something you can introspect, build on, extend, and trust long-term. Huge thanks to everyone building open-source robotics. The next decade of Physical AI will be built in the open.

Rerun

29,684 views • 5 months ago

Programmable Bandwidth is crypto’s next meta. Gm rent your spare Wi-Fi to AI. AI needs more data. AGI is coming. Are you ready? Bandwidth = how much data your connection moves per second. Residential IP = your home’s street address on the internet, trusted as human traffic that doesn’t get blocked. Together, Bandwidth + Residential IP = clean, high-trust traffic that data buyers and AI teams actually want. The community becomes the network. LLMs eat data. The more and the higher quality, the smarter they get. Owning the data pipeline = owning the power. Proof it’s valuable: big platforms license conversational & user-generated data for serious money. Many 8-figure+ deals are public, many more done under the table. Proprietary datasets are the real edge in this AI world. Why residential IPs matter: datacenter IPs get blocked by anti-scraping shields. Residential IP networks are resilient. Grass showed the playbook: idle bandwidth can pay twice 1️⃣ Web data collection via your node 2️⃣ Renting those nodes to GPU grids for training Data + compute = compounding revenue. Hub ( takes it further: aggregate community bandwidth → build a Residential IP Supernetwork → sell real-time data APIs to enterprises (from Meta to Web2 startups). The value loop: Community miners → Bandwidth pool → Enterprise feeds → Revenue → Rewards. If revenue outpaces incentives, everyone wins. Balancing it won’t be easy, time will tell. For miners: run on the network, earn points (likely $HUB at mainnet). Early participation can matter. Why DePIN? Community-run networks scale faster, are harder to censor, and more resilient than centralized systems. We are long programmable bandwidth thesis. In an agent-driven world, whoever controls the live data feed mints the money. DYOR. Register NOW

Programmable Bandwidth is crypto’s next meta. Gm rent your spare Wi-Fi to AI. AI needs more data. AGI is coming. Are you ready? Bandwidth = how much data your connection moves per second. Residential IP = your home’s street address on the internet, trusted as human traffic that doesn’t get blocked. Together, Bandwidth + Residential IP = clean, high-trust traffic that data buyers and AI teams actually want. The community becomes the network. LLMs eat data. The more and the higher quality, the smarter they get. Owning the data pipeline = owning the power. Proof it’s valuable: big platforms license conversational & user-generated data for serious money. Many 8-figure+ deals are public, many more done under the table. Proprietary datasets are the real edge in this AI world. Why residential IPs matter: datacenter IPs get blocked by anti-scraping shields. Residential IP networks are resilient. Grass showed the playbook: idle bandwidth can pay twice 1️⃣ Web data collection via your node 2️⃣ Renting those nodes to GPU grids for training Data + compute = compounding revenue. Hub ( takes it further: aggregate community bandwidth → build a Residential IP Supernetwork → sell real-time data APIs to enterprises (from Meta to Web2 startups). The value loop: Community miners → Bandwidth pool → Enterprise feeds → Revenue → Rewards. If revenue outpaces incentives, everyone wins. Balancing it won’t be easy, time will tell. For miners: run on the network, earn points (likely $HUB at mainnet). Early participation can matter. Why DePIN? Community-run networks scale faster, are harder to censor, and more resilient than centralized systems. We are long programmable bandwidth thesis. In an agent-driven world, whoever controls the live data feed mints the money. DYOR. Register NOW

Q42

39,768 views • 10 months ago

In the next 2-3 years, the skills you need to build an ETL process will fundamentally differ from anything we've done in the last decade. The EXTRACT and LOAD steps of an ETL pipeline have been solved forever. They are boring, and I suspect they will continue to be boring. But the TRANSFORM step is where the money is at (and where innovation will continue happening!) An ETL pipeline that can use Large Language Models effectively to transform data is a whole new ballgame. I recorded a video to talk about this. And more importantly, I talk about how you can build ETL pipelines to transform UNSTRUCTURED data (the kind of data that's really hard to handle).

In the next 2-3 years, the skills you need to build an ETL process will fundamentally differ from anything we've done in the last decade. The EXTRACT and LOAD steps of an ETL pipeline have been solved forever. They are boring, and I suspect they will continue to be boring. But the TRANSFORM step is where the money is at (and where innovation will continue happening!) An ETL pipeline that can use Large Language Models effectively to transform data is a whole new ballgame. I recorded a video to talk about this. And more importantly, I talk about how you can build ETL pipelines to transform UNSTRUCTURED data (the kind of data that's really hard to handle).

Santiago

56,412 views • 1 year ago

Better data for robot hands! 🤲🏼 Embodied reasoning is the bottleneck now. It’s the data we are missing. Specifically, understanding what the robot's hands are doing throughout a task. Perceptron AI new Egocentric offering tracks both hands through an entire video instead of guessing from sampled frames. That matters because a lot happens between frames. Hand positions shift. Contacts change. The robot adjusts grip. You miss all that if you're sampling. 0.280 semantic end-to-end F1 score on WGO-Bench vs 0.158 from the best pipelines built on Gemini Robotics-ER 1.6. Training a robot to manipulate objects requires understanding every moment of hand-object interaction. If your training data has gaps: missing frames, missed contacts, unclear hand positions, the policy learns from incomplete information. You end up with robots that work in demos but fail in real tasks. Continuous hand tracking gives you the full picture. The policy learns from cleaner, more complete data. That compounds into better real-world performance. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Better data for robot hands! 🤲🏼 Embodied reasoning is the bottleneck now. It’s the data we are missing. Specifically, understanding what the robot's hands are doing throughout a task. Perceptron AI new Egocentric offering tracks both hands through an entire video instead of guessing from sampled frames. That matters because a lot happens between frames. Hand positions shift. Contacts change. The robot adjusts grip. You miss all that if you're sampling. 0.280 semantic end-to-end F1 score on WGO-Bench vs 0.158 from the best pipelines built on Gemini Robotics-ER 1.6. Training a robot to manipulate objects requires understanding every moment of hand-object interaction. If your training data has gaps: missing frames, missed contacts, unclear hand positions, the policy learns from incomplete information. You end up with robots that work in demos but fail in real tasks. Continuous hand tracking gives you the full picture. The policy learns from cleaner, more complete data. That compounds into better real-world performance. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

17,926 views • 15 days ago

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Chris Barber (in SF)

130,324 views • 2 months ago

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,162 views • 8 months ago

Your data is NOT yours—and it’s a global crisis! 🚨 Every 39 seconds, a cyberattack occurs. That’s not just a stat, it’s a ticking time bomb targeting OUR sensitive information. From social media profiles to our bank details, corporations are hoarding, storing, and SELLING our data. These centralized giants have made us a product, leaving us exposed to breaches, hacks, and exploitation. Our digital identity isn’t just at risk, it’s being hijacked. This is the cost of centralized systems: zero transparency, zero ownership, and zero security. As a solution, we introduced Pangea’s self-sovereign identity. 🔒 Powered by next-gen cryptography, it’s your ticket to taking back control. Own. Protect. Liberate. With decentralized technology, your data is YOURS, secure, untouchable, and free from exploitation. This isn’t just about privacy, it’s about POWER. It’s about FREEDOM. The future belongs to those who dare to demand it. Don’t wait to be the next statistic. Reclaim what’s yours. The revolution is decentralized, and it’s already here. 🌐

Your data is NOT yours—and it’s a global crisis! 🚨 Every 39 seconds, a cyberattack occurs. That’s not just a stat, it’s a ticking time bomb targeting OUR sensitive information. From social media profiles to our bank details, corporations are hoarding, storing, and SELLING our data. These centralized giants have made us a product, leaving us exposed to breaches, hacks, and exploitation. Our digital identity isn’t just at risk, it’s being hijacked. This is the cost of centralized systems: zero transparency, zero ownership, and zero security. As a solution, we introduced Pangea’s self-sovereign identity. 🔒 Powered by next-gen cryptography, it’s your ticket to taking back control. Own. Protect. Liberate. With decentralized technology, your data is YOURS, secure, untouchable, and free from exploitation. This isn’t just about privacy, it’s about POWER. It’s about FREEDOM. The future belongs to those who dare to demand it. Don’t wait to be the next statistic. Reclaim what’s yours. The revolution is decentralized, and it’s already here. 🌐

Tonomy

18,135 views • 1 year ago

For the past 3 months, I've been heads down building and extending our multimodal data capture rig for Physical AI training. What you're seeing here is a single synchronized capture from our system — RGB, depth (stereo IR camera) feed; real-time 3D hand reconstruction, and a 23×20 tactile pressure grid from sensor gloves, all aligned and timestamped together. Getting here took a lot of unglamorous iteration. We reworked the pipeline to deliver clean, gap-free depth at scale after identifying early depth frame drops caused by data throughput bottlenecks in our capture box. We built per-channel calibration models that convert raw resistance signals from our flex sensor gloves into accurate, drift-corrected force values — solving the creep behavior that made a steady grip falsely appear to fade over time. Even though we’ve stayed quiet on socials, word of mouth spread — our rigs have already been deployed in data factories, manufacturing lines, and homes across the US, Europe, China, and Southeast Asia by our customers and ourselves. In the next few days, I'll be sharing more about how we're tackling rights-cleared data capture from the hardware level, and our closed-loop data-model flywheel — using model evaluation results to identify gaps and guide what data our customers need. More soon, stay tuned…

For the past 3 months, I've been heads down building and extending our multimodal data capture rig for Physical AI training. What you're seeing here is a single synchronized capture from our system — RGB, depth (stereo IR camera) feed; real-time 3D hand reconstruction, and a 23×20 tactile pressure grid from sensor gloves, all aligned and timestamped together. Getting here took a lot of unglamorous iteration. We reworked the pipeline to deliver clean, gap-free depth at scale after identifying early depth frame drops caused by data throughput bottlenecks in our capture box. We built per-channel calibration models that convert raw resistance signals from our flex sensor gloves into accurate, drift-corrected force values — solving the creep behavior that made a steady grip falsely appear to fade over time. Even though we’ve stayed quiet on socials, word of mouth spread — our rigs have already been deployed in data factories, manufacturing lines, and homes across the US, Europe, China, and Southeast Asia by our customers and ourselves. In the next few days, I'll be sharing more about how we're tackling rights-cleared data capture from the hardware level, and our closed-loop data-model flywheel — using model evaluation results to identify gaps and guide what data our customers need. More soon, stay tuned…

Grace Zhang

52,665 views • 1 month ago

In my experience, robot 'generalists' are often jacks of all trades but masters of none. In training across multiple tasks and environments, robot policies fail to generalize robustly and effectively to each particular test setting. What if at test time, we non-parametrically *retrieved* “relevant” data from the training set and used it to significantly improve the performance of few-shot imitation learning to be robust to various test time scenes. Notably, we are *not* collecting lots of new data, just training more on sub-components of the same training data! Now, we’re certainly not the first to suggest retrieval, but in our new work - STRAP, we show how retrieving relevant *sub-trajectories* from offline datasets can significantly increase data reuse across tasks, when paired with an appropriate metric space. A 🧵 (1/7)

In my experience, robot 'generalists' are often jacks of all trades but masters of none. In training across multiple tasks and environments, robot policies fail to generalize robustly and effectively to each particular test setting. What if at test time, we non-parametrically retrieved “relevant” data from the training set and used it to significantly improve the performance of few-shot imitation learning to be robust to various test time scenes. Notably, we are not collecting lots of new data, just training more on sub-components of the same training data! Now, we’re certainly not the first to suggest retrieval, but in our new work - STRAP, we show how retrieving relevant sub-trajectories from offline datasets can significantly increase data reuse across tasks, when paired with an appropriate metric space. A 🧵 (1/7)

Abhishek Gupta

12,045 views • 1 year ago

Telecom companies offer Recharge Plans with ‘𝐃𝐚𝐢𝐥𝐲 𝐃𝐚𝐭𝐚 𝐋𝐢𝐦𝐢𝐭𝐬’ like 1.5GB, 2GB or 3GB per day, resetting every 24 hours. Any Unused Data EXPIRES at midnight, despite being fully paid for. 𝐘𝐨𝐮 𝐚𝐫𝐞 𝐛𝐢𝐥𝐥𝐞𝐝 𝐟𝐨𝐫 𝟐𝐆𝐁. 𝐘𝐨𝐮 𝐮𝐬𝐞 𝟏.𝟓𝐆𝐁. 𝐓𝐡𝐞 𝐫𝐞𝐦𝐚𝐢𝐧𝐢𝐧𝐠 𝟎.𝟓𝐆𝐁 𝐝𝐢𝐬𝐚𝐩𝐩𝐞𝐚𝐫𝐬 𝐚𝐬 𝐝𝐚𝐲 𝐞𝐧𝐝𝐬. No refund. No rollover. Just gone. This is not an accident. This is policy. Use it unnecessarily, or lose it by midnight. That’s how mobile data works today. I raised this issue in Parliament - 𝐖𝐡𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐝𝐚𝐭𝐚 𝐭𝐡𝐚𝐭 𝐰𝐞 𝐡𝐚𝐯𝐞 𝐩𝐚𝐢𝐝 𝐟𝐨𝐫 𝐛𝐞 𝐅𝐎𝐑𝐅𝐄𝐈𝐓𝐄𝐃? UNUSED DATA should carry forward into the next cycle, so consumers can use what they have already paid for. My demands are clear: 𝟏. 𝐀𝐥𝐥𝐨𝐰 𝐃𝐚𝐭𝐚 𝐜𝐚𝐫𝐫𝐲-𝐟𝐨𝐫𝐰𝐚𝐫𝐝/ 𝐃𝐚𝐭𝐚 𝐫𝐨𝐥𝐥𝐨𝐯𝐞𝐫 𝐟𝐨𝐫 𝐚𝐥𝐥 𝐮𝐬𝐞𝐫𝐬 All telecom operators should provide Rollover of Unused Data. What remains unused at the end of the day, should be added to the next day’s Daily Data Limit, not erased the moment validity ends. 𝟐. 𝐆𝐢𝐯𝐞 𝐨𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐨𝐟 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐧𝐞𝐱𝐭 𝐦𝐨𝐧𝐭𝐡’𝐬 𝐑𝐞𝐜𝐡𝐚𝐫𝐠𝐞 𝐀𝐦𝐨𝐮𝐧𝐭𝐬 If a consumer consistently under-utilises their data over multiple cycles, there should be a mechanism for Adjustment or Discount of that value, from the following month’s Recharge Amount. Consumers should not repeatedly pay for capacity they do not use. 𝟑. 𝐀𝐥𝐥𝐨𝐰 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐨𝐟 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐫𝐞𝐥𝐚𝐭𝐢𝐯𝐞𝐬 & 𝐟𝐫𝐢𝐞𝐧𝐝𝐬 Unused data should be treated as the consumer’s digital property. Users should be allowed to transfer their unused data to others, from their Daily Data Limit, just as transfer money to others. As we build a Digital India, access cannot depend on data that disappears. If you’ve paid for it, it should carry forward and remain yours to use.

Telecom companies offer Recharge Plans with ‘𝐃𝐚𝐢𝐥𝐲 𝐃𝐚𝐭𝐚 𝐋𝐢𝐦𝐢𝐭𝐬’ like 1.5GB, 2GB or 3GB per day, resetting every 24 hours. Any Unused Data EXPIRES at midnight, despite being fully paid for. 𝐘𝐨𝐮 𝐚𝐫𝐞 𝐛𝐢𝐥𝐥𝐞𝐝 𝐟𝐨𝐫 𝟐𝐆𝐁. 𝐘𝐨𝐮 𝐮𝐬𝐞 𝟏.𝟓𝐆𝐁. 𝐓𝐡𝐞 𝐫𝐞𝐦𝐚𝐢𝐧𝐢𝐧𝐠 𝟎.𝟓𝐆𝐁 𝐝𝐢𝐬𝐚𝐩𝐩𝐞𝐚𝐫𝐬 𝐚𝐬 𝐝𝐚𝐲 𝐞𝐧𝐝𝐬. No refund. No rollover. Just gone. This is not an accident. This is policy. Use it unnecessarily, or lose it by midnight. That’s how mobile data works today. I raised this issue in Parliament - 𝐖𝐡𝐲 𝐬𝐡𝐨𝐮𝐥𝐝 𝐝𝐚𝐭𝐚 𝐭𝐡𝐚𝐭 𝐰𝐞 𝐡𝐚𝐯𝐞 𝐩𝐚𝐢𝐝 𝐟𝐨𝐫 𝐛𝐞 𝐅𝐎𝐑𝐅𝐄𝐈𝐓𝐄𝐃? UNUSED DATA should carry forward into the next cycle, so consumers can use what they have already paid for. My demands are clear: 𝟏. 𝐀𝐥𝐥𝐨𝐰 𝐃𝐚𝐭𝐚 𝐜𝐚𝐫𝐫𝐲-𝐟𝐨𝐫𝐰𝐚𝐫𝐝/ 𝐃𝐚𝐭𝐚 𝐫𝐨𝐥𝐥𝐨𝐯𝐞𝐫 𝐟𝐨𝐫 𝐚𝐥𝐥 𝐮𝐬𝐞𝐫𝐬 All telecom operators should provide Rollover of Unused Data. What remains unused at the end of the day, should be added to the next day’s Daily Data Limit, not erased the moment validity ends. 𝟐. 𝐆𝐢𝐯𝐞 𝐨𝐩𝐭𝐢𝐨𝐧 𝐨𝐟 𝐀𝐝𝐣𝐮𝐬𝐭𝐦𝐞𝐧𝐭 𝐨𝐟 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐧𝐞𝐱𝐭 𝐦𝐨𝐧𝐭𝐡’𝐬 𝐑𝐞𝐜𝐡𝐚𝐫𝐠𝐞 𝐀𝐦𝐨𝐮𝐧𝐭𝐬 If a consumer consistently under-utilises their data over multiple cycles, there should be a mechanism for Adjustment or Discount of that value, from the following month’s Recharge Amount. Consumers should not repeatedly pay for capacity they do not use. 𝟑. 𝐀𝐥𝐥𝐨𝐰 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐨𝐟 𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐫𝐞𝐥𝐚𝐭𝐢𝐯𝐞𝐬 & 𝐟𝐫𝐢𝐞𝐧𝐝𝐬 Unused data should be treated as the consumer’s digital property. Users should be allowed to transfer their unused data to others, from their Daily Data Limit, just as transfer money to others. As we build a Digital India, access cannot depend on data that disappears. If you’ve paid for it, it should carry forward and remain yours to use.

Raghav Chadha

425,426 views • 4 months ago

There’s a serious issue that deserves transparency and accountability: Historical climate records exist, and they should be consistent, traceable, and publicly verifiable. When adjustments are made, the public deserves clear explanations, not blind trust. Right now, many people are questioning whether data is being altered in ways that exaggerate trends to justify extreme policies. That includes concerns about geoengineering proposals and the potential health and environmental risks tied to atmospheric spraying. This isn’t about denying science; it’s about demanding honesty in it. If decisions are being made that impact our air, our health, and our future, then the data behind those decisions must be open, accurate, and beyond manipulation. We’re asking questions. And we’re not going to stop until we get real answers. Follow•Like•Share Nicole Pearson, Esq. The GeoFight Babooschka

There’s a serious issue that deserves transparency and accountability: Historical climate records exist, and they should be consistent, traceable, and publicly verifiable. When adjustments are made, the public deserves clear explanations, not blind trust. Right now, many people are questioning whether data is being altered in ways that exaggerate trends to justify extreme policies. That includes concerns about geoengineering proposals and the potential health and environmental risks tied to atmospheric spraying. This isn’t about denying science; it’s about demanding honesty in it. If decisions are being made that impact our air, our health, and our future, then the data behind those decisions must be open, accurate, and beyond manipulation. We’re asking questions. And we’re not going to stop until we get real answers. Follow•Like•Share Nicole Pearson, Esq. The GeoFight Babooschka

Tom Renz

15,408 views • 3 months ago

🦔Workers in India are wearing head-mounted cameras for 12 cents an hour to collect training data for humanoid robots. The footage of them doing everyday tasks like cooking, cleaning, sorting, and walking through public spaces gets sold to robotics companies building the models meant to replace those same kinds of jobs in higher-wage countries. The arrangement has been running for roughly two years. Workers do not own the data, do not get residuals, and in many cases are not told what their footage is being used to train. My Take The workers wearing the cameras live in a country where robotics automation will hit decades later, so they are training their own future replacements at a delay that hides the consequence from them personally. The companies buying the data are mostly US and Chinese, building humanoid robots aimed at warehouses, retail, and service jobs in countries paying $15 to $25 an hour rather than 12 cents. Robotics companies need motion data that mimics how humans actually move through real environments, and synthetic data has not been good enough yet. Paying 12 cents an hour in Bengaluru is cheaper than running motion capture studios in Boston, and it works at scale because the worker absorbs the cost of the camera, the discomfort of wearing it, and the long-term loss of any rights to their own movement data. The robotics labor market that eventually emerges from this footage will displace far more wages than the data collection cost to gather. That is the trade investors funding humanoid robotics startups are betting will pay off, and the workers in the videos are the ones paying the tab up front. Hedgie🤗

🦔Workers in India are wearing head-mounted cameras for 12 cents an hour to collect training data for humanoid robots. The footage of them doing everyday tasks like cooking, cleaning, sorting, and walking through public spaces gets sold to robotics companies building the models meant to replace those same kinds of jobs in higher-wage countries. The arrangement has been running for roughly two years. Workers do not own the data, do not get residuals, and in many cases are not told what their footage is being used to train. My Take The workers wearing the cameras live in a country where robotics automation will hit decades later, so they are training their own future replacements at a delay that hides the consequence from them personally. The companies buying the data are mostly US and Chinese, building humanoid robots aimed at warehouses, retail, and service jobs in countries paying $15 to $25 an hour rather than 12 cents. Robotics companies need motion data that mimics how humans actually move through real environments, and synthetic data has not been good enough yet. Paying 12 cents an hour in Bengaluru is cheaper than running motion capture studios in Boston, and it works at scale because the worker absorbs the cost of the camera, the discomfort of wearing it, and the long-term loss of any rights to their own movement data. The robotics labor market that eventually emerges from this footage will displace far more wages than the data collection cost to gather. That is the trade investors funding humanoid robotics startups are betting will pay off, and the workers in the videos are the ones paying the tab up front. Hedgie🤗

Hedgie

161,757 views • 2 months ago

Lake Powell is currently at critically low levels You see the lines on the cliffs where the water level used to be? Right now It’s only at about 23% of full capacity 24 large hyperscale data centers and 379 smaller data centers are located across the 7 Colorado River Basin states The Colorado River is over allocated. Cities, farms, especially alfalfa and industry in 7 states pull more water than the river reliably supplies The Upper Basin states are using the water before it reaches Lake Powell To make things even worse, A high-profile proposed $10 billion data center is being planned right next to Lake Powell Developers claim they’ll use closed-loop systems and secure their own supply (yeah right)

Wall Street Apes

94,504 views • 2 months ago