Загрузка видео...

Не удалось загрузить видео

На главную

Real world data is the new meta. On Poseidon: Contributors upload specialized data → Story attaches programmable IP licenses → buyers mint usage rights → royalties flow onchain. The result: clean, high-signal data tracks built for LLMs. jacob goes deep ↴

StoryProtocol's profile picture

Story

646,358 subscribers

17,722 просмотров • 1 год назад •via X (Twitter)

Наука и технологииОбразованиеФинансы
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

AI is moving beyond the browser and into the real world. The bottleneck? Data. Today we’re announcing a $15M seed round led by a16z crypto to build infra that collects, curates, and licenses high-quality data for physical AI. Incubated by and built on @StoryProtocol.
0:37

AI is moving beyond the browser and into the real world. The bottleneck? Data. Today we’re announcing a $15M seed round led by a16z crypto to build infra that collects, curates, and licenses high-quality data for physical AI. Incubated by and built on @StoryProtocol.

Poseidon

292,510 просмотров • 1 год назад

Why we built a new Kafka client for Node.js The Node.js world needs better tools. Apache Kafka is vital for real-time data. It powers many businesses, especially in Fintech and Media. These fields see heavy data usage and need reliable solutions.
1:45

Why we built a new Kafka client for Node.js The Node.js world needs better tools. Apache Kafka is vital for real-time data. It powers many businesses, especially in Fintech and Media. These fields see heavy data usage and need reliable solutions.

Matteo Collina

31,039 просмотров • 1 год назад

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
0:24

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 просмотров • 11 месяцев назад

Our new teaser is live. One minute to reveal what Kirha really is: the private data layer powering the next generation of LLMs and agents. Fresh, verified, real world data on demand. Deterministic. Reliable. Built for scale. This is only the beginning. Our roadmap is packed with new verticals and integrations. Give your agents the context they deserve ⚡️
1:08

Our new teaser is live. One minute to reveal what Kirha really is: the private data layer powering the next generation of LLMs and agents. Fresh, verified, real world data on demand. Deterministic. Reliable. Built for scale. This is only the beginning. Our roadmap is packed with new verticals and integrations. Give your agents the context they deserve ⚡️

Kirha

303,437 просмотров • 9 месяцев назад

The 375ai Mainnet is live! 375ai indexes the real world, transforming video, audio and environmental data into real time intelligence using edge AI. A new data layer for the physical world, now on @Solana
0:55

The 375ai Mainnet is live! 375ai indexes the real world, transforming video, audio and environmental data into real time intelligence using edge AI. A new data layer for the physical world, now on @Solana

375ai

227,641 просмотров • 9 месяцев назад

Highlights of the OpenLedger journey: OpenLedger is the AI Blockchain unlocking liquidity for data, models, and agents, backed by Polychain, Balaji Srinivasan (ex-Coinbase CTO), and more. $14.7M+ in revenue from global enterprise projects and $91M+ in investment commitments secured. Worked with global enterprises like Walmart, Sony, Meta LLaMA, Viacom, and others. Built on Stanford’s data attribution research paper, enabling verifiable, fair rewards for contributors. 40+ projects, including ex-Google DeepMind teams and top DeFi builders, are building specialized AI models. 1M+ AI nodes, 25M+ transactions, and 20K+ models, real-world usage at scale.
0:58

Highlights of the OpenLedger journey: OpenLedger is the AI Blockchain unlocking liquidity for data, models, and agents, backed by Polychain, Balaji Srinivasan (ex-Coinbase CTO), and more. $14.7M+ in revenue from global enterprise projects and $91M+ in investment commitments secured. Worked with global enterprises like Walmart, Sony, Meta LLaMA, Viacom, and others. Built on Stanford’s data attribution research paper, enabling verifiable, fair rewards for contributors. 40+ projects, including ex-Google DeepMind teams and top DeFi builders, are building specialized AI models. 1M+ AI nodes, 25M+ transactions, and 20K+ models, real-world usage at scale.

OpenLedger

738,514 просмотров • 11 месяцев назад

As robotics teams scale, collecting real-world data can quickly become slow and expensive. 📉 Lightwheel is changing that with a simulation-first platform built on our technology, helping customers generate a 100:1 simulated-to-real data ratio for training autonomous systems. Together, we’re turning synthetic and real data into more capable, more reliable robots. Read the full success story 🔗
0:47

As robotics teams scale, collecting real-world data can quickly become slow and expensive. 📉 Lightwheel is changing that with a simulation-first platform built on our technology, helping customers generate a 100:1 simulated-to-real data ratio for training autonomous systems. Together, we’re turning synthetic and real data into more capable, more reliable robots. Read the full success story 🔗

NVIDIA Robotics

10,840 просмотров • 4 месяцев назад

VAAP Highlights: Devium, built on zkPass. Devium enables private order data + AI by storing users' order data on Devium's decentralized network nodes. It categorizes the data into vertical sectors and trains specialized AI large models for these vertical domains. These large models support enterprise operations, product development, and upgrades within their respective industries. By replacing data hosting with verifiable proofs, Devium reduces platform risks and minimizes user friction. Devium empowers teams to build real-world applications on verifiable infrastructure such as zkTLS.
0:40

VAAP Highlights: Devium, built on zkPass. Devium enables private order data + AI by storing users' order data on Devium's decentralized network nodes. It categorizes the data into vertical sectors and trains specialized AI large models for these vertical domains. These large models support enterprise operations, product development, and upgrades within their respective industries. By replacing data hosting with verifiable proofs, Devium reduces platform risks and minimizes user friction. Devium empowers teams to build real-world applications on verifiable infrastructure such as zkTLS.

zkPass

10,856 просмотров • 6 месяцев назад

Built AI Launch Radar this week. One thing I underestimated was how hard it is to get reliable, structured web data for monitoring product launches and market sentiment. After trying different approaches, I ended up using Nimble's APIs to power the data layer, which saved me a lot of time I'd otherwise spend on scraping and data cleanup. The result is a dashboard that tracks launches, sentiment, and emerging products in real time. Try Nimble now: Here's a quick look 👇
0:58

Built AI Launch Radar this week. One thing I underestimated was how hard it is to get reliable, structured web data for monitoring product launches and market sentiment. After trying different approaches, I ended up using Nimble's APIs to power the data layer, which saved me a lot of time I'd otherwise spend on scraping and data cleanup. The result is a dashboard that tracks launches, sentiment, and emerging products in real time. Try Nimble now: Here's a quick look 👇

Nayeem Sheikh

12,349 просмотров • 1 месяц назад

Leju Robotics has launched a massive 10,000-square-meter data training center in Beijing, aimed at solving the industry’s data shortage and bridging the "reality gap" for AI models. The facility features 1:1 replicas of 16 real-world scenarios across industrial, home, and healthcare sectors. ► Scale: Over 10,000 sq. meters, featuring 16 detailed real-world scenarios. ► Capacity: Annual production capacity of over 6 million high-quality data entries. ► Training: The Kuavo humanoid robot is already training on-site, achieving a 95%+ success rate on practical tasks like sorting, packing, and inspection. ► Quality: Data sourced from real machine runs, certified at a 99% single-data qualification rate.
1:41

Leju Robotics has launched a massive 10,000-square-meter data training center in Beijing, aimed at solving the industry’s data shortage and bridging the "reality gap" for AI models. The facility features 1:1 replicas of 16 real-world scenarios across industrial, home, and healthcare sectors. ► Scale: Over 10,000 sq. meters, featuring 16 detailed real-world scenarios. ► Capacity: Annual production capacity of over 6 million high-quality data entries. ► Training: The Kuavo humanoid robot is already training on-site, achieving a 95%+ success rate on practical tasks like sorting, packing, and inspection. ► Quality: Data sourced from real machine runs, certified at a 99% single-data qualification rate.

RoboHub🤖

43,066 просмотров • 10 месяцев назад

What is the best data for training humanoid & robotics foundation models? Pete Florence Pete Florence (CEO Eric Nehrlich, ex-Google DeepMind) dropped his live data tier list in this 7-minute clip on TBPN: - S-tier: Real-world robot experience (especially glove/sensor high-dexterity data) - A/B-tier: Internet/YouTube videos. Surprisingly powerful for transfer learning (the “web data” moment for physical AI) - B-tier: Text/common crawl (Reddit, books, etc.). Useful priors, but not enough alone - C-tier: Motion Capture. Great for whole-body motion, weak on finger dexterity - C or lower: Simulation / synthetic / world models. High potential, still waiting for strong real-world proof Generalist has collected 270,000+ hours of real-world manipulation data (scaling ~10k hours/week). And Pete stressed one key point: “The quality of data is incredibly important.” It’s not just about volume. It’s developing intuition for what actually drives performance through hands-on work. As Physical AI scales, curated real-world and high-quality internet video looks like a winning combo. h/t YUJI | AI Robotics
7:06

What is the best data for training humanoid & robotics foundation models? Pete Florence Pete Florence (CEO Eric Nehrlich, ex-Google DeepMind) dropped his live data tier list in this 7-minute clip on TBPN: - S-tier: Real-world robot experience (especially glove/sensor high-dexterity data) - A/B-tier: Internet/YouTube videos. Surprisingly powerful for transfer learning (the “web data” moment for physical AI) - B-tier: Text/common crawl (Reddit, books, etc.). Useful priors, but not enough alone - C-tier: Motion Capture. Great for whole-body motion, weak on finger dexterity - C or lower: Simulation / synthetic / world models. High potential, still waiting for strong real-world proof Generalist has collected 270,000+ hours of real-world manipulation data (scaling ~10k hours/week). And Pete stressed one key point: “The quality of data is incredibly important.” It’s not just about volume. It’s developing intuition for what actually drives performance through hands-on work. As Physical AI scales, curated real-world and high-quality internet video looks like a winning combo. h/t YUJI | AI Robotics

Michelle Sun

15,351 просмотров • 2 месяцев назад

our next chapter is here. it’s called Real-World AI. Larry Pang shared at our flagship event in singapore how IoTeX will power a new generation of AI built on verified real-world data. watch the full keynote below.
16:01

our next chapter is here. it’s called Real-World AI. Larry Pang shared at our flagship event in singapore how IoTeX will power a new generation of AI built on verified real-world data. watch the full keynote below.

IoTeX

61,620 просмотров • 10 месяцев назад

The first world-class clean AI video model is here. Marey by Moonvalley—built for filmmakers, trained exclusively on licensed data. Brought to you by the leading minds in AI and film. The future starts now. Join the waitlist:
1:03

The first world-class clean AI video model is here. Marey by Moonvalley—built for filmmakers, trained exclusively on licensed data. Brought to you by the leading minds in AI and film. The future starts now. Join the waitlist:

Moonvalley

73,478 просмотров • 1 год назад

“The narrower the top of the funnel, the more efficient everything downstream becomes." METANOVA $2.6B per drug on average, and most models built on theoretical rather than real-world data SN68 Nova is breaking the mould as a decentralized high‑speed race for drug research breakthroughs Full episode on YouTube: #subnet68 #SN68
2:23

“The narrower the top of the funnel, the more efficient everything downstream becomes." METANOVA $2.6B per drug on average, and most models built on theoretical rather than real-world data SN68 Nova is breaking the mould as a decentralized high‑speed race for drug research breakthroughs Full episode on YouTube: #subnet68 #SN68

Openτensor Foundaτion

28,475 просмотров • 1 год назад

GDP. Onchain. Officially 🇺🇸 The U.S. Commerce Department is moving to publish GDP stats directly to a public blockchain This isn't about crypto adoption. This is the federal government using public ledgers for data integrity That changes everything ⤵️ GDP is the most-watched macro metric in the world It informs: → Policy decisions → Rate moves → Capital flows → Institutional macro strategy Every revision shifts billions And now it’s getting a verifiable onchain timestamp Commerce Secretary Lutnick confirmed it Official GDP figures will be pushed onchain by the U.S. government First time a major federal agency is treating a public blockchain as a trust layer for national data That precedent will cascade What this unlocks: ➤ Time-stamped economic releases ➤ Immutable macro signals ➤ Trustless rails for modeling risk, yield, credit The data is no longer just public It’s now programmable The implications for financial tooling are massive You could build: ✔︎ RWAs keyed to GDP triggers ✔︎ Onchain yield curves linked to macro signals ✔︎ Predictive markets on economic prints All syncing in real time This opens up a new category of composable macro data And more importantly, it spotlights the infrastructure stack Because the oracles, chains, and protocols that secure this data? They won’t just distribute it They’ll route the capital that follows The rails are already being laid Watch what gets integrated Because whoever secures this data flow Wins the next layer of macro-fintech capital
0:25

GDP. Onchain. Officially 🇺🇸 The U.S. Commerce Department is moving to publish GDP stats directly to a public blockchain This isn't about crypto adoption. This is the federal government using public ledgers for data integrity That changes everything ⤵️ GDP is the most-watched macro metric in the world It informs: → Policy decisions → Rate moves → Capital flows → Institutional macro strategy Every revision shifts billions And now it’s getting a verifiable onchain timestamp Commerce Secretary Lutnick confirmed it Official GDP figures will be pushed onchain by the U.S. government First time a major federal agency is treating a public blockchain as a trust layer for national data That precedent will cascade What this unlocks: ➤ Time-stamped economic releases ➤ Immutable macro signals ➤ Trustless rails for modeling risk, yield, credit The data is no longer just public It’s now programmable The implications for financial tooling are massive You could build: ✔︎ RWAs keyed to GDP triggers ✔︎ Onchain yield curves linked to macro signals ✔︎ Predictive markets on economic prints All syncing in real time This opens up a new category of composable macro data And more importantly, it spotlights the infrastructure stack Because the oracles, chains, and protocols that secure this data? They won’t just distribute it They’ll route the capital that follows The rails are already being laid Watch what gets integrated Because whoever secures this data flow Wins the next layer of macro-fintech capital

eye zen hour 🥶

36,020 просмотров • 11 месяцев назад

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!
2:24

Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

85,012 просмотров • 1 год назад

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
29:51

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 просмотров • 2 месяцев назад

The DATA Network has just received clearance to onboard audit receipts for over 500,000 hours of video content spanning 30,000 pieces of entertainment, including Bugs Bunny, Woody Woodpecker, Tennessee Tuxedo, Tom & Jerry, The Pink Panther, Popeye, The Twilight Zone, Garfield, and thousands of other classics. Kled is onboarding this content to Trace so its ownership, provenance, licensing rights, and usage history can be independently verified before it is sold to AI labs. This is the future of the AI data market. The world’s most valuable IP will increasingly require permanent, transparent audit trails before it can be licensed for AI training, and those records will live on the DATA Network.
0:20

The DATA Network has just received clearance to onboard audit receipts for over 500,000 hours of video content spanning 30,000 pieces of entertainment, including Bugs Bunny, Woody Woodpecker, Tennessee Tuxedo, Tom & Jerry, The Pink Panther, Popeye, The Twilight Zone, Garfield, and thousands of other classics. Kled is onboarding this content to Trace so its ownership, provenance, licensing rights, and usage history can be independently verified before it is sold to AI labs. This is the future of the AI data market. The world’s most valuable IP will increasingly require permanent, transparent audit trails before it can be licensed for AI training, and those records will live on the DATA Network.

The DATA Foundation

49,957 просмотров • 14 дней назад

Some news: I'm launching a new show called FULL SIGNAL. We built this show for independent investors who are optimistic about where the world is heading, and the role markets and data play in explaining it. First episode drops Wednesday. Time to get to work.
0:46

Some news: I'm launching a new show called FULL SIGNAL. We built this show for independent investors who are optimistic about where the world is heading, and the role markets and data play in explaining it. First episode drops Wednesday. Time to get to work.

Phil Rosen

13,428 просмотров • 9 месяцев назад