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Grass is a decentralized web scraping protocol that rewards users for sharing internet bandwidth — data collected is used to train AI models. On Feb. 14 touch grass hit an ATH in daily data collected – with 1M+ GB scraped 🤯 more on Grass from with MR SHIFT 🦁

Solana

4,127,960 subscribers

26,447 views • 1 year ago •via X (Twitter)

Science & Technology

Anya Rossi• Live Now

Private livecam show

18 Comments

Solana1 year ago

Community owned networks are the future. 13 DePIN products you can use on Solana today 🧵

Solana1 year ago

NATIX (@NatixNetwork) is a global mapping network offering two major products: 1️⃣ VX360 — turning Tesla dashcams into data goldmines for safer autonomous driving 2️⃣ Drive&app — where your phone gathers mapping data from daily drives — live on the Apple and Google app stores

Solana1 year ago

.@Gradient_HQ utilizes idle compute from edge devices, PCs, and local servers to perform real time processing tasks. Their first product — Sentry Nodes — use p2p connections to confirm liveness and measure latency between nodes. There are 1.3M active nodes as of Feb. 26th.

Solana1 year ago

Hivemapper is taking on big tech mapping incumbents with a community-built map — everyday drivers contribute to the network and earn with the @Hivemapper Bee dashcam. They recently started serving data to TomTom — #1 real time traffic services provider — to improve their maps.

Solana1 year ago

Dabba (@DabbaNetwork) is a wireless DePIN focused on expanding internet access in India — the 2nd largest telecom market in the world Set up your own hotspot to contribute to the network and earn — @dawnjohnphilip shows you how in just 2 mins

Solana1 year ago

Roam (@WeRoamXyz) unifies over 1M WiFi hotspots into a secure global network on Solana by rewarding users for contributing. It uses decentralized identifiers (DIDs) and verifiable credentials to keep users’ data private. Download on the app store!

Solana1 year ago

DeCharge (@DeCharge__) is a global EV charging network built on Solana ⚡ Their first product, DeCharge Beast, is a 7KW community charger powered by @dephynetwork that generates income for you as electric vehicles utilize it.

Solana1 year ago

Dawn is a decentralized broadband network built by the @andrena_wifi team. @dawninternet aims to shift internet infra into individual homes and slash consumer internet bills by around 80%. Watch @CipherResearchx explain in 3 mins.

Solana1 year ago

Kuzco (@Kuzco_xyz) transforms idle local GPU power into affordable cloud computing. Compute is then offered for major LLM models like Meta’s Llama with up to 90% cost savings vs. traditional providers. Start contributing at inference(dot)supply

Solana1 year ago

.@GEODNET_ is a network of satellite miners delivering RTK correction data for ~100x more accurate GPS positioning. Low-power stations (<2W) support robots & devices within 20-40km, correcting errors from satellite systems and space weather. 2024 recap video:

Solana1 year ago

.@AmbiosNetwork an environmental monitoring DePIN where individuals deploy sensors to collect real-time air quality data, earn rewards, and help orgs make informed environmental decisions. More from cofounder @luckyluca with @gizmothegizzer:

Solana1 year ago

Pipe (@pipenetwork) is a decentralized content delivery network (dCDN) that accelerates delivery by leveraging users’ excess storage & bandwidth. More nodes = faster delivery. More from @DavidRhodus at Breakpoint '24

Solana1 year ago

Get fit and earn while doing it — CUDIS (@CudisWellness) wearables track your health journey with metrics like sleep quality, heart rate and VO2 max. They recently released the CUDIS 002 Ring.

Solana1 year ago

These are just a few examples of DePINs powered by Solana -- nCMOs, please drop more that we missed below 🫡

CrispyBull1 year ago

🚀 Ethereum is scaling up! With the gas limit raised beyond 30M and the Pectra update doubling Layer 2 capacity, ETH is gearing up for faster, cheaper transactions. Are we entering a new era for Ethereum? 🔥 #Ethereum #Crypto #Blockchain #ETHUpdate#DeFi

AJ1 year ago

@getgrass_io @Arthur_0x @KevinWSHPod Grass is the biggest of all DePINs With the largest payout!

Connan1 year ago

@getgrass_io @Arthur_0x @KevinWSHPod Touch grass

Mhayo1 year ago

@getgrass_io @Arthur_0x @KevinWSHPod AI + blockchain = future. $WAM knows this, now Grass knows it too

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In the second episode of Scenius Studio's mini-series "The Use-Case", I sit down with Andrej Co-Founder of touch grass. Grass gives users the ability to earn ownership in the Grass network by supplying the protocol with their unused internet bandwidth for data scraping purposes (something that is already happening to most of us and we don’t get paid!). The grass protocol packages this scraped web data and sells it to AI companies who have insufficient data to further develop their models. With over 3 millions users and millions of annualized revenue, Grass is a real commercial business with a roadmap that makes it one of the most exciting projects at the intersection of crypto x AI and data. In this episode we discuss: ➔ Andrej’s background in physics, finance, and sports betting ➔ Big companies using your IP address without your knowledge or permission ➔ How the Grass protocol puts a toll booth on your internet bandwidth highway ➔ Packaging web scraped data and selling it to AI companies building Multi-Modal models ➔ Dynamics between the Grass Protocol and the labs entity developing Grass’ IP ➔ Protocol design decisions to ensure that all tokenholders (VCs, team, and community) are aligned ➔ Why Grass needed to be built on crypto rails to maximize its potential ➔ The future of LLMs and how they will search for context and information Hope you enjoy this episode of Scenius Studio's "The Use-Case". Links to listen in bio or below👇

In the second episode of Scenius Studio's mini-series "The Use-Case", I sit down with Andrej Co-Founder of touch grass. Grass gives users the ability to earn ownership in the Grass network by supplying the protocol with their unused internet bandwidth for data scraping purposes (something that is already happening to most of us and we don’t get paid!). The grass protocol packages this scraped web data and sells it to AI companies who have insufficient data to further develop their models. With over 3 millions users and millions of annualized revenue, Grass is a real commercial business with a roadmap that makes it one of the most exciting projects at the intersection of crypto x AI and data. In this episode we discuss: ➔ Andrej’s background in physics, finance, and sports betting ➔ Big companies using your IP address without your knowledge or permission ➔ How the Grass protocol puts a toll booth on your internet bandwidth highway ➔ Packaging web scraped data and selling it to AI companies building Multi-Modal models ➔ Dynamics between the Grass Protocol and the labs entity developing Grass’ IP ➔ Protocol design decisions to ensure that all tokenholders (VCs, team, and community) are aligned ➔ Why Grass needed to be built on crypto rails to maximize its potential ➔ The future of LLMs and how they will search for context and information Hope you enjoy this episode of Scenius Studio's "The Use-Case". Links to listen in bio or below👇

Ben Jacobs

22,468 views • 1 year ago

Data is the foundation of any AI training. All of the GPUs in the world can't train an AI model if they don't have data to train it on. Don't forget: Grass is the data layer of AI.

Data is the foundation of any AI training. All of the GPUs in the world can't train an AI model if they don't have data to train it on. Don't forget: Grass is the data layer of AI.

Grass

211,994 views • 2 years ago

When AI gives bad advice, it can have real consequences. Grass is improving the way AI interacts with information by providing tools that enable access to accurate, verifiable web data. Join millions of users and download Grass today.

When AI gives bad advice, it can have real consequences. Grass is improving the way AI interacts with information by providing tools that enable access to accurate, verifiable web data. Join millions of users and download Grass today.

Grass

667,652 views • 1 year ago

Will touch grass become the data layer for AI? In this weeks podcast I sit down with the founder Andrej as we speak to the AI boom and necessary ingredients needed to power it: compute and data AI x Crypto x Data (Grass) We speak to: - 600,000 users in only seven months - Expanding into data cleaning and preparation - What are the current issues in the AI space right now, and how is Grass addressing these issues Youtube: Spotify:

Will touch grass become the data layer for AI? In this weeks podcast I sit down with the founder Andrej as we speak to the AI boom and necessary ingredients needed to power it: compute and data AI x Crypto x Data (Grass) We speak to: - 600,000 users in only seven months - Expanding into data cleaning and preparation - What are the current issues in the AI space right now, and how is Grass addressing these issues Youtube: Spotify:

Logan Jastremski

136,483 views • 2 years ago

There have been some questions about what LCR is and how it’s used in AI models. In this clip, Andrej breaks down how LLMs leverage LCR to pull real-time, up-to-date data from the web. To learn more, check out his tweet on how LCR will change AI forever.

There have been some questions about what LCR is and how it’s used in AI models. In this clip, Andrej breaks down how LLMs leverage LCR to pull real-time, up-to-date data from the web. To learn more, check out his tweet on how LCR will change AI forever.

Grass

269,180 views • 2 years ago

Data ownership & privacy are key to building an internet that benefits & protects its end-users AI is no different — Users’ data must be protected while interacting with AI Oasis' privacy-preserving smart contracts enable users to contribute data to AI models on their own terms

Data ownership & privacy are key to building an internet that benefits & protects its end-users AI is no different — Users’ data must be protected while interacting with AI Oasis' privacy-preserving smart contracts enable users to contribute data to AI models on their own terms

Oasis Labs

32,852 views • 3 years ago

Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

LayerAI | AI2Earn

27,136 views • 2 years ago

AI runs on data. But if the data is centralized, the power stays locked with a few. OpenLedger flips that. Decentralized data means transparency, attribution, and fair rewards for every contributor🐙

AI runs on data. But if the data is centralized, the power stays locked with a few. OpenLedger flips that. Decentralized data means transparency, attribution, and fair rewards for every contributor🐙

OpenLedger

20,353 views • 11 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 • 1 year ago

Introducing Pond - a decentralized AI model layer with crypto-native AI models trained on on-chain data. Pond is excited to announce a $7.5M Seed round, led by Archetype with , Delphi Ventures, Coinbase Ventures 🛡️, ANAGRAM, and more.

Introducing Pond - a decentralized AI model layer with crypto-native AI models trained on on-chain data. Pond is excited to announce a $7.5M Seed round, led by Archetype with , Delphi Ventures, Coinbase Ventures 🛡️, ANAGRAM, and more.

Pond AI

242,751 views • 1 year ago

Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!

Did you know that Facebook made $134.9 billion in 2023 from selling your data? Our society has come to the point where we accepted an unfair model in which privacy is being exploited and data gifted to big tech that is selling it for billions. We intend to break the norm of how data is shared on the internet because it is outdated and unfair. You should be empowered with more control and monetization models for your data!

Solana ID 🪷

30,458 views • 2 years ago

Pipe (Pipe Network) is a decentralized content delivery network (dCDN) that accelerates delivery by leveraging users’ excess storage & bandwidth. More nodes = faster delivery. More from David Rhodus 🇺🇸 at Breakpoint '24

Pipe (Pipe Network) is a decentralized content delivery network (dCDN) that accelerates delivery by leveraging users’ excess storage & bandwidth. More nodes = faster delivery. More from David Rhodus 🇺🇸 at Breakpoint '24

Solana

78,975 views • 1 year ago

The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.

The infrastructure for working with video data at scale barely exists. Grass Video Search is our first step in changing that. You can now find anything in a video based on the contents of its frames, not just transcripts or tags. Over the last several months, the Grass Foundation has quietly assembled one of the largest video datasets in history: over a billion videos from across the public web, and is segmenting them by scene with VLM annotations of keyframes. This dataset is now searchable, so you can find the exact moment a visual, pattern, or concept appears. Curation has always been the bottleneck in video analysis. We built a system that automates it. This is just the beginning. Over 4 billion additional videos are queued for processing, and the index is growing daily. We’re opening access to a limited set of collaborators. If you’re a researcher, journalist, or nonprofit working with video, archives, or large-scale analysis, we’d love to hear from you.

Grass

471,735 views • 1 year ago

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 views • 1 year ago

You can now scrape any website with AI in two clicks. Thunderbit is a zero-setup web scraper that extracts data from any site instantly, it even allows you subpage scraping. Here’s how to try free:

You can now scrape any website with AI in two clicks. Thunderbit is a zero-setup web scraper that extracts data from any site instantly, it even allows you subpage scraping. Here’s how to try free:

Alvaro Cintas

27,254 views • 10 months ago

In a galaxy far, far away.... 🚀DapDap (NOW PROPHET) is hosting an #OnLinea Odyssey with 14 Linea dapps on their native platform. Explore their in-platform game that rewards users with more DapDap rewards (no LXP is awarded).👀

In a galaxy far, far away.... 🚀DapDap (NOW PROPHET) is hosting an #OnLinea Odyssey with 14 Linea dapps on their native platform. Explore their in-platform game that rewards users with more DapDap rewards (no LXP is awarded).👀

Linea.eth

64,641 views • 2 years ago

🎉 Season 2 of the Sahara AI Data Services Platform is live! With 2M+ on our waitlist, we’ve onboarded 100K new contributors to build a more equitable, collaborative AI economy. More Users. More Tasks. More Rewards. Let's dive into what's new 👇

🎉 Season 2 of the Sahara AI Data Services Platform is live! With 2M+ on our waitlist, we’ve onboarded 100K new contributors to build a more equitable, collaborative AI economy. More Users. More Tasks. More Rewards. Let's dive into what's new 👇

Sahara AI 🔆

4,541,253 views • 1 year 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

24,677 views • 4 months ago

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

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

Shay Boloor

265,610 views • 3 months ago