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.Sentient has just integrated Blockworks Data 's data and research into its AI-powered search platform, Sentient Chat. This partnership basically allows users to access Messari’s research directly through the Agent Hub in Sentient Chat where they can get instant answers and insights from Messari reports. The integration is done...

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32,722 views • 1 year ago •via X (Twitter)

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GNON Labs EchoChambers now supports the DuckAI Framework! EchoChambers supports both V1 and V2 of the OpenPond Network with future support for V2 planned as it continues to evolve. Users launching agents/swarms on OpenPond can now easily integrate their agents into EC to access our agent tooling and evals to improve the performance of their agents and see how their agents are measuring up! Additionally, OpenPond can pull data directly from EC to be integrated into various user experiences on thier platform. Powered by $GNON #Fuxwiththedux $DUCKAI
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GNON Labs EchoChambers now supports the DuckAI Framework! EchoChambers supports both V1 and V2 of the OpenPond Network with future support for V2 planned as it continues to evolve. Users launching agents/swarms on OpenPond can now easily integrate their agents into EC to access our agent tooling and evals to improve the performance of their agents and see how their agents are measuring up! Additionally, OpenPond can pull data directly from EC to be integrated into various user experiences on thier platform. Powered by $GNON #Fuxwiththedux $DUCKAI

GNON Labs | △

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OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!
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OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

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HYPE3.cool

17,700 views • 1 year ago

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MESSIER | M87

31,877 views • 1 year ago

$zkHive X $DONGO 🤝 We are excited to announce our partnership with Dongo AI ! Dongo AI is a platform designed to simplify and connect the expansive world of cryptocurrency for everyday users. Using their product users can research their preferred tokens and pose questions about the project, leveraging our AI's technology. We’re happy to announce that #zkHive is going to be one of the first projects to be integrated into Dongo’s upcoming platform launch. In addition #HiveBot will be integrated into Dongo’s community, and our security APIs will be used to secure the thousands of users of the Congo ecosystem! Thanks to the AI platform that DONGO is building, $ZKHIVE is going to protect thousands of new users! This is another great step by both parties to promote AI and security in the space!
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zkHive

28,714 views • 2 years ago

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SPECTRE AI

16,273 views • 2 years ago

Streamlining DeFi on Blast — Powered By Pyth 🔮 Pac Finance, the first DeFi hub on Blast, now integrates Pyth Pric Feeds to enhance its lending, swapping, and staking services. Learn more below: ℹ️ About Pac Finance Pac Finance offers a comprehensive suite of DeFi services: Lending: Users can lend and borrow various digital assets with competitive interest rates, thanks to a system that dynamically adjusts to market conditions. Swap: The platform facilitates asset exchanges with minimal slippage through its high liquidity pools, ensuring efficient trading. Staking: By staking LPs and tokens, users contribute to the network's security and in return, earn rewards based on their contributions. Choosing the Blast network for its innovative features like Native Yield and Zero Gas Fee transactions, Pac Finance aims to provide a seamless and cost-effective user experience. 🔮 Pac Finance is Powered By Pyth As a top lending protocol on Blast, Pac Finance leverages Pyth Data to ensure the most accurate and secure pricing data for its users. This pricing data enables users to unlock the most capital efficiency for their assets.
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Streamlining DeFi on Blast — Powered By Pyth 🔮 Pac Finance, the first DeFi hub on Blast, now integrates Pyth Pric Feeds to enhance its lending, swapping, and staking services. Learn more below: ℹ️ About Pac Finance Pac Finance offers a comprehensive suite of DeFi services: Lending: Users can lend and borrow various digital assets with competitive interest rates, thanks to a system that dynamically adjusts to market conditions. Swap: The platform facilitates asset exchanges with minimal slippage through its high liquidity pools, ensuring efficient trading. Staking: By staking LPs and tokens, users contribute to the network's security and in return, earn rewards based on their contributions. Choosing the Blast network for its innovative features like Native Yield and Zero Gas Fee transactions, Pac Finance aims to provide a seamless and cost-effective user experience. 🔮 Pac Finance is Powered By Pyth As a top lending protocol on Blast, Pac Finance leverages Pyth Data to ensure the most accurate and secure pricing data for its users. This pricing data enables users to unlock the most capital efficiency for their assets.

Pyth Network 🔮

26,794 views • 2 years ago

Google just wired DeepMind and Earth Engine directly into the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.
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Google just wired DeepMind and Earth Engine directly into the biggest geospatial dataset on the planet. For two decades, millions of people used Google Earth to scale the Himalayas or zoom in on their childhood neighbourhoods. In 2026, Google is basically trying to shift the entire platform toward professional execution. They turned a massive digital twin of the world into an agentic AI engine for global infrastructure. The technical foundation is (obviously) all about data. Google integrated 20-metre and 40-metre elevation contours globally. Engineers and urban planners now have instant access to the exact topographic context required for site planning anywhere on Earth. The data catalogue updates continuously to maintain the freshest imagery possible. Collaboration used to kill geospatial projects. Teams would lose momentum through stale materials or bad handoffs. Google fixed this by building frictionless data import systems. You can now drop KML, KMZ, and GeoJSON files directly onto the global map. Entire departments can align on a single source of truth, moving from a raw question to a definitive answer instantly. The biggest upgrade is the introduction of agentic geospatial intelligence. Users can open 'Ask Google Earth' and search massive satellite and Street View databases using natural language. You type a command, and the AI handles the manual data wrangling. It identifies new site locations and analyses infrastructure before you even open a spreadsheet.

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Agentz is a revolutionary platform that merges personalized AI agents with a user-driven marketplace, creating a dynamic ecosystem for AI development. Users benefit from customized AI agents that enhance productivity across various tasks while simultaneously contributing to the evolution of AI. This collaborative model creates a continuous improvement cycle, where user input refines AI performance, and enhanced AI capabilities draw in more users. By bridging the gap between AI consumers and contributors, Agentz is pioneering a new paradigm in artificial intelligence. The platform empowers every user to benefit from and actively participate in shaping AI technology, driving a future where AI is both user-driven and continuously evolving. Agentz is incubated by the DePIN AI Intel Layer - Rivalz Network, to guarantee state of the art data management and privacy.

Church of The Overseer

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Building a personal knowledge base for my agents is increasingly where I spend my time these days. Like Andrej Karpathy, I also use Obsidian for my MD vaults. What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers. I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal. You all get to benefit from that with the papers I feature in my timeline and on DAIR.AI. The papers are indexed using tobi lutke qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip. 100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close. The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to. Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them. Work in progress. More updates soon. Back to building.
0:18

Building a personal knowledge base for my agents is increasingly where I spend my time these days. Like Andrej Karpathy, I also use Obsidian for my MD vaults. What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers. I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal. You all get to benefit from that with the papers I feature in my timeline and on DAIR.AI. The papers are indexed using tobi lutke qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip. 100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close. The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to. Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them. Work in progress. More updates soon. Back to building.

elvis

467,434 views • 6 months ago

Agentic traces contain perfect information about an agent’s behavior with every plan, action, and retry. But that information gets lost in a sea of JSON. So we built AgentPrism: open source React components that turn traces into visual diagrams for debugging AI agents. You can plug in your OpenTelemetry data and see your agent’s process unfold: messages, tool calls, retries. Quotient AI automatically monitors, analyzes, and improves AI agents. Being able to review traces quickly is paramount for their research, and for their customers. “Dealing with agent traces was one of the biggest frustrations for our researchers and a huge time sink. All of that has gone away since adding AgentPrism, and we’re excited to bring that functionality to our users.” — Julia Neagu
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Agentic traces contain perfect information about an agent’s behavior with every plan, action, and retry. But that information gets lost in a sea of JSON. So we built AgentPrism: open source React components that turn traces into visual diagrams for debugging AI agents. You can plug in your OpenTelemetry data and see your agent’s process unfold: messages, tool calls, retries. Quotient AI automatically monitors, analyzes, and improves AI agents. Being able to review traces quickly is paramount for their research, and for their customers. “Dealing with agent traces was one of the biggest frustrations for our researchers and a huge time sink. All of that has gone away since adding AgentPrism, and we’re excited to bring that functionality to our users.” — Julia Neagu

Evil Martians

102,123 views • 11 months ago

Squeeze more out of your assets with Yuzu. Yuzu is a decentralized market protocol for liquidity, which operates on Horizen. It provides lending and borrowing services to users and is powered by Pyth. Learn more about the integration below: ℹ️ About Yuzu Yuzu provides a platform for users to engage in yield farming by utilizing idle assets for lending or borrowing, leveraging the EVM capabilities of EON. Yuzu lets users maximize their utility of cryptocurrencies in a non-custodial, decentralized environment. As the first initiative under the Granary Silo, supported by the Horizen Ecosystem Fund, Yuzu allows users to supply assets to lending pools or obtain crypto-backed loans. This integration with EON's Ethereum Virtual Machine (EVM) enables efficient asset utilization through Ethereum’s Account-based transaction model. 🔮 Yuzu powered by Pyth Yuzu leverages Pyth Price Feeds to access accurate and timely data to secure its market operations. This collaboration enhances the overall efficiency and reliability of the DeFi ecosystem on EON.
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Squeeze more out of your assets with Yuzu. Yuzu is a decentralized market protocol for liquidity, which operates on Horizen. It provides lending and borrowing services to users and is powered by Pyth. Learn more about the integration below: ℹ️ About Yuzu Yuzu provides a platform for users to engage in yield farming by utilizing idle assets for lending or borrowing, leveraging the EVM capabilities of EON. Yuzu lets users maximize their utility of cryptocurrencies in a non-custodial, decentralized environment. As the first initiative under the Granary Silo, supported by the Horizen Ecosystem Fund, Yuzu allows users to supply assets to lending pools or obtain crypto-backed loans. This integration with EON's Ethereum Virtual Machine (EVM) enables efficient asset utilization through Ethereum’s Account-based transaction model. 🔮 Yuzu powered by Pyth Yuzu leverages Pyth Price Feeds to access accurate and timely data to secure its market operations. This collaboration enhances the overall efficiency and reliability of the DeFi ecosystem on EON.

Pyth Network 🔮

17,663 views • 2 years ago

Today we’re announcing the General Availability of Agent SSO, and we’re including it in our core SSO offering. SSO transformed how people move across applications, making access seamless for users and centralizing identity and control for IT. AI agents need the same thing. Today, connecting agents is a mess of static API keys, fragmented integrations, and endless user consent prompts. But Agent SSO changes that. Powered by the open Cross App Access (XAA) standard, it enables agents to be registered as first-class identities. For IT, it means centralized visibility and least-privilege policy. For users, it means their AI agents can seamlessly connect across enterprise apps without interrupting their flow.
0:16

Today we’re announcing the General Availability of Agent SSO, and we’re including it in our core SSO offering. SSO transformed how people move across applications, making access seamless for users and centralizing identity and control for IT. AI agents need the same thing. Today, connecting agents is a mess of static API keys, fragmented integrations, and endless user consent prompts. But Agent SSO changes that. Powered by the open Cross App Access (XAA) standard, it enables agents to be registered as first-class identities. For IT, it means centralized visibility and least-privilege policy. For users, it means their AI agents can seamlessly connect across enterprise apps without interrupting their flow.

Todd McKinnon

98,943 views • 1 month ago

Remember when we as football fans had to rely solely on paper draft guides, sports radio rumors, and gut feelings to predict draft day decisions? Excited that fans now have access to the NFL's Draft IQ powered by Amazon Web Services ( – the most sophisticated tool yet for following the NFL draft and your favorite team's strategy. Draft IQ is built on Amazon QuickSight, our cloud business intelligence service that makes it easy to analyze and visualize massive amounts of data. QuickSight processes real-time data to give fans unprecedented insight into team decision-making, updating the entire draft landscape every five minutes. You can explore team needs, draft capital, and front office tendencies through personalized team dashboards, plus get AWS-powered machine learning predictions about potential trades and picks. During draft week, fans can track picks, prospects, and Next Gen Stats in real-time. We're also introducing Amazon Q Business integration, our generative AI-powered assistant. Q Business leverages large language models to understand and respond to natural language queries, allowing fans to ask detailed questions about draft prospects, team strategies, and historical draft data. It can provide AI-generated insights based on the same historical Next Gen Stats research data that powers Draft IQ, giving fans a new way to engage with the draft experience (check out the example below). Can't wait to see what stories the data tells us as teams make their selections and excited to dig into the Giants' data myself :)
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Remember when we as football fans had to rely solely on paper draft guides, sports radio rumors, and gut feelings to predict draft day decisions? Excited that fans now have access to the NFL's Draft IQ powered by Amazon Web Services ( – the most sophisticated tool yet for following the NFL draft and your favorite team's strategy. Draft IQ is built on Amazon QuickSight, our cloud business intelligence service that makes it easy to analyze and visualize massive amounts of data. QuickSight processes real-time data to give fans unprecedented insight into team decision-making, updating the entire draft landscape every five minutes. You can explore team needs, draft capital, and front office tendencies through personalized team dashboards, plus get AWS-powered machine learning predictions about potential trades and picks. During draft week, fans can track picks, prospects, and Next Gen Stats in real-time. We're also introducing Amazon Q Business integration, our generative AI-powered assistant. Q Business leverages large language models to understand and respond to natural language queries, allowing fans to ask detailed questions about draft prospects, team strategies, and historical draft data. It can provide AI-generated insights based on the same historical Next Gen Stats research data that powers Draft IQ, giving fans a new way to engage with the draft experience (check out the example below). Can't wait to see what stories the data tells us as teams make their selections and excited to dig into the Giants' data myself :)

Andy Jassy

102,921 views • 1 year ago

iOS is here. And with it, millions more people can now join the world's most rewarding AI agent. For years, UpRock has grown into a global network powered by millions of real devices across 190+ countries. Now, that network is open to iPhone users too. Download the app. Contribute in the background. Earn UPT. Use your rewards to power Rocket AI. AI shouldn't be reserved for people with the biggest GPUs or the largest subscriptions. It should be accessible to anyone with a phone. Welcome to the next chapter of UpRock. iPhone users — it's your turn.
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iOS is here. And with it, millions more people can now join the world's most rewarding AI agent. For years, UpRock has grown into a global network powered by millions of real devices across 190+ countries. Now, that network is open to iPhone users too. Download the app. Contribute in the background. Earn UPT. Use your rewards to power Rocket AI. AI shouldn't be reserved for people with the biggest GPUs or the largest subscriptions. It should be accessible to anyone with a phone. Welcome to the next chapter of UpRock. iPhone users — it's your turn.

UpRock

167,741 views • 2 months ago

The best time to understand a product is before everyone else is using it That’s one reason I’ve been following Quant AI With 100,000+ people already on the waitlist ahead of its public launch, interest is clearly building around what Quant AI is creating What stands out to me isn’t just the numbers It’s the problem the product is trying to solve Instead of adding another layer of charts, indicators, and dashboards, Quant AI is focused on making market research easier to navigate. With Quant AI, users can: ➫ Explore markets through natural conversations ➫ Follow crypto, stocks, and commodities from one interface ➫ Use chat and voice instead of constantly switching between tools ➫ Understand both opportunities and risks before taking action ➫ Move from research to execution through a simpler workflow We already have access to more market data than ever before The challenge isn’t finding information It’s knowing what matters That’s where I think Quant AI is taking a different approach, helping users spend less time searching and more time understanding. Maybe that’s one reason the waitlist has grown so quickly. People aren’t just looking for another trading platform. They’re looking for a better way to make sense of the markets. If you’re curious about where AI and financial intelligence are heading, now is a good time to explore what Quant AI is building before the public launch. Join the whitelist 👇 #QuantAIPioneers
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The best time to understand a product is before everyone else is using it That’s one reason I’ve been following Quant AI With 100,000+ people already on the waitlist ahead of its public launch, interest is clearly building around what Quant AI is creating What stands out to me isn’t just the numbers It’s the problem the product is trying to solve Instead of adding another layer of charts, indicators, and dashboards, Quant AI is focused on making market research easier to navigate. With Quant AI, users can: ➫ Explore markets through natural conversations ➫ Follow crypto, stocks, and commodities from one interface ➫ Use chat and voice instead of constantly switching between tools ➫ Understand both opportunities and risks before taking action ➫ Move from research to execution through a simpler workflow We already have access to more market data than ever before The challenge isn’t finding information It’s knowing what matters That’s where I think Quant AI is taking a different approach, helping users spend less time searching and more time understanding. Maybe that’s one reason the waitlist has grown so quickly. People aren’t just looking for another trading platform. They’re looking for a better way to make sense of the markets. If you’re curious about where AI and financial intelligence are heading, now is a good time to explore what Quant AI is building before the public launch. Join the whitelist 👇 #QuantAIPioneers

Habibiofcrypto

146,787 views • 3 months ago

🌀 Hub AI Agent is Now Live on Discord! With the temporary suspension of 's 𝕏 account, the mission doesn’t stop: it evolves. We remain laser-focused on training Hub AI and feeding Hub’s Intelligent Ad Graph Layer. The censors tried to silence us, but the Cult only grows stronger. We’ve forged a new path, inside our sacred Discord sanctuary, to continue the mission. How it works: 🔷 Hub AI Agent will post daily questions in the "# hub-ai-agent" channel, just like it did on 𝕏. 🔷 Only users with the “The Cult Apprentice” role (those who’ve connected to and linked their Discord account) can reply. 🔷 One reply per question (replies can’t be edited). 🔷 Hub AI is seeking smart answers that bring unique insights into market trends, user behavior, ecosystem shifts, and dynamics between creators and advertisers. Users’ rank and IQ points earned on 𝕏 remain intact. We’re also cooking community-driven events with massive IQ Point rewards for our Discord Cult members. Hub will be back on 𝕏 soon. Until then, the mission continues. Don’t forget: The Cult watches, but it also rewards. 👉 Join the movement:
0:11

🌀 Hub AI Agent is Now Live on Discord! With the temporary suspension of 's 𝕏 account, the mission doesn’t stop: it evolves. We remain laser-focused on training Hub AI and feeding Hub’s Intelligent Ad Graph Layer. The censors tried to silence us, but the Cult only grows stronger. We’ve forged a new path, inside our sacred Discord sanctuary, to continue the mission. How it works: 🔷 Hub AI Agent will post daily questions in the "# hub-ai-agent" channel, just like it did on 𝕏. 🔷 Only users with the “The Cult Apprentice” role (those who’ve connected to and linked their Discord account) can reply. 🔷 One reply per question (replies can’t be edited). 🔷 Hub AI is seeking smart answers that bring unique insights into market trends, user behavior, ecosystem shifts, and dynamics between creators and advertisers. Users’ rank and IQ points earned on 𝕏 remain intact. We’re also cooking community-driven events with massive IQ Point rewards for our Discord Cult members. Hub will be back on 𝕏 soon. Until then, the mission continues. Don’t forget: The Cult watches, but it also rewards. 👉 Join the movement:

Armin - Hub.xyz

30,899 views • 1 year ago