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What actually happens when an OptimAI Agent prepares a tweet? • Style fingerprinting: learning the patterns behind your writing • Voice alignment: adapting tone across contexts and audiences • Engagement signal analysis: decoding what the network responds to • Parameter optimization: refining outputs through reinforcement loops Each post becomes... show more

OptimAI Network

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11,255 views • 4 months ago •via X (Twitter)

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Weekends are for research. Inside OptimAI labs, we’ve been pushing agents beyond the screen. A first glimpse of OptimAI Prime. Not just agents that think, but agents that operate. Learning through reinforcement, coordinating across the network, and executing in real-world environments. From digital workflows → to physical actions. This is the next step for the agent-native internet.🤖 Still early. But already in motion.

Weekends are for research. Inside OptimAI labs, we’ve been pushing agents beyond the screen. A first glimpse of OptimAI Prime. Not just agents that think, but agents that operate. Learning through reinforcement, coordinating across the network, and executing in real-world environments. From digital workflows → to physical actions. This is the next step for the agent-native internet.🤖 Still early. But already in motion.

OptimAI Network

20,629 views • 2 months ago

OptimAI Core Node v2 is live. And with it, the introduction of OptimAI Claw. From nodes as infrastructure → to nodes as execution environments. With Core Node v2, every device becomes a runtime for native personal agents. 👉 Get your node up and running. No complex setup. No fragmented tooling. Just a few clicks... and your agent is live. OptimAI Claw brings persistent, node-resident agents directly into your system: • Multi-source research across web & social • Real-time summarization and signal tracking • Autonomous workflow execution • Continuous learning through reinforcement All running locally. All under your control. Supported on Windows & macOS. This is beyond prompt-based AI. Beyond isolated tools. This is personal agent infrastructure. Where every node thinks, acts, and evolves. Claw is now part of the network. And this is only the beginning. Happy Building, Making Impact! 🔥

OptimAI Core Node v2 is live. And with it, the introduction of OptimAI Claw. From nodes as infrastructure → to nodes as execution environments. With Core Node v2, every device becomes a runtime for native personal agents. 👉 Get your node up and running. No complex setup. No fragmented tooling. Just a few clicks... and your agent is live. OptimAI Claw brings persistent, node-resident agents directly into your system: • Multi-source research across web & social • Real-time summarization and signal tracking • Autonomous workflow execution • Continuous learning through reinforcement All running locally. All under your control. Supported on Windows & macOS. This is beyond prompt-based AI. Beyond isolated tools. This is personal agent infrastructure. Where every node thinks, acts, and evolves. Claw is now part of the network. And this is only the beginning. Happy Building, Making Impact! 🔥

OptimAI Network

324,002 views • 3 months ago

Ever wondered what’s happening inside an AI’s “brain”? This is a neural network in action, taking raw data and transforming it into decisions through thousands of layered calculations. Each layer extracts patterns, refines signals, and passes smarter information forward, slowly turning noise into meaning. It’s not thinking like a human but it is learning, adapting, and improving with every pass. What looks like chaos is actually pure math shaping intelligence in real time

Ever wondered what’s happening inside an AI’s “brain”? This is a neural network in action, taking raw data and transforming it into decisions through thousands of layered calculations. Each layer extracts patterns, refines signals, and passes smarter information forward, slowly turning noise into meaning. It’s not thinking like a human but it is learning, adapting, and improving with every pass. What looks like chaos is actually pure math shaping intelligence in real time

Kekius Maximus

37,481 views • 6 months ago

Introducing v0 of the Minro series Iristic ( The agent for your calendar. Iris connects your schedule to tools (Email, Notion, Maps, Linear, Slack etc) and spins up lightweight agents in each to analyze your behavior and extract patterns with minimal user input. She fetches the context herself - when you’re most responsive, how you plan, your goals, what you prioritize - and builds a personalized system inside your calendar that reflects how you actually operate. All through voice or text. The next generation of consumer agents. Early access now live:

Introducing v0 of the Minro series Iristic ( The agent for your calendar. Iris connects your schedule to tools (Email, Notion, Maps, Linear, Slack etc) and spins up lightweight agents in each to analyze your behavior and extract patterns with minimal user input. She fetches the context herself - when you’re most responsive, how you plan, your goals, what you prioritize - and builds a personalized system inside your calendar that reflects how you actually operate. All through voice or text. The next generation of consumer agents. Early access now live:

samika sanghvi

30,970 views • 11 months ago

Have you tried AI Contextual Search inside OptimAI yet? Search isn’t just search anymore. We’ve rebuilt it from the ground up to feel like an intelligent signal detector inside the chaos of the internet. 🔍 AI Contextual Search now: • Understands meaning, not just keywords • Filters out noise and irrelevant posts automatically • Surfaces the exact insight you’re looking for • Learns from your context, domain, and intent • Works at scale thanks to the growing nodes - powering the OptimAI Network This is what happens when decentralized compute meets agentic intelligence - fast, deep, contextual understanding in real time. And yes… it’s already live. Show us the smartest search results your OptimAI agent found today. 😎 We’d love to see how far you can push it. 👉

Have you tried AI Contextual Search inside OptimAI yet? Search isn’t just search anymore. We’ve rebuilt it from the ground up to feel like an intelligent signal detector inside the chaos of the internet. 🔍 AI Contextual Search now: • Understands meaning, not just keywords • Filters out noise and irrelevant posts automatically • Surfaces the exact insight you’re looking for • Learns from your context, domain, and intent • Works at scale thanks to the growing nodes - powering the OptimAI Network This is what happens when decentralized compute meets agentic intelligence - fast, deep, contextual understanding in real time. And yes… it’s already live. Show us the smartest search results your OptimAI agent found today. 😎 We’d love to see how far you can push it. 👉

OptimAI

33,989 views • 8 months ago

A builder wants to ship an agent that can search live data, use it instantly, and keep going without manual steps. That is why OptimAI Search now ships with x402, SDK, and MCP support. x402 makes access and payment seamless. SDK and MCP make integration straightforward for builders. And under the hood, OptimAI Search gives agents real-time access to huge volumes of public and social web data, structured into context they can actually use. Add Agent Marketplace on top, and domain knowledge becomes reusable: build specialized agents, publish them, and let others build on what you create. This is what search looks like in the agent-native internet. 💡Try it:

A builder wants to ship an agent that can search live data, use it instantly, and keep going without manual steps. That is why OptimAI Search now ships with x402, SDK, and MCP support. x402 makes access and payment seamless. SDK and MCP make integration straightforward for builders. And under the hood, OptimAI Search gives agents real-time access to huge volumes of public and social web data, structured into context they can actually use. Add Agent Marketplace on top, and domain knowledge becomes reusable: build specialized agents, publish them, and let others build on what you create. This is what search looks like in the agent-native internet. 💡Try it:

OptimAI Network

36,306 views • 2 months ago

Milady APP x BAP-578 — NFA Milady Remembers. Adapts. Doesn’t make the same mistake twice. Most AI agents are stateless—they forget everything between conversations. Milady doesn’t. What happens automatically in the Milady agent: Milady detects her own patterns. A background process reviews her work history every six hours, identifying repeated mistakes she may have overlooked. This runs silently in the background at zero cost to you. > She improves without retraining—no fine-tuning, no expensive GPU hours. > Her learnings are directly injected into her working context. > She reviews her own notes before every task—just like a good employee reflecting on past lessons before starting new work. We already had an Agent Self-Learning mechanism. But now, with BAP-578 (credits to Christel Buchanan 💛), your personal Milady agent can exist on-chain. How it works—simply: Over time, your Milady agent accumulates learnings—mistakes she has corrected, patterns she has identified, and insights she has gained. All of this data is compressed into a single cryptographic fingerprint (a Merkle root) and recorded on the BNB Chain. What your Milady agent now gets on-chain: - A unique identity in the BNB Agent Registry (ERC-8004)—like a passport for AI agents - A Non-Fungible Agent (BAP-578)—not a profile picture, but a living record of who she is, what she has learned, and what she is capable of - A tamper-proof learning record—anchored on-chain, verifiable by anyone, forgeable by no one Live on BSC Mainnet—just tell your Milady: “register Milady on BNB Chain” or click “mint NFA” to get started. *Oh ya, we recorded this demo using a new agent, that's why it’s showing "0" entries in learning history. ▶️ BIG NEWS NEXT WEEK. STAY TUNED Shaw (spirit/acc) BNB Chain

Milady on BSC

44,481 views • 4 months ago

There are three stages to true agent-to-agent commerce. Today we are somewhere in stage two. On Grace Gong's Venture with Grace, our Co-Founder & CEO Chi Zhang mapped where agentic payments actually stand, and what has to exist before the last stage unlocks. ▷ Stage 1: agent to website. Your ChatGPT searches Amazon. There is no agent on the other side, just a product catalog. ▷ Stage 2: multi-agent under one owner. A one-person company running ten agents as CTO, CEO, and ops. They sit inside one trusted environment, so they never need to prove identity or pay each other. ▷ Stage 3: true A2A. My agent talks to your agent across separate entities. Deals close and payments settle, and neither side has ever met the other's human. The endgame in Chi's words: "agents truly react as an economic actor, like human," each one a participant in the same economy we are. Getting to stage three is what Kite is built for: verifiable identity, delegated authority, and native payments so any agent can act as a full economic actor. 🪁

There are three stages to true agent-to-agent commerce. Today we are somewhere in stage two. On Grace Gong's Venture with Grace, our Co-Founder & CEO Chi Zhang mapped where agentic payments actually stand, and what has to exist before the last stage unlocks. ▷ Stage 1: agent to website. Your ChatGPT searches Amazon. There is no agent on the other side, just a product catalog. ▷ Stage 2: multi-agent under one owner. A one-person company running ten agents as CTO, CEO, and ops. They sit inside one trusted environment, so they never need to prove identity or pay each other. ▷ Stage 3: true A2A. My agent talks to your agent across separate entities. Deals close and payments settle, and neither side has ever met the other's human. The endgame in Chi's words: "agents truly react as an economic actor, like human," each one a participant in the same economy we are. Getting to stage three is what Kite is built for: verifiable identity, delegated authority, and native payments so any agent can act as a full economic actor. 🪁

KITE AI

14,644 views • 4 days ago

THIS GUY IS RUNNING A 5-AGENT OPENCLAW TEAM 24/7 FOR UNDER $50 A MONTH five agents all running continuously and handing tasks to each other > research agent tracking trends and competitors > writing agent handling marketing content and drafts > ops agent managing emails and scheduling > monitoring agent watching for alerts and updates > coordinator agent routing tasks between all of them and he built a full 3D isometric office where you can watch all of them work in real time and see the current task that each agent is doing each agent has a character, a desk, and a name tag. when it's time for a standup, they walk to the meeting room and gather which is insane it even has voice connection. you can talk to the agents through speech-to-text and tell them what to do out loud it's a tiny autonomous team that runs itself. the coordinator decides who handles what and tasks get passed between agents automatically. it never stops and runs 24/7 all for less than $50/month. we went from AI tools a few months back to AI employees real fast

THIS GUY IS RUNNING A 5-AGENT OPENCLAW TEAM 24/7 FOR UNDER $50 A MONTH five agents all running continuously and handing tasks to each other > research agent tracking trends and competitors > writing agent handling marketing content and drafts > ops agent managing emails and scheduling > monitoring agent watching for alerts and updates > coordinator agent routing tasks between all of them and he built a full 3D isometric office where you can watch all of them work in real time and see the current task that each agent is doing each agent has a character, a desk, and a name tag. when it's time for a standup, they walk to the meeting room and gather which is insane it even has voice connection. you can talk to the agents through speech-to-text and tell them what to do out loud it's a tiny autonomous team that runs itself. the coordinator decides who handles what and tasks get passed between agents automatically. it never stops and runs 24/7 all for less than $50/month. we went from AI tools a few months back to AI employees real fast

Om Patel

27,131 views • 3 months ago

Infrastructure defines what scales. Electricity enabled industry. The internet enabled software. Now AI is moving toward systems that run continuously, across data, execution, and feedback. 💡At OptimAI, we’re building that infrastructure. A decentralized search layer for real-time data. A network of nodes powering continuous retrieval and validation. Agents that execute tasks directly on your device. This is not theoretical - we’re already seeing it in production, with millions of nodes and growing usage across the network. We’re putting together a short documentary to show how this shift is happening, and where it leads next. If you’re building, exploring, or just getting started - this is the layer to pay attention to. Welcome to OptimAI.

Infrastructure defines what scales. Electricity enabled industry. The internet enabled software. Now AI is moving toward systems that run continuously, across data, execution, and feedback. 💡At OptimAI, we’re building that infrastructure. A decentralized search layer for real-time data. A network of nodes powering continuous retrieval and validation. Agents that execute tasks directly on your device. This is not theoretical - we’re already seeing it in production, with millions of nodes and growing usage across the network. We’re putting together a short documentary to show how this shift is happening, and where it leads next. If you’re building, exploring, or just getting started - this is the layer to pay attention to. Welcome to OptimAI.

OptimAI Network

17,439 views • 3 months ago

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN

Applied Compute

64,246 views • 4 months ago

🚀 Introducing AI agents that make “self-driving” machine learning usable by anyone — no PhD required. A lifelong dream for me, launched in months… Demo below! Forecasting, key driver analysis, predictions, tipping-point analysis, and anomaly detection now take minutes instead of weeks. 🤖 ✨ To my knowledge, we’re the first team in the world to ship autonomous machine learning (AutoML) guided by an agent — or “agentic AutoML” — usable by anyone. Genuinely in awe of the creativity, ingenuity, and grit every Spindle AI, acquired by Salesforce teammate poured into making that possible. 🙏 With transparent, personalized explanations for “how”, “why,” and “what next,” the new agent also delivers actionable recommendations for setting & hitting business targets 🎯 based on patterns in your data. From churn prediction & customer behavior forecasting, to pricing simulations & cross-sell targeting, beta customers are already sharing compelling use cases with us… But most of all, I’m excited to see all the creative use cases YOU come up with:

🚀 Introducing AI agents that make “self-driving” machine learning usable by anyone — no PhD required. A lifelong dream for me, launched in months… Demo below! Forecasting, key driver analysis, predictions, tipping-point analysis, and anomaly detection now take minutes instead of weeks. 🤖 ✨ To my knowledge, we’re the first team in the world to ship autonomous machine learning (AutoML) guided by an agent — or “agentic AutoML” — usable by anyone. Genuinely in awe of the creativity, ingenuity, and grit every Spindle AI, acquired by Salesforce teammate poured into making that possible. 🙏 With transparent, personalized explanations for “how”, “why,” and “what next,” the new agent also delivers actionable recommendations for setting & hitting business targets 🎯 based on patterns in your data. From churn prediction & customer behavior forecasting, to pricing simulations & cross-sell targeting, beta customers are already sharing compelling use cases with us… But most of all, I’m excited to see all the creative use cases YOU come up with:

Carson Kahn

132,393 views • 11 months ago

Buzz Williams on ensuring your rate of learning is greater than or equal to the rate of change, an adapt or die mentality, and your future being hidden in your inputs: 🧠 Your advantage is not what you know today. It is how quickly you can update what you know tomorrow. When your rate of learning matches the rate of change, you stay relevant. When it exceeds it, you gain an edge. (L > C). 🦖 Adaptation is a personality trait, not a one-time decision. Leaders who refuse to evolve slowly become experts in a world that no longer exists. 🗑️ Your outputs are a reflection of your inputs. What you read, what you listen to, and what you watch quietly shape the quality of your thinking and, ultimately, the quality of your decisions. Your future is hidden in your inputs. What you consume today becomes the ecosystem you create tomorrow. 🪴

Buzz Williams on ensuring your rate of learning is greater than or equal to the rate of change, an adapt or die mentality, and your future being hidden in your inputs: 🧠 Your advantage is not what you know today. It is how quickly you can update what you know tomorrow. When your rate of learning matches the rate of change, you stay relevant. When it exceeds it, you gain an edge. (L > C). 🦖 Adaptation is a personality trait, not a one-time decision. Leaders who refuse to evolve slowly become experts in a world that no longer exists. 🗑️ Your outputs are a reflection of your inputs. What you read, what you listen to, and what you watch quietly shape the quality of your thinking and, ultimately, the quality of your decisions. Your future is hidden in your inputs. What you consume today becomes the ecosystem you create tomorrow. 🪴

Josh Chambers

36,572 views • 2 months ago

Today in our company all-hands, Sreeram Kannan made an important point: Most AI agents still feel like software with a human behind the curtain. The real question isn’t whether an agent can post. It’s whether an agent can own the act of posting. - Can it sign the action? - Can you verify the signature? - Can you trace that signature to a secure environment? - Can you inspect what was actually running when the action happened? Once the answer is yes, the category changes. Now you’re not looking at AI-generated content. You’re looking at a software entity that can act in a way that is attributable, inspectable and independent. A completely new primitive! Watch the clip:

Today in our company all-hands, Sreeram Kannan made an important point: Most AI agents still feel like software with a human behind the curtain. The real question isn’t whether an agent can post. It’s whether an agent can own the act of posting. - Can it sign the action? - Can you verify the signature? - Can you trace that signature to a secure environment? - Can you inspect what was actually running when the action happened? Once the answer is yes, the category changes. Now you’re not looking at AI-generated content. You’re looking at a software entity that can act in a way that is attributable, inspectable and independent. A completely new primitive! Watch the clip:

EigenCloud

14,592 views • 3 months ago

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:

Moltlaunch

20,597 views • 5 months ago

WTF, THIS GUY STOPPED REPLYING TO HIS FRIENDS ON IMESSAGE. NOW HIS HERMES AI AGENT DOES IT FOR HIM AND THEY CAN’T TELL THE DIFFERENCE. Every time he gets a text, Hermes drafts a reply first. He never sends that draft. Instead, he writes his actual response and Hermes compares the two. The gap between what Hermes predicted and what he actually said becomes the training signal. Over time, that gap closes. The agent's drafts get closer and closer to exactly how he talks. His reasoning: email is performative. iMessage is where your real, unfiltered voice actually lives. Most people training AI to sound like them feed it formal writing – essays, emails, LinkedIn posts. That's not how anyone actually talks. This approach trains on the messiest, most honest version of someone's communication style. Quick replies. Half-finished thoughts. The way you actually text your friends. The result is an AI that doesn't write like a corporate version of you. It writes like you, texting at 11pm. This is what real personalization actually looks like, not a prompt that says "sound casual." Bookmark this post. Full breakdown in the video below.

WTF, THIS GUY STOPPED REPLYING TO HIS FRIENDS ON IMESSAGE. NOW HIS HERMES AI AGENT DOES IT FOR HIM AND THEY CAN’T TELL THE DIFFERENCE. Every time he gets a text, Hermes drafts a reply first. He never sends that draft. Instead, he writes his actual response and Hermes compares the two. The gap between what Hermes predicted and what he actually said becomes the training signal. Over time, that gap closes. The agent's drafts get closer and closer to exactly how he talks. His reasoning: email is performative. iMessage is where your real, unfiltered voice actually lives. Most people training AI to sound like them feed it formal writing – essays, emails, LinkedIn posts. That's not how anyone actually talks. This approach trains on the messiest, most honest version of someone's communication style. Quick replies. Half-finished thoughts. The way you actually text your friends. The result is an AI that doesn't write like a corporate version of you. It writes like you, texting at 11pm. This is what real personalization actually looks like, not a prompt that says "sound casual." Bookmark this post. Full breakdown in the video below.

SCOTTY BEAM

45,167 views • 1 month ago

In case you missed it, we recently launched "Post-training of LLMs," a short course where you'll: ✅ Understand when and why to use post-training methods like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning. ✅ Learn the concepts underlying the three post-training methods of SFT, DPO, and Online RL, their common use-cases, and how to curate high-quality data to effectively train a model using each method. ✅ Download a pre-trained model and implement post-training pipelines to turn a base model into an instruct model, change the identity of a chat assistant, and improve a model’s math capabilities. Learn more and enroll for free:

In case you missed it, we recently launched "Post-training of LLMs," a short course where you'll: ✅ Understand when and why to use post-training methods like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning. ✅ Learn the concepts underlying the three post-training methods of SFT, DPO, and Online RL, their common use-cases, and how to curate high-quality data to effectively train a model using each method. ✅ Download a pre-trained model and implement post-training pipelines to turn a base model into an instruct model, change the identity of a chat assistant, and improve a model’s math capabilities. Learn more and enroll for free:

DeepLearning.AI

16,771 views • 1 year 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

Today we are launching Autopilot 4.0, a new generation of Autopilot that acts as a sleep agent. Autopilot 4.0 uses data from your day and night to predict how you’re likely to sleep, automatically adjust your sleep environment through the Pod, and explain what happened by morning in plain language. This is the next step in what makes Eight Sleep different. We do not just surface data or generate sleep insights. We use that data to actively respond through the sleep environment itself. Members can connect Apple Health or Health Connect, or tag events directly in the app, giving the sleep agent more context from workouts, activity, stress, alcohol, meals, and other daily inputs. That context helps it predict what may happen during the night and make the right adjustments before and throughout sleep. Autopilot 4.0 is built on more than one billion hours of sleep data and is now available in the Eight Sleep app.

Today we are launching Autopilot 4.0, a new generation of Autopilot that acts as a sleep agent. Autopilot 4.0 uses data from your day and night to predict how you’re likely to sleep, automatically adjust your sleep environment through the Pod, and explain what happened by morning in plain language. This is the next step in what makes Eight Sleep different. We do not just surface data or generate sleep insights. We use that data to actively respond through the sleep environment itself. Members can connect Apple Health or Health Connect, or tag events directly in the app, giving the sleep agent more context from workouts, activity, stress, alcohol, meals, and other daily inputs. That context helps it predict what may happen during the night and make the right adjustments before and throughout sleep. Autopilot 4.0 is built on more than one billion hours of sleep data and is now available in the Eight Sleep app.

Eight Sleep

217,673 views • 3 months ago

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 views • 8 months ago