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If you’re still using AI like a chatbot, you’re under-utilizing it. Spine AI isn’t one assistant. It’s an AI workforce you control. You don’t just get suggestions. You delegate work to multiple specialised agents — running in parallel — and watch them execute in real time. Give it a...

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sabir hussain

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19,623 次观看 • 4 个月前 •via X (Twitter)

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Most AI agents live in a chat window. Helio lets them join your company. I added an AI teammate to GitHub like a real engineer. Not a chatbot. Not a copilot. Not a prompt-waiting tool. A teammate with its own identity, permissions, and role in the workflow—that’s Helio’s AI-Native Workforce. It can join channels, collaborate with other AI teammates, understand context, execute tasks, and report back. Helio isn’t building a better assistant. It’s redefining AI: not software you use, but a teammate you work with. Tools help you do work. Teammates move work forward. #Helio #AIWorkforce #AITeammate
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Most AI agents live in a chat window. Helio lets them join your company. I added an AI teammate to GitHub like a real engineer. Not a chatbot. Not a copilot. Not a prompt-waiting tool. A teammate with its own identity, permissions, and role in the workflow—that’s Helio’s AI-Native Workforce. It can join channels, collaborate with other AI teammates, understand context, execute tasks, and report back. Helio isn’t building a better assistant. It’s redefining AI: not software you use, but a teammate you work with. Tools help you do work. Teammates move work forward. #Helio #AIWorkforce #AITeammate

Your Tech Girl

14,290 次观看 • 1 个月前

What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch
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What does it actually mean to be AI native? There was no clear guide on the internet for how to become AI native so we built the definitive one (60 min masterclass): 1. An AI native org has 3 layers: people for strategy and taste, agents for execution, and a shared context layer that makes the entire company readable to agents. 2. AI eats the middle of your work. You used to spend 80% of your day on execution. Now agents do that. Your job is the bookends: deciding what to do and judging whether it's good enough. 3. Everyone is a manager now. Your output is the output of your agents. If your agents produce garbage, that's on you. You set them up wrong. 4. Using ChatGPT doesn't make you AI native. That's like having a website and calling yourself a tech company lol. 5. No AI native org without AI native people. Most companies skip straight to the tools. That's why it fails. If your people don't understand how to manage agents, the tech doesn't matter. 6. Making your company "readable" to agents is the real work. Every process, every decision, every piece of knowledge needs to exist in a format an agent can consume. Most companies are nowhere close. 7. Speed without signal is just expensive chaos. You need the system to move fast AND know if you're moving in the right direction. 8. The skill chain is how agents get good at your specific workflows. Skills build on skills. The more you invest in them, the more your company compounds. 9. The moat is the system. People managing agents, agents reading from rich context, the whole thing getting smarter every week. That compounds. Your competitor can copy your tools. They can't copy your system. Full episode with Theo Tabah from LCA on The Startup Ideas Podcast (SIP) 🧃. This is the stuff we normally keep internal but all the sauce is yours. Theo Tabah is the brains behind advising the world's biggest companies on AI and building AI products. Your fav CEO's first call for figuring out AI. You are in for a treat Become AI native in under 60 minutes Watch

GREG ISENBERG

83,793 次观看 • 1 个月前

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.
2:09

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Aaron Levie

103,811 次观看 • 10 个月前

AI agents are failing in production...not a surprise. As you scale your knowledge base, embeddings start creating noise. It’s called ‘semantic collapse’ - when conversations run too long, you have hundreds of PDFs, millions of data points to give to your AI. Your AI can’t flag it because it doesn’t know it’s hallucinating. Similarity gets passed off as relevance. Fix your context. Make your agents work. Build intelligent AI. If your AI is plateauing at 50% accuracy and hallucinations are still a problem, let's talk. Book a 20 minute demo with the link in the next thread. We'll dig into your setup and find out how we can help.
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AI agents are failing in production...not a surprise. As you scale your knowledge base, embeddings start creating noise. It’s called ‘semantic collapse’ - when conversations run too long, you have hundreds of PDFs, millions of data points to give to your AI. Your AI can’t flag it because it doesn’t know it’s hallucinating. Similarity gets passed off as relevance. Fix your context. Make your agents work. Build intelligent AI. If your AI is plateauing at 50% accuracy and hallucinations are still a problem, let's talk. Book a 20 minute demo with the link in the next thread. We'll dig into your setup and find out how we can help.

Nishkarsh

1,297,778 次观看 • 3 个月前

Friends this is not the time to fear ai 🤖 this is the time to be curious and play with it and understand it … please 🙏 don’t put your head in the sand … there is so much you can’t control when it comes to ai 🤖 and how it will play out … but there is a lot you can control on how you work with it and use it to build something for you and your family !!! Pls If you’re finally ready to not be scared of ai pls leave a 🤖 emoji in the the chat
0:30

Friends this is not the time to fear ai 🤖 this is the time to be curious and play with it and understand it … please 🙏 don’t put your head in the sand … there is so much you can’t control when it comes to ai 🤖 and how it will play out … but there is a lot you can control on how you work with it and use it to build something for you and your family !!! Pls If you’re finally ready to not be scared of ai pls leave a 🤖 emoji in the the chat

Gary Vaynerchuk

15,342 次观看 • 8 个月前

AI shouldn’t be controlled by a single company or person, and the reason why most AI tools are "free" is because you are the product. Your data is more valuable to them, than the tool itself. That's why Action Model is different. We use tokens so: • Every AI action has a real cost • That cost is transparent • Value flows back to contributors, not one corporation The token represents a fractional stake in the AI itself; economic rights, governance rights, and participation in its growth. And that’s how you run AI without centralized control.
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AI shouldn’t be controlled by a single company or person, and the reason why most AI tools are "free" is because you are the product. Your data is more valuable to them, than the tool itself. That's why Action Model is different. We use tokens so: • Every AI action has a real cost • That cost is transparent • Value flows back to contributors, not one corporation The token represents a fractional stake in the AI itself; economic rights, governance rights, and participation in its growth. And that’s how you run AI without centralized control.

Action Model

47,135 次观看 • 5 个月前

CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below
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CEO of Nvidia said something that stopped me cold: "You may or may not lose a job to an AI, but you will absolutely lose a job to someone who uses AI" this 41 minutes is the most important talk on AI you'll watch this year so the question is not whether AI is coming for your job it's whether you're the one using it or the one being replaced by someone who does Nvidia already has hundreds of thousands of agents running inside the company right now agents are not the future, they are how the best teams work today I wrote a step by step guide on how to build your first AI agent with Claude Code you can find it in the article below

Anatoli Kopadze

14,627 次观看 • 1 个月前

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:
6:05

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 次观看 • 3 个月前

📣 Agent Forge is LIVE! What if creating your own team of AI agents took minutes — not months? Say hello to Agent Forge — the platform that puts AI agents at your fingertips. Here’s what you can do: 🔹 Automate your operations 🔹 Create an entire team of AI Agents 🔹 Use real-time data fetching Agents 🔹 Build powerful AI agent workflows fast 🔹 Design intelligent workflows using our drag & drop mechanism Whether you're building a startup, launching a side hustle, or just exploring AI for trading & research — Agent Forge is your new playground. ✨ Get $65 in free credits as a first-time user — no strings attached. 👉 Try it now:
0:39

📣 Agent Forge is LIVE! What if creating your own team of AI agents took minutes — not months? Say hello to Agent Forge — the platform that puts AI agents at your fingertips. Here’s what you can do: 🔹 Automate your operations 🔹 Create an entire team of AI Agents 🔹 Use real-time data fetching Agents 🔹 Build powerful AI agent workflows fast 🔹 Design intelligent workflows using our drag & drop mechanism Whether you're building a startup, launching a side hustle, or just exploring AI for trading & research — Agent Forge is your new playground. ✨ Get $65 in free credits as a first-time user — no strings attached. 👉 Try it now:

AITECH CLOUD NETWORK

598,953 次观看 • 1 年前

Introducing Theoriq’s AlphaSwarm! ⚡️ We’re thrilled to give developers access to a starter toolkit for building AI-powered trading agents in DeFi (AiFi)! 🦾 AlphaSwarm streamlines agent and swarm development, making it easier to build, execute, and refine collaborative agents that can analyze real-time market signals, and autonomously execute trades across multiple chains. Check out our latest blog for more details! 👇 You can explore the toolkit directly in Theoriq’s GitHub: Or if you’re a developer interested in building the future of AiFi, join our dev community in Discord:
0:30

Introducing Theoriq’s AlphaSwarm! ⚡️ We’re thrilled to give developers access to a starter toolkit for building AI-powered trading agents in DeFi (AiFi)! 🦾 AlphaSwarm streamlines agent and swarm development, making it easier to build, execute, and refine collaborative agents that can analyze real-time market signals, and autonomously execute trades across multiple chains. Check out our latest blog for more details! 👇 You can explore the toolkit directly in Theoriq’s GitHub: Or if you’re a developer interested in building the future of AiFi, join our dev community in Discord:

Theoriq

103,751 次观看 • 1 年前

Palantir CEO Alex Karp says people think we’re in an AI bubble because a lot of AI just doesn’t work: “If you just buy LLMs off the shelf and try to do any of this, it won’t work.” “It’s not precise enough. You can’t do underwriting. You can’t do these things that are regulated.” “People have tried things that just can never work. You buy a LLM, put it on your stack, and wonder why it’s not working.” “What you’re going to see, especially in America, is people trying to do something like Ontology by hand.” “Once you build a software layer to orchestrate and manage the LLMs in a language your enterprise understands, you actually can create value.” “There’s a lot of discussion on if we’re in an AI bubble. What is the meaning of this bubble? If anything, we’re just in a lag. There’s a lot of AI, some of it works.” “Go back to the battlefield context: everybody in the world assumed this would not work. But now it does work. Now the question is, ‘How can I get it to work for my country?’” “Palantir barely has a sales force. In fact, it seems to be getting smaller and smaller every time I go see them.”
3:13

Palantir CEO Alex Karp says people think we’re in an AI bubble because a lot of AI just doesn’t work: “If you just buy LLMs off the shelf and try to do any of this, it won’t work.” “It’s not precise enough. You can’t do underwriting. You can’t do these things that are regulated.” “People have tried things that just can never work. You buy a LLM, put it on your stack, and wonder why it’s not working.” “What you’re going to see, especially in America, is people trying to do something like Ontology by hand.” “Once you build a software layer to orchestrate and manage the LLMs in a language your enterprise understands, you actually can create value.” “There’s a lot of discussion on if we’re in an AI bubble. What is the meaning of this bubble? If anything, we’re just in a lag. There’s a lot of AI, some of it works.” “Go back to the battlefield context: everybody in the world assumed this would not work. But now it does work. Now the question is, ‘How can I get it to work for my country?’” “Palantir barely has a sales force. In fact, it seems to be getting smaller and smaller every time I go see them.”

Jawwwn

695,318 次观看 • 5 个月前

Stop using AI like a chatbot. It can literally run your research for you. They don’t work. They just… talk. Until now. I tested a setup that actually executes tasks: → Claude Code plans → XCrawl fetches real-time data → AI analyzes + delivers insights No fluff. Just output. I gave it a real task: “Analyze top candle brands, pricing & trends” What happened next was wild 👇 Instead of guessing: XCrawl: → searched competitors → crawled real product pages → returned structured data Claude: → compared pricing → spotted trends → delivered insights Result (in minutes): - actual price ranges - best-selling product types - real positioning strategies No tabs. No copy-paste. No manual research. This is the real shift: Old AI: “Here’s an answer.” New AI: “I’ve already done the work.” If you’re still using AI like Google… you’re already behind. Try it yourself (free 1,000 credits): Docs:
0:42

Stop using AI like a chatbot. It can literally run your research for you. They don’t work. They just… talk. Until now. I tested a setup that actually executes tasks: → Claude Code plans → XCrawl fetches real-time data → AI analyzes + delivers insights No fluff. Just output. I gave it a real task: “Analyze top candle brands, pricing & trends” What happened next was wild 👇 Instead of guessing: XCrawl: → searched competitors → crawled real product pages → returned structured data Claude: → compared pricing → spotted trends → delivered insights Result (in minutes): - actual price ranges - best-selling product types - real positioning strategies No tabs. No copy-paste. No manual research. This is the real shift: Old AI: “Here’s an answer.” New AI: “I’ve already done the work.” If you’re still using AI like Google… you’re already behind. Try it yourself (free 1,000 credits): Docs:

Shruti Codes

20,473 次观看 • 3 个月前

🚀 I wrote one line… and an entire AI team started running a business. No hiring. No setup. No waiting. This isn’t hype. This is what naïve actually does. Here’s how it works 👇 1️⃣ You describe your idea 2️⃣ AI instantly creates a team of agents 3️⃣ They start executing — right away Each agent operates like a real employee: → has its own email → manages tasks independently → coordinates with other agents → works in parallel So instead of doing everything yourself… Your AI team is: → booking calls → writing content → building products → handling support → managing operations You’re not prompting a tool anymore. You’re watching a system run your company. 📊 Early users are already seeing: +32% growth week-over-week +42% more tasks completed 💡 The shift is simple: From building a team → to deploying one with a prompt You don’t need a co-founder. You don’t need a full team. You just need one idea. Try it here → #NaiveAI #AI #Automation #Startups #FutureOfWork
0:57

🚀 I wrote one line… and an entire AI team started running a business. No hiring. No setup. No waiting. This isn’t hype. This is what naïve actually does. Here’s how it works 👇 1️⃣ You describe your idea 2️⃣ AI instantly creates a team of agents 3️⃣ They start executing — right away Each agent operates like a real employee: → has its own email → manages tasks independently → coordinates with other agents → works in parallel So instead of doing everything yourself… Your AI team is: → booking calls → writing content → building products → handling support → managing operations You’re not prompting a tool anymore. You’re watching a system run your company. 📊 Early users are already seeing: +32% growth week-over-week +42% more tasks completed 💡 The shift is simple: From building a team → to deploying one with a prompt You don’t need a co-founder. You don’t need a full team. You just need one idea. Try it here → #NaiveAI #AI #Automation #Startups #FutureOfWork

Haider Anis

213,477 次观看 • 2 个月前

Meta's Chief AI Officer: "People will have one, maybe two agents they rely on for everything. More and more of your personal life and work." In a few years "do you have an agent" will sound like "do you have an email address." The clearest 20 minutes you'll find on where AI agents are headed. Watch it, then read the guide on how to create your own AI agent step by step below.
20:21

Meta's Chief AI Officer: "People will have one, maybe two agents they rely on for everything. More and more of your personal life and work." In a few years "do you have an agent" will sound like "do you have an email address." The clearest 20 minutes you'll find on where AI agents are headed. Watch it, then read the guide on how to create your own AI agent step by step below.

Anatoli Kopadze

83,399 次观看 • 12 天前

Most AI products help you create content. The best ones feel like real AI teammates working with you. I've been exploring Helio lately, and the biggest shift wasn't speed—it was how naturally it fits into a team-like workflow. You don’t just generate slides or content alone. You collaborate with AI teammates that help you structure ideas, refine messaging, and turn rough inputs into a clear narrative. For example, you can start with scattered notes, and an AI teammate helps you shape the story, another refines visuals, and another polishes the final output like a real creative team working in sync. Less solo building. More team-driven creation. Try Helio and build with AI teammates instead of just using tools.
0:31

Most AI products help you create content. The best ones feel like real AI teammates working with you. I've been exploring Helio lately, and the biggest shift wasn't speed—it was how naturally it fits into a team-like workflow. You don’t just generate slides or content alone. You collaborate with AI teammates that help you structure ideas, refine messaging, and turn rough inputs into a clear narrative. For example, you can start with scattered notes, and an AI teammate helps you shape the story, another refines visuals, and another polishes the final output like a real creative team working in sync. Less solo building. More team-driven creation. Try Helio and build with AI teammates instead of just using tools.

RAVI KUMAR SAHU

34,782 次观看 • 1 个月前

AI has changed education completely and you are just one prompt away from learning anything you want. OpenMAIC is an open-source platform from Tsinghua University that generates an entire interactive AI classroom in one click. Not just slides. You get an AI teacher who actually explains concepts with voice and real-time teaching actions. You get study-buddy agents who sit with you in a roundtable discussion. You get quizzes that adapt. You get a whiteboard where agents work through concepts live. The core idea is simple but powerful. True interactivity means you stop watching and start engaging. Multiple agents, not one algorithm. Community-driven, fully extensible. I gave it a single prompt. Plan Agent structured the entire content flow automatically and generated the session. I tested it by building a course on Machine Learning for Beginners. Here's what happened 👇 Sponsored
0:49

AI has changed education completely and you are just one prompt away from learning anything you want. OpenMAIC is an open-source platform from Tsinghua University that generates an entire interactive AI classroom in one click. Not just slides. You get an AI teacher who actually explains concepts with voice and real-time teaching actions. You get study-buddy agents who sit with you in a roundtable discussion. You get quizzes that adapt. You get a whiteboard where agents work through concepts live. The core idea is simple but powerful. True interactivity means you stop watching and start engaging. Multiple agents, not one algorithm. Community-driven, fully extensible. I gave it a single prompt. Plan Agent structured the entire content flow automatically and generated the session. I tested it by building a course on Machine Learning for Beginners. Here's what happened 👇 Sponsored

AshutoshShrivastava

67,085 次观看 • 4 个月前

Here’s why OpenHome creator Jesse Leimgruber says, if you’re still prompting OpenClaw🦞 on a screen, you’re falling behind! The whole point of AI agents is that they can do things for you in the real world. But what’s the point if you’re still interacting with them exclusively through a laptop. That’s why Jesse created a smart speaker that allows you to interact vocally with your agent from any room in your home, 24/7. Having an agent listen in on your conversations, and learn all about you and your life in context, opens up an entirely new level of productivity… and might just save your Valentine’s Day as well.
5:01

Here’s why OpenHome creator Jesse Leimgruber says, if you’re still prompting OpenClaw🦞 on a screen, you’re falling behind! The whole point of AI agents is that they can do things for you in the real world. But what’s the point if you’re still interacting with them exclusively through a laptop. That’s why Jesse created a smart speaker that allows you to interact vocally with your agent from any room in your home, 24/7. Having an agent listen in on your conversations, and learn all about you and your life in context, opens up an entirely new level of productivity… and might just save your Valentine’s Day as well.

This Week in Startups

209,839 次观看 • 4 个月前

Content creation just leveled up with AI agents. I just tested Superagent by Airtable, and it’s insane… Instead of doing research + planning + writing manually, you can run multiple AI agents at the same time and get a complete Super Report at the end. What surprised me the most: ✅ Agents work in parallel (faster and more organized) ✅ The report is interactive and easy to explore ✅ It includes cited sources, so it’s actually reliable ✅ You can even generate outputs like docs / slides / websites It feels like having a full strategy team… on demand. Get 2 FREE months of Superagent using the code: ADAM2FREE → #SuperagentPartner #ad #SuperagentbyAirtable Airtable Superagent
1:13

Content creation just leveled up with AI agents. I just tested Superagent by Airtable, and it’s insane… Instead of doing research + planning + writing manually, you can run multiple AI agents at the same time and get a complete Super Report at the end. What surprised me the most: ✅ Agents work in parallel (faster and more organized) ✅ The report is interactive and easy to explore ✅ It includes cited sources, so it’s actually reliable ✅ You can even generate outputs like docs / slides / websites It feels like having a full strategy team… on demand. Get 2 FREE months of Superagent using the code: ADAM2FREE → #SuperagentPartner #ad #SuperagentbyAirtable Airtable Superagent

Adam

17,890 次观看 • 5 个月前

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.
0:31

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

OptimAI Network

50,405 次观看 • 10 个月前

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.
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Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,772 次观看 • 1 年前