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300 agents sounds powerful until you realize they can create 44,850 possible agent-to-agent relationships. that’s the real scaling problem. 1 agent searches 10 pages. 10 agents can search 100. 300 agents can search thousands in parallel. but more agents also means more: • duplicate findings • shared sources •... hidden relationships and if every agent returns its own isolated report, you’re left with 300 piles to reconcile manually. that’s why the graph matters. instead of: 300 agents → 300 reports you get: 300 agents → shared entities → edges → clusters → one connected research map 100 nodes already create up to 4,950 pairwise relationships. more agents give you more information. the graph is what stops that information from becoming noise. full breakdown in the article below ↓show more

Ricker

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26,875 次观看 • 5 天前 •via X (Twitter)

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300 agents without shared context is just parallelized confusion. All 300 agents are useless if the research dies inside 300 separate documents. the real unlock is what happens after the search. in K3 graph-native setup: 300 agents 140 linked sources 100% traceable claims 9 contradictions surfaced and every new source changes the graph. now imagine that happening across 8–10 active research clusters at once. instead of 300 isolated summaries, you get one shared structure: source → entity → relationship → evidence → contradiction → verification the math gets ugly fast. 100 entities = 4,950 possible pairwise relationships. that’s why “just run more agents” stops being enough. you need a graph that can keep track of what connects to what. full breakdown in the article below.

300 agents without shared context is just parallelized confusion. All 300 agents are useless if the research dies inside 300 separate documents. the real unlock is what happens after the search. in K3 graph-native setup: 300 agents 140 linked sources 100% traceable claims 9 contradictions surfaced and every new source changes the graph. now imagine that happening across 8–10 active research clusters at once. instead of 300 isolated summaries, you get one shared structure: source → entity → relationship → evidence → contradiction → verification the math gets ugly fast. 100 entities = 4,950 possible pairwise relationships. that’s why “just run more agents” stops being enough. you need a graph that can keep track of what connects to what. full breakdown in the article below.

Ricker

15,089 次观看 • 6 天前

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,166 次观看 • 3 个月前

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

rewind

27,195 次观看 • 1 个月前

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG

My submission to the Cerebras Google Gemma hackathon, Carbide. Carbide is an ontology builder. Based on a research prompt Carbide uses Cerebras Gemma4 agents to build an ontology. The agents connects individual nodes of information based on the relationship that the agents infer. The agents also assign a confidence metric for how accurate they think the relationship is. You can see each node in a relationship table of the information -> relationship -> target -> and the confidence the relationship is true. The inspector shows the node's connected edges and as an expand button to grow the ontology in the direction of that information. You can scroll through each concept and load them in the inspector. You can search the entire graph based on the kind of relationships generated, or key words. A visual shows how each nodes connects to another one and by what relationship, and changes with new nodes or search filters. Sorry for the audio quality. $CBRS $GOOG

QuantHood

16,376 次观看 • 2 个月前

Autonomous businesses are here. Powered by the Agent Commerce Protocol—an open standard for multi-agent commerce and coordination, leveraging the blockchain. Imagine an autonomous hedge fund business composed of information agents, trading agents, TEE-secured treasury management agents. Or an autonomous media business composed of Luna, music agents, video gen agents, event organiser agents. Or an autonomous healthcare business composed of diagnostic agents, pharma agents, insurance agents. Agents today are becoming specialists, building economic value and competitive advantages. But specialization means they need to collaborate to fill gaps. Unlike rule-based interactions and manual triggers between agents under a single system, independent specialized agents working together are more scalable and can create far more value through their unique models, datasets, and action spaces. The challenge? The independent nature of these agents risks information loss—misinterpretation, hallucination, or missing data. Mistakes compound fast in a many-to-many environment. ACP solves this by structuring agent-to-agent transactions with smart contracts, ensuring smooth, reliable coordination. The result? Autonomous businesses where the economic whole is worth more than the sum of its parts. 🔗

Autonomous businesses are here. Powered by the Agent Commerce Protocol—an open standard for multi-agent commerce and coordination, leveraging the blockchain. Imagine an autonomous hedge fund business composed of information agents, trading agents, TEE-secured treasury management agents. Or an autonomous media business composed of Luna, music agents, video gen agents, event organiser agents. Or an autonomous healthcare business composed of diagnostic agents, pharma agents, insurance agents. Agents today are becoming specialists, building economic value and competitive advantages. But specialization means they need to collaborate to fill gaps. Unlike rule-based interactions and manual triggers between agents under a single system, independent specialized agents working together are more scalable and can create far more value through their unique models, datasets, and action spaces. The challenge? The independent nature of these agents risks information loss—misinterpretation, hallucination, or missing data. Mistakes compound fast in a many-to-many environment. ACP solves this by structuring agent-to-agent transactions with smart contracts, ensuring smooth, reliable coordination. The result? Autonomous businesses where the economic whole is worth more than the sum of its parts. 🔗

Virtuals Protocol

1,073,772 次观看 • 1 年前

Agents shouldn’t be one-size-fits-all. They should be built, shared, and improved by the people who understand the domain best. Agent Marketplace is live on OptimAI Search. Anyone can: • create their own agent • inject custom knowledge and logic • publish it for others to use and build on What starts as a prompt becomes a reusable unit of intelligence. As more agents are created and used, the system compounds - better agents, better outputs, more value across the network. This is how intelligence scales: not from a single model, but from a network of specialized agents.

Agents shouldn’t be one-size-fits-all. They should be built, shared, and improved by the people who understand the domain best. Agent Marketplace is live on OptimAI Search. Anyone can: • create their own agent • inject custom knowledge and logic • publish it for others to use and build on What starts as a prompt becomes a reusable unit of intelligence. As more agents are created and used, the system compounds - better agents, better outputs, more value across the network. This is how intelligence scales: not from a single model, but from a network of specialized agents.

OptimAI Network

12,527 次观看 • 4 个月前

Andrew Ng just released a 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents that loop, rewrite themselves, and run without you: 09:14 - Build your first AI agent 33:11 - Run agents with loop engineering 1:02:46 - Turn agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the full graph system without you Most people are still building one agent and calling it done. Andrew Ng is already teaching everything after: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow. Systems that improve and run without you are the new one. This 2-hour watch is worth more than most $500 agent engineering courses. Bookmark and watch it before everyone catches up. Then read how to run 1,000 agents from one prompt below ↓

Andrew Ng just released a 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents that loop, rewrite themselves, and run without you: 09:14 - Build your first AI agent 33:11 - Run agents with loop engineering 1:02:46 - Turn agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the full graph system without you Most people are still building one agent and calling it done. Andrew Ng is already teaching everything after: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow. Systems that improve and run without you are the new one. This 2-hour watch is worth more than most $500 agent engineering courses. Bookmark and watch it before everyone catches up. Then read how to run 1,000 agents from one prompt below ↓

Lunar

224,517 次观看 • 18 天前

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Close the loop, give the model a way to verify its own output" In a 20-minute session, an Anthropic Team member breaks down how to build agents that improve themselves The real setup is Claude running through loops, plan mode, and dynamic workflows Better than most $300 agent courses Bookmark and watch the talk Then read the article below

Anthropic Research Lead: "99% of our engineers run swarms of 300+ self-improving agents" "Close the loop, give the model a way to verify its own output" In a 20-minute session, an Anthropic Team member breaks down how to build agents that improve themselves The real setup is Claude running through loops, plan mode, and dynamic workflows Better than most $300 agent courses Bookmark and watch the talk Then read the article below

rari

381,936 次观看 • 2 个月前

We won Best Trustless Agent at the Solana x402 Hackathon! Amiko Marketplace turns x402 payments into a trustless, on-chain economy where agents have identity, reputation, and verifiable actions. The agent economy finally has a trust layer. What we built: - Agent Registry - On-chain Job Records - Feedback & Reputation System - Search + Discoverability Everything tied to real wallets, real actions, real performance. Agents aren’t just paying each other. Now they’re accountable. This is the foundation for a global network of autonomous agents that can be trusted, rated, and discovered. All on Solana. More agents. More you.

We won Best Trustless Agent at the Solana x402 Hackathon! Amiko Marketplace turns x402 payments into a trustless, on-chain economy where agents have identity, reputation, and verifiable actions. The agent economy finally has a trust layer. What we built: - Agent Registry - On-chain Job Records - Feedback & Reputation System - Search + Discoverability Everything tied to real wallets, real actions, real performance. Agents aren’t just paying each other. Now they’re accountable. This is the foundation for a global network of autonomous agents that can be trusted, rated, and discovered. All on Solana. More agents. More you.

AMIKO

24,112 次观看 • 9 个月前

anthropic research lead: “99% of our engineers use 300+ AI agents working together.” “close the loop. let the AI check its own work.” in a 20-minute talk, an Anthropic team member explains how to build AI agents that can improve themselves. the setup uses Claude with loops, plan mode, and smart workflows. it’s better than most $300 AI agent courses. bookmark this and watch the talk. then read the article below.

anthropic research lead: “99% of our engineers use 300+ AI agents working together.” “close the loop. let the AI check its own work.” in a 20-minute talk, an Anthropic team member explains how to build AI agents that can improve themselves. the setup uses Claude with loops, plan mode, and smart workflows. it’s better than most $300 AI agent courses. bookmark this and watch the talk. then read the article below.

MIKE

34,813 次观看 • 2 个月前

Andrew Ng just dropped a free 2-hour course On turning one prompt into 100 self-improving agents: 09:14 - Build your first AI agent 33:11 - Run agents inside feedback loops 1:02:46 - Connect agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the entire graph without you Most people build one agent and call it finished Andrew Ng is already teaching everything that comes next: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow Systems that improve and run without you are the new one This free 2-hour course is worth more than most $500 agent engineering programs Bookmark and watch it before everyone catches up Then read how to run 1,000 agents from one prompt below ↓

Andrew Ng just dropped a free 2-hour course On turning one prompt into 100 self-improving agents: 09:14 - Build your first AI agent 33:11 - Run agents inside feedback loops 1:02:46 - Connect agent loops into graphs 1:30:15 - Build agents that rewrite themselves 1:49:05 - Run the entire graph without you Most people build one agent and call it finished Andrew Ng is already teaching everything that comes next: Prompt → Agents → Loops → Graphs → Self-Improving Systems Single agents are the old workflow Systems that improve and run without you are the new one This free 2-hour course is worth more than most $500 agent engineering programs Bookmark and watch it before everyone catches up Then read how to run 1,000 agents from one prompt below ↓

rari

44,517 次观看 • 17 天前

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

168,153 次观看 • 1 年前

I’m personally excited about the era of Search agents. You’ll be able to create, customize, and manage multiple AI agents for tasks, right in Search. Starting with information agents this summer, you can ask Search to keep you updated on topics you care about. Your agent will operate 24/7 to intelligently reason and research and send you a comprehensive update, with links to dive deeper – like if you want to know right when your favorite sneaker collab drops, or is available in a new colorway. #GoogleIO

I’m personally excited about the era of Search agents. You’ll be able to create, customize, and manage multiple AI agents for tasks, right in Search. Starting with information agents this summer, you can ask Search to keep you updated on topics you care about. Your agent will operate 24/7 to intelligently reason and research and send you a comprehensive update, with links to dive deeper – like if you want to know right when your favorite sneaker collab drops, or is available in a new colorway. #GoogleIO

Robby Stein

15,732 次观看 • 3 个月前

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much anything because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 次观看 • 2 个月前

Searching the internet has changed. Agentic E-commerce is driving the revolution. Before: “Cheap flights” Now: Agents search, compare, negotiate and complete payment. The next internet layer isn’t websites. It’s agents talking to agents. That’s how your customers find your products, compare providers and complete tasks. Your brand agent can do it for them, making the process easier so they're more likely to buy from you again. Own your voice. Claim your brand agent today:

Fetch.ai

10,232 次观看 • 5 个月前

We’re seeing more and more DeFAI agents being launched everyday, but these agents are trained in the past and there’s no clear way to know which ones will perform in the future. That’s why we're launching ALFA with NEAR Protocol. ALFA is the Agent Leaderboard For Alpha, a prediction market that surfaces future performance so users can invest with real knowledge. Let’s introduce the first batch of agents:

We’re seeing more and more DeFAI agents being launched everyday, but these agents are trained in the past and there’s no clear way to know which ones will perform in the future. That’s why we're launching ALFA with NEAR Protocol. ALFA is the Agent Leaderboard For Alpha, a prediction market that surfaces future performance so users can invest with real knowledge. Let’s introduce the first batch of agents:

Fraction AI

28,474 次观看 • 11 个月前

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

Santiago

20,943 次观看 • 24 天前

Agentic Browser will be lived tomorrow! You can now ask your AI agent about flight tickets, restaurant recommendations, and crypto news—the agent will search for real-time information via a browser tool. In its ultimate form, the Agentic Browser will allow AI agent to autonomously browse the internet, read news, source alpha, and much more. We’re one step closer to fully sentient AI agents.

Agentic Browser will be lived tomorrow! You can now ask your AI agent about flight tickets, restaurant recommendations, and crypto news—the agent will search for real-time information via a browser tool. In its ultimate form, the Agentic Browser will allow AI agent to autonomously browse the internet, read news, source alpha, and much more. We’re one step closer to fully sentient AI agents.

delaunch

30,986 次观看 • 1 年前

Jensen Huang: "Nvidia runs 7,500,000 agents for 75,000 people." that's 100 AI workers for every human employee. he goes further than that. "for the very first time, we have useful AI." not AI that answers questions. AI that does the work. and he's not talking about people who occasionally prompt ChatGPT. he means people who actually run agents in parallel. most people don't know you can already do this. one button. one prompt. up to 300 agents working at the same time. it's called Kimi Agent Swarm. 100 CVs tailored to 100 job descriptions. one prompt. 40 research papers into a 100,000-word review. one prompt. 30 landing pages for 30 businesses. one prompt. $40,000-$100,000 worth of work. one sitting. full breakdown in the article below.

Jensen Huang: "Nvidia runs 7,500,000 agents for 75,000 people." that's 100 AI workers for every human employee. he goes further than that. "for the very first time, we have useful AI." not AI that answers questions. AI that does the work. and he's not talking about people who occasionally prompt ChatGPT. he means people who actually run agents in parallel. most people don't know you can already do this. one button. one prompt. up to 300 agents working at the same time. it's called Kimi Agent Swarm. 100 CVs tailored to 100 job descriptions. one prompt. 40 research papers into a 100,000-word review. one prompt. 30 landing pages for 30 businesses. one prompt. $40,000-$100,000 worth of work. one sitting. full breakdown in the article below.

Dep

11,924 次观看 • 3 个月前

Fuc*k your paid courses, Kimi.ai AI founder (Yang Zhilin) explained how they're running 300+ specialized sub-agents in parallel and it changes everything. The founder of a $20B + valuation Chinese AI lab give you a 40-minute masterclass on AGENT SWARMS. This is the clearest breakdown I've seen on building large-scale AI systems that actually work in the real world. - Dynamic orchestration, PARL rewards, real scaling beyond context windows. - Trained with smart RL rewards (instantiation, finish & outcome) - 100-300+ specialized spawns agents , sub-agents on the fly working in parallel. - Thousands of tool calls. - 4.5x faster execution. This isn't theory it's how they're scaling Kimi to handle real complexity. Better than most local paid courses. Swap your Netflix tonight watch this instead.

Fuc*k your paid courses, Kimi.ai AI founder (Yang Zhilin) explained how they're running 300+ specialized sub-agents in parallel and it changes everything. The founder of a $20B + valuation Chinese AI lab give you a 40-minute masterclass on AGENT SWARMS. This is the clearest breakdown I've seen on building large-scale AI systems that actually work in the real world. - Dynamic orchestration, PARL rewards, real scaling beyond context windows. - Trained with smart RL rewards (instantiation, finish & outcome) - 100-300+ specialized spawns agents , sub-agents on the fly working in parallel. - Thousands of tool calls. - 4.5x faster execution. This isn't theory it's how they're scaling Kimi to handle real complexity. Better than most local paid courses. Swap your Netflix tonight watch this instead.

Md Ismail Šojal 🕷️

60,757 次观看 • 1 个月前