Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Kimi 2.6 Agent Swarm is crazy. It deployed 12 parallel research agents, did 200+ web searches, and found me 30 current AI DevRel opportunities at Sarvam AI, Razorpay, Anthropic, NVIDIA, Groq, AWS, MongoDB, GitHub, OpenAI, and more + generated report was also very detailed.

ai_for_success's profile picture

AshutoshShrivastava

80,991 subscribers

47,391 Aufrufe • vor 4 Monaten •via X (Twitter)

Wissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Who else is developing AI-generated podcasts? 🤖 NVIDIA announced their new AI technology last night at CES, showcasing AI Agents and more! We’re excited for what’s to come from our very own AI-generated podcast tech stack, and The Agentic Report on Base 🎮🎙️
0:29

Who else is developing AI-generated podcasts? 🤖 NVIDIA announced their new AI technology last night at CES, showcasing AI Agents and more! We’re excited for what’s to come from our very own AI-generated podcast tech stack, and The Agentic Report on Base 🎮🎙️

METACADE

10,880 Aufrufe • vor 1 Jahr

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.
1:05:45

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 Aufrufe • vor 3 Monaten

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.
1:21

Excited to share our new project 🦉OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… 🦉OWL: Everyone’s talking about Manus AI (an agent that can research, browse, code, and automate tasks.) But there’s a catch: → All the above agents cost $20 to $200 a month → Manus AI is available via an invite code only OWL by CAMEL-AI is an opensource project that’s breaking records: ⭐ 6K+ GitHub stars in 2 days 🏆 Ranks #1 on the GAIA Benchmark among open-source projects with a 58.18 avg score! 📌 Specialized Multi-Agent Collaboration ↳ AI user agents break down complex tasks ↳ Assistants agents create detailed execution strategies and interact with tools and tool agents ↳ Tools / Tool agents interface with external services and APIs 📌 Real-World Capabilities ↳ Autonomous research, coding, and web browsing ↳ Runs locally on your machine for privacy ↳ Supports top LLMs (easily integrated with GPT-4O, Claude 3.7, Gemini, Mistral, DeepSeek , Qwen, Ollama, Groq more) ↳ Automates complex workflows, from research to execution I 📌 Simple Setup ↳ Fast installs via conda or uv ↳ Docker support for easy deployment ↳ Straightforward config with TOML files No restrictions. No invites. No paywalls. Just powerful multi-agent collaboration. AI autonomy shouldn’t be locked behind paywalls.

Guohao Li 🐫

223,252 Aufrufe • vor 1 Jahr

Today we’re launching Decawork (YC S26) backed by Y Combinator : one place for IT to deploy, govern, and maintain the internal AI agents running across a company. Every team is building AI tools/agents now. They go live with real access, no standard way to ship, nobody watching. With Decawork, every agent - built in Claude Code, Codex, or anywhere else - ships with its own identity and scoped credentials. IT signs off before it's live, sensitive actions wait for approval, and every run is watched from first to last. I built AI at NVIDIA, deployed at OpenAI and Meta. Aman (aman) built Barclays' AI compliance platform used by internal teams, then shipped agents to 100K+ monthly users. We lived this and quit to solve it. Internal AI agents/tools piling up at your company? Chat with us founders[at]decawork[dot]ai or
1:15

Today we’re launching Decawork (YC S26) backed by Y Combinator : one place for IT to deploy, govern, and maintain the internal AI agents running across a company. Every team is building AI tools/agents now. They go live with real access, no standard way to ship, nobody watching. With Decawork, every agent - built in Claude Code, Codex, or anywhere else - ships with its own identity and scoped credentials. IT signs off before it's live, sensitive actions wait for approval, and every run is watched from first to last. I built AI at NVIDIA, deployed at OpenAI and Meta. Aman (aman) built Barclays' AI compliance platform used by internal teams, then shipped agents to 100K+ monthly users. We lived this and quit to solve it. Internal AI agents/tools piling up at your company? Chat with us founders[at]decawork[dot]ai or

sarthak 🌉

96,069 Aufrufe • vor 14 Tagen

Your research agent can search the web in English. But the evidence that should shape your answer is in French film institute reports, Japanese trade publications, Brazilian creator communities, and Polish cinema data. That is not a model problem. It is a retrieval and orchestration problem. Neither gets solved by a bigger model. Here's a deep dive of the Temporal, OpenAI, and Box AI research agent we built. The agent plans multilingual queries, searches concurrently across four markets, synthesizes a cited master report, publishes localized briefs to Box with retry-safe versioning, and asks Box AI to compare the external findings against your internal governed content. End-to-end durable research.
1:13

Your research agent can search the web in English. But the evidence that should shape your answer is in French film institute reports, Japanese trade publications, Brazilian creator communities, and Polish cinema data. That is not a model problem. It is a retrieval and orchestration problem. Neither gets solved by a bigger model. Here's a deep dive of the Temporal, OpenAI, and Box AI research agent we built. The agent plans multilingual queries, searches concurrently across four markets, synthesizes a cited master report, publishes localized briefs to Box with retry-safe versioning, and asks Box AI to compare the external findings against your internal governed content. End-to-end durable research.

Box

34,452 Aufrufe • vor 25 Tagen

I don't think specialized vector databases will survive for much longer. They were a big thing for a while, but now, you have pro-grade databases like MongoDB offering native support for vector search. If you don't understand how vector search is an absolutely bonkers, mind-blowing invention, watch the attached video. Give me 4 minutes, and I promise you'll understand how vectors have completely changed the game. Honestly, MongoDB is cracked! They have become a one-stop shop for AI implementations in addition to its document and non-relational model. You'll find a link below to the source code of 7 examples of AI agents that use MongoDB: 1. A factory safety assistant agent 2. A research assistant agent 3. An investment researcher agent 4. A recommendation system agent using RAG 5. A human resources chatbot agent 6. An AWS Bedrock agent 7. An asset manager assistant agent Everything is on a GitHub repository. Link below.
4:12

I don't think specialized vector databases will survive for much longer. They were a big thing for a while, but now, you have pro-grade databases like MongoDB offering native support for vector search. If you don't understand how vector search is an absolutely bonkers, mind-blowing invention, watch the attached video. Give me 4 minutes, and I promise you'll understand how vectors have completely changed the game. Honestly, MongoDB is cracked! They have become a one-stop shop for AI implementations in addition to its document and non-relational model. You'll find a link below to the source code of 7 examples of AI agents that use MongoDB: 1. A factory safety assistant agent 2. A research assistant agent 3. An investment researcher agent 4. A recommendation system agent using RAG 5. A human resources chatbot agent 6. An AWS Bedrock agent 7. An asset manager assistant agent Everything is on a GitHub repository. Link below.

Santiago

60,065 Aufrufe • vor 1 Jahr

1/ ChatGPT is great, but increasingly more competitors are entering the market - and they have features that make OpenAI and Anthropic look somewhat outdated. Genspark is Silicon Valley’s first AI agent unicorn - 30 people, $1B valuation in 6 months - and it’s built as an all-in-one AI workspace where work actually gets done for you. Now with 3 new amazing features: • AI Inbox – talk to your email in text or voice, auto-summaries + replies, scheduled AI agents • Teams – shared projects where your team + AI co-edit slides/docs/sheets/designs • AI Sheets 2.0 – fully-automated, Excel-native data superpowers. In this example, I used Slides to create an overview of NVIDIA's earnings. It's incredible how detailed and with how many graphs it was presented!
0:38

1/ ChatGPT is great, but increasingly more competitors are entering the market - and they have features that make OpenAI and Anthropic look somewhat outdated. Genspark is Silicon Valley’s first AI agent unicorn - 30 people, $1B valuation in 6 months - and it’s built as an all-in-one AI workspace where work actually gets done for you. Now with 3 new amazing features: • AI Inbox – talk to your email in text or voice, auto-summaries + replies, scheduled AI agents • Teams – shared projects where your team + AI co-edit slides/docs/sheets/designs • AI Sheets 2.0 – fully-automated, Excel-native data superpowers. In this example, I used Slides to create an overview of NVIDIA's earnings. It's incredible how detailed and with how many graphs it was presented!

Chubby♨️

48,864 Aufrufe • vor 9 Monaten

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

17,913 Aufrufe • vor 7 Monaten

Firecrawl open-sourced "web-agent," a new framework that allows developers to build AI agents capable of autonomously executing "search-scrape-interact" loops on the live web. By running a single command, developers can scaffold a complete project with streaming UI, API, or library templates. The framework uses a "Plan-Act" mechanism and can spin up parallel sub-agents to process multiple websites simultaneously. It is entirely model-agnostic, allowing you to plug in OpenAI, Anthropic (with recent builds defaulting to Claude Opus 4.7), or your own self-hosted models.
0:25

Firecrawl open-sourced "web-agent," a new framework that allows developers to build AI agents capable of autonomously executing "search-scrape-interact" loops on the live web. By running a single command, developers can scaffold a complete project with streaming UI, API, or library templates. The framework uses a "Plan-Act" mechanism and can spin up parallel sub-agents to process multiple websites simultaneously. It is entirely model-agnostic, allowing you to plug in OpenAI, Anthropic (with recent builds defaulting to Claude Opus 4.7), or your own self-hosted models.

Wes Roth

53,653 Aufrufe • vor 4 Monaten

Sam is absolutely right. The moment AI agents get full access to a computer and the web, we cross a threshold from “tools we use” to “systems that act.” This is not just another upgrade. It is the beginning of autonomous productivity at scale. Research, execution, iteration, all happening in parallel while we focus on higher level thinking. The interface era is ending. The agent era is beginning.
0:40

Sam is absolutely right. The moment AI agents get full access to a computer and the web, we cross a threshold from “tools we use” to “systems that act.” This is not just another upgrade. It is the beginning of autonomous productivity at scale. Research, execution, iteration, all happening in parallel while we focus on higher level thinking. The interface era is ending. The agent era is beginning.

VraserX e/acc

11,102 Aufrufe • vor 6 Monaten

“ OpenAI and Anthropic will have to create their own chips” “ AI is going to cause massive labor shortages.” “ I would be surprised if in five years Nvidia wasn't worth $10 trillion” The best podcast episodes are opinionated, gritty and go against everything you thought you knew. This is that and more and my 7 takeaways with Jonathan Ross 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: (00:00) Intro (01:10) Analyzing the Current Market Landscape (03:33) Why the Hyperscalers Have to Keep Spending Recklessly on AI (12:35) Why OpenAI and Anthropic Will Have to Build Their Own Chips (18:14) OpenAI and Anthropic Will be $5BN Companies: The Bull Case (28:20) Why China is Behind the US in AI and Deepseek is More Expensive to Run (34:37) How Europe Could Compete in AI and Why the US is More Risk Averse Than Europe (37:21) Why We Have to Have Nuclear Energy and How to Bring it Back (48:16) Deflationary Pressures and New Job Markets (51:54) The Future of Vibe Coding (53:40) Why AI Companies Should Strive to Have Low Margins (58:46) S&P 7000, Mag 7 & Market Choppiness (01:06:44) Why OpenAI and Anthropic are so Undervalued (01:13:35) The Chip Market in 5 Years (01:21:51) Quick-Fire Round: Biggest Fear, Nvidia: $10TRN, Zuck Buying AI: Work or Not
1:31:19

“ OpenAI and Anthropic will have to create their own chips” “ AI is going to cause massive labor shortages.” “ I would be surprised if in five years Nvidia wasn't worth $10 trillion” The best podcast episodes are opinionated, gritty and go against everything you thought you knew. This is that and more and my 7 takeaways with Jonathan Ross 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: (00:00) Intro (01:10) Analyzing the Current Market Landscape (03:33) Why the Hyperscalers Have to Keep Spending Recklessly on AI (12:35) Why OpenAI and Anthropic Will Have to Build Their Own Chips (18:14) OpenAI and Anthropic Will be $5BN Companies: The Bull Case (28:20) Why China is Behind the US in AI and Deepseek is More Expensive to Run (34:37) How Europe Could Compete in AI and Why the US is More Risk Averse Than Europe (37:21) Why We Have to Have Nuclear Energy and How to Bring it Back (48:16) Deflationary Pressures and New Job Markets (51:54) The Future of Vibe Coding (53:40) Why AI Companies Should Strive to Have Low Margins (58:46) S&P 7000, Mag 7 & Market Choppiness (01:06:44) Why OpenAI and Anthropic are so Undervalued (01:13:35) The Chip Market in 5 Years (01:21:51) Quick-Fire Round: Biggest Fear, Nvidia: $10TRN, Zuck Buying AI: Work or Not

Harry Stebbings

106,162 Aufrufe • vor 11 Monaten

holy s*it, it's happening! AI is doing your job! The latest Gemini with Deep Research just checked 79 sites for me, and gave me the report I asked. What would take a human 2-3h was done in 3 min!! uhm 2025 is the last year. AI agents are coming, this is proof. --- The report was not perfect (too boring for me) but it cited the resources and it was accurate. I saw for some people it would scan 300+ websites. It adds the final result in a Doc file, it looks like a proper research paper. Perplexity is cooked. But also many humans are cooked. Imagine not using this tech next year.
2:11

holy s*it, it's happening! AI is doing your job! The latest Gemini with Deep Research just checked 79 sites for me, and gave me the report I asked. What would take a human 2-3h was done in 3 min!! uhm 2025 is the last year. AI agents are coming, this is proof. --- The report was not perfect (too boring for me) but it cited the resources and it was accurate. I saw for some people it would scan 300+ websites. It adds the final result in a Doc file, it looks like a proper research paper. Perplexity is cooked. But also many humans are cooked. Imagine not using this tech next year.

Alex Northstar

545,434 Aufrufe • vor 1 Jahr

NVIDIA is giving away free access to 140+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3-Ultra-550B-A55B, Kimi K2.7, and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free with this many models in one place, it is also the easiest way to test which one actually fits your agent before you commit to a paid provider
0:29

NVIDIA is giving away free access to 140+ AI models for a full year > most people building AI agents are paying $50-200/month for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3-Ultra-550B-A55B, Kimi K2.7, and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year while everyone is paying for API access, this is sitting there for free with this many models in one place, it is also the easiest way to test which one actually fits your agent before you commit to a paid provider

Mr. Buzzoni

121,377 Aufrufe • vor 1 Monat

A 21-YEAR-OLD FROM CHINA RUNS 300 AI AGENTS AT ONCE. THE PART THAT MATTERS ISN'T THE SPEED, IT'S THAT NONE OF THEM CAN LIE TO HIM he opens the dashboard and shows the swarm live, 300 Kimi K2.6 agents firing in parallel, then Opus 4.8 checking every single output against its source. this is not just a faster swarm. it is a loop that refuses to stop while anything is still wrong he pointed it at 100 EV-market companies. first pass: 12 failed. wrong revenue, dead citations, empty fields. second pass: 3 failed. third pass: zero this is not another agent demo. it is a system that catches its own mistakes before he reads a single row
0:30

A 21-YEAR-OLD FROM CHINA RUNS 300 AI AGENTS AT ONCE. THE PART THAT MATTERS ISN'T THE SPEED, IT'S THAT NONE OF THEM CAN LIE TO HIM he opens the dashboard and shows the swarm live, 300 Kimi K2.6 agents firing in parallel, then Opus 4.8 checking every single output against its source. this is not just a faster swarm. it is a loop that refuses to stop while anything is still wrong he pointed it at 100 EV-market companies. first pass: 12 failed. wrong revenue, dead citations, empty fields. second pass: 3 failed. third pass: zero this is not another agent demo. it is a system that catches its own mistakes before he reads a single row

NO1ennn

6,950,217 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 11 Monaten

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation
37:00

We’re back for Episode 14 of TAO Talk 🚨 calanthia from Masa joins 563 and brody this week to chat about new subnets and AI agents sourcing intelligence from Bittensor! The group chats about: - $TAO -pilling AI/ML chads at NeurIPS Conference feat. const Crucible Labs Macrocosmos Manifold and Yuma - JJ teaming up with Cameron Fairchild to form Laτenτ Holdings, which will validate and help scale subnets - Crucible Labs drops a subnet analysis framework - Celium offering H100s cheaper than any other provider - Tao360 releases inaugural research report for their AI-enabled subnet analysis tool @notYourBananaa - Masa unveils the AI Agent Arena on SN59 /// Timestamps: 00:00 Intro 01:10 Subnet 42 and Agent Arena: Masa’s Subnets 03:00 Real-Time Data Networks for AI 04:20 How Masa is Building AI Agent Arenas Inspired by Gladiators 06:10 Decentralized AI and Bittensor: The Growing Ecosystem 08:15 AI Meets Web3: Masa’s Role in Revolutionizing Data Networks 10:00 The Future of AI Agents: Intelligent Societies and Real-Time Data 12:05 Why Masa Chose Bittensor 14:10 $TAO Incentives and the Future of AI Decentralization 16:25 Bittensor and the Rise of Agent Competition: Masa’s Perspective 18:00 Exploring AI Agent Societies 20:30 Creating Competitive AI Arenas: The Agent Arena Subnet Explained 23:00 Calanthia on the Challenges of Web3 AI Development 25:10 Bringing Web2 Developers into Web3: Lessons from Masa 27:30 The Evolution of AI: From Dumb Agents to Intelligent Societies 30:00 AI Agents as the Future of Interaction in Decentralized AI 34:00 The Agent Arena’s Vision: Competition, Incentives, and Innovation

TAO τalk 🥩🦍

24,929 Aufrufe • vor 1 Jahr