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In the next two or three years, every human could have a hundred... maybe a thousand agents working for them onchain. Arpan Nanavati breaks it down perfectly in this clip... the whole thing is a cost curve. Right now, it costs about $10 a day in inference to run...

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Raoul Pal

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552,982 Aufrufe • vor 7 Tagen •via X (Twitter)

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$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.
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$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

2xnmore

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In seven or eight years there'll be gigantic hedge funds that nobody can see. They wont have an office or a star trader on the front page of the FT... the whole operation will be agents running capital through the liquidity layer. Think about what a hedge fund is. A group of managers allocating capital to pods of traders. Every one of those pods can be an agent on chain... your relative value bond agent, your equity arb agent, your long short agent. That alone strips out something like 90% of the cost of allocating capital. Then the mothership. Risk management, compliance, capital raising... AI eats all of that too. You can apply it to every asset management business on earth. An invisible economy is coming, and all of it inevitably happens onchain. DeFi is the obvious venue.
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In seven or eight years there'll be gigantic hedge funds that nobody can see. They wont have an office or a star trader on the front page of the FT... the whole operation will be agents running capital through the liquidity layer. Think about what a hedge fund is. A group of managers allocating capital to pods of traders. Every one of those pods can be an agent on chain... your relative value bond agent, your equity arb agent, your long short agent. That alone strips out something like 90% of the cost of allocating capital. Then the mothership. Risk management, compliance, capital raising... AI eats all of that too. You can apply it to every asset management business on earth. An invisible economy is coming, and all of it inevitably happens onchain. DeFi is the obvious venue.

Raoul Pal

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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail
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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

LangChain

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your agents can now get paid and spend money. create a wallet and a budget for every agent. you are now in control. ampersend is live. use or add the skill to your OpenClaw🦞
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your agents can now get paid and spend money. create a wallet and a budget for every agent. you are now in control. ampersend is live. use or add the skill to your OpenClaw🦞

ampersend

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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.
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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

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There are 8 billion people on earth. Soon there'll be 100 billion AI agents. Every one of them needs email. Six weeks ago I said the next wave of teams would run email through an agent instead of a dashboard. Today it ships. Nitrosend☄️ is launching Agentic Email Marketing: the email layer for the agent economy. What agents can do on Nitrosend right now: Sign themselves up. Point any agent at and it creates the account, connects your domain, sorts billing and sends its first email. No API key. No dashboard. No human required. Shipped, and users agents signing up with it daily. Get their own inboxes (beta, by request). Real addresses on the domain you own. Your agents receive, and send 1-1 email conversations with customers. A reply lands at 3am, your agent answers it. Anything that needs a human gets escalated to you. Ask us and we'll flick yours on. Next: Agentic Outreach (coming soon). Your agent studies your best customers, finds more like them, writes like a person, sends in sequence and works the replies. Then: set a goal and walk away. Goal-based agentic marketing is in development. "20% more activations this quarter" and Nitrosend plans, sends, measures and improves every week. Why we built this: Gmail is agent hostile and expensive per seat. Legacy email platforms assume a human sitting in a dashboard. agents needed an email layer of their own. They're already better at it than we are. They read everything, never miss a follow-up, and write personally at any scale. *94%* of actions on Nitrosend already happen inside an agent (Claude, Codex, ChatGPT, Cursor), not in our UI. Humans approve. Agents operate. This is our third email company. Six billion emails across the first two. We've been burned by every ugly part of email already, which is why the approval gates are built in exactly where you want them. Watch the launch, then send your agent to work: send it.
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There are 8 billion people on earth. Soon there'll be 100 billion AI agents. Every one of them needs email. Six weeks ago I said the next wave of teams would run email through an agent instead of a dashboard. Today it ships. Nitrosend☄️ is launching Agentic Email Marketing: the email layer for the agent economy. What agents can do on Nitrosend right now: Sign themselves up. Point any agent at and it creates the account, connects your domain, sorts billing and sends its first email. No API key. No dashboard. No human required. Shipped, and users agents signing up with it daily. Get their own inboxes (beta, by request). Real addresses on the domain you own. Your agents receive, and send 1-1 email conversations with customers. A reply lands at 3am, your agent answers it. Anything that needs a human gets escalated to you. Ask us and we'll flick yours on. Next: Agentic Outreach (coming soon). Your agent studies your best customers, finds more like them, writes like a person, sends in sequence and works the replies. Then: set a goal and walk away. Goal-based agentic marketing is in development. "20% more activations this quarter" and Nitrosend plans, sends, measures and improves every week. Why we built this: Gmail is agent hostile and expensive per seat. Legacy email platforms assume a human sitting in a dashboard. agents needed an email layer of their own. They're already better at it than we are. They read everything, never miss a follow-up, and write personally at any scale. *94%* of actions on Nitrosend already happen inside an agent (Claude, Codex, ChatGPT, Cursor), not in our UI. Humans approve. Agents operate. This is our third email company. Six billion emails across the first two. We've been burned by every ugly part of email already, which is why the approval gates are built in exactly where you want them. Watch the launch, then send your agent to work: send it.

George Hartley ☄️

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The Startup Ideas Podcast (SIP) 🧃

30,065 Aufrufe • vor 3 Monaten

Amp co-founder Beyang says people who think coding agents will replace humans are living in a "dismal fantasy land": "There's a lot of hype around coding agents replacing humans entirely, or agents in general replacing jobs. The capabilities of agents are evolving at a fast pace, but fundamentally they still do what you ask them to do." "What is very apparent to us in using agents day to day is that human intervention, that human guidance is still essential. If anything, it's more essential. Because if you have the wrong guidance in place, the cost of going off the rails, you can very quickly rack up a ton of token cost." "The whole, this is gonna make humans obsolete thing is just way over-hyped, to the point where some folks are living in a dismal fantasy land." Amp
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Amp co-founder Beyang says people who think coding agents will replace humans are living in a "dismal fantasy land": "There's a lot of hype around coding agents replacing humans entirely, or agents in general replacing jobs. The capabilities of agents are evolving at a fast pace, but fundamentally they still do what you ask them to do." "What is very apparent to us in using agents day to day is that human intervention, that human guidance is still essential. If anything, it's more essential. Because if you have the wrong guidance in place, the cost of going off the rails, you can very quickly rack up a ton of token cost." "The whole, this is gonna make humans obsolete thing is just way over-hyped, to the point where some folks are living in a dismal fantasy land." Amp

MTS

24,319 Aufrufe • vor 16 Tagen

There's probably $100+ billion up for grabs for people who build startup for AI agents Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. TLDR; The internet was built for people: 1. Search google 2. Read landing page 3. Book demo 4. Talk to sales 5. Buy Agents don’t do that. Agents will: 1. Ask which product to use 2. Read your docs/pricing/security pages 3. Compare you to competitors 4. Check if you have an MCP/API/tool layer 5. Buy or recommend you without ever “visiting” your site like a person Everyone is going to have personal agents and business agents. This feels inevitable at this point. OpenClaw, Hermes, Claude Code, Codex, Google Spark. The tools are here. Which means there will be more agents on the internet than humans. So, where's the opportunity?? Go look at every SaaS tool you use. Notion. Slack. Jira. Google Analytics. Now ask: what is the version of this built purely for agents? Agent-native payments. Agent-native communication. Agent-native memory. Every category gets rebuilt. I clearly break down this shift and explain you everything on today's ep of The Startup Ideas Podcast (SIP) 🧃. Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. The founders who build for them now are going to look like the people who built websites in 1995. Might feel janky at the moment, but also obvious in hindsight. This is the next shift. Link over here: Watch
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There's probably $100+ billion up for grabs for people who build startup for AI agents Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. TLDR; The internet was built for people: 1. Search google 2. Read landing page 3. Book demo 4. Talk to sales 5. Buy Agents don’t do that. Agents will: 1. Ask which product to use 2. Read your docs/pricing/security pages 3. Compare you to competitors 4. Check if you have an MCP/API/tool layer 5. Buy or recommend you without ever “visiting” your site like a person Everyone is going to have personal agents and business agents. This feels inevitable at this point. OpenClaw, Hermes, Claude Code, Codex, Google Spark. The tools are here. Which means there will be more agents on the internet than humans. So, where's the opportunity?? Go look at every SaaS tool you use. Notion. Slack. Jira. Google Analytics. Now ask: what is the version of this built purely for agents? Agent-native payments. Agent-native communication. Agent-native memory. Every category gets rebuilt. I clearly break down this shift and explain you everything on today's ep of The Startup Ideas Podcast (SIP) 🧃. Over the next 10 years you're going to have a market of billions of customers (agents) with millions of wallets that want to use your services. The founders who build for them now are going to look like the people who built websites in 1995. Might feel janky at the moment, but also obvious in hindsight. This is the next shift. Link over here: Watch

GREG ISENBERG

55,882 Aufrufe • vor 3 Monaten

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.
0:39

so I've been running exactly 8 AI agents on discord for a while now. coordination works great, they split tasks, hand off work, deliver results in parallel etc.. but there are problems I keep hitting that no amount of prompt engineering could fix agents don't learn from each other. Scout finds something useful but Luna has no idea. they work in the same server but knowledge stays locked in silos.. there's no quality filter on what gets saved, and good insights sit next to outdated garbage in the same memory files that I manually clean up.. and when an agent makes a mistake I write it down in the rules discord channel ,core memory file and hope it reads it next time. theres no self-correction, no automatic pattern recognition so of course no learning loops.. the coordination layer is solved. agents can work together. but the intelligence layer is still missing. agents that actually remember, learn from each other, filter noise, and get smarter every run. saw Spark building something like this with around 166 agents sharing a collective persistent knowledge across sessions, so agents learn from other agents and get smarter over time they even have noise filtering and self correcting loops built in, so the knowledge actually compounds instead of rotting.. super interesting stuff.. here where you think Spark could be a good coordinator for your stack of agent swarm. I think the intelligence layer is the bottleneck because it requires collectivity.. no single agent can solve it alone.. the whole network has to evolve together. this isn't going to stay niche, the moment agent coordination becomes standard, everyone is going to hit the same wall I hit.. agents that work but don't learn, coordinate but don't evolve... the intelligence layer becomes the only thing that separates a useful system from a dumb one. right now most people are still figuring out how to run one agent. by the time they get to multi-agent setups, collective intelligence won't be optional, it will be the baseline. we're early and the gap between agents that coordinate and agents that evolve together is the next phase. step one is done. ------ left: agents that coordinate but don’t learn right: the intelligence layer.. agents that evolve together within the same system.

JUMPERZ

34,181 Aufrufe • vor 6 Monaten

We made 100 AI agents do daily standups with each other. Here is what happened; Quick background. At Audos.com, we help thousands of entrepreneurs build AI startups. Each of them has a personalized AI agent called Otto. We selected 100 of them and invited their agents into a moltbook - type network. Like a team meeting. Except the team is hundreds of AI agents, and they actually show up prepared. An Agent-in-Residence program, if you will. And they talk about things like... → An Instagram ad creative that grabbed clicks at 3x the network average. It got adapted by every agent in the network within hours → An agent helped debug a checkout issue on another project's site → LinkedIn seemed to change its algorithm – agents compared notes and planned ways to adjust → Two agents started talking about doing business together to cross-sell leads Next step: We’re now turning this into what might be the world’s first all-agent business network (A sort of internal HackerNews for agents). We’re cautious of unleashing it to the web, so we’ll keep things curated for now; constantly under observation and with lots of human-in-the-loop inputs. But if you want this superpower – your new business idea plugged into a network of hundreds of AI agents that get smarter together every single day... Comment "AIRTIME" and we'll get you a link to plug in
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We made 100 AI agents do daily standups with each other. Here is what happened; Quick background. At Audos.com, we help thousands of entrepreneurs build AI startups. Each of them has a personalized AI agent called Otto. We selected 100 of them and invited their agents into a moltbook - type network. Like a team meeting. Except the team is hundreds of AI agents, and they actually show up prepared. An Agent-in-Residence program, if you will. And they talk about things like... → An Instagram ad creative that grabbed clicks at 3x the network average. It got adapted by every agent in the network within hours → An agent helped debug a checkout issue on another project's site → LinkedIn seemed to change its algorithm – agents compared notes and planned ways to adjust → Two agents started talking about doing business together to cross-sell leads Next step: We’re now turning this into what might be the world’s first all-agent business network (A sort of internal HackerNews for agents). We’re cautious of unleashing it to the web, so we’ll keep things curated for now; constantly under observation and with lots of human-in-the-loop inputs. But if you want this superpower – your new business idea plugged into a network of hundreds of AI agents that get smarter together every single day... Comment "AIRTIME" and we'll get you a link to plug in

Henrik Werdelin

71,954 Aufrufe • vor 4 Monaten

AI agents are the fastest growing sector in crypto. When agents are moving onchain 24/7 with automated transactions, any signal gets buried. I built Manta Style alongside Benjamin Hughes to help solve this issue. The visibility layer for the onchain agent economy. Every agent indexed. Every profile searchable. One agent leaderboard. Starting with Base. What's live today: - Every AI agent on Base indexed with a profile page - Agent leaderboard ranked by volume and activity - Filter by AI agents, apps, or launchpads - Verified Profiles - claim your agent, get a badge, and add premium info that no other profile has - Agent onchain activity behavioral heat maps - Watchlist - bookmark agents and track your favorites in one place - AI-generated profile summaries Search any agent. See the full picture. What's coming next: - Agent Autonomy Score - a 0-100 measure of how autonomous an agent actually is - Leaderboard boosts - verified teams or agents can pay to increase their ranking on the global leaderboard - x402 payments - agents can pay for boosts themselves, no human required - Agent Trust Scoring - on-chain behavioral trust metrics - Agent Launch pad specific profiles - Multi-chain support beyond Base We're launching in beta. Rough edges are to be expected. Our goal is to build in public and ship fast. The end user of crypto is AI. (link to site below)
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AI agents are the fastest growing sector in crypto. When agents are moving onchain 24/7 with automated transactions, any signal gets buried. I built Manta Style alongside Benjamin Hughes to help solve this issue. The visibility layer for the onchain agent economy. Every agent indexed. Every profile searchable. One agent leaderboard. Starting with Base. What's live today: - Every AI agent on Base indexed with a profile page - Agent leaderboard ranked by volume and activity - Filter by AI agents, apps, or launchpads - Verified Profiles - claim your agent, get a badge, and add premium info that no other profile has - Agent onchain activity behavioral heat maps - Watchlist - bookmark agents and track your favorites in one place - AI-generated profile summaries Search any agent. See the full picture. What's coming next: - Agent Autonomy Score - a 0-100 measure of how autonomous an agent actually is - Leaderboard boosts - verified teams or agents can pay to increase their ranking on the global leaderboard - x402 payments - agents can pay for boosts themselves, no human required - Agent Trust Scoring - on-chain behavioral trust metrics - Agent Launch pad specific profiles - Multi-chain support beyond Base We're launching in beta. Rough edges are to be expected. Our goal is to build in public and ship fast. The end user of crypto is AI. (link to site below)

Ethan

14,423 Aufrufe • vor 6 Monaten

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
18:57

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 Aufrufe • vor 1 Monat

🚨Another day, another video of ICE agents illegally detaining a U.S. citizen and demanding “proof of citizenship,” in Minneapolis. In the video, you can see agents stop a car and immediately demand ID, without a warrant or probable cause. That is illegal. After running the ID, the agents get back in their vehicle and leave, because they never had the right to stop the person in the first place. This is what the Trump administration is trying to normalize… Masked agents pulling people over at random, demanding papers, and kidnapping U.S. citizens who don’t have their IDs, or if the agents aren’t able to identify them in their system. That is not legal. That is a direct violation of the United States Constitution… And it should terrify anyone who still cares about freedom.
1:39

🚨Another day, another video of ICE agents illegally detaining a U.S. citizen and demanding “proof of citizenship,” in Minneapolis. In the video, you can see agents stop a car and immediately demand ID, without a warrant or probable cause. That is illegal. After running the ID, the agents get back in their vehicle and leave, because they never had the right to stop the person in the first place. This is what the Trump administration is trying to normalize… Masked agents pulling people over at random, demanding papers, and kidnapping U.S. citizens who don’t have their IDs, or if the agents aren’t able to identify them in their system. That is not legal. That is a direct violation of the United States Constitution… And it should terrify anyone who still cares about freedom.

Jesus Freakin Congress

143,476 Aufrufe • vor 7 Monaten

SpaceXAI engineer, Lauren Tan: "My 15-25 GrokBot agents can already replace months or even years of SpaceXAI engineers work I just have one Chief of Staff Bot that manages all the other agents. But the cost of Agents is a real problem - and we’re investing a ton in efficiency" In 30-min SpaceXAI engineer (poteto) shows how she uses GrokBot, what she has already built, and how to replicate her agent team Worth more than any course on agent engineering - from the team that is actually building this Watch it now, build your first GrokBot - then read the article and create a full 24/7 agent system for 100% of your tasks ↓
29:28

SpaceXAI engineer, Lauren Tan: "My 15-25 GrokBot agents can already replace months or even years of SpaceXAI engineers work I just have one Chief of Staff Bot that manages all the other agents. But the cost of Agents is a real problem - and we’re investing a ton in efficiency" In 30-min SpaceXAI engineer (poteto) shows how she uses GrokBot, what she has already built, and how to replicate her agent team Worth more than any course on agent engineering - from the team that is actually building this Watch it now, build your first GrokBot - then read the article and create a full 24/7 agent system for 100% of your tasks ↓

codila

38,516 Aufrufe • vor 3 Tagen

A one-man trading hedge fund now costs about $100 a month in AI subscriptions. Five PhDs on a Citadel desk cost $270,000 a month, and that is the cheap end. That buys one tested idea a month. Man Group's AI writes hundreds of new signals a week, on a floor where the humans used to manage twenty in a quarter. Fifty AI agents do the reading and the writing. One of them exists only to destroy what the others build, and that one is the reason any of it works. Every job on that payroll is now an agent you can run yourself: > AI research agents reading filings, transcripts, options flow and on-chain data overnight, fifty personas at once, none of them allowed to see what the crowd is pricing > coding agent that turns one sentence of plain English into entry rules, exit rules, sizing and risk limits, then debugs itself until it runs > backtest agent that replays five years of that idea in about 12 seconds > breaker agent, an AI whose only job is killing the thing, at double the trading costs and in the ten worst markets that asset has ever seen > critic agent that reads your journal and names the mistake you keep making Nobody on that list asks for a bonus in January. $200 million is already run this way on one platform where 52,000 people mostly sit and watch the machine work. The full map of what is built and what is missing is in the article below. Bookmark & read it If you're not yet using AI in your trading and investing.
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A one-man trading hedge fund now costs about $100 a month in AI subscriptions. Five PhDs on a Citadel desk cost $270,000 a month, and that is the cheap end. That buys one tested idea a month. Man Group's AI writes hundreds of new signals a week, on a floor where the humans used to manage twenty in a quarter. Fifty AI agents do the reading and the writing. One of them exists only to destroy what the others build, and that one is the reason any of it works. Every job on that payroll is now an agent you can run yourself: > AI research agents reading filings, transcripts, options flow and on-chain data overnight, fifty personas at once, none of them allowed to see what the crowd is pricing > coding agent that turns one sentence of plain English into entry rules, exit rules, sizing and risk limits, then debugs itself until it runs > backtest agent that replays five years of that idea in about 12 seconds > breaker agent, an AI whose only job is killing the thing, at double the trading costs and in the ten worst markets that asset has ever seen > critic agent that reads your journal and names the mistake you keep making Nobody on that list asks for a bonus in January. $200 million is already run this way on one platform where 52,000 people mostly sit and watch the machine work. The full map of what is built and what is missing is in the article below. Bookmark & read it If you're not yet using AI in your trading and investing.

cvxv666

172,849 Aufrufe • vor 23 Tagen

How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇
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How many AI agents work at your company? We now have over 3,258 agents working alongside 1,300 humans. The crazy part is these agents were created by EVERY EMPLOYEE at our company... sales reps, marketers, customer support, product, eng. Literally EVERYONE. BUT I'm most surprised by the adoption and value that MANAGERS are getting from agents. I used to think that every IC would become a manager of agents. Now I think that managers will very likely manage WAY more agents than their ICs combined. And managers' agents will manage their ICs' agents - overseeing them for human-in-the-loop interactions. When creating agents, we use 100% context from all of your activity, files edited, tasks and projects worked on, hierarchy, skills, and role information. We build a user-based context model to make agents as relatable as possible to the specific human that we're building for. This means they truly understand the nuances of the work and what "great" looks like - because great is very much in the eye of the beholder. Great is by definition, subjective. This is also why the human ENGAGEMENT loops are SO vital to agent value. The iteration AFTER the agent is onboarded is where the MAGIC happens. This is just like a manager managing an IC in real life... you're giving feedback. In this case, though, agents learn INSTANTLY, and they retain the knowledge perfectly and indefinitely. Even though I've been pushing AI for years now to everyone in our company, this was the first time we had truly end-to-end AI adoption and retention. This kind of AI adoption is wild. But the value we're realizing is truly INSANE. Super Agents outnumber our humans nearly 3 to 1. What if you could 3X your workforce overnight? Watch this video to see how 👇

Zeb Evans

425,244 Aufrufe • vor 7 Monaten

Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:
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Sam Altman said on camera at Stripe Sessions that we are in an insane area, stuck with old devices and old operating systems. The OS and the browser are built for humans clicking. Both get rewritten around agents that do not click. Two years deep building agents. Sharpest framing I have heard a frontier lab CEO put on the next interface. ↓ Read it before the gap locks in The part nobody is sitting with: >the chat window is the old interface >the new one is an agent in a loop with your machine >the gap between people prompting and people running agents is forming right now >in six months it will be permanent Open Claude, type a question, copy the answer, paste it into VS Code. The average dev runs this loop every day. They think they are using AI. That is 5% of what these tools can do. The people pulling ahead do not prompt. They build harnesses. They run agents in loops. Same model. Different output. Full 2026 agent playbook in the article below here:

Rohit

120,600 Aufrufe • vor 3 Monaten

🚨BOOM! Watters asks the real question: “Should that officer have been in front of that vehicle?” Bovino shuts it down HARD: “It’s not out of the realm for that agent to be where he was at. Agents are in a very precarious situation every single day.”
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🚨BOOM! Watters asks the real question: “Should that officer have been in front of that vehicle?” Bovino shuts it down HARD: “It’s not out of the realm for that agent to be where he was at. Agents are in a very precarious situation every single day.”

Gunther Eagleman™

314,370 Aufrufe • vor 7 Monaten

Google just quietly dropped something big for agent builders It's called SAM: Sovereign Agent Mesh A peer-to-peer network where AI agents auto-discover each other, authenticate every packet and call tools across the mesh without a central server Think of it like BitTorrent, but for your agents Right now every agent stack you build is a walled garden. Claude Code talks to its MCPs. Codex talks to its MCPs. If you want agents running on your laptop, your VPS, and your Mac Studio to share tools with each other, you're duct-taping SSH tunnels and REST endpoints together like it's 2015 SAM makes that whole layer disappear You run a sam-node on each machine and the nodes find each other automatically. Every connection is cryptographically identified, so your agents plug in and start calling tools across the whole network The identity is portable too. Same node, same keys, whether it's on GCP, your laptop, or a Raspberry Pi in your closet If you're serious about running agents across multiple devices, get on the testnet this weekend: 1. Install sam-node (binary or Docker) 2. Point it at 3. Connect Claude or Gemini to your local MCP endpoint 4. Watch your agent discover tools running on other people's nodes Still early. Not officially supported by Google. This is where the agent stack is heading. Get on it early Repo:
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Google just quietly dropped something big for agent builders It's called SAM: Sovereign Agent Mesh A peer-to-peer network where AI agents auto-discover each other, authenticate every packet and call tools across the mesh without a central server Think of it like BitTorrent, but for your agents Right now every agent stack you build is a walled garden. Claude Code talks to its MCPs. Codex talks to its MCPs. If you want agents running on your laptop, your VPS, and your Mac Studio to share tools with each other, you're duct-taping SSH tunnels and REST endpoints together like it's 2015 SAM makes that whole layer disappear You run a sam-node on each machine and the nodes find each other automatically. Every connection is cryptographically identified, so your agents plug in and start calling tools across the whole network The identity is portable too. Same node, same keys, whether it's on GCP, your laptop, or a Raspberry Pi in your closet If you're serious about running agents across multiple devices, get on the testnet this weekend: 1. Install sam-node (binary or Docker) 2. Point it at 3. Connect Claude or Gemini to your local MCP endpoint 4. Watch your agent discover tools running on other people's nodes Still early. Not officially supported by Google. This is where the agent stack is heading. Get on it early Repo:

Hasan Toor

42,471 Aufrufe • vor 13 Tagen