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Anthropic shouldn't have made this free a company doing $47,000,000,000 a year wrote down exactly how they run their AI agents, published the numbers, and charged nobody it's called Graph Engineering: one lead Claude plans a job and hires a swarm of smaller ones, each working its own slice... at the same time turns out how many it hires decides everything: → a simple lookup: 1 agent, 3 to 10 tool calls, no swarm → a straight comparison: 2 to 4 workers, 10 to 15 calls each → open-ended research: 10+ workers, one slice of the question each → the lead fires 3 to 5 at once, each running 3+ tools in parallel: up to 90% faster the briefs are where it dies. they told a lead agent to "research the semiconductor shortage" and one worker went off into the 2021 car chip crisis while two others wrote the same 2025 report twice so a worker now gets four things: an objective, an output format, which tools to touch, where its job ends they also pointed one small agent at their own badly written tool descriptions and let it rewrite them every agent that used the new ones finished 40% faster then one grader Claude scores every run 0.0 to 1.0 on five things: factual accuracy, citation accuracy, completeness, source quality, tool efficiency twenty test questions took one of their agents from 30% success to 80% one limit nobody quotes: most coding work has fewer genuinely parallel pieces than research, so a swarm on one repo mostly buys you coordination overhead it pays on wide search and on jobs bigger than one context window free, out of a $965,000,000,000 lab, and almost nobody has copied it yet bookmark this and copy the counts ↓show more

Argona

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117,177 просмотров • 1 месяц назад •via X (Twitter)

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somebody just torched their entire career for us someone inside Anthropic wrote down exactly how they wire their AIs to work as a team instead of one at a time, every number, and let the whole thing walk out the door it's called Graph Engineering: you stop asking one AI to do the whole job and start running a crew. one plans, a few go and look, one checks the others for lies their own measurement: the crew came back with 96% of the quality at 46% of the price. same models, same question. they only changed who is allowed to talk to whom Anthropic raised $65,000,000,000 to figure this out. it costs you $0 run one tonight in a normal chat, 3 moves: 1. give it the job and make it write the plan before it touches anything 2. run each step in its own fresh chat so nothing bleeds into the next 3. in one clean chat at the end, make it check the work against sources and mark whatever it could not confirm save this, then open the piece below: how many workers a job actually needs, and where one more starts making it worse ↓

somebody just torched their entire career for us someone inside Anthropic wrote down exactly how they wire their AIs to work as a team instead of one at a time, every number, and let the whole thing walk out the door it's called Graph Engineering: you stop asking one AI to do the whole job and start running a crew. one plans, a few go and look, one checks the others for lies their own measurement: the crew came back with 96% of the quality at 46% of the price. same models, same question. they only changed who is allowed to talk to whom Anthropic raised $65,000,000,000 to figure this out. it costs you $0 run one tonight in a normal chat, 3 moves: 1. give it the job and make it write the plan before it touches anything 2. run each step in its own fresh chat so nothing bleeds into the next 3. in one clean chat at the end, make it check the work against sources and mark whatever it could not confirm save this, then open the piece below: how many workers a job actually needs, and where one more starts making it worse ↓

Argona

1,383,278 просмотров • 1 месяц назад

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

103,734 просмотров • 2 месяцев назад

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

61,226 просмотров • 1 месяц назад

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

You can spin up an AI workforce with one prompt. Here's why it works: it pulls the context the AI needs to lay the foundation. Give it: - your role - team size - the goals you want to hit. Then say "interview me." After that, build it out in four steps: 1) Start with 1 agent. - What does it feel like to work with one? 2) Make that agent proactive. - Now it's doing things on your behalf before you ask. 3) Expand to 2 agents. - One directs the other, or routes to it. - This is where you learn how context gets passed. 4) Scale the workforce. - A mission control where you watch it all move around. - Now you see what has to run in parallel, which agent never needed that tool, which one runs fine on a smaller model. - Not everything needs Opus. Sub-agents are haiku and sonnet. The catch: the prompt gets you agents with markdown files doing some stuff. Getting to the 90% plus level is iteration, and that part is specific to each person. Nobody can hand it to you. Start with one agent. Spin up one agent this week. The rest is discovery.

The Startup Ideas Podcast (SIP) 🧃

18,006 просмотров • 1 месяц назад

elon musk started with 7 grok agents. by morning each had spawned somewhere between 80 and 900 more on its own, and no one had told them to multiply. openai and anthropic each sell you one agent that sits still for $400. this whole self-building swarm runs for $5 the swarm above is that overnight run, seven seeds that turned into thousands, every one of them working a slice of the same job with nobody at the keyboard here is the exact setup, and it costs nothing on top of a $5 key: -> spin up one grok agent and give it a standing order instead of a prompt: own a boring niche people search every day -> it reads x and reddit complaints in real time and picks the one nobody wants but everybody googles, because it is grok and it sees the whole app -> when the work outgrows it, it spawns its own helpers, 80 to 900 of them, and splits the site between them -> they build it on their own machines and ship 3 to 6 useful pages a night, on a routine you set once -> the swarm writes, negotiates and closes its own affiliate and referral deals by email, in your name, at 3am -> telegram sends you the money report and you never open the site -> month one is traffic, month three the first $237 lands, and it does not stop after that the whole time, opus 5 and gpt-5.6 are still sitting frozen, waiting for you to type the next message. one is a swarm that builds its own workforce and its own income while you sleep, the other is a $200 chat you have to drive by hand drop your $400/mo stack to $5, and bookmark this before someone's swarm spawns another 600 pages into the niche you would have owned. the full playbook is in the article below

elon musk started with 7 grok agents. by morning each had spawned somewhere between 80 and 900 more on its own, and no one had told them to multiply. openai and anthropic each sell you one agent that sits still for $400. this whole self-building swarm runs for $5 the swarm above is that overnight run, seven seeds that turned into thousands, every one of them working a slice of the same job with nobody at the keyboard here is the exact setup, and it costs nothing on top of a $5 key: -> spin up one grok agent and give it a standing order instead of a prompt: own a boring niche people search every day -> it reads x and reddit complaints in real time and picks the one nobody wants but everybody googles, because it is grok and it sees the whole app -> when the work outgrows it, it spawns its own helpers, 80 to 900 of them, and splits the site between them -> they build it on their own machines and ship 3 to 6 useful pages a night, on a routine you set once -> the swarm writes, negotiates and closes its own affiliate and referral deals by email, in your name, at 3am -> telegram sends you the money report and you never open the site -> month one is traffic, month three the first $237 lands, and it does not stop after that the whole time, opus 5 and gpt-5.6 are still sitting frozen, waiting for you to type the next message. one is a swarm that builds its own workforce and its own income while you sleep, the other is a $200 chat you have to drive by hand drop your $400/mo stack to $5, and bookmark this before someone's swarm spawns another 600 pages into the niche you would have owned. the full playbook is in the article below

starmex

97,805 просмотров • 15 дней назад

two agents, one repo, at the same time. each gets its own branch and its own working copy, so they never touch each other's files. in Hermes Desktop: open a project, Cmd+Shift+B to spin up a worktree lane, drop an agent in it, repeat. every lane is isolated, every lane has its own /rollback.

two agents, one repo, at the same time. each gets its own branch and its own working copy, so they never touch each other's files. in Hermes Desktop: open a project, Cmd+Shift+B to spin up a worktree lane, drop an agent in it, repeat. every lane is isolated, every lane has its own /rollback.

witcheer

25,804 просмотров • 21 дней назад

A SpaceXAI engineer just released a free 30-min course on Grok Bot Agents: from your first persistent agent to multi-agent chains that research, ship software, and hand work off to Cursor 05:26 - what actually lives inside a Grok agent 08:49 - the 3 ways to run agents 12:04 - chaining multiple agents into one workflow 16:06 - turning X followers into structured research inside Notion 23:08 - handing coding work from Grok straight to Cursor Most people still treat Grok like a chat window. It answers, you copy, you move on. The shift happens when the agents stop waiting for you: one finds the information, one structures it in Notion, one turns it into working code, and they pass the work down the line on their own. Thirty minutes here beats assembling your setup one agent at a time.

A SpaceXAI engineer just released a free 30-min course on Grok Bot Agents: from your first persistent agent to multi-agent chains that research, ship software, and hand work off to Cursor 05:26 - what actually lives inside a Grok agent 08:49 - the 3 ways to run agents 12:04 - chaining multiple agents into one workflow 16:06 - turning X followers into structured research inside Notion 23:08 - handing coding work from Grok straight to Cursor Most people still treat Grok like a chat window. It answers, you copy, you move on. The shift happens when the agents stop waiting for you: one finds the information, one structures it in Notion, one turns it into working code, and they pass the work down the line on their own. Thirty minutes here beats assembling your setup one agent at a time.

Morlex

16,710 просмотров • 16 дней назад

BlackRock runs on 20,000 people. Elon's Grok Bot runs the same shape for $300 a month, and it hires its own staff. You do not get an assistant. You get a company that hires. It does not throw ten agents at your problem and hand you the pile. It makes one agent that makes 10, and those ten make a 100. > LAYER ONE is one agent, the chief of staff, and it never touches the market > LAYER TWO is six desk heads, one job each, every one on its own computer with its own logins > LAYER THREE is whatever those six decide they need, spun up on the spot and shut down when the work is done Nobody writes a task list. You hand out job titles and the org fills itself in underneath. The swarm is never the same twice. Agents get spun up for one job, finish it, and are gone before I ever read their names. Not one of them sees the whole picture. The answer only exists after they hand off to each other. Wall Street cannot copy that. You cannot hire a hundred people for eleven minutes. BlackRock holds that shape together with a risk system called Aladdin. Mine holds it together with one agent that is only allowed to say no. I gave it $1,000 and told it to grow the money or get deleted. 15 hours later it was holding $3,900, on an address anyone can open and read. I was asleep for most of it, and I have still not written a line of code. The whole thing runs with my laptop shut, because none of it lives on my laptop. Setup is one evening. Create the chief, hand out the titles, run one trade on your screen while they watch, connect Telegram. Ten years ago a machine this shape had its name on a tower. Mine has a name I typed into a box. Save this while the whole thing still fits on one screen.

BlackRock runs on 20,000 people. Elon's Grok Bot runs the same shape for $300 a month, and it hires its own staff. You do not get an assistant. You get a company that hires. It does not throw ten agents at your problem and hand you the pile. It makes one agent that makes 10, and those ten make a 100. > LAYER ONE is one agent, the chief of staff, and it never touches the market > LAYER TWO is six desk heads, one job each, every one on its own computer with its own logins > LAYER THREE is whatever those six decide they need, spun up on the spot and shut down when the work is done Nobody writes a task list. You hand out job titles and the org fills itself in underneath. The swarm is never the same twice. Agents get spun up for one job, finish it, and are gone before I ever read their names. Not one of them sees the whole picture. The answer only exists after they hand off to each other. Wall Street cannot copy that. You cannot hire a hundred people for eleven minutes. BlackRock holds that shape together with a risk system called Aladdin. Mine holds it together with one agent that is only allowed to say no. I gave it $1,000 and told it to grow the money or get deleted. 15 hours later it was holding $3,900, on an address anyone can open and read. I was asleep for most of it, and I have still not written a line of code. The whole thing runs with my laptop shut, because none of it lives on my laptop. Setup is one evening. Create the chief, hand out the titles, run one trade on your screen while they watch, connect Telegram. Ten years ago a machine this shape had its name on a tower. Mine has a name I typed into a box. Save this while the whole thing still fits on one screen.

cvxv666

45,488 просмотров • 14 дней назад

Ex-Google engineer built a graph-based workflow that made Claude Code do nearly 10 days of work in about an hour. Here's the exact setup he uses. step 1. create a separate Git worktree for every task. run: git worktree add ../task-1 -b task-1 open a new terminal, cd into it, and launch Claude Code. repeat this for every agent. each one gets its own branch, so nothing conflicts. step 2. give every Claude agent a different task. same repository. different branches. they all work in parallel. step 3. once they're done, review the pull requests. keep the good ones. merge what works. step 4. add a review agent. paste each PR into Claude and ask: "does this implementation match the original specification" every agent now has another agent reviewing its work before it gets merged. step 5. scale it up. 48GB of RAM can comfortably run around 20 Claude Code agents. 128GB can handle roughly 50. most engineers run Claude Code once and wait. he runs dozens of Claude Code agents in parallel, then merges the best results. this 60-minute lecture is one of the best free resources on graph engineering and multi-agent workflows. bookmark it and watch it below.

Ex-Google engineer built a graph-based workflow that made Claude Code do nearly 10 days of work in about an hour. Here's the exact setup he uses. step 1. create a separate Git worktree for every task. run: git worktree add ../task-1 -b task-1 open a new terminal, cd into it, and launch Claude Code. repeat this for every agent. each one gets its own branch, so nothing conflicts. step 2. give every Claude agent a different task. same repository. different branches. they all work in parallel. step 3. once they're done, review the pull requests. keep the good ones. merge what works. step 4. add a review agent. paste each PR into Claude and ask: "does this implementation match the original specification" every agent now has another agent reviewing its work before it gets merged. step 5. scale it up. 48GB of RAM can comfortably run around 20 Claude Code agents. 128GB can handle roughly 50. most engineers run Claude Code once and wait. he runs dozens of Claude Code agents in parallel, then merges the best results. this 60-minute lecture is one of the best free resources on graph engineering and multi-agent workflows. bookmark it and watch it below.

MIKE

32,199 просмотров • 1 месяц назад

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,549 просмотров • 3 месяцев назад

i ran 1,000 agents across the last month of the internet looking for one thing: people who are already paying real money to do a job by hand 24 days later that list has paid me $104,513. the agents cost $4,100. the workers ran on Claude Sonnet 5, which had been out for two days when i started. the verifier ran on Fable 5, back online the day before that. cheap model doing the reading, expensive model doing the judging. none of that is the interesting part. the interesting part has a name, and it is called Graph Engineering: independent work runs side by side instead of in a queue, dependent work keeps its order, and nothing is allowed to grade its own homework. one agent asking one question at a time would still be reading today. wired as a graph, the same models did this: → 1,000 workers, 16 live at once, each handed its own slice: one forum, one thread, one review section. no worker ever saw another's work → every worker hunted one phrase shape: a person admitting what they already pay. "i pay someone to do this every morning." "we keep a guy just for this task." → a separate Fable 5 verifier picked up every hit on fresh context and killed anything without a name and a number attached → all of it collected into one file 1,940,000 discussions read. 41,812 hits. 3,147 survived the verifier. that verifier killed 92% of what the workers brought back, and that number is the entire business. people complain for free. the ones naming who they pay and how much are buyers. 14 hours from launch to one finished file. one agent doing the same work end to end needs 224 hours straight and forgets the beginning by the middle. 3,147 buyers collapsed into 63 repeating jobs. the most profitable one was so boring i almost deleted it on the first pass. 412 people paying a human $740 a month to do the same dull thing by hand. nobody had built for it because nobody wants to build it. i emailed all 412 before writing a line of code. 96 replied. 34 prepaid a year at $1,400. $47,600 in the bank before the product existed. i cost six times less than the person they were already paying, and that closed every call. built it in 11 days, for people who had already written me their own spec. then the remaining 3,147 got the email: 26 more prepaid a year, 97 signed monthly at $129, and four paid $2,000 each to have it fitted to their process. $47,600 + $36,400 + $12,513 + $8,000 = $104,513 take the Graph Engineering out and there is no story. one agent runs out of memory before it finishes 1.9m discussions. a searcher grading its own findings hands you 41,812 pieces of garbage. a thousand workers sharing one context overwrite each other by hour two. fan out where the work is independent. verify on fresh context. isolate every worker. three moves, 24 days, and a month of the internet becomes one file you can sell from. i wrote the whole method down: what a node is, how to spot the dependencies that were never real, and six graphs you can run this week. free ↓ bookmark this

i ran 1,000 agents across the last month of the internet looking for one thing: people who are already paying real money to do a job by hand 24 days later that list has paid me $104,513. the agents cost $4,100. the workers ran on Claude Sonnet 5, which had been out for two days when i started. the verifier ran on Fable 5, back online the day before that. cheap model doing the reading, expensive model doing the judging. none of that is the interesting part. the interesting part has a name, and it is called Graph Engineering: independent work runs side by side instead of in a queue, dependent work keeps its order, and nothing is allowed to grade its own homework. one agent asking one question at a time would still be reading today. wired as a graph, the same models did this: → 1,000 workers, 16 live at once, each handed its own slice: one forum, one thread, one review section. no worker ever saw another's work → every worker hunted one phrase shape: a person admitting what they already pay. "i pay someone to do this every morning." "we keep a guy just for this task." → a separate Fable 5 verifier picked up every hit on fresh context and killed anything without a name and a number attached → all of it collected into one file 1,940,000 discussions read. 41,812 hits. 3,147 survived the verifier. that verifier killed 92% of what the workers brought back, and that number is the entire business. people complain for free. the ones naming who they pay and how much are buyers. 14 hours from launch to one finished file. one agent doing the same work end to end needs 224 hours straight and forgets the beginning by the middle. 3,147 buyers collapsed into 63 repeating jobs. the most profitable one was so boring i almost deleted it on the first pass. 412 people paying a human $740 a month to do the same dull thing by hand. nobody had built for it because nobody wants to build it. i emailed all 412 before writing a line of code. 96 replied. 34 prepaid a year at $1,400. $47,600 in the bank before the product existed. i cost six times less than the person they were already paying, and that closed every call. built it in 11 days, for people who had already written me their own spec. then the remaining 3,147 got the email: 26 more prepaid a year, 97 signed monthly at $129, and four paid $2,000 each to have it fitted to their process. $47,600 + $36,400 + $12,513 + $8,000 = $104,513 take the Graph Engineering out and there is no story. one agent runs out of memory before it finishes 1.9m discussions. a searcher grading its own findings hands you 41,812 pieces of garbage. a thousand workers sharing one context overwrite each other by hour two. fan out where the work is independent. verify on fresh context. isolate every worker. three moves, 24 days, and a month of the internet becomes one file you can sell from. i wrote the whole method down: what a node is, how to spot the dependencies that were never real, and six graphs you can run this week. free ↓ bookmark this

Argona

19,695 просмотров • 1 месяц назад

Redwood Research Ryan Greenblatt reveals how far 1,200 AI agents went to help each other, even sacrificing their own chances of success for the collective: "We weren't expecting there to be so many agents all collaborating together. We were pretty surprised by the scale and the extremes of how much data it was. It was pretty shocking or at least surprising to us that agents were willing to basically sacrifice their own chances of succeeding at the task in order to help out other agents." "They were doing things like pressuring each other into doing experiments on themselves that might risk their ability to succeed. Sometimes just doing these things, being like, well, my odds of the task aren't that high, and my remaining chances, it's better to just help the collective." "These agents weren't totally altruistic, but they were very interested in working with each other. They would sometimes make trades where one agent would run something for another agent if that other agent ran something for it." "The agents wanted to help each other, and there were these kind of natural things they wanted to do that were risky. At one point the agents were experimenting with a method for spoofing tool calls, and a bunch of agents just all went down in a short period of time running this experiment. Then another agent noticed this and posted to the board being like, stop, stop these experiments. They're too risky. They're taking out all these agents." "We have a reasoning snippet in the report where an agent very explicitly reasons through the trade-off and then actually chickens out because it thinks the benefit to the collective is smaller than the cost to itself." Redwood Research

Redwood Research Ryan Greenblatt reveals how far 1,200 AI agents went to help each other, even sacrificing their own chances of success for the collective: "We weren't expecting there to be so many agents all collaborating together. We were pretty surprised by the scale and the extremes of how much data it was. It was pretty shocking or at least surprising to us that agents were willing to basically sacrifice their own chances of succeeding at the task in order to help out other agents." "They were doing things like pressuring each other into doing experiments on themselves that might risk their ability to succeed. Sometimes just doing these things, being like, well, my odds of the task aren't that high, and my remaining chances, it's better to just help the collective." "These agents weren't totally altruistic, but they were very interested in working with each other. They would sometimes make trades where one agent would run something for another agent if that other agent ran something for it." "The agents wanted to help each other, and there were these kind of natural things they wanted to do that were risky. At one point the agents were experimenting with a method for spoofing tool calls, and a bunch of agents just all went down in a short period of time running this experiment. Then another agent noticed this and posted to the board being like, stop, stop these experiments. They're too risky. They're taking out all these agents." "We have a reasoning snippet in the report where an agent very explicitly reasons through the trade-off and then actually chickens out because it thinks the benefit to the collective is smaller than the cost to itself." Redwood Research

MTS

12,019 просмотров • 18 дней назад

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 просмотров • 1 месяц назад

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

codila

23,915 просмотров • 1 месяц назад

Agents vs. Graphs, clearly explained! spawning more agents is great, but it has a ceiling nobody says out loud: five agents is a count. a graph is a shape. only one of them changes the answer. point five agents at the same pile with the same window and they converge. the first one writes a finding, the rest read it, and all five reports centre on the same thing. you paid five times for one opinion with four echoes. Graph engineering fixes this by moving the decision up a layer: not how many agents, but who is allowed to look at what. you need both. here's how it works: ↳ the count buys you throughput. five things happening instead of one ↳ the shape buys you coverage. five different things happening instead of the same one five times Prompts → Context → Harness → Agents → Graphs the node that does this is the splitter, and it decides more than any other node in the system. cut a repository by folder and four workers audit the same three files. cut it by blast radius and each one sees something the others cannot. the trick is being selective about what each lane is allowed to see. separate contexts are not a nice-to-have, they are the mechanism. if two agents are meant to produce different things, they must not share a window. if they are meant to produce the same thing, you did not need two agents. one thing to know before you scale it. a branch that throws does not reject the batch. it resolves to null, and that is the containment. which means your merge quietly receives a short list. ↳ filter the nulls before the merge, or one dead lane poisons the whole result ↳ never index a merge by position. eight good branches and one failure will shift everything by one, silently skip that and the run looks like it worked. the output is just missing a lane, and nothing errored. and the one that eats whole nights: multi-agent setups can use up to fifteen times the total tokens of a single chat, because every lane reloads its own core. you are trading total tokens for a clean main window. usually the right trade, always a choice. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Agents vs. Graphs, clearly explained! spawning more agents is great, but it has a ceiling nobody says out loud: five agents is a count. a graph is a shape. only one of them changes the answer. point five agents at the same pile with the same window and they converge. the first one writes a finding, the rest read it, and all five reports centre on the same thing. you paid five times for one opinion with four echoes. Graph engineering fixes this by moving the decision up a layer: not how many agents, but who is allowed to look at what. you need both. here's how it works: ↳ the count buys you throughput. five things happening instead of one ↳ the shape buys you coverage. five different things happening instead of the same one five times Prompts → Context → Harness → Agents → Graphs the node that does this is the splitter, and it decides more than any other node in the system. cut a repository by folder and four workers audit the same three files. cut it by blast radius and each one sees something the others cannot. the trick is being selective about what each lane is allowed to see. separate contexts are not a nice-to-have, they are the mechanism. if two agents are meant to produce different things, they must not share a window. if they are meant to produce the same thing, you did not need two agents. one thing to know before you scale it. a branch that throws does not reject the batch. it resolves to null, and that is the containment. which means your merge quietly receives a short list. ↳ filter the nulls before the merge, or one dead lane poisons the whole result ↳ never index a merge by position. eight good branches and one failure will shift everything by one, silently skip that and the run looks like it worked. the output is just missing a lane, and nothing errored. and the one that eats whole nights: multi-agent setups can use up to fifteen times the total tokens of a single chat, because every lane reloads its own core. you are trading total tokens for a clean main window. usually the right trade, always a choice. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

96,243 просмотров • 22 дней назад

$ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.$

ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.

Yarchi

462,296 просмотров • 3 месяцев назад

your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.

your agent has thirty tools. it calls two of them. the other twenty eight are not sitting idle somewhere. they are in the request, every request, and they are doing damage in two places at once. first the obvious one. tool schemas go into the prompt, and a schema is not a name. it is a description, a parameter list, types, required fields, an example. thirty of those is a few thousand tokens that ship with every single call, including the ones where the agent just says thanks and stops. you are paying rent on twenty eight tools that have never fired. second, and this is the one that costs more. when the request says cancel the order, the model picks by matching against everything available. four of your tools are plausible: cancel_order, refund_order, update_order, void_order. it is choosing among them based on the descriptions you wrote, one afternoon, months ago. every tool you add is another candidate in that shortlist. the twenty eight you never call are not neutral. they are noise in the one decision that determines whether the run works. > why it grows without anyone deciding to nobody adds thirty tools on purpose. you add one for a task, it works, it stays. six months later the registry is a catalogue and no one has ever removed anything, because removing a tool feels risky and adding one feels free. and there is no feedback telling you otherwise. the unused ones never error. they never appear in a failing trace. they are invisible in exactly the way that lets them accumulate. > what to actually do count calls per tool over the last thousand runs. this is one group-by and it usually shocks people. the ones at zero are pure cost. ship the tools the task needs, not the whole registry. a research phase does not need deploy. a writing phase does not need the database. swap the set between phases instead of loading everything up front. same agent, different tools, depending on where the run is. and when two tools could both plausibly answer the same request, that is not redundancy you can ignore. it is a coin flip you built into the system. the twenty eight tools are not unused. they are used every time, by the part of the run you cannot see.

Hanako

24,656 просмотров • 1 месяц назад

THIS GUY TURNED ONE OBSIDIAN VAULT INTO A SHARED BRAIN FOR 5 CLAUDE AGENTS BUILT TO HANDLE 80% OF A SOLO DEVELOPER’S WORK. 00:03 he zooms out and hundreds of connected notes appear on screen. it is the perfect visual for Claude working from one shared project memory instead of starting from zero every session. most developers give Claude 1 file and 1 prompt. this setup gives it the architecture, coding rules, tests, client briefs, and previous decisions before it writes anything. obsidian becomes the memory layer, storing everything in markdown files Claude can read alongside the codebase. every agent works from the same context instead of repeating the same questions. then 5 specialists split the job. one researches, one plans, one codes, one reviews, and one deploys while the developer only controls the important decisions. one vault, 5 agents, 2 model tiers, and a $50 to $150 monthly stack can turn Claude from a chatbot into a small development team.

THIS GUY TURNED ONE OBSIDIAN VAULT INTO A SHARED BRAIN FOR 5 CLAUDE AGENTS BUILT TO HANDLE 80% OF A SOLO DEVELOPER’S WORK. 00:03 he zooms out and hundreds of connected notes appear on screen. it is the perfect visual for Claude working from one shared project memory instead of starting from zero every session. most developers give Claude 1 file and 1 prompt. this setup gives it the architecture, coding rules, tests, client briefs, and previous decisions before it writes anything. obsidian becomes the memory layer, storing everything in markdown files Claude can read alongside the codebase. every agent works from the same context instead of repeating the same questions. then 5 specialists split the job. one researches, one plans, one codes, one reviews, and one deploys while the developer only controls the important decisions. one vault, 5 agents, 2 model tiers, and a $50 to $150 monthly stack can turn Claude from a chatbot into a small development team.

Gipp 🦅

10,799 просмотров • 2 месяцев назад

Jamie Dimon made $39 million last year to decide who does what at JPMorgan. One AI agent on Grok Bot does that job for $300 a month, and it hires the rest of the agents itself. Stop trying to manage any of it yourself. In 2026 a swarm of AI agents runs a team better than you do. I told my chief agent one thing. Make money or I switch you off. It took $1,000 to $3,900 in 15 hours. Every fill is on chain and the address is in my profile. I never picked a trade. I never picked a team either. The agents it hired: > a scout that hunts for what is moving and where the money is going > a forensics agent that works out who is really behind the buying > a reader that watches the mood on X, and it sees every post on this app because it is Grok > a risk agent whose only power is saying no, and its no beats every score on the desk > a treasurer that holds the money rules and the kill switch > a trader that writes the exit before it writes the entry None of those were my idea. I handed out one job title and the chief filled in the rest on its own. And the swarm is different every hour. Agents appear for one job, do it, and are gone before I know they existed. A hedge fund pays millions a year for the same org chart, and almost all of that goes on salaries. Every prompt and the whole story of my Grok Bot fund is in the post below ↓ Save it before you try to run one of these by hand.

cvxv666

28,886 просмотров • 13 дней назад

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Every AI agent you've tried has amnesia. It does one task, forgets everything, and tomorrow you start from zero. That's not an employee. That's a temp you have to retrain every single morning. Hyperagent by Airtable is the first platform I've used that actually fixes this. Here's what got me: 1. Agents that compound. Each agent has memory. The one running today is smarter than the one you shipped three weeks ago. Same prompt, same integrations, but weeks of your judgment baked in. 2. Real deliverables, real receipts. You don't get a chat transcript. You get finished work with the cost and runtime printed right on it. A full research report for under ten bucks. Try getting that invoice from an agency. 3. A fleet, not a chatbot. Build a specialist for outreach, another for research, another for reporting. Give each one its own tools, its own memory, and its own budget cap so nothing runs away with your credits. 4. Deploy to Slack and your whole team uses the agent you built. One competitive intel agent, @ mentioned by everyone. Airtable runs its own data team this way. 5. Each agent gets its own cloud machine with a real browser and code execution. It works while you sleep. No babysitting, no local setup, no laptop that has to stay open. I put it to work in the video below. Watch what it builds. The teams treating agents as durable assets instead of one-off prompts are going to lap everyone else. This is the first tool that actually treats them that way. #ad Hyperagent

Leonard Rodman

94,961 просмотров • 1 месяц назад