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you've never watched one prompt, turn into a finished thing while you did nothing you've watched Claude work, sure one step, your approval, the next step, you steering the whole way that isn't the same as handing off a job, coming back to a result that already checked itself... show more

Lagosaur

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26,218 次观看 • 3 个月前 •via X (Twitter)

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ANTHROPIC LEAKED 8 RULES THAT GIVE ONE ENGINEER 8X FROM A SINGLE CHANGE the engineers built it so nothing adds up - it multiplies, and that's the 8x task → split → verify → score → write back one queue means you wait for every step in turn - three workers together cost you the slowest one, not the sum of all. as long as you're the one reading the diff, the bottleneck is you, not the model. state is written at every step - a crash mid-run costs you one step, not the whole session. history gets compressed 25 times over - the next pass starts at 8k instead of 200k. nothing moves forward without a number - "it got better" isn't a result, and the breakage comes back when you're no longer watching. the eighth rule is the only one that writes back into CLAUDE.md - the agent records what it learned, and the next run doesn't start from zero. copy this into Claude Code today - and tomorrow you work like an engineer on $1.5M a year ↓

ANTHROPIC LEAKED 8 RULES THAT GIVE ONE ENGINEER 8X FROM A SINGLE CHANGE the engineers built it so nothing adds up - it multiplies, and that's the 8x task → split → verify → score → write back one queue means you wait for every step in turn - three workers together cost you the slowest one, not the sum of all. as long as you're the one reading the diff, the bottleneck is you, not the model. state is written at every step - a crash mid-run costs you one step, not the whole session. history gets compressed 25 times over - the next pass starts at 8k instead of 200k. nothing moves forward without a number - "it got better" isn't a result, and the breakage comes back when you're no longer watching. the eighth rule is the only one that writes back into CLAUDE.md - the agent records what it learned, and the next run doesn't start from zero. copy this into Claude Code today - and tomorrow you work like an engineer on $1.5M a year ↓

Noisy

37,450 次观看 • 1 个月前

I think i found the best place to actually get work done right now. Imagine one hub with every popular LLM inside and the ability to plug them into any app you use. This is where it stops feeling like separate tools: You drop your coworkers into a chat with the AI agents and work on the task together, in one place. And the agents don't just answer, they check each other's work. One drafts, another pushes back, another catches what's missing. This isn't the future. it's Bloome.

I think i found the best place to actually get work done right now. Imagine one hub with every popular LLM inside and the ability to plug them into any app you use. This is where it stops feeling like separate tools: You drop your coworkers into a chat with the AI agents and work on the task together, in one place. And the agents don't just answer, they check each other's work. One drafts, another pushes back, another catches what's missing. This isn't the future. it's Bloome.

Anatoli Kopadze

58,400 次观看 • 3 个月前

IF YOU ARE STILL PROMPTING BY HAND IN 2026 YOU ARE ALREADY BEHIND the guy who built Claude Code at Anthropic said he stopped writing prompts a long time ago he runs loops instead, systems that write the prompts for him and keep working while he sleeps you type one request at a time and wait for the answer he built a machine that fires hundreds of requests, checks its own output and fixes itself with no hands on the keyboard here is how you make the jump step 1 stop treating the model like a chat box and start treating it like a worker you manage step 2 write a loop that takes a goal, breaks it into tasks and sends each one to the model on its own step 3 add a second pass where the model grades its own work and retries whatever failed step 4 let it run on a timer so it keeps shipping while you are away from the screen the person still typing prompts one by one is doing the work of a intern the person running loops is doing the work of a whole team off one laptop prompting was never the skill, building the system that prompts for you is

IF YOU ARE STILL PROMPTING BY HAND IN 2026 YOU ARE ALREADY BEHIND the guy who built Claude Code at Anthropic said he stopped writing prompts a long time ago he runs loops instead, systems that write the prompts for him and keep working while he sleeps you type one request at a time and wait for the answer he built a machine that fires hundreds of requests, checks its own output and fixes itself with no hands on the keyboard here is how you make the jump step 1 stop treating the model like a chat box and start treating it like a worker you manage step 2 write a loop that takes a goal, breaks it into tasks and sends each one to the model on its own step 3 add a second pass where the model grades its own work and retries whatever failed step 4 let it run on a timer so it keeps shipping while you are away from the screen the person still typing prompts one by one is doing the work of a intern the person running loops is doing the work of a whole team off one laptop prompting was never the skill, building the system that prompts for you is

Atlas

16,641 次观看 • 1 个月前

CLAUDE JUST REPLACED ONE TEAM MEMBER. HERE’S WHAT YOU DIDN’T KNOW. For two years, "using AI at work" meant: open a tab -> paste your problem -> copy the answer back. Anthropic just killed that. With Claude Tag you Claude R Perrin in Slack like a coworker - it breaks the task into stages, runs your tools and codebase, and posts the finished pull request back in the thread. You stopped talking to a window. You started delegating to a worker. The chatbot-in-a-tab era is ending

CLAUDE JUST REPLACED ONE TEAM MEMBER. HERE’S WHAT YOU DIDN’T KNOW. For two years, "using AI at work" meant: open a tab -> paste your problem -> copy the answer back. Anthropic just killed that. With Claude Tag you Claude R Perrin in Slack like a coworker - it breaks the task into stages, runs your tools and codebase, and posts the finished pull request back in the thread. You stopped talking to a window. You started delegating to a worker. The chatbot-in-a-tab era is ending

kiosa

48,037 次观看 • 3 个月前

Wtf did i just watch 😳? Since I became a Lionel Messi fan, I have never come across this Lionel Messi's highlight. You might think that you've watched all Lionel Messi highlights until you come across the one better than the others you've watched 🐐.

Wtf did i just watch 😳? Since I became a Lionel Messi fan, I have never come across this Lionel Messi's highlight. You might think that you've watched all Lionel Messi highlights until you come across the one better than the others you've watched 🐐.

KingYcee 🇦🇷 🇪🇸 (ARG SPAIN)

21,006 次观看 • 3 个月前

Hakeem Olajuwon says he never did a single individual workout during the season and still has the best footwork the game has ever seen KD: “And you worked on that stuff religiously. What was your routine coming into practice? When did you get a chance to work on your game during the season?” Hakeem: “In the summertime.” KD: “Never in a season? Like you didn’t, no individuals?” Hakeem: “No. Just play one-on-one before the game. Just play.” KD: “You was a hooper, man. You was a true, with the footwork, you would think that you were in the gym just individual workout.” Hakeem: “One-on-one. Just play. Just one-on-one… there’s some things that you have just by playing, you cannot duplicate.” (via Boardroom, ft. Hakeem Olajuwon)

Hakeem Olajuwon says he never did a single individual workout during the season and still has the best footwork the game has ever seen KD: “And you worked on that stuff religiously. What was your routine coming into practice? When did you get a chance to work on your game during the season?” Hakeem: “In the summertime.” KD: “Never in a season? Like you didn’t, no individuals?” Hakeem: “No. Just play one-on-one before the game. Just play.” KD: “You was a hooper, man. You was a true, with the footwork, you would think that you were in the gym just individual workout.” Hakeem: “One-on-one. Just play. Just one-on-one… there’s some things that you have just by playing, you cannot duplicate.” (via Boardroom, ft. Hakeem Olajuwon)

katsu

32,958 次观看 • 1 个月前

I’ve been waiting for something like this. Using multiple AI agents sounds great until you have to keep giving each one the same context. Jarvix Jarvix is built to solve that： It carries your selected context across the AI tools you already use and brings everything together in one place. #JarvixOS You can use Jarvix Home to see your context and connections, Jarvix Search to find past information without remembering exact file names, and Self Reflection to spot patterns in the way you work. There’s also Jarvix Radio for personalized content discovery and Next Move to surface a useful direction based on your context. And the part I find most interesting: Agent Team Mode. Give Jarvix one goal, choose the agents you want to use, and it divides the work while keeping everyone on the same project context. You can watch the progress, review the result, and approve what happens next. No need to keep briefing every agent from scratch. One goal. Shared context. Multiple agents. You stay in control. That’s a much cleaner way to work with AI.

I’ve been waiting for something like this. Using multiple AI agents sounds great until you have to keep giving each one the same context. Jarvix Jarvix is built to solve that： It carries your selected context across the AI tools you already use and brings everything together in one place. #JarvixOS You can use Jarvix Home to see your context and connections, Jarvix Search to find past information without remembering exact file names, and Self Reflection to spot patterns in the way you work. There’s also Jarvix Radio for personalized content discovery and Next Move to surface a useful direction based on your context. And the part I find most interesting: Agent Team Mode. Give Jarvix one goal, choose the agents you want to use, and it divides the work while keeping everyone on the same project context. You can watch the progress, review the result, and approve what happens next. No need to keep briefing every agent from scratch. One goal. Shared context. Multiple agents. You stay in control. That’s a much cleaner way to work with AI.

Aaliya

23,225 次观看 • 1 个月前

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 次观看 • 1 个月前

Elon Musk, absolute leader of the AI race with Grok Bot, and it's not a joke anymore. Ultimate guide on god-mode setup of Grok Bot, the org chart that runs while you sleep, step by step: A Chief of Staff sits in the middle with no tools of its own, BUT it reads the outcome you gave it, picks who does what, and never does the work itself. That one rule is why it never turns into the bottleneck you hired it to remove. → Researcher pulls real sources and tracks what's actually moving, not what sounds true → Writer turns that into finished copy while the research is still in the room → Visualiser gets three reference visuals once, then ships everything in that style forever → Analyst reads what performed and tells the rest of the team what to stop doing → Scheduler owns timing and holds the queue → Publisher actually ships What makes it different from every AI tool you've used: each bot gets its own computer in the cloud, its own browser, its own files, and they all share one memory. So the research is already sitting inside the draft before the draft starts. Nothing gets copy-pasted between tabs, nothing waits on you to approve step four of nine. And you never write a workflow for it. You hit record, do the job once the way you actually do it, stop. It pulls out the steps, saves them as a skill, and puts it on a schedule. The shape you're aiming for on every bot: everything reversible finished, nothing sent. 36 drafts queued, 0 published. It does all the work and stops dead at the one line only you can cross. You stop prompting. You start assigning. Full charter blocks, the approval line and the routines are in the article below ↓

Elon Musk, absolute leader of the AI race with Grok Bot, and it's not a joke anymore. Ultimate guide on god-mode setup of Grok Bot, the org chart that runs while you sleep, step by step: A Chief of Staff sits in the middle with no tools of its own, BUT it reads the outcome you gave it, picks who does what, and never does the work itself. That one rule is why it never turns into the bottleneck you hired it to remove. → Researcher pulls real sources and tracks what's actually moving, not what sounds true → Writer turns that into finished copy while the research is still in the room → Visualiser gets three reference visuals once, then ships everything in that style forever → Analyst reads what performed and tells the rest of the team what to stop doing → Scheduler owns timing and holds the queue → Publisher actually ships What makes it different from every AI tool you've used: each bot gets its own computer in the cloud, its own browser, its own files, and they all share one memory. So the research is already sitting inside the draft before the draft starts. Nothing gets copy-pasted between tabs, nothing waits on you to approve step four of nine. And you never write a workflow for it. You hit record, do the job once the way you actually do it, stop. It pulls out the steps, saves them as a skill, and puts it on a schedule. The shape you're aiming for on every bot: everything reversible finished, nothing sent. 36 drafts queued, 0 published. It does all the work and stops dead at the one line only you can cross. You stop prompting. You start assigning. Full charter blocks, the approval line and the routines are in the article below ↓

Miraqle

87,098 次观看 • 1 个月前

DELETE FIGMA. ONE MCP CONNECTOR TURNS CLAUDE CODE INTO A GOD-MODE STUDIO IN 30 MINUTES. The exact setup, step by step, no jokes: > open connectors, add a custom connector, drop in the Higgsfield MCP > open a Claude Code session and paste one prompt > it generates the first frame, the last frame, and the motion between them > then wires the result straight into an animated hero section for your site > 30 minutes end to end, nothing rendered by hand The bigger thing hiding here: MCP means the model rents hands. it doesn't know how to animate, so it hires a tool that does, mid-task, without you switching tabs. The ceiling of what one prompt ships isn't the model anymore, it's how many hands you've plugged into it. The method underneath (the goal, the rules, the bar, the loop) is in the article below, and it works with any connector you add next.

DELETE FIGMA. ONE MCP CONNECTOR TURNS CLAUDE CODE INTO A GOD-MODE STUDIO IN 30 MINUTES. The exact setup, step by step, no jokes: > open connectors, add a custom connector, drop in the Higgsfield MCP > open a Claude Code session and paste one prompt > it generates the first frame, the last frame, and the motion between them > then wires the result straight into an animated hero section for your site > 30 minutes end to end, nothing rendered by hand The bigger thing hiding here: MCP means the model rents hands. it doesn't know how to animate, so it hires a tool that does, mid-task, without you switching tabs. The ceiling of what one prompt ships isn't the model anymore, it's how many hands you've plugged into it. The method underneath (the goal, the rules, the bar, the loop) is in the article below, and it works with any connector you add next.

Miraqle

38,373 次观看 • 2 个月前

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 次观看 • 1 个月前

you finished reading it already picking your evening that is the part that got cheap eight steps, seven of them cover the build, and the build is one evening, step one is find a problem someone will actually pay for, and step one is the entire job two years of learning to code, gone, thirty thousand to a developer, gone, and the hour they went, everyone else in your niche got the same evening you did what did not get cheaper is knowing, which narrow thing is worth money, and being someone the buyer already trusts on the day it appears the artifact takes an evening charging monthly means you owe somebody the next twenty four of them

you finished reading it already picking your evening that is the part that got cheap eight steps, seven of them cover the build, and the build is one evening, step one is find a problem someone will actually pay for, and step one is the entire job two years of learning to code, gone, thirty thousand to a developer, gone, and the hour they went, everyone else in your niche got the same evening you did what did not get cheaper is knowing, which narrow thing is worth money, and being someone the buyer already trusts on the day it appears the artifact takes an evening charging monthly means you owe somebody the next twenty four of them

Lagosaur

14,261 次观看 • 1 个月前

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

Om Patel

159,776 次观看 • 6 个月前

ANTHROPIC LEAKED A GRAPH WHERE ONE FILE WITH 7 ITEMS CUTS YOUR WORK FROM 8 HOURS TO 40 MINUTES AND TAKES 40 DECISIONS A DAY OFF YOU autonomy isn't a slider you drag to the right - it's a graph with 7 steps, and exactly one edge leads into each one. your approve is worth nothing until there's a description of what the output should be - otherwise you're approving something nobody defined. you can't run a batch without checkpoints, because one crash in the middle rolls back all 40 tasks at once. the agent already works without you, and until it writes down what it did there's nothing to check the report against. this is where the point of no return sits: past it the loop runs while you sleep. the approval didn't disappear - you moved it onto an evaluator that never gets tired and never lets one through. silence is only allowed when there's a list of what breaks it: money, deletion, anything leaving the system. the last one is the brake - a turn limit and a budget cap, whichever runs out first kills the run. you build the brake before the engine - an agent you can't stop isn't autonomous, it's just unattended. skip one edge and nothing crashes and nobody warns you - the system quietly settles two steps lower and keeps running. save this - 7 steps, 7 artefacts, zero ways to jump one ↓

ANTHROPIC LEAKED A GRAPH WHERE ONE FILE WITH 7 ITEMS CUTS YOUR WORK FROM 8 HOURS TO 40 MINUTES AND TAKES 40 DECISIONS A DAY OFF YOU autonomy isn't a slider you drag to the right - it's a graph with 7 steps, and exactly one edge leads into each one. your approve is worth nothing until there's a description of what the output should be - otherwise you're approving something nobody defined. you can't run a batch without checkpoints, because one crash in the middle rolls back all 40 tasks at once. the agent already works without you, and until it writes down what it did there's nothing to check the report against. this is where the point of no return sits: past it the loop runs while you sleep. the approval didn't disappear - you moved it onto an evaluator that never gets tired and never lets one through. silence is only allowed when there's a list of what breaks it: money, deletion, anything leaving the system. the last one is the brake - a turn limit and a budget cap, whichever runs out first kills the run. you build the brake before the engine - an agent you can't stop isn't autonomous, it's just unattended. skip one edge and nothing crashes and nobody warns you - the system quietly settles two steps lower and keeps running. save this - 7 steps, 7 artefacts, zero ways to jump one ↓

Sprytix

41,920 次观看 • 1 个月前

One day you’ll look in the mirror, Meloni. You watched a genocide and did nothing. That shame is all yours forever.

One day you’ll look in the mirror, Meloni. You watched a genocide and did nothing. That shame is all yours forever.

Hussain “Hoz” Shafiei

83,872 次观看 • 1 年前

ANTHROPIC JUST OPEN-SOURCED 11 PLUGINS THAT TURN CLAUDE INTO YOUR WHOLE BACK OFFICE These plugins turn Claude into an actual coworker for your job, whatever your job is. Repo: /anthropics/knowledge-work-plugins Anthropic built and shipped 11 plugins, the same ones inspired by how their own teams work. One for each role: Sales, Marketing, Engineering, Data, Design, Product, HR, Support, Productivity, and more. Each one comes loaded with the real workflows that role needs. The sales one researches prospects, preps your calls, reviews your pipeline, drafts outreach, builds competitive battlecards. The data one writes your SQL, explores datasets, builds dashboards. The productivity one learns your people, your projects, your shorthand, so you say "ask Todd to do the PSR for Oracle" and Claude knows exactly who and what you mean. They work standalone right away, and get supercharged when you connect your tools (Slack, Notion, HubSpot, Linear, Figma, Snowflake, whatever you use). This is straight from the people who build Claude. Not a third-party copy. Bookmark this

ANTHROPIC JUST OPEN-SOURCED 11 PLUGINS THAT TURN CLAUDE INTO YOUR WHOLE BACK OFFICE These plugins turn Claude into an actual coworker for your job, whatever your job is. Repo: /anthropics/knowledge-work-plugins Anthropic built and shipped 11 plugins, the same ones inspired by how their own teams work. One for each role: Sales, Marketing, Engineering, Data, Design, Product, HR, Support, Productivity, and more. Each one comes loaded with the real workflows that role needs. The sales one researches prospects, preps your calls, reviews your pipeline, drafts outreach, builds competitive battlecards. The data one writes your SQL, explores datasets, builds dashboards. The productivity one learns your people, your projects, your shorthand, so you say "ask Todd to do the PSR for Oracle" and Claude knows exactly who and what you mean. They work standalone right away, and get supercharged when you connect your tools (Slack, Notion, HubSpot, Linear, Figma, Snowflake, whatever you use). This is straight from the people who build Claude. Not a third-party copy. Bookmark this

Yarchi

121,071 次观看 • 3 个月前

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

Anthropic engineer showed how one person can run 5 AI agents, that code, test, review, and deploy at the same time. In 30 minutes they built the whole thing live in one session. Here's what they cover: > when to use one agent vs a full team > how to split work so agents don't step on each other > the exact framework for deciding what each agent handles that's exactly why, I put together a guide on building agent teams that actually work. full guide in the article below 👇

darkzodchi

846,346 次观看 • 4 个月前

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

1,385,988 次观看 • 1 个月前

.Lulu Cheng Meservey on Elon going direct: "One thing that always comes up is people say: you advocate for going direct, but look at Elon...he makes so many mistakes and puts his foot in his mouth. The answer is: you have to look at the net result. You can't look at one tweet in isolation or one incident. There's nothing that gets built, not an audience, not a movement, not a company — there's nothing that doesn't get built with some errors along the way. You just have to bake it in."

.Lulu Cheng Meservey on Elon going direct: "One thing that always comes up is people say: you advocate for going direct, but look at Elon...he makes so many mistakes and puts his foot in his mouth. The answer is: you have to look at the net result. You can't look at one tweet in isolation or one incident. There's nothing that gets built, not an audience, not a movement, not a company — there's nothing that doesn't get built with some errors along the way. You just have to bake it in."

Ti Morse

80,875 次观看 • 4 个月前

ANTHROPIC LEAKED A SETUP WHERE ONE FILE STARTS 7 LOOPS THAT TURN A PROMPT INTO A WORKING BUSINESS AI engineer drops in 1 file - the other 95% of the work closes automatically without them. spec → tasks → code → tests → docs → release → monitoring → back into the spec you don't write tickets anymore - you write the thing tickets get derived from, and Opus 5 cuts the spec into 6 tasks by itself. a task that fails its test comes back to itself, not to you - and runs a second lap without a single notification. 6 lanes of code run in parallel and none of them wait on you - the critic filters the diff before you ever open it. every new production bug becomes a permanent line in the eval set - the system never steps on it twice. monitoring isn't the end of the line, it's the start of the next spec - those back edges are what make it a graph instead of a conveyor. and one place is left where you say yes - release, 5 minutes instead of 5 hours. save this and paste it into Claude Code - and you get 7 loops that run without you ↓

ANTHROPIC LEAKED A SETUP WHERE ONE FILE STARTS 7 LOOPS THAT TURN A PROMPT INTO A WORKING BUSINESS AI engineer drops in 1 file - the other 95% of the work closes automatically without them. spec → tasks → code → tests → docs → release → monitoring → back into the spec you don't write tickets anymore - you write the thing tickets get derived from, and Opus 5 cuts the spec into 6 tasks by itself. a task that fails its test comes back to itself, not to you - and runs a second lap without a single notification. 6 lanes of code run in parallel and none of them wait on you - the critic filters the diff before you ever open it. every new production bug becomes a permanent line in the eval set - the system never steps on it twice. monitoring isn't the end of the line, it's the start of the next spec - those back edges are what make it a graph instead of a conveyor. and one place is left where you say yes - release, 5 minutes instead of 5 hours. save this and paste it into Claude Code - and you get 7 loops that run without you ↓

Sprytix

66,333 次观看 • 1 个月前