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EVERYONE HAS BEEN TALKING ABOUT AI AGENTS. BUT THE REAL UNLOCK ISN'T BETTER PROMPTS: IT'S BETTER LOOPS. Everyone is chasing the perfect prompt. The top AI engineers are chasing something else: loops. Instead of chatting back and forth with AI, they give it one goal and let it continuously... improve its own work until the objective is met. Here's the framework: • Define the goal once • AI discovers the work required • Specialized agents tackle tasks in parallel • A verification agent checks the results • If the goal isn't met: loop again. If it is: deliver. The key ingredient is memory. Each cycle remembers what was completed, what failed, and what still needs work, so every iteration gets smarter instead of starting from scratch. There are two approaches: • Open loops: Maximum autonomy, but higher token costs as AI explores freely. • Closed loops: Clear boundaries, defined success metrics, lower costs, and far more practical for everyday workflows. Don't ask AI to complete a task. Build a system where AI can plan, execute, verify, remember, and repeat until the task is actually finished. Prompt engineering got us here. Loop engineering is what takes AI from assistant to autonomous teammate.show more

Dami-Defi

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CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.

Dami-Defi

10,127 Aufrufe • vor 2 Monaten

Andrej Karpathy explains why human-AI collaboration often fails: we've got the workflow backwards and the bottleneck wrong. He points out that when working with AI, there's a clear pattern. The AI generates solutions quickly while humans verify the output. The goal is making this loop as fast as possible to get real work done. The first way to speed this up is through better verification tools. GUIs are crucial because they tap into our brain's visual processing power. Reading through walls of text is slow and painful, but visual interfaces create a highway to understanding. The second approach is keeping AI constrained. Karpathy warns that people are getting too excited about autonomous agents. An AI that instantly generates 10,000 lines of code isn't helpful when a human still needs hours to verify it's bug-free and secure. The fundamental problem is that even instant AI generation becomes useless if humans can't verify fast enough. The human becomes the bottleneck, having to check for bugs, correct implementation, and security issues in massive outputs. His solution is simple: constrain AI output to maintain manageable verification loops. Don't let AI run wild with massive changes. Keep it on a leash so humans can actually review and approve the work effectively.

Andrej Karpathy explains why human-AI collaboration often fails: we've got the workflow backwards and the bottleneck wrong. He points out that when working with AI, there's a clear pattern. The AI generates solutions quickly while humans verify the output. The goal is making this loop as fast as possible to get real work done. The first way to speed this up is through better verification tools. GUIs are crucial because they tap into our brain's visual processing power. Reading through walls of text is slow and painful, but visual interfaces create a highway to understanding. The second approach is keeping AI constrained. Karpathy warns that people are getting too excited about autonomous agents. An AI that instantly generates 10,000 lines of code isn't helpful when a human still needs hours to verify it's bug-free and secure. The fundamental problem is that even instant AI generation becomes useless if humans can't verify fast enough. The human becomes the bottleneck, having to check for bugs, correct implementation, and security issues in massive outputs. His solution is simple: constrain AI output to maintain manageable verification loops. Don't let AI run wild with massive changes. Keep it on a leash so humans can actually review and approve the work effectively.

Aish

111,476 Aufrufe • vor 1 Jahr

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch

HOW TO USE AI LOOPS TO RUN YOUR BUSINESS 24/7 A lot has been written about loop engineering for building products. Almost nothing about using loops to run the business itself. That's the bigger idea. A loop is when you give an agent a goal, a way to check its own work, and permission to keep trying until it hits that goal. Build. Verify. Repeat. Stop when the condition is met. Here's what it looks like in practice: 1/SEO loop You're position 30 for a term you want. The loop runs once a month, makes changes, checks where you rank, and keeps pushing until you're on page one. This is running in production right now on Inbox Zero. 2/Ads loop You're spending $100 a day and losing money. The loop tests creative, checks profitability, kills what fails, and keeps going until the account is in the black. 3/Eval loop Your AI feature is only 88% accurate. The loop keeps adjusting the prompt and swapping the model until it passes 90%. 4/LLM visibility loop People search in ChatGPT now, not just Google. Same loop, new scoreboard. Are we the answer or not? The whole thing hinges on one thing: a metric that comes back black and white. Where do I rank? Did it hit profitability? Did the evals pass? Give an agent that scoreboard and it runs for months. Loops used to run for 30 minutes. These run for a year. Take a step, sleep, wake up next month, take another one. You're basically hiring an agency that never sleeps, gets paid in tokens instead of invoices, and undoes its own mistakes when the number goes down. Full episode on The Startup Ideas Podcast (SIP) 🧃 watch

GREG ISENBERG

82,714 Aufrufe • vor 1 Monat

Do not spend 2 years learning AI agents the slow way Andrew Ng just shared a complete 2-hour roadmap On becoming an agentic AI engineer in 2026: 00:00 - Learn the foundations of AI agents 12:12 - Design agentic workflows 53:27 - Build agents that work in practice 1:20:30 - Create self-improving loops 1:30:19 - Orchestrate multi-agent systems Most people are still learning how to prompt one model Andrew Ng is already teaching the complete stack: Agents → Workflows → Loops → Multi-Agent Systems Prompting is the old workflow Building autonomous systems is the new one Anthropic pays top engineers up to $750K a year to understand this stack Bookmark this and give it two hours today Then read the full graph engineering guide below

Do not spend 2 years learning AI agents the slow way Andrew Ng just shared a complete 2-hour roadmap On becoming an agentic AI engineer in 2026: 00:00 - Learn the foundations of AI agents 12:12 - Design agentic workflows 53:27 - Build agents that work in practice 1:20:30 - Create self-improving loops 1:30:19 - Orchestrate multi-agent systems Most people are still learning how to prompt one model Andrew Ng is already teaching the complete stack: Agents → Workflows → Loops → Multi-Agent Systems Prompting is the old workflow Building autonomous systems is the new one Anthropic pays top engineers up to $750K a year to understand this stack Bookmark this and give it two hours today Then read the full graph engineering guide below

rari

56,677 Aufrufe • vor 7 Tagen

Learning about loops and how they can affect your life and business. AI engineers have been talking about them here on X, so I wanted to learn more from someone who is building them into their business. Learned loops really means "long running multiple AI agents." Trung Le gives me a master class on loops, he's the founder of "Give agents a shared workspace to plan, use terminals and browsers, coordinate with people, and deliver reviewable work—using local runtimes and the providers you choose."

Learning about loops and how they can affect your life and business. AI engineers have been talking about them here on X, so I wanted to learn more from someone who is building them into their business. Learned loops really means "long running multiple AI agents." Trung Le gives me a master class on loops, he's the founder of "Give agents a shared workspace to plan, use terminals and browsers, coordinate with people, and deliver reviewable work—using local runtimes and the providers you choose."

Robert Scoble

18,682 Aufrufe • vor 26 Tagen

THE GUY RUNNING CLAUDE CODE AT ANTHROPIC JUST SAID HE DOESN’T PROMPT AI ANYMORE. He writes loops. That’s a huge difference. A prompt tells Claude what to do once. A loop gives Claude a goal. Then Claude keeps checking its own work… Finding mistakes. Writing its next prompt. Trying again. Over and over… Until it actually reaches the target you defined. But here’s what most people miss. A loop is only as smart as the context you give it. That’s exactly why Claude Repositories matter. I broke down how to connect your entire codebase so Claude can actually reason across your project instead of a single file. Full guide below

THE GUY RUNNING CLAUDE CODE AT ANTHROPIC JUST SAID HE DOESN’T PROMPT AI ANYMORE. He writes loops. That’s a huge difference. A prompt tells Claude what to do once. A loop gives Claude a goal. Then Claude keeps checking its own work… Finding mistakes. Writing its next prompt. Trying again. Over and over… Until it actually reaches the target you defined. But here’s what most people miss. A loop is only as smart as the context you give it. That’s exactly why Claude Repositories matter. I broke down how to connect your entire codebase so Claude can actually reason across your project instead of a single file. Full guide below

kiosa

16,808 Aufrufe • vor 1 Monat

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

AI AGENTS 101 (58 minute free masterclass) send this to anyone who wants to understand ai agents, claude skills, md files, how to get the most out of AI etc in plain english: 1. chat vs agents - chat models answer questions in a back and forth while agents take a goal, figure out the steps, and deliver a result 2. agents don’t stop after one response. they keep running until the task is actually finishedno babysitting required 3. everything runs on a loop. they gather context, decide what to do, take an action, then repeat until done 4. the loop is the system. they look at files, tools, and the internet. decide the next step. execute and then feed that back into the next step. over and over until completion 5. the model is just one piece. gpt, claude, gemini are the reasoning layer. the key is model + loop + tools + context 6. mcp is how agents use tools. it connects things like browser, code, apis, and your internal software. once connected, the agent decides when to use them to get the job done 7. context beats prompt all day. you don't need to write perfect prompts. load your agent with context about your business, style, and goals and then simple instructions work 8. claude.md or agents.md is the onboarding doc it tells the agent who it is, how to behave, what it knows, and what tools it can use. this gets loaded every time before it starts 9. memory.md is how it improves. agents don’t remember by default. this file stores preferences, corrections, and patterns you tell the agent to update it, and it gets better over time 10. skills + harnesses make it usable. skills are reusable tasks like writing, research, analysis the harness is the environment like claude code or openclaw that runs everything. basiclaly, different interfaces, same system underneath this episode with remy on The Startup Ideas Podcast (SIP) 🧃 was one of the clearest ways of understanding a lot of the core concepts of ai agents could be the best beginners course for ai agents 58 mins. all free. no advertisers. i just want to see you build cool stuff. im rooting for you. send to a friend watch

GREG ISENBERG

376,293 Aufrufe • vor 5 Monaten

Right now, most developers are still doing the job AI should be doing. Assign a task. Wait. Review the result. Fix it. Repeat. You're not building with AI. You're babysitting it. The shift happening now is much bigger than better coding agents. It's loop engineering. Instead of managing every AI agent yourself, you build a system that manages them for you. One loop assigns the work. Another checks the output. A third retries failures. Everything keeps moving without waiting for your next prompt. Your job doesn't disappear. It changes. You stop acting like a project manager... ...and start designing the system that manages the entire team. That's exactly the transition I break down in the article below.

Right now, most developers are still doing the job AI should be doing. Assign a task. Wait. Review the result. Fix it. Repeat. You're not building with AI. You're babysitting it. The shift happening now is much bigger than better coding agents. It's loop engineering. Instead of managing every AI agent yourself, you build a system that manages them for you. One loop assigns the work. Another checks the output. A third retries failures. Everything keeps moving without waiting for your next prompt. Your job doesn't disappear. It changes. You stop acting like a project manager... ...and start designing the system that manages the entire team. That's exactly the transition I break down in the article below.

Pure

11,257 Aufrufe • vor 1 Monat

For 20+ years, we've stayed ahead of every major shift in technology. Today we take the next one: we're introducing the world's first AI-native tech services model. Most of the industry is adding AI to the way it already works. Like bolting a copilot onto old processes and calling it transformation because it's faster than before. That's not what enterprises actually need. works differently. It provides instant access to AI Pods (service units run by AI agents and supervised by Globant experts) that deliver tech services at machine speed, but with the rigor, the governance, and the quality that enterprise-grade work demands. It is a smarter model, running the right AI through parallel agents, loops, workflows, and deterministic processes. With organizations access AI services with transparent, output or consumption-linked pricing, not per hour or per seat, and they own everything they produce. We are bringing the speed AI promises, with the rigor and quality any business needs. Join the waitlist at

For 20+ years, we've stayed ahead of every major shift in technology. Today we take the next one: we're introducing the world's first AI-native tech services model. Most of the industry is adding AI to the way it already works. Like bolting a copilot onto old processes and calling it transformation because it's faster than before. That's not what enterprises actually need. works differently. It provides instant access to AI Pods (service units run by AI agents and supervised by Globant experts) that deliver tech services at machine speed, but with the rigor, the governance, and the quality that enterprise-grade work demands. It is a smarter model, running the right AI through parallel agents, loops, workflows, and deterministic processes. With organizations access AI services with transparent, output or consumption-linked pricing, not per hour or per seat, and they own everything they produce. We are bringing the speed AI promises, with the rigor and quality any business needs. Join the waitlist at

Martin Migoya

390,212 Aufrufe • vor 11 Tagen

Built an AI maturity assessment. It's designed to answer... • how AI-native your team really is • where the bottlenecks are • what good looks like • and what to do next We turned the AI transformation model into something practical you can actually use. Takes about 3 minutes. Chat with it and it gives back: • a detailed AI maturity assessment • strengths and gaps across your workflows • practical recommendations • a downloadable report you can share internally The goal wasn’t to make a gimmicky quiz (please give me direct feedback on how to improve). It’s meant to be a practical diagnostic for how modern teams are actually adopting AI at work. Give it shot!

Built an AI maturity assessment. It's designed to answer... • how AI-native your team really is • where the bottlenecks are • what good looks like • and what to do next We turned the AI transformation model into something practical you can actually use. Takes about 3 minutes. Chat with it and it gives back: • a detailed AI maturity assessment • strengths and gaps across your workflows • practical recommendations • a downloadable report you can share internally The goal wasn’t to make a gimmicky quiz (please give me direct feedback on how to improve). It’s meant to be a practical diagnostic for how modern teams are actually adopting AI at work. Give it shot!

Hurley

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Google just released a free 2-hour+ course on full agent engineering. How to go from one prompt to a system that runs while you sleep: 38:46 - Build your first AI agent 54:46 - Connect MCP tools 1:12:43 - Run agents with four loop patterns 1:20:57 - Turn agent loops into graphs 2:22:31 - Build the complete autonomous system Most people are still building one agent and stopping there. Google is already teaching the full stack: Agents → Tools → Loops → Graphs → Autonomous Systems Single agents are the old workflow. Systems that run without you are the new one. This course is worth more than most paid agent engineering bootcamps. Bookmark and watch it today Then read the full graph engineering playbook below

Google just released a free 2-hour+ course on full agent engineering. How to go from one prompt to a system that runs while you sleep: 38:46 - Build your first AI agent 54:46 - Connect MCP tools 1:12:43 - Run agents with four loop patterns 1:20:57 - Turn agent loops into graphs 2:22:31 - Build the complete autonomous system Most people are still building one agent and stopping there. Google is already teaching the full stack: Agents → Tools → Loops → Graphs → Autonomous Systems Single agents are the old workflow. Systems that run without you are the new one. This course is worth more than most paid agent engineering bootcamps. Bookmark and watch it today Then read the full graph engineering playbook below

Lunar

196,123 Aufrufe • vor 18 Tagen

HERMES CAN NOW RUN FOR FREE FOREVER A new Gemma 4 update just removed the biggest problem with local AI agents. The Upgrade: → Gemma 4 runs around 90% faster on Apple Silicon using Ollama + MLX → Plug it into Hermes with a few terminal commands → Run private agent workflows without subscriptions, token limits, or API bills What It Can Actually Do: ✓ Build and preview apps locally ✓ Use /learn to turn tutorials into permanent skills ✓ Read files, analyse landing pages, summarise emails, and track AI news ✓ Run loops until a task is complete instead of waiting for another prompt ✓ Launch multiple Gemma 4 subagents to work on separate tasks in parallel No Apple Silicon? ✔ OpenRouter offers the 31B model through a free API The real win is not faster chat. It is having Hermes build, retry, check its work, and operate in the background without charging you for every loop.

HERMES CAN NOW RUN FOR FREE FOREVER A new Gemma 4 update just removed the biggest problem with local AI agents. The Upgrade: → Gemma 4 runs around 90% faster on Apple Silicon using Ollama + MLX → Plug it into Hermes with a few terminal commands → Run private agent workflows without subscriptions, token limits, or API bills What It Can Actually Do: ✓ Build and preview apps locally ✓ Use /learn to turn tutorials into permanent skills ✓ Read files, analyse landing pages, summarise emails, and track AI news ✓ Run loops until a task is complete instead of waiting for another prompt ✓ Launch multiple Gemma 4 subagents to work on separate tasks in parallel No Apple Silicon? ✔ OpenRouter offers the 31B model through a free API The real win is not faster chat. It is having Hermes build, retry, check its work, and operate in the background without charging you for every loop.

Julian Goldie SEO

11,234 Aufrufe • vor 1 Monat

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Aaron Levie

103,811 Aufrufe • vor 11 Monaten

Can AI actually automate jobs? Scale AI and are launching the Remote Labor Index (RLI), the first benchmark and public leaderboard that test how well AI agents can complete real, paid freelance work in domains like software engineering, design, architecture, data analysis, and more. Early results show the limits of today’s models. The top AI agent completed just 2.5% of real freelance jobs better than humans. AI is powerful, but not yet reliable enough to replace skilled labor. RLI gives us a transparent way to track progress over time and bring clarity to the future of work.

Can AI actually automate jobs? Scale AI and are launching the Remote Labor Index (RLI), the first benchmark and public leaderboard that test how well AI agents can complete real, paid freelance work in domains like software engineering, design, architecture, data analysis, and more. Early results show the limits of today’s models. The top AI agent completed just 2.5% of real freelance jobs better than humans. AI is powerful, but not yet reliable enough to replace skilled labor. RLI gives us a transparent way to track progress over time and bring clarity to the future of work.

Bing Liu

424,454 Aufrufe • vor 9 Monaten

Claude Code creator Boris Cherny: "Verification is the single most important part of closed-loop development" "Companies underinvest in it, and that is the biggest mistake" In a 26-minute talk, Boris breaks down real AI coding loops with Spotify's Chief Engineer: 00:00 - Closed-loop development 08:33 - Why one-shot code generation fails 10:02 - Agentic verifier and PR success rate 12:00 - Verification loops without humans in the loop 14:00 - Tests, skills, CLAUDE.md, and MCP as the loop infrastructure 20:11 - Why consistent codebases make agents work better This one video is worth more than 100 Claude coding setup tutorials Bookmark and watch it today Then read the full guide on loop engineering below

Claude Code creator Boris Cherny: "Verification is the single most important part of closed-loop development" "Companies underinvest in it, and that is the biggest mistake" In a 26-minute talk, Boris breaks down real AI coding loops with Spotify's Chief Engineer: 00:00 - Closed-loop development 08:33 - Why one-shot code generation fails 10:02 - Agentic verifier and PR success rate 12:00 - Verification loops without humans in the loop 14:00 - Tests, skills, CLAUDE.md, and MCP as the loop infrastructure 20:11 - Why consistent codebases make agents work better This one video is worth more than 100 Claude coding setup tutorials Bookmark and watch it today Then read the full guide on loop engineering below

rari

73,045 Aufrufe • vor 1 Monat

I thought I was testing another AI website builder. I was wrong. Teamily AI turns ideas into interactive experiences inside a shared conversation where people and AI agents can work together in the same space. Instead of creating something alone, teammates can join the discussion, bring in AI specialists, and refine the result together. The interesting part isn't just what AI creates... It's how humans and AI collaborate to build it. From the first idea to the final experience everyone stays connected through one shared workflow. 🔗 Teamily AI

I thought I was testing another AI website builder. I was wrong. Teamily AI turns ideas into interactive experiences inside a shared conversation where people and AI agents can work together in the same space. Instead of creating something alone, teammates can join the discussion, bring in AI specialists, and refine the result together. The interesting part isn't just what AI creates... It's how humans and AI collaborate to build it. From the first idea to the final experience everyone stays connected through one shared workflow. 🔗 Teamily AI

marcus

33,863 Aufrufe • vor 25 Tagen

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside one graph: 17:44 - Build your first AI agent 39:30 - Run agents with loop engineering 1:12:38 - Turn agent loops into graphs 1:34:26 - Build agents that throttle themselves 1:55:05 - Orchestrate the full multi-agent system Most people build one agent and stop there. Google is teaching the full stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read the full architecture below ↓

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside one graph: 17:44 - Build your first AI agent 39:30 - Run agents with loop engineering 1:12:38 - Turn agent loops into graphs 1:34:26 - Build agents that throttle themselves 1:55:05 - Orchestrate the full multi-agent system Most people build one agent and stop there. Google is teaching the full stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read the full architecture below ↓

Lunar

396,236 Aufrufe • vor 10 Tagen

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

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

Lunar

221,126 Aufrufe • vor 7 Tagen

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 Aufrufe • vor 26 Tagen

Spotify Chief Architect, Niklas Gustavsson: "Once we added loops, our agent success rate went from 20-30% to 80%." Everyone's chasing a smarter model. Spotify just built a better loop. 26 min with Claude Code creator Boris Cherny on how they run agents across a 20M-line monorepo. 73% of PRs are AI-assisted. 4,500 deploys a day. 2,900 engineers. loops + a verifier that runs CI + a house-style codebase + auto-merge - that's the secret. close the loop: let the agent run the build, read what failed, and fix itself. Watch the talk, then save the playbook.

Spotify Chief Architect, Niklas Gustavsson: "Once we added loops, our agent success rate went from 20-30% to 80%." Everyone's chasing a smarter model. Spotify just built a better loop. 26 min with Claude Code creator Boris Cherny on how they run agents across a 20M-line monorepo. 73% of PRs are AI-assisted. 4,500 deploys a day. 2,900 engineers. loops + a verifier that runs CI + a house-style codebase + auto-merge - that's the secret. close the loop: let the agent run the build, read what failed, and fix itself. Watch the talk, then save the playbook.

shmidt

21,748 Aufrufe • vor 1 Monat