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Claude Code team just built an agent loop system with 18,000 agents 140,000 steps and 90% of the work runs with zero humans typing 0% → 02:11 - move from API calls to managed agents 25% → 04:33 - ship agents 10-15x faster 50% → 08:48 - cut P95...

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Lunar

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10,130 просмотров • 3 дней назад •via X (Twitter)

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Claude Code team just built an agent loop system of 18,000 agents 140,000 steps and 90% of the work runs with zero humans typing 02:11 - From API to managed agents 04:33 - Ship agents 10-15x faster 08:48 - Cut P95 latency by over 90% 24:33 - Claude finds the root cause 33:04 - Subagents, memory, dreaming, and vaults This one video is worth more than any paid agentic AI course Bookmark and watch it tonight Then read the article below
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Claude Code team just built an agent loop system of 18,000 agents 140,000 steps and 90% of the work runs with zero humans typing 02:11 - From API to managed agents 04:33 - Ship agents 10-15x faster 08:48 - Cut P95 latency by over 90% 24:33 - Claude finds the root cause 33:04 - Subagents, memory, dreaming, and vaults This one video is worth more than any paid agentic AI course Bookmark and watch it tonight Then read the article below

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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

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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 ↓

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Anthropic's Claude team just showed how to give your AI agents real memory in under 18 minutes. Free. From the people who built it. You've been building agents that forget everything between sessions. This fixes that tonight. One person + 10 agents with memory + dreaming = a team that runs 24/7, learns from every task, and improves itself overnight. Worth more than any $500 AI agent course. Bookmark it. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

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SpaceXAI engineer (ex. Cursor): "at SpaceXAI, 95% of our code is written by AI agents. Most of engineers are running 10-20+ agents in parallel. I'm running 20+ GrokBot agents with /loop, /goal, /swarm inside pstack, which lets agents fully own, verify and ship tasks." in a 1-hour workshop, SpaceXAI engineer showed how they build self-improving coding agent team from scratch worth more than 10 paid courses on building agents watch today, then read how to build a team of self-learning GrokBot agents from scratch in article below

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Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to an agent graph that can build itself: 0% → 10:16 - build your first AI agent 25% → 41:05 - master prompt engineering 50% → 54:45 - turn agents into graphs 75% → 1:20:10 - run loops inside agent graphs 100% → 1:43:33 - build a graph that builds itself Most people build one agent and stop there. Google is teaching everything that comes after: Prompt → Agents → Graphs → Loops → Self-Building Systems Single agents are the old workflow. Graphs that evolve themselves are the next one. This 2-hour course is worth more than most paid agent engineering courses. Bookmark it and watch today Then read how to run 1,000 agents from one prompt below ↓

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Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.
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Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

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SpaceXAI engineer (ex-Cursor): "I'm running 10-20 GrokBot agents in a loop. I have a Chief of Staff agent, it knows other agents and manages everything my GrokBot agents team shipped 1000+ PRs last month, and I will double it this month. In a 1-hour session, a SpaceXAI engineer showed how to build a team of agents that will work for you 24/7 worth more than 10 paid courses on agentic engineering watch today, then read how to build a Grok agents team from scratch in the article below

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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 ↓
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99% of AI engineers still don't know about Claude skills. Andrew Ng just dropped a 2 hour course with Anthropic on building AI agentic skills from scratch. • 00:00 - build agentic skills with Claude • 22:29 - Claude pre-built skills • 41:04 - skills vs MCP and subagents • 1:06:03 - skills for long running agents • 1:51:59 - live demo to build agentic skills Prompting is dead. Claude skills is how top engineers build AI agents now. I watched it last night. Halfway through, I realized I have been using Claude completely wrong for years. Bookmark this and read the article below.

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Anthropic engineer: "Stop re-prompting Claude all day. Build the loop once. Let it do the work." 00:00 - Build your first autonomous agent 05:02 - Make the loop fix its own mistakes 16:43 - Give Claude memory across sessions Prompting is the old workflow. Loops are the new one. This 30-minute watch is worth more than most paid agent courses. Bookmark and watch it today Then read the step-by-step guide below
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Anthropic engineer: "Stop re-prompting Claude all day. Build the loop once. Let it do the work." 00:00 - Build your first autonomous agent 05:02 - Make the loop fix its own mistakes 16:43 - Give Claude memory across sessions Prompting is the old workflow. Loops are the new one. This 30-minute watch is worth more than most paid agent courses. Bookmark and watch it today Then read the step-by-step guide below

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