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Anthropic hired this engineer at $250k-$750k/year because he can build knowledge Graphs for multi-agent systems: in this 15-min workshop he explain exactly how, using loops - from scratch step 1 → start from the Claude Agent SDK - you get the loop, context, and sandbox for free - bring...

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codila

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82,301 次观看 • 1 天前 •via X (Twitter)

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Anthropic hired this engineer at $250k-$600k/year because he can built projects with zero lines of code - in Claude chat: in this 30-min workshop he showed exactly how, using loops - from 0% to 100% here's workflow, step by step: step 1 → set your rules once at the top: "no preamble, use artifacts, you're a React expert" - every reply after that stays on rails step 2 → load the docs plus a random Medium article about the API - the human explanation teaches it more than the reference step 3 → wrap your ask in XML tags and tell it to ask you questions first. Answer 5, and the scope is locked before a single file exists step 4 → now run the loop: screenshot the broken UI, paste it back, let it fix, screenshot again - each pass gets closer - this is the agentic loop in its simplest form step 5 → push to GitHub, then paste the repo link in - every next loop iteration builds on working code, not chat history most people never did this - it pays for itself on day one watch & bookmark - then read how to get Claude Certified Architect in the article below ↓
26:23

Anthropic hired this engineer at $250k-$600k/year because he can built projects with zero lines of code - in Claude chat: in this 30-min workshop he showed exactly how, using loops - from 0% to 100% here's workflow, step by step: step 1 → set your rules once at the top: "no preamble, use artifacts, you're a React expert" - every reply after that stays on rails step 2 → load the docs plus a random Medium article about the API - the human explanation teaches it more than the reference step 3 → wrap your ask in XML tags and tell it to ask you questions first. Answer 5, and the scope is locked before a single file exists step 4 → now run the loop: screenshot the broken UI, paste it back, let it fix, screenshot again - each pass gets closer - this is the agentic loop in its simplest form step 5 → push to GitHub, then paste the repo link in - every next loop iteration builds on working code, not chat history most people never did this - it pays for itself on day one watch & bookmark - then read how to get Claude Certified Architect in the article below ↓

codila

288,600 次观看 • 10 天前

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 ↓
38:54

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

60,826 次观看 • 24 天前

Anthropic engineer: "You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day" in 45 minutes he shows exactly how to do it from scratch, step by step most people are still doing this manually watch the session, then save the guide below
44:36

Anthropic engineer: "You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day" in 45 minutes he shows exactly how to do it from scratch, step by step most people are still doing this manually watch the session, then save the guide below

Anatoli Kopadze

632,756 次观看 • 2 个月前

Anthropic engineer: "You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day." In 45 minutes he shows exactly how to do it from scratch, step by step. Most people are still doing all of this by hand. Watch the session, then save the guide below.
44:38

Anthropic engineer: "You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day." In 45 minutes he shows exactly how to do it from scratch, step by step. Most people are still doing all of this by hand. Watch the session, then save the guide below.

Anatoli Kopadze

1,496,090 次观看 • 1 个月前

Anthropic engineer: "You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day." In 45 minutes he shows exactly how to build them from scratch, step by step. Most people are still doing all of this by hand. Watch the session, then read the step by step guide below on building all of them in a single chat, prompts included.
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Anthropic engineer: "You can build 5 assistants in one afternoon. Each one handles a task you've been doing manually every single day." In 45 minutes he shows exactly how to build them from scratch, step by step. Most people are still doing all of this by hand. Watch the session, then read the step by step guide below on building all of them in a single chat, prompts included.

Anatoli Kopadze

215,342 次观看 • 10 天前

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓
1:04:05

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓

codila

542,428 次观看 • 28 天前

Andrew Ng built agentic knowledge graph that runs agents loops from scratch: his agents searched thousands of tools through the graph, found the "right" one by meaning, and called it agent efficiency increased by 90% here's how: step 1 → embed every tool, search by meaning - not just keywords step 2 → but "similar" ≠ "correct" - it grabs the wrong one, skips the step before it step 3 → map tools as a graph - nodes are tools, edges are what runs before what step 4 → retrieve the path, not one tool - every tool the task needs, in order step 5 → bake the process into the graph - the sequence becomes data, not a guess most agents fail here and no one knows why - 1-hour brilliant course about it watch & bookmark - then read full course about graph engineering ↓
1:04:49

Andrew Ng built agentic knowledge graph that runs agents loops from scratch: his agents searched thousands of tools through the graph, found the "right" one by meaning, and called it agent efficiency increased by 90% here's how: step 1 → embed every tool, search by meaning - not just keywords step 2 → but "similar" ≠ "correct" - it grabs the wrong one, skips the step before it step 3 → map tools as a graph - nodes are tools, edges are what runs before what step 4 → retrieve the path, not one tool - every tool the task needs, in order step 5 → bake the process into the graph - the sequence becomes data, not a guess most agents fail here and no one knows why - 1-hour brilliant course about it watch & bookmark - then read full course about graph engineering ↓

codila

16,276 次观看 • 20 天前

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 ↓
45:37

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 次观看 • 20 天前

Kimi CEO Zhilin Yang: "a smart agent isn't enough - it has to run long Kimi 3 new attention makes the context 10x faster, so the agent never has to stop" here's how built it: step 1 → chase token efficiency, not compute - Muon optimizer, 2x more learning per token step 2 → make context cheap, not just long - new attention runs 10x faster, so the agent never stalls step 3 → let it run 200-300 steps alone - K2 already solves problems its own builders can't step 4 → give it a strong prior - the agent searches less and finds the answer faster step 5 → stop chasing a smarter model - chase one that never has to stop in 26-min talk is him revealing the exact architecture behind K3 and how to properly build agents on it watch & bookmark - start with the article below ↓
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Kimi CEO Zhilin Yang: "a smart agent isn't enough - it has to run long Kimi 3 new attention makes the context 10x faster, so the agent never has to stop" here's how built it: step 1 → chase token efficiency, not compute - Muon optimizer, 2x more learning per token step 2 → make context cheap, not just long - new attention runs 10x faster, so the agent never stalls step 3 → let it run 200-300 steps alone - K2 already solves problems its own builders can't step 4 → give it a strong prior - the agent searches less and finds the answer faster step 5 → stop chasing a smarter model - chase one that never has to stop in 26-min talk is him revealing the exact architecture behind K3 and how to properly build agents on it watch & bookmark - start with the article below ↓

codila

11,898 次观看 • 23 天前

Anthropic engineer: "Harness engineering is a really rare skill right now - and it's hard to know if you're even good at it." how he actually thinks about building the full system: • step 1 → the model. already smarter than we use it. "freeze development - you'd find new capabilities for another 6-12 months" • step 2 → the harness. delete the steering. "we stopped using plan mode - the model thinks correctly" • step 3 → the agents. workflows let Claude build its own harness per task and spin sub-agents. what didn't fit in one context window now runs across many • step 4 → the context. "RAG is an anti-pattern. use grep." memory via files, clean context window, prompt caching. that's the stack most people optimize step 1. he optimizes all four. Watch the full video, then read the guide below on how to build an agentic system.
46:31

Anthropic engineer: "Harness engineering is a really rare skill right now - and it's hard to know if you're even good at it." how he actually thinks about building the full system: • step 1 → the model. already smarter than we use it. "freeze development - you'd find new capabilities for another 6-12 months" • step 2 → the harness. delete the steering. "we stopped using plan mode - the model thinks correctly" • step 3 → the agents. workflows let Claude build its own harness per task and spin sub-agents. what didn't fit in one context window now runs across many • step 4 → the context. "RAG is an anti-pattern. use grep." memory via files, clean context window, prompt caching. that's the stack most people optimize step 1. he optimizes all four. Watch the full video, then read the guide below on how to build an agentic system.

Codez

43,764 次观看 • 25 天前

ex-Anthropic engineer: "I left Anthropic to build agents systems in Google but nothing beats Claude as an agent yet - in coding and tool use, the rest are still behind " in this 90-min podcast, he showed how to build effective agent system with Claude from scratch to get into Anthropic, he hand-coded Karpathy's nanoGPT as a physicist Claude → tools → loops of verification → a real agent and why, at Google, he says Claude is the best watch & bookmark - then read the guide to agent systems ↓
1:33:11

ex-Anthropic engineer: "I left Anthropic to build agents systems in Google but nothing beats Claude as an agent yet - in coding and tool use, the rest are still behind " in this 90-min podcast, he showed how to build effective agent system with Claude from scratch to get into Anthropic, he hand-coded Karpathy's nanoGPT as a physicist Claude → tools → loops of verification → a real agent and why, at Google, he says Claude is the best watch & bookmark - then read the guide to agent systems ↓

codila

126,242 次观看 • 22 天前

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
31:36

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

Lunar

316,814 次观看 • 21 天前

Anthropic engineer: "You're not supposed to watch Claude Code work. You're supposed to wake up and review what it shipped" in 22 minutes she builds the full workflow from scratch, step by step a routine that ships work while you sleep most developers have never seen Claude Code run without them watching watch the session, then save the guide below
21:33

Anthropic engineer: "You're not supposed to watch Claude Code work. You're supposed to wake up and review what it shipped" in 22 minutes she builds the full workflow from scratch, step by step a routine that ships work while you sleep most developers have never seen Claude Code run without them watching watch the session, then save the guide below

Anatoli Kopadze

36,192 次观看 • 2 个月前

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → order matters: tools first, then system prompt, then your docs. anything that changes goes last step 2 → drop the breakpoint after the last stable block. everything above it gets stored step 3 → the match has to be byte-perfect. one extra space and you're back to full price step 4 → 5 minutes of idle and it's gone. but every hit restarts the timer step 5 → cache reads don't touch your rate limit. that's throughput you're not paying for 99% people never configure this - one afternoon and it pays for itself save this - the full 1-hour course is right below ↓
1:06:45

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → order matters: tools first, then system prompt, then your docs. anything that changes goes last step 2 → drop the breakpoint after the last stable block. everything above it gets stored step 3 → the match has to be byte-perfect. one extra space and you're back to full price step 4 → 5 minutes of idle and it's gone. but every hit restarts the timer step 5 → cache reads don't touch your rate limit. that's throughput you're not paying for 99% people never configure this - one afternoon and it pays for itself save this - the full 1-hour course is right below ↓

codila

84,462 次观看 • 6 天前

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.
8:29

Amazon AI engineer just explained how to build agentic loops from scratch in under 10-minutes. this is one of the clearest explanations of AI agentic loops you’ll find anywhere. Loop + model + agent + hooks + MCP = an autonomous, self-improving system. People are paying $500 for courses that teach less than this. Watch it, then read the step-by-step guide below on building loops for your own agents.

Codez

26,868 次观看 • 1 个月前

Anthropic Claude Code engineer: "The moment you're reviewing every line Claude writes, you became its tester. You weren't hired for that." this 36 min talk is the clearest breakdown of how to get yourself out of the loop entirely the shift is simple: you stop prompting and start building agents that work instead of you agents that run, check themselves, and keep going while your laptop is closed watch it, then build your own with the step-by-step guide below
36:48

Anthropic Claude Code engineer: "The moment you're reviewing every line Claude writes, you became its tester. You weren't hired for that." this 36 min talk is the clearest breakdown of how to get yourself out of the loop entirely the shift is simple: you stop prompting and start building agents that work instead of you agents that run, check themselves, and keep going while your laptop is closed watch it, then build your own with the step-by-step guide below

Anatoli Kopadze

28,042 次观看 • 1 个月前

Karpathy just proved the structure beats the model - mapped the whole path from loops to graphs into a 15-page PDF: he pointed one agent at code he'd already hand-optimized. 700 experiments in two days, then he said the single loop was never the target here's the full system: step 1 → write the scorer before anything else. one number, automatic, under 10 minutes. no scorer, no loop step 2 → split the files. one the agent edits, one holding the scorer it can never reach step 3 → fix the wall clock per attempt. comparability comes from the budget, not the attempts step 4 → write the never-stop line verbatim or your agent becomes a slower human step 5 → add a second agent only after the first ships real commits. one git worktree each or they overwrite each other step 6 → at thirty agents, isolation costs more than it saves. that's when they need one shared DAG instead of thirty private histories 2.02 hours to 1.80 hours on code its author had already tuned. 90,900 stars in four months. read this PDF and paste it into your Claude, you won't regret it bookmark, then read the article on building graphs from scratch ↓
2:00

Karpathy just proved the structure beats the model - mapped the whole path from loops to graphs into a 15-page PDF: he pointed one agent at code he'd already hand-optimized. 700 experiments in two days, then he said the single loop was never the target here's the full system: step 1 → write the scorer before anything else. one number, automatic, under 10 minutes. no scorer, no loop step 2 → split the files. one the agent edits, one holding the scorer it can never reach step 3 → fix the wall clock per attempt. comparability comes from the budget, not the attempts step 4 → write the never-stop line verbatim or your agent becomes a slower human step 5 → add a second agent only after the first ships real commits. one git worktree each or they overwrite each other step 6 → at thirty agents, isolation costs more than it saves. that's when they need one shared DAG instead of thirty private histories 2.02 hours to 1.80 hours on code its author had already tuned. 90,900 stars in four months. read this PDF and paste it into your Claude, you won't regret it bookmark, then read the article on building graphs from scratch ↓

Carnage

53,533 次观看 • 14 天前

Anthropic engineer: "90% of our engineers were using self-improving loops. Now everyone shifted to building Graphs." In 20 minutes he shows how Anthropic builds agents that remember across sessions and learn while they sleep. The missing piece most engineers miss: a loop resets, a graph compounds. Watch it, then try your first graph with the step-by-step guide below
20:43

Anthropic engineer: "90% of our engineers were using self-improving loops. Now everyone shifted to building Graphs." In 20 minutes he shows how Anthropic builds agents that remember across sessions and learn while they sleep. The missing piece most engineers miss: a loop resets, a graph compounds. Watch it, then try your first graph with the step-by-step guide below

Mahax

45,566 次观看 • 8 天前

Anthropic just dropped a 1-hour workshop on how to actually build with AI in 2026: 00:26 - Why they stopped prompting 16:57 - The system that builds, tests, and fixes itself 25:27 - One engineer replacing a 5-person team 36:25 - Building your first loop from scratch This 1-hour watch could replace 10 AI engineering courses on the internet. Watch it today, then read the step-by-step guide on building loops below.
1:13:54

Anthropic just dropped a 1-hour workshop on how to actually build with AI in 2026: 00:26 - Why they stopped prompting 16:57 - The system that builds, tests, and fixes itself 25:27 - One engineer replacing a 5-person team 36:25 - Building your first loop from scratch This 1-hour watch could replace 10 AI engineering courses on the internet. Watch it today, then read the step-by-step guide on building loops below.

Raytar

87,900 次观看 • 1 个月前

How to turn Claude 4 into your AI super agent (FREE): Step 1: Download Claude Desktop Step 2: Enable MCP in developer settings Step 3: Configure browser + laptop control Step 4: Give it tasks like "research the latest news" Step 5: Watch it work for hours while you do other things Real example from today: - Asked it to check my free disk space - It responded: "91GB available, 14% used" - Then navigated to websites and gathered data This is what "AI agents" actually means. Not chatbots—full computer control. Save this video, you'll automate everything. Want the SOP? DM me. 💬
1:09:00

How to turn Claude 4 into your AI super agent (FREE): Step 1: Download Claude Desktop Step 2: Enable MCP in developer settings Step 3: Configure browser + laptop control Step 4: Give it tasks like "research the latest news" Step 5: Watch it work for hours while you do other things Real example from today: - Asked it to check my free disk space - It responded: "91GB available, 14% used" - Then navigated to websites and gathered data This is what "AI agents" actually means. Not chatbots—full computer control. Save this video, you'll automate everything. Want the SOP? DM me. 💬

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32,494 次观看 • 1 年前