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LOOP + GRAPH + CONTEXT ENGINEERING = THE KIMI K3 STACK THAT KEEPS RUNNING 300 Kimi K3 agents built one graph out of 100 companies. no 300 documents to read yourself kimi-swarm/ ├── swarm_launch.md task, sources, stop condition ├── CONSTRAINTS.md corrections from every past run ├── graph/ 384 entities,...

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Mr. Buzzoni

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15,462 views • 6 days ago •via X (Twitter)

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Google just released free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 10% → 17:44 - build your first agent 30% → 39:30 - Loop engineering: iterate, check, break 60% → 1:12:38 - Graph engineering 75% → 1:34:26 - agents that throttle themselves 100% → 1:55:05 - full graph for multi-agentic systems everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph watch the course, build the graph - then read the full architecture below ↓
2:03:00

Google just released free 2-hour course on full Graph engineering: 1 prompt → 100 agents → loops → graphs from 0% to 100%: 10% → 17:44 - build your first agent 30% → 39:30 - Loop engineering: iterate, check, break 60% → 1:12:38 - Graph engineering 75% → 1:34:26 - agents that throttle themselves 100% → 1:55:05 - full graph for multi-agentic systems everyone builds one agent and calls it done - this is the full system where agents wire themselves into a graph watch the course, build the graph - then read the full architecture below ↓

codila

463,265 views • 24 days ago

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below
0:40

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below

Hanako

19,160 views • 27 days ago

one agent → a loop → a graph → a system that throttles itself Google just put the whole ladder in one free course. most people never leave the first rung. 2 hours 4 minutes, taught by two Google Cloud engineers. no signup, no paywall. 17:55 - build your first agent 39:55 - loop engineering: iterate, check, break 1:13:25 - graph engineering 1:35:27 - agents that throttle themselves 1:56:19 - the full graph for multi-agent systems the fourth one is the step nobody talks about. an agent that cannot throttle itself is not autonomous, it is just unattended. everyone builds one agent and calls it done. this is the version where the agents wire themselves into a graph and decide their own rate. and the thing that decides when one of them is allowed to stop is not in the course. that part you build yourself. bookmark and watch it today, then read the eval setup below
2:04:19

one agent → a loop → a graph → a system that throttles itself Google just put the whole ladder in one free course. most people never leave the first rung. 2 hours 4 minutes, taught by two Google Cloud engineers. no signup, no paywall. 17:55 - build your first agent 39:55 - loop engineering: iterate, check, break 1:13:25 - graph engineering 1:35:27 - agents that throttle themselves 1:56:19 - the full graph for multi-agent systems the fourth one is the step nobody talks about. an agent that cannot throttle itself is not autonomous, it is just unattended. everyone builds one agent and calls it done. this is the version where the agents wire themselves into a graph and decide their own rate. and the thing that decides when one of them is allowed to stop is not in the course. that part you build yourself. bookmark and watch it today, then read the eval setup below

Hanako

53,671 views • 22 days ago

Google dropped a free 2 hour course on full graph engineering, taking you from a single prompt to a hundred agents wired into loops and graphs 17:44 – Building your first agent 39:30 – Loop engineering, iterate, check, break 1:12:38 – Graph engineering 1:34:26 – Agents that throttle themselves 1:55:05 – Full graph for multi agentic systems Most people build a single agent and stop there. This course shows the full system, where agents wire themselves into a graph on their own Watch it today, then check out the graph engineering playbook below for a deeper breakdown
2:03:00

Google dropped a free 2 hour course on full graph engineering, taking you from a single prompt to a hundred agents wired into loops and graphs 17:44 – Building your first agent 39:30 – Loop engineering, iterate, check, break 1:12:38 – Graph engineering 1:34:26 – Agents that throttle themselves 1:55:05 – Full graph for multi agentic systems Most people build a single agent and stop there. This course shows the full system, where agents wire themselves into a graph on their own Watch it today, then check out the graph engineering playbook below for a deeper breakdown

Wayne

140,885 views • 11 days ago

Google just released free 2-hour course on full Graph engineering: 1 prompt → agent teams → loops → graphs from 0% to 100%: 10% → 10:16 - build your first agent 40% → 41:05 - Prompt engineering 50% → 54:45 - Graph engineering 75% → 1:20:10 - Loop engineering (with graphs) 100% → 1:43:33 - the graph that builds itself most people ship one agent and stop - this is everything after watch it today - then read how to run 1000 agents from one prompt below ↓
1:56:52

Google just released free 2-hour course on full Graph engineering: 1 prompt → agent teams → loops → graphs from 0% to 100%: 10% → 10:16 - build your first agent 40% → 41:05 - Prompt engineering 50% → 54:45 - Graph engineering 75% → 1:20:10 - Loop engineering (with graphs) 100% → 1:43:33 - the graph that builds itself most people ship one agent and stop - this is everything after watch it today - then read how to run 1000 agents from one prompt below ↓

codila

104,682 views • 18 days ago

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 ↓
2:05:53

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

399,182 views • 19 days ago

the loop is not the whole agent it is only one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls iteration turns retries budgets stop conditions success checks stalled progress use durable execution when the run must survive crashes → GRAPH ENGINEERING controls structure nodes edges state branches checkpoints routing handoffs build stateful agents as explicit graphs → inspect the topology without putting a model in the loop → HARNESS ENGINEERING controls exposure tools permissions memory sandboxes evals traces humans isolate generated code from the host machine → test behavior before users ever see it → preserve one trace across every model step and tool call → without loop control it keeps running without graph structure the path stays invisible without harness control it can reach anything the loop runs inside the graph the graph runs inside the harness the model is only one component debug the layer that actually owns the failure save this then read the article below
0:20

the loop is not the whole agent it is only one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls iteration turns retries budgets stop conditions success checks stalled progress use durable execution when the run must survive crashes → GRAPH ENGINEERING controls structure nodes edges state branches checkpoints routing handoffs build stateful agents as explicit graphs → inspect the topology without putting a model in the loop → HARNESS ENGINEERING controls exposure tools permissions memory sandboxes evals traces humans isolate generated code from the host machine → test behavior before users ever see it → preserve one trace across every model step and tool call → without loop control it keeps running without graph structure the path stays invisible without harness control it can reach anything the loop runs inside the graph the graph runs inside the harness the model is only one component debug the layer that actually owns the failure save this then read the article below

rari

20,934 views • 21 days ago

Google just released 1-hour workshop on "Graph Engineering" for agentic systems from 0% to 100%: "loop agent is getting deprecated soon, so I’m going to focus on graph workflows" 0% → 8:01 - build your first agent from scratch 30% → 24:14 - Prompt engineering 55% → 44:48 - Graphs: kill the tree, draw the graph 80% → 1:00:47 - the router that never asks the model 100% → 1:19:49 - Parallel engineering: fan out without losing control most agent tutorials tell you to add another agent - this one deletes three of them and puts plain functions in their place watch it, open the lab, build the graph - then read the full architecture below ↓
1:20:54

Google just released 1-hour workshop on "Graph Engineering" for agentic systems from 0% to 100%: "loop agent is getting deprecated soon, so I’m going to focus on graph workflows" 0% → 8:01 - build your first agent from scratch 30% → 24:14 - Prompt engineering 55% → 44:48 - Graphs: kill the tree, draw the graph 80% → 1:00:47 - the router that never asks the model 100% → 1:19:49 - Parallel engineering: fan out without losing control most agent tutorials tell you to add another agent - this one deletes three of them and puts plain functions in their place watch it, open the lab, build the graph - then read the full architecture below ↓

codila

22,265 views • 11 days ago

Loops vs. Graphs, clearly explained! loops are great, but they have a ceiling: a loop makes one unit of work better. it cannot decide which units exist. so you end up with a very good agent running the wrong three steps, in the wrong order, one at a time. Graph engineering fixes this by moving the decision up a layer: what runs, what runs at the same time, and what never runs at all. you need both. here's how it works: a graph splits your system into two kinds of decision. ↳ inside a unit: the loop. produce, check, correct, repeat until green ↳ between units: the graph. split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs you get parallel work, isolated contexts, and steps that stop running when nothing needs them. the trick is being selective about what becomes a node. only spend a model where judgment lives. merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict. a graph where every edge is an agent pays rent on its own wiring. one thing to know before you scale it. a graph has two return paths, and almost everyone builds one. ↳ the correction edge is short. a gate rejects one unit back to the step that produced it, and it fixes the run you are in ↳ the learning edge is long. an accepted result goes back to the splitter as a constraint, and it fixes every run after skip the second and you get a graph that is fast and never gets smarter. next week it starts from the same place with the same blind spots. and a smaller one that eats whole nights: when a unit fails, return that unit, not the batch. send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓
0:41

Loops vs. Graphs, clearly explained! loops are great, but they have a ceiling: a loop makes one unit of work better. it cannot decide which units exist. so you end up with a very good agent running the wrong three steps, in the wrong order, one at a time. Graph engineering fixes this by moving the decision up a layer: what runs, what runs at the same time, and what never runs at all. you need both. here's how it works: a graph splits your system into two kinds of decision. ↳ inside a unit: the loop. produce, check, correct, repeat until green ↳ between units: the graph. split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs you get parallel work, isolated contexts, and steps that stop running when nothing needs them. the trick is being selective about what becomes a node. only spend a model where judgment lives. merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict. a graph where every edge is an agent pays rent on its own wiring. one thing to know before you scale it. a graph has two return paths, and almost everyone builds one. ↳ the correction edge is short. a gate rejects one unit back to the step that produced it, and it fixes the run you are in ↳ the learning edge is long. an accepted result goes back to the splitter as a constraint, and it fixes every run after skip the second and you get a graph that is fast and never gets smarter. next week it starts from the same place with the same blind spots. and a smaller one that eats whole nights: when a unit fails, return that unit, not the batch. send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Hanako

65,507 views • 5 days ago

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 views • 1 month ago

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to an agent graph that builds itself: 10:16 - Build your first AI agent 41:05 - Master prompt engineering 54:45 - Turn agents into graphs 1:20:10 - Run loops inside agent graphs 1:43:33 - Build a graph that builds itself Most people ship one agent and stop there. Google is teaching everything after: Prompt → Agents → Graphs → Loops → Self-Building Systems Single agents are the old workflow. Graphs that evolve themselves are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read how to run 1,000 agents from one prompt below ↓
1:54:59

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

Lunar

49,332 views • 18 days ago

KIMI K3 + OBSIDIAN + LOOP ENGINEERING = A VAULT THAT RUNS ITSELF the core idea: the vault is the loop's state, not the chat window everything Kimi K3 knows lives in a .md file the loop: > capture - a thought lands in 00-inbox > context - K3 pulls links, tags, and neighbouring notes > draft - edits happen inside a git worktree, never the live vault > review - a critic agent checks the diff before anything ships > commit - appended to the vault, nothing gets rewritten the key insight: frontmatter fields like supports, contradicts, and supersedes are graph edges, not metadata - the note format is the write API start with a plain loop, it runs about 2-4x the cost of one direct call > only move to a full graph once state has to outlive the session, several agents need to coordinate, or you have to explain what changed - that jump can run 10-50x one review assistant climbed from 55% to 72% to 84% just by moving through these shapes in order
0:20

KIMI K3 + OBSIDIAN + LOOP ENGINEERING = A VAULT THAT RUNS ITSELF the core idea: the vault is the loop's state, not the chat window everything Kimi K3 knows lives in a .md file the loop: > capture - a thought lands in 00-inbox > context - K3 pulls links, tags, and neighbouring notes > draft - edits happen inside a git worktree, never the live vault > review - a critic agent checks the diff before anything ships > commit - appended to the vault, nothing gets rewritten the key insight: frontmatter fields like supports, contradicts, and supersedes are graph edges, not metadata - the note format is the write API start with a plain loop, it runs about 2-4x the cost of one direct call > only move to a full graph once state has to outlive the session, several agents need to coordinate, or you have to explain what changed - that jump can run 10-50x one review assistant climbed from 55% to 72% to 84% just by moving through these shapes in order

Mr. Buzzoni

10,680 views • 5 days ago

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 ↓
1:48:23

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

223,862 views • 15 days ago

ok. kimi k3 is insane. you can build an autonomous analyst that runs your entire competitive intelligence operation while your team sleeps. here’s how to do do it: 1. give it the market: “build a context graph containing every competitor, product, launch, price, integration, positioning claim, and trusted source in my market.” > now the analyst has one living map of everything your team tracks. 2. put it on watch: “compare today’s evidence with the current graph. ignore anything unchanged. surface only new, updated, disputed, or stale information.” > your team stops reading repeat research and wakes up to the changes worth caring about. 3. connect each change to an action: “for every material change, identify what it affects and which team should respond.” > a pricing change updates the battlecard. > a new integration creates a partnership opportunity. > a positioning shift gives marketing something to counter. 4. make every correction permanent: “when a human corrects a fact, source, relationship, or rule, save it and apply it to every future run.” > the analyst stops returning with the same mistakes. > kimi keeps that entire map current and brings your team the few moves worth acting on with the evidence behind them. don’t leave this sitting in your bookmarks. open the post below, paste the full article into kimi, and tell it to build the first version for your market.
0:40

ok. kimi k3 is insane. you can build an autonomous analyst that runs your entire competitive intelligence operation while your team sleeps. here’s how to do do it: 1. give it the market: “build a context graph containing every competitor, product, launch, price, integration, positioning claim, and trusted source in my market.” > now the analyst has one living map of everything your team tracks. 2. put it on watch: “compare today’s evidence with the current graph. ignore anything unchanged. surface only new, updated, disputed, or stale information.” > your team stops reading repeat research and wakes up to the changes worth caring about. 3. connect each change to an action: “for every material change, identify what it affects and which team should respond.” > a pricing change updates the battlecard. > a new integration creates a partnership opportunity. > a positioning shift gives marketing something to counter. 4. make every correction permanent: “when a human corrects a fact, source, relationship, or rule, save it and apply it to every future run.” > the analyst stops returning with the same mistakes. > kimi keeps that entire map current and brings your team the few moves worth acting on with the evidence behind them. don’t leave this sitting in your bookmarks. open the post below, paste the full article into kimi, and tell it to build the first version for your market.

J.B.

702,730 views • 6 days ago

Google just released free 2-hour course on full agent engineering: 1 prompt → agent teams → loops → graphs from 0% to 100%: 10% → 38:46 - build your first agent 30% → 54:46 - connect MCP tools 55% → 1:12:43 - Loop engineering: 4 ways to run agents 70% → 1:20:57 - Graph engineering 100% → 2:22:31 - the full system that works while you sleep most people build one agent and stop there - this is the full path to a system that runs without you from scratch watch it today - then read the full graph engineering playbook below ↓
2:10:34

Google just released free 2-hour course on full agent engineering: 1 prompt → agent teams → loops → graphs from 0% to 100%: 10% → 38:46 - build your first agent 30% → 54:46 - connect MCP tools 55% → 1:12:43 - Loop engineering: 4 ways to run agents 70% → 1:20:57 - Graph engineering 100% → 2:22:31 - the full system that works while you sleep most people build one agent and stop there - this is the full path to a system that runs without you from scratch watch it today - then read the full graph engineering playbook below ↓

codila

530,675 views • 27 days ago

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
2:14:04

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

196,723 views • 26 days ago

Kimi has a feature 90% of users have never opened it's called Agent Swarm you write one project brief, Kimi spins up to 300 domain-specialized agents, and delivers real files as output > 100 job descriptions -> 100 tailored CVs, one prompt > 40 academic papers -> 100k-word literature review with citations in 1 prompt > 30 businesses without websites -> 30 landing pages, one prompt a content agency charges $15k-$20k for that last task, but Agent Swarm does it for $0 extra on your subscription this is the feature Kimi built that nobody talks about full breakdown + 15 rules for running it in the article below
1:14

Kimi has a feature 90% of users have never opened it's called Agent Swarm you write one project brief, Kimi spins up to 300 domain-specialized agents, and delivers real files as output > 100 job descriptions -> 100 tailored CVs, one prompt > 40 academic papers -> 100k-word literature review with citations in 1 prompt > 30 businesses without websites -> 30 landing pages, one prompt a content agency charges $15k-$20k for that last task, but Agent Swarm does it for $0 extra on your subscription this is the feature Kimi built that nobody talks about full breakdown + 15 rules for running it in the article below

Chrome

16,660 views • 3 months ago

300 agents can still give you bad research. unless the sources can check each other. the interesting part is what happens when every source can see every other source. K3 swarm doesn't just dump research into 300 separate write-ups. • every source becomes a node. • shared entities become edges. • single-source claims stick out as weak. so instead of: `300 agents → 300 answers` you get: `300 agents → sources → links → one research graph` and that's where things start showing up that no individual agent was explicitly asked to find. the article's example ends with 140 linked sources, every claim traceable to its origin, and 9 contradictions surfaced instead of silently shipped as facts. esearch stops being a pile of tabs. it becomes a system where the sources check each other. Book this & read full breakdown below
0:40

300 agents can still give you bad research. unless the sources can check each other. the interesting part is what happens when every source can see every other source. K3 swarm doesn't just dump research into 300 separate write-ups. • every source becomes a node. • shared entities become edges. • single-source claims stick out as weak. so instead of: `300 agents → 300 answers` you get: `300 agents → sources → links → one research graph` and that's where things start showing up that no individual agent was explicitly asked to find. the article's example ends with 140 linked sources, every claim traceable to its origin, and 9 contradictions surfaced instead of silently shipped as facts. esearch stops being a pile of tabs. it becomes a system where the sources check each other. Book this & read full breakdown below

Ricker

32,618 views • 7 days ago

This free 2-hour course on "Graph Engineering" gets you Anthropic official "Claude Certified Architect" certificate with it you'll be hired at $250K/year in Anthropic's ecosystem 10% → 22:30 - build your first coordinator agent 30% → 48:18 - agentic loop foundations 45% → 58:14 - teams of agents work while you sleep 70% → 1:14:24 - Loop engineering 100% → 1:41:00 - you are Graph Architect after this 2-hour section - full freeCodeCamp tutorial (12 hours) - go to Anthropic's website and take the exam - you're ready even if you never take the exam - this is still the best graph engineering course I've seen watch it today - then read how to pass this "Claude Certified Architect" exam in the article below ↓
2:16:56

This free 2-hour course on "Graph Engineering" gets you Anthropic official "Claude Certified Architect" certificate with it you'll be hired at $250K/year in Anthropic's ecosystem 10% → 22:30 - build your first coordinator agent 30% → 48:18 - agentic loop foundations 45% → 58:14 - teams of agents work while you sleep 70% → 1:14:24 - Loop engineering 100% → 1:41:00 - you are Graph Architect after this 2-hour section - full freeCodeCamp tutorial (12 hours) - go to Anthropic's website and take the exam - you're ready even if you never take the exam - this is still the best graph engineering course I've seen watch it today - then read how to pass this "Claude Certified Architect" exam in the article below ↓

codila

22,859 views • 21 days ago

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓
1:06:12

Google just released a free 1-hour lecture from Chief Scientist Jeff Dean on full AI Engineering. How to go from one LLM to coordinating 100 AI agents: 01:45 - Build an LLM from scratch 17:22 - Learn how to actually use AI models 30:03 - Master prompt engineering 52:35 - Coordinate 100 agents as one human Most people are still learning how to prompt one model. Jeff Dean is showing the full stack: LLMs → Prompts → Agents → Agent Teams → Graphs Prompting is the old workflow. Coordinating agent systems is the new one. This is 27 years of Google AI compressed into one hour. Bookmark and watch it today Then read the full Graph Engineering guide below ↓

Lunar

101,704 views • 18 days ago