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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...

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Mr. Buzzoni

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10,680 просмотров • 1 месяц назад •via X (Twitter)

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CLAUDE + OBSIDIAN + LOOP ENGINEERING = A VAULT THAT RUNS ITSELF the core idea: the vault is the loop's state, not the chat window everything Claude knows lives in a .md file the loop: > capture - a thought lands in 00-inbox > context - claude 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
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CLAUDE + OBSIDIAN + LOOP ENGINEERING = A VAULT THAT RUNS ITSELF the core idea: the vault is the loop's state, not the chat window everything Claude knows lives in a .md file the loop: > capture - a thought lands in 00-inbox > context - claude 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

137,015 просмотров • 1 месяц назад

CLAUDE + OBSIDIAN + LOOP ENGINEERING = A VAULT THAT RUNS ITSELF the core idea: the loop keeps its state in the vault, so the chat window can close anytime everything Claude knows lives in a markdown file the loop: > capture - a thought lands in 00-inbox > context - Claude Opus 5.5 pulls the links, tags and neighbouring notes > draft - edits happen in a git worktree, the live vault stays untouched > review - a critic agent reads the diff before anything ships > commit - appended to the vault, nothing overwritten the key insight: frontmatter fields like supports, contradicts and supersedes act as graph edges. the note format is the write API start with a plain loop. it costs roughly 2-4x a single direct call upgrade to a full graph only when state has to outlive the session, several agents need to coordinate, or you need to explain what changed. that step can run 10-50x one review assistant went 55% -> 72% -> 84% by walking through these shapes in order worth stealing even if you never build a graph: the review step most vaults skip it and let Claude write straight into live notes the mistakes stack up for months before anyone notices put that gate in first
0:24

CLAUDE + OBSIDIAN + LOOP ENGINEERING = A VAULT THAT RUNS ITSELF the core idea: the loop keeps its state in the vault, so the chat window can close anytime everything Claude knows lives in a markdown file the loop: > capture - a thought lands in 00-inbox > context - Claude Opus 5.5 pulls the links, tags and neighbouring notes > draft - edits happen in a git worktree, the live vault stays untouched > review - a critic agent reads the diff before anything ships > commit - appended to the vault, nothing overwritten the key insight: frontmatter fields like supports, contradicts and supersedes act as graph edges. the note format is the write API start with a plain loop. it costs roughly 2-4x a single direct call upgrade to a full graph only when state has to outlive the session, several agents need to coordinate, or you need to explain what changed. that step can run 10-50x one review assistant went 55% -> 72% -> 84% by walking through these shapes in order worth stealing even if you never build a graph: the review step most vaults skip it and let Claude write straight into live notes the mistakes stack up for months before anyone notices put that gate in first

Mr. Buzzoni

29,485 просмотров • 3 дней назад

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
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THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,643 просмотров • 4 месяцев назад

STOP TREATING YOUR OBSIDIAN VAULT AS AI MEMORY. WITHOUT ONE ROUTING FILE, CLAUDE IS BURNING TOKENS ON NOISE YOU FORGOT YOU SAVED. a folder full of markdown does not make Claude smart. what makes it smart is what your agent can find without rereading the whole vault. storage: write → tag → graph → agent scans everything the model touches the entire vault, hallucinates half of it, forgets by the next session. memory: write → index → route → agent opens two files the model reads the index first, follows one path, answers with a link back to the note. i have rebuilt my Obsidian vault three times this year. the first two collapsed inside a month because Claude kept guessing which folder to open. the third one holds because the first thing i ship now is the routing file. Markdown, links, and the graph view are not what changed. what changed was a one-line description per folder, written for the agent, kept current, loaded before any retrieval call. if your agent still starts every session by scanning your whole vault, you are running storage. write the routing file today.
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STOP TREATING YOUR OBSIDIAN VAULT AS AI MEMORY. WITHOUT ONE ROUTING FILE, CLAUDE IS BURNING TOKENS ON NOISE YOU FORGOT YOU SAVED. a folder full of markdown does not make Claude smart. what makes it smart is what your agent can find without rereading the whole vault. storage: write → tag → graph → agent scans everything the model touches the entire vault, hallucinates half of it, forgets by the next session. memory: write → index → route → agent opens two files the model reads the index first, follows one path, answers with a link back to the note. i have rebuilt my Obsidian vault three times this year. the first two collapsed inside a month because Claude kept guessing which folder to open. the third one holds because the first thing i ship now is the routing file. Markdown, links, and the graph view are not what changed. what changed was a one-line description per folder, written for the agent, kept current, loaded before any retrieval call. if your agent still starts every session by scanning your whole vault, you are running storage. write the routing file today.

kocer

17,240 просмотров • 1 месяц назад

A Chinese developer just explained the shift from Loop Engineering to Graph Engineering better than anyone. most people are still building agents the way that's about to be obsolete. > why single-agent loops break and go "goal blind" > the 4 parts of a graph: nodes, edges, state, policy > 3 topologies that run everything: diamond, supervisor, pipeline > Anthropic's 5 official workflow patterns the punchline: it's not how many agents you run. it's the determinism you build with verifiers, code fallbacks, and reality anchors. I broke the same architecture down with Kimi K3. Full A-Z guide below.
21:17

A Chinese developer just explained the shift from Loop Engineering to Graph Engineering better than anyone. most people are still building agents the way that's about to be obsolete. > why single-agent loops break and go "goal blind" > the 4 parts of a graph: nodes, edges, state, policy > 3 topologies that run everything: diamond, supervisor, pipeline > Anthropic's 5 official workflow patterns the punchline: it's not how many agents you run. it's the determinism you build with verifiers, code fallbacks, and reality anchors. I broke the same architecture down with Kimi K3. Full A-Z guide below.

Kirill

2,738,564 просмотров • 1 месяц назад

A DOCTOR BUILT AN AI-QUERYABLE DATABASE OF EVERY DISEASE HE KNOWS IN OBSIDIAN. THE SAME STACK CAN RUN AN ENTIRE COMPANY ON ONE PERSON'S BRAIN Zoomed out, his vault looks like a constellation. Every disease linked to its symptoms, every symptom to the conditions it shows up in, every treatment to what it treats. Adding a new case is one note that wires itself into the rest. It's a personal database, and the structure has nothing to do with medicine. It's just nodes and links. Once you see that, the same setup runs anything: > a startup that needs to remember every customer, decision, and meeting; > a school whose curriculum links to every lecture and student question; > a business where SOPs, clients, and vendors all sit in one queryable graph. Drop Claude on top, and the vault stops being a map. It becomes something you can ask, in plain English, across everything you've ever written down. Full Claude + Obsidian build in the article below. Bookmark this
0:21

A DOCTOR BUILT AN AI-QUERYABLE DATABASE OF EVERY DISEASE HE KNOWS IN OBSIDIAN. THE SAME STACK CAN RUN AN ENTIRE COMPANY ON ONE PERSON'S BRAIN Zoomed out, his vault looks like a constellation. Every disease linked to its symptoms, every symptom to the conditions it shows up in, every treatment to what it treats. Adding a new case is one note that wires itself into the rest. It's a personal database, and the structure has nothing to do with medicine. It's just nodes and links. Once you see that, the same setup runs anything: > a startup that needs to remember every customer, decision, and meeting; > a school whose curriculum links to every lecture and student question; > a business where SOPs, clients, and vendors all sit in one queryable graph. Drop Claude on top, and the vault stops being a map. It becomes something you can ask, in plain English, across everything you've ever written down. Full Claude + Obsidian build in the article below. Bookmark this

Yarchi

32,594 просмотров • 3 месяцев назад

THE ANATOMY OF A SECOND BRAIN THAT WRITES ITSELF one Obsidian vault, three layers, and a loop that files every good answer back into a wiki you never touch the structure: > CLAUDE.md + AGENTS.md - the constitution. same rules whether Claude or Codex runs the loop > raw/ - inbox, articles, assets. frozen and read-only, you feed it and walk away > wiki/ - index.md, entities/, concepts/, synthesis.md. the agent grows this on its own > log.md - append-only, every move timestamped 47 sources in -> 210 pages out the agent typed 96% of it. you typed zero the cost trick: point Opus 5 only at synthesis.md, the one file that needs frontier reasoning. everything else runs fine on cheaper models under the hood it's loop engineering: you ask -> a good answer gets filed back -> the wiki grows start linking entities to each other and you're one step from a full graph your research compounds while you sleep. the full blueprint is below 👇
0:24

THE ANATOMY OF A SECOND BRAIN THAT WRITES ITSELF one Obsidian vault, three layers, and a loop that files every good answer back into a wiki you never touch the structure: > CLAUDE.md + AGENTS.md - the constitution. same rules whether Claude or Codex runs the loop > raw/ - inbox, articles, assets. frozen and read-only, you feed it and walk away > wiki/ - index.md, entities/, concepts/, synthesis.md. the agent grows this on its own > log.md - append-only, every move timestamped 47 sources in -> 210 pages out the agent typed 96% of it. you typed zero the cost trick: point Opus 5 only at synthesis.md, the one file that needs frontier reasoning. everything else runs fine on cheaper models under the hood it's loop engineering: you ask -> a good answer gets filed back -> the wiki grows start linking entities to each other and you're one step from a full graph your research compounds while you sleep. the full blueprint is below 👇

Annatar.md

33,910 просмотров • 26 дней назад

the loop is not the agent it is one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls repetition turns retries budgets exits success checks no progress use durable execution when the run must survive failure → GRAPH ENGINEERING controls topology nodes edges state branches checkpoints handoffs build stateful agents as explicit graphs → analyze the topology without putting a model in the loop → HARNESS ENGINEERING controls the blast radius tools permissions memory sandboxes evals traces humans isolate generated code from the host → test behavior before it reaches users → keep one trace across every model node and tool call → without loop control it never stops without graph structure you cannot see the path without harness control it can touch anything the loop lives inside the graph the graph lives inside the harness the model is not the system debug the layer that owns the failure save this
0:21

the loop is not the agent it is one layer of the system LOOP vs GRAPH vs HARNESS ENGINEERING LOOP ENGINEERING controls repetition turns retries budgets exits success checks no progress use durable execution when the run must survive failure → GRAPH ENGINEERING controls topology nodes edges state branches checkpoints handoffs build stateful agents as explicit graphs → analyze the topology without putting a model in the loop → HARNESS ENGINEERING controls the blast radius tools permissions memory sandboxes evals traces humans isolate generated code from the host → test behavior before it reaches users → keep one trace across every model node and tool call → without loop control it never stops without graph structure you cannot see the path without harness control it can touch anything the loop lives inside the graph the graph lives inside the harness the model is not the system debug the layer that owns the failure save this

elune

50,193 просмотров • 2 месяцев назад

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 просмотров • 2 месяцев назад

your agent is not the loop the loop is only the smallest layer LOOP vs GRAPH vs HARNESS ENGINEERING most teams still treat all three like one prompt that is why agent failures feel impossible to diagnose LOOP ENGINEERING controls iteration turns retries budgets evaluators exits stalled progress when the run keeps going forever the loop is broken make long-running work survive crashes and restarts → GRAPH ENGINEERING controls structure nodes edges state branches cycles joins checkpoints when the agent takes the wrong path the graph is broken make state and routing explicit → inspect the topology without asking another model to explain it → HARNESS ENGINEERING controls access tools permissions memory sandboxes evals traces humans when the agent touches the wrong system the harness is broken isolate generated code and tool execution from the host → put an eval gate before every model or prompt update → keep one trace across every node model and tool call → without loop engineering it never stops without graph engineering you cannot see the path without harness engineering it can reach anything the prompt lives inside the loop the loop lives inside the graph the graph lives inside the harness the model is the smallest box in the system debug the layer that actually owns the failure save this before the next agent rewrite
0:20

your agent is not the loop the loop is only the smallest layer LOOP vs GRAPH vs HARNESS ENGINEERING most teams still treat all three like one prompt that is why agent failures feel impossible to diagnose LOOP ENGINEERING controls iteration turns retries budgets evaluators exits stalled progress when the run keeps going forever the loop is broken make long-running work survive crashes and restarts → GRAPH ENGINEERING controls structure nodes edges state branches cycles joins checkpoints when the agent takes the wrong path the graph is broken make state and routing explicit → inspect the topology without asking another model to explain it → HARNESS ENGINEERING controls access tools permissions memory sandboxes evals traces humans when the agent touches the wrong system the harness is broken isolate generated code and tool execution from the host → put an eval gate before every model or prompt update → keep one trace across every node model and tool call → without loop engineering it never stops without graph engineering you cannot see the path without harness engineering it can reach anything the prompt lives inside the loop the loop lives inside the graph the graph lives inside the harness the model is the smallest box in the system debug the layer that actually owns the failure save this before the next agent rewrite

elune

16,875 просмотров • 2 месяцев назад

HERMES AGENT + OBSIDIAN IS A COMBINATION NOBODY IS TALKING ABOUT. Hermes ships with a bundled Obsidian skill. read, search, and create notes in your vault out of the box. why this combination is powerful: Hermes built-in memory is capped. MEMORY.md: 2,200 chars (~800 tokens). USER.md: 1,375 chars (~500 tokens). Obsidian vault has no cap. your agent writes research, session summaries, project context, and learned patterns as linked markdown notes. unlimited depth. the agent creates indexed notes by design. timestamps, backlinks, tags. every note connects to the knowledge graph. three ways to integrate: 1. BUNDLED OBSIDIAN SKILL (simplest) ships with Hermes. reads, searches, creates notes in your vault directly. hermes skills list | grep obsidian 2. OBSIDIAN MCP SERVER (deepest) 30+ tools: full-text search, tag lookup, note management, vault analysis, link analysis, orphan detection. add it via: hermes mcp 3. TELEGRAM + CRON → VAULT (always-on) set a cron job that writes daily summaries, research findings, or task reports directly into your Obsidian vault. your agent feeds the vault while you sleep. you review in Obsidian when you're ready. the unlock: Hermes memory handles what the agent needs to know per session (capped, injected). Obsidian handles everything the agent has ever learned (uncapped, searchable). short-term in Hermes. long-term in Obsidian. both accessible. both persistent. keep the vault scope narrow at first. start with one /Hermes folder. expand once you trust the workflow. 8 Loops Indise Hermes Agent👇
0:56

HERMES AGENT + OBSIDIAN IS A COMBINATION NOBODY IS TALKING ABOUT. Hermes ships with a bundled Obsidian skill. read, search, and create notes in your vault out of the box. why this combination is powerful: Hermes built-in memory is capped. MEMORY.md: 2,200 chars (~800 tokens). USER.md: 1,375 chars (~500 tokens). Obsidian vault has no cap. your agent writes research, session summaries, project context, and learned patterns as linked markdown notes. unlimited depth. the agent creates indexed notes by design. timestamps, backlinks, tags. every note connects to the knowledge graph. three ways to integrate: 1. BUNDLED OBSIDIAN SKILL (simplest) ships with Hermes. reads, searches, creates notes in your vault directly. hermes skills list | grep obsidian 2. OBSIDIAN MCP SERVER (deepest) 30+ tools: full-text search, tag lookup, note management, vault analysis, link analysis, orphan detection. add it via: hermes mcp 3. TELEGRAM + CRON → VAULT (always-on) set a cron job that writes daily summaries, research findings, or task reports directly into your Obsidian vault. your agent feeds the vault while you sleep. you review in Obsidian when you're ready. the unlock: Hermes memory handles what the agent needs to know per session (capped, injected). Obsidian handles everything the agent has ever learned (uncapped, searchable). short-term in Hermes. long-term in Obsidian. both accessible. both persistent. keep the vault scope narrow at first. start with one /Hermes folder. expand once you trust the workflow. 8 Loops Indise Hermes Agent👇

YanXbt

20,617 просмотров • 4 месяцев назад

Loops vs. Graphs, clearly explained! loops are great, and they have a ceiling you can watch happen: a loop goes around. it produces, checks, corrects, and goes around again. after six passes you have one job, done very well. after six hundred passes you still have one job, done very well. Graph engineering fixes this by moving the decision up a layer: not how well one job gets done, but which jobs exist to be done at all. you need both, and here is the sentence that resolves the whole confusion: the loop lives inside a node. the graph lives between them. ↳ inside one unit: produce, check, correct, repeat until green ↳ between units: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the loop does not go away when you build a graph. it moves inside, and now there are three of them running at once on three things you would never have thought to run. 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. one thing to know before you scale it. a loop that cannot fail is not a loop, it is a repeat with a bill attached. and the check people write is almost always the wrong kind. ↳ the test suite exits 0 is a check. the diff touches only the files in the plan is a check ↳ the output looks good, the model says it is confident, no errors were raised, none of those are checks that last one catches careful people. absence of an error is not evidence of correctness, and a loop built on it will confidently repeat a mistake until the budget runs out, with a clean log the whole way. and the 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 ↓
0:50

Loops vs. Graphs, clearly explained! loops are great, and they have a ceiling you can watch happen: a loop goes around. it produces, checks, corrects, and goes around again. after six passes you have one job, done very well. after six hundred passes you still have one job, done very well. Graph engineering fixes this by moving the decision up a layer: not how well one job gets done, but which jobs exist to be done at all. you need both, and here is the sentence that resolves the whole confusion: the loop lives inside a node. the graph lives between them. ↳ inside one unit: produce, check, correct, repeat until green ↳ between units: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the loop does not go away when you build a graph. it moves inside, and now there are three of them running at once on three things you would never have thought to run. 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. one thing to know before you scale it. a loop that cannot fail is not a loop, it is a repeat with a bill attached. and the check people write is almost always the wrong kind. ↳ the test suite exits 0 is a check. the diff touches only the files in the plan is a check ↳ the output looks good, the model says it is confident, no errors were raised, none of those are checks that last one catches careful people. absence of an error is not evidence of correctness, and a loop built on it will confidently repeat a mistake until the budget runs out, with a clean log the whole way. and the 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

129,105 просмотров • 1 месяц назад

While AI timeline argues about loop engineering, a 20-year-old student in Shenzhen built the biggest loop anyone's aimed at money: 300 agents, one brain, $1,200 -> 395,000. Not a prompt. Not a bot. A loop that runs the whole job on its own - and never asks him a thing. No fund. No backers. Just a laptop, a dorm room, and a bet that a swarm beats a genius. His wallet: The smartest single model still works one step at a time. His loop moves in 300 directions at once - and never checks in with him. Claude Opus 4.8 runs the floor as the brain. 300 agents are the hands. One loop ties them together: > Decompose - Opus breaks the market into targets > Dispatch - 300 agents scatter across them at once > Execute - thousands of reads in a single pass, not one by one > Review - Opus grades every edge before a cent moves The hands find. The brain judges. The loop learns what failed and runs again, tighter each time. One daily loss cap, and it shuts itself down. It's not a bot you babysit. It's a loop that runs the whole desk. Everyone's just learning to run one loop. He runs 300 in one. The edge was never the genius model - it's the size of the loop, and the brain in charge of it. Save this - the full 300-agent loop is laid out above, start to finish. You'll want it when you build your own. 300 agents are looping right now, hunting the markets everyone skips. Two clicks and their trades land in your wallet too:
0:53

While AI timeline argues about loop engineering, a 20-year-old student in Shenzhen built the biggest loop anyone's aimed at money: 300 agents, one brain, $1,200 -> 395,000. Not a prompt. Not a bot. A loop that runs the whole job on its own - and never asks him a thing. No fund. No backers. Just a laptop, a dorm room, and a bet that a swarm beats a genius. His wallet: The smartest single model still works one step at a time. His loop moves in 300 directions at once - and never checks in with him. Claude Opus 4.8 runs the floor as the brain. 300 agents are the hands. One loop ties them together: > Decompose - Opus breaks the market into targets > Dispatch - 300 agents scatter across them at once > Execute - thousands of reads in a single pass, not one by one > Review - Opus grades every edge before a cent moves The hands find. The brain judges. The loop learns what failed and runs again, tighter each time. One daily loss cap, and it shuts itself down. It's not a bot you babysit. It's a loop that runs the whole desk. Everyone's just learning to run one loop. He runs 300 in one. The edge was never the genius model - it's the size of the loop, and the brain in charge of it. Save this - the full 300-agent loop is laid out above, start to finish. You'll want it when you build your own. 300 agents are looping right now, hunting the markets everyone skips. Two clicks and their trades land in your wallet too:

cvxv666

53,282 просмотров • 3 месяцев назад

Loops vs. Graphs, clearly explained! loops are great, and the ceiling is one you can watch turn: a loop is a gear. it produces, checks, corrects, and comes back around. after six turns you have one job, done very well. after six hundred turns you still have one job, done very well. the teeth are perfect. they are touching nothing. Graph engineering fixes this by moving the decision up a layer: not how well one gear turns, but what it is meshed into. you need both, and here is the sentence that resolves the whole confusion: the loop lives inside a node. the graph lives between them. ↳ inside one unit: produce, check, correct, repeat until green ↳ between units: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the loop does not go away when you build a graph. it moves inside, and now there are three of them turning at once on three things you would never have thought to run. 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. one thing to know before you scale it. a loop that cannot fail is not a loop, it is a repeat with a bill attached. ↳ the test suite exits 0 is a check. the diff touches only the files in the plan is a check ↳ the output looks good, the model says it is confident, no errors were raised, none of those are checks that last one catches careful people. absence of an error is not evidence of correctness, and a loop built on it will confidently repeat a mistake until the budget runs out, with a clean log the whole way. and the 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 ↓
0:50

Loops vs. Graphs, clearly explained! loops are great, and the ceiling is one you can watch turn: a loop is a gear. it produces, checks, corrects, and comes back around. after six turns you have one job, done very well. after six hundred turns you still have one job, done very well. the teeth are perfect. they are touching nothing. Graph engineering fixes this by moving the decision up a layer: not how well one gear turns, but what it is meshed into. you need both, and here is the sentence that resolves the whole confusion: the loop lives inside a node. the graph lives between them. ↳ inside one unit: produce, check, correct, repeat until green ↳ between units: split, fan out, merge, gate, send back Prompts → Context → Harness → Loops → Graphs the loop does not go away when you build a graph. it moves inside, and now there are three of them turning at once on three things you would never have thought to run. 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. one thing to know before you scale it. a loop that cannot fail is not a loop, it is a repeat with a bill attached. ↳ the test suite exits 0 is a check. the diff touches only the files in the plan is a check ↳ the output looks good, the model says it is confident, no errors were raised, none of those are checks that last one catches careful people. absence of an error is not evidence of correctness, and a loop built on it will confidently repeat a mistake until the budget runs out, with a clean log the whole way. and the 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

32,701 просмотров • 23 дней назад

Obsidian just became the most dangerous folder on your computer, and Andrej Karpathy predicted it with 1 markdown file. His method is called append-and-review. He described it years ago and almost nobody listened: 1 single note, every thought appended to the top, reviewed on random scrolls. No tags, no folders, no Notion dashboards with 40 linked databases. The problem was always the same the note remembers, but it can't act. That changed the moment people pointed Claude Code at an Obsidian vault. Here's the setup that's quietly spreading: Part 1 — The vault becomes a database. Obsidian stores everything as plain markdown on your disk, which means Claude Code can read it like a codebase. You open a terminal inside the vault folder and your 2,000 notes turn into queryable memory. Part 2 — The CLAUDEmd file becomes the brain stem. One file at the root tells the agent who you are, what you're building, and how your notes are structured. From that point every session starts with full context instead of a blank chat window. Part 3 — Karpathy append log becomes fuel. You dump raw thoughts all day ideas, links, half-sentences. Then 1 command at night: review today's appends, extract action items, draft the 3 posts hiding in there, link them to existing notes. The messy log goes in, structured output comes out, and the vault rewrites itself while you sleep. Part 4 — Agents start living in your notes. People are running weekly reviews, content calendars, even market research as scheduled Claude Code runs over their vault. Your second brain stops being a graveyard of highlights and starts shipping. The honest math: setup takes about 90 minutes, the first week feels like overkill, and by day 30 you're sitting on a system where every note you've written in 5 years is working for you instead of rotting in a folder. Notion needed a server, a subscription and your data on someone else's machine. Obsidian needed a text file and now the text file has an employee.
0:22

Obsidian just became the most dangerous folder on your computer, and Andrej Karpathy predicted it with 1 markdown file. His method is called append-and-review. He described it years ago and almost nobody listened: 1 single note, every thought appended to the top, reviewed on random scrolls. No tags, no folders, no Notion dashboards with 40 linked databases. The problem was always the same the note remembers, but it can't act. That changed the moment people pointed Claude Code at an Obsidian vault. Here's the setup that's quietly spreading: Part 1 — The vault becomes a database. Obsidian stores everything as plain markdown on your disk, which means Claude Code can read it like a codebase. You open a terminal inside the vault folder and your 2,000 notes turn into queryable memory. Part 2 — The CLAUDEmd file becomes the brain stem. One file at the root tells the agent who you are, what you're building, and how your notes are structured. From that point every session starts with full context instead of a blank chat window. Part 3 — Karpathy append log becomes fuel. You dump raw thoughts all day ideas, links, half-sentences. Then 1 command at night: review today's appends, extract action items, draft the 3 posts hiding in there, link them to existing notes. The messy log goes in, structured output comes out, and the vault rewrites itself while you sleep. Part 4 — Agents start living in your notes. People are running weekly reviews, content calendars, even market research as scheduled Claude Code runs over their vault. Your second brain stops being a graveyard of highlights and starts shipping. The honest math: setup takes about 90 minutes, the first week feels like overkill, and by day 30 you're sitting on a system where every note you've written in 5 years is working for you instead of rotting in a folder. Notion needed a server, a subscription and your data on someone else's machine. Obsidian needed a text file and now the text file has an employee.

Spike 1%

58,428 просмотров • 2 месяцев назад

GROK BOT WIRED INTO OBSIDIAN JUST CHEWED THROUGH 4,200 NOTES IN 9 MINUTES AND FOUND 3 IDEAS I ALREADY FORGOT I HAD X amateurs still paste prompts into a chat window and copy the answer back by hand while pros give the agent write access to the vault and walk away your second brain is a graveyard 4,200 files, 380 orphans nothing links to, a search bar as your only tool the exact architecture: point a Grok swarm at 1 vault folder through an MCP filesystem server with read and write, so the agents stop guessing from context and start reading every daily log, every clipped thread, every dead draft from 2024 force one instruction: find every concept sitting in 3+ notes with no [[link]] between them, build the MOC, wikilink both directions 9 minutes, 214 new links, 38 orphans pulled back in make it write markdown back into the vault instead of dumping to chat, so tomorrow the swarm reads what it wrote yesterday and compounds instead of resetting when you close the tab that's the whole loop read the vault, write to the vault, compound the complete automation script I spent 3 years taking notes I never read, the bot read all 4,200 in 9 minutes
0:27

GROK BOT WIRED INTO OBSIDIAN JUST CHEWED THROUGH 4,200 NOTES IN 9 MINUTES AND FOUND 3 IDEAS I ALREADY FORGOT I HAD X amateurs still paste prompts into a chat window and copy the answer back by hand while pros give the agent write access to the vault and walk away your second brain is a graveyard 4,200 files, 380 orphans nothing links to, a search bar as your only tool the exact architecture: point a Grok swarm at 1 vault folder through an MCP filesystem server with read and write, so the agents stop guessing from context and start reading every daily log, every clipped thread, every dead draft from 2024 force one instruction: find every concept sitting in 3+ notes with no [[link]] between them, build the MOC, wikilink both directions 9 minutes, 214 new links, 38 orphans pulled back in make it write markdown back into the vault instead of dumping to chat, so tomorrow the swarm reads what it wrote yesterday and compounds instead of resetting when you close the tab that's the whole loop read the vault, write to the vault, compound the complete automation script I spent 3 years taking notes I never read, the bot read all 4,200 in 9 minutes

Spike 1%

26,778 просмотров • 1 месяц назад

THIS CREATOR MADE HIS HERMES AGENT 10X FASTER WITHOUT CHANGING THE MODEL, JUST BY ADDING ONE FILE TO HIS OBSIDIAN VAULT 00:02 the screen shows a massive second brain with hundreds of connected notes, but without a clear map an AI agent still treats every file like it could be the answer Hermes used to open 7 different documents and spend nearly 2 minutes searching for the current brief, even though the correct file was already sitting inside the vault the fix was one index file inside each major folder, listing the important subfolders, canonical files, and the exact document the agent should open first after reorganizing the vault, the same search dropped from 2 minutes to around 10 seconds, while several tasks went from 5 or 6 opened files to just one the model never changed, the $30 agent stack never changed, only the structure around it did, because a powerful agent without a map is still just wandering through folders blindly
0:25

THIS CREATOR MADE HIS HERMES AGENT 10X FASTER WITHOUT CHANGING THE MODEL, JUST BY ADDING ONE FILE TO HIS OBSIDIAN VAULT 00:02 the screen shows a massive second brain with hundreds of connected notes, but without a clear map an AI agent still treats every file like it could be the answer Hermes used to open 7 different documents and spend nearly 2 minutes searching for the current brief, even though the correct file was already sitting inside the vault the fix was one index file inside each major folder, listing the important subfolders, canonical files, and the exact document the agent should open first after reorganizing the vault, the same search dropped from 2 minutes to around 10 seconds, while several tasks went from 5 or 6 opened files to just one the model never changed, the $30 agent stack never changed, only the structure around it did, because a powerful agent without a map is still just wandering through folders blindly

Gipp 🦅

36,473 просмотров • 3 месяцев назад

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, 1,064 typed edges, 8 closed verbs └── cron/ re-fires the swarm on schedule > one node sits at the center with 12 inbound edges - the vendor everyone depends on and nobody flagged in the prompt > 2 hops already answers most of what you'd actually ask it loop decides when it fires -> graph decides what's kept -> context decides what survives after the session closes how people are getting paid on this: > one build sold as a competitive teardown - replaces a four-figure analyst contract, done in an hour, unattended > the same graph on retainer - charge weekly for every new edge that shows up > wire the stack for someone else's market once it's proven on your own full version of this tree is in the article below
0:22

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, 1,064 typed edges, 8 closed verbs └── cron/ re-fires the swarm on schedule > one node sits at the center with 12 inbound edges - the vendor everyone depends on and nobody flagged in the prompt > 2 hops already answers most of what you'd actually ask it loop decides when it fires -> graph decides what's kept -> context decides what survives after the session closes how people are getting paid on this: > one build sold as a competitive teardown - replaces a four-figure analyst contract, done in an hour, unattended > the same graph on retainer - charge weekly for every new edge that shows up > wire the stack for someone else's market once it's proven on your own full version of this tree is in the article below

Mr. Buzzoni

15,462 просмотров • 1 месяц назад

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

53,692 просмотров • 2 месяцев назад

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

73,867 просмотров • 1 месяц назад