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this is free f*cking gold a second brain article hit 8 million views, so the guy behind it put the entire setup in one place the repo, the guide, the tools, the learning path. all of it, free • the guide > 10 sections, 65 pages, concept through troubleshooting...

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Annatar.md

2,203 subscribers

193,376 Aufrufe • vor 3 Tagen •via X (Twitter)

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15 Kommentare

Profilbild von Chris Johnson
Chris Johnsonvor 3 Tagen

Why do you meat proxies never give actual credit? It’s always the “guy behind it”. Srsly delete your account - you are the problem

Profilbild von magsimich
magsimichvor 3 Tagen

Free resources like this are useful

Profilbild von 烦70·BG小野
烦70·BG小野vor 3 Tagen

这波不收藏真的对不起知识库了

Profilbild von DeepRead.com
DeepRead.comvor 3 Tagen

When /ingest-pdf pulls in a book, does the ingestor keep its chapter structure, or does everything flatten into whatever links the curator infers? We hit the same flattening question building DeepRead: a book opens as a cone you move through, chapters intact, highlights sitting where they happened. Ideas keep their address, so returning is cheap.

Profilbild von Emma Rodriguez
Emma Rodriguezvor 3 Tagen

nah giving all of this away for free is wild 😂

Profilbild von LyLy Trann
LyLy Trannvor 3 Tagen

Vàng ròng miễn phí thiệt hả, mà 65 trang thì đọc tới bao giờ mới hết đây 😅

Profilbild von why
whyvor 3 Tagen

The best second-brain systems usually win on retrieval and maintenance, not just capture. Having the troubleshooting section included is a strong sign.

Profilbild von BG饭70小墨
BG饭70小墨vor 3 Tagen

这才是互联网最该有的样子太硬核了

Profilbild von The Upside Ledger
The Upside Ledgervor 3 Tagen

Or we could just use our own brain. Surprisingly, humans have been running that system for about 300,000 years.

Profilbild von Adriana
Adrianavor 3 Tagen

Sounds like a lot of work

Profilbild von ROI of AI
ROI of AIvor 3 Tagen

That’s fire 🔥

Profilbild von Jaime Roma
Jaime Romavor 3 Tagen

bessent is busy trying to hold crude down and the S&P UP, he forgot about his role as treasury SEC.

Profilbild von Mo Aye
Mo Ayevor 3 Tagen

LMFAO, integrating M3 into an obsidian vault is a 10000% sure-fire way to wreck it. It can not properly write and link Markdown, creates dead links, and doesn't properly lint. You will have a disaster on your hands. I know from experience trying it when Minimax first came out.

Profilbild von Crio Songo
Crio Songovor 3 Tagen

That's such a generous share, all complete resources are free. I'll check the repo right away.

Profilbild von 柠儿说修车
柠儿说修车vor 3 Tagen

Nice one

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🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!
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🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!

Kanika

38,795 Aufrufe • vor 1 Monat

CLAUDE + OBSIDIAN + LOOP ENGINEERING = AN AGENT THAT LIVES INSIDE YOUR NOTES Karpathy's second brain runs on something close to this two years of growth he typed almost none of it himself • the loop, four steps on repeat > read - Claude Opus 5 opens the vault instead of a chat window > write - notes and links get edited inside a branch > check - a critic reads the diff and verifies every link > keep - the good change stays, everything else is left alone • the math > cost: 2-4x a single prompt > pays off past a 5% gain > zero notes overwritten, the vault only grows • the whole system, six plain files > CLAUDE.md, skills, subagents, hooks, MCP, plugins > nothing hidden • three ways in > desktop connector - three clicks > Claude Code - full control > Obsidian plugin - you never leave the app the rules that keep it safe: append, never overwrite measure the gain before adding another step don't let it rewrite the whole vault in one turn a loop that never deletes a note is compound interest on your own thinking the stack: 📁 Claude ↳ 📁 Obsidian ↳
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CLAUDE + OBSIDIAN + LOOP ENGINEERING = AN AGENT THAT LIVES INSIDE YOUR NOTES Karpathy's second brain runs on something close to this two years of growth he typed almost none of it himself • the loop, four steps on repeat > read - Claude Opus 5 opens the vault instead of a chat window > write - notes and links get edited inside a branch > check - a critic reads the diff and verifies every link > keep - the good change stays, everything else is left alone • the math > cost: 2-4x a single prompt > pays off past a 5% gain > zero notes overwritten, the vault only grows • the whole system, six plain files > CLAUDE.md, skills, subagents, hooks, MCP, plugins > nothing hidden • three ways in > desktop connector - three clicks > Claude Code - full control > Obsidian plugin - you never leave the app the rules that keep it safe: append, never overwrite measure the gain before adding another step don't let it rewrite the whole vault in one turn a loop that never deletes a note is compound interest on your own thinking the stack: 📁 Claude ↳ 📁 Obsidian ↳

Mr. Buzzoni

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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
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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

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

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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
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A 26-YEAR-OLD AI ENGINEER WHOSE BIRMINGHAM THESIS OUTPERFORMED GOOGLE SCHOLAR BY 50% JUST SHIPPED GRAPHIFY: ONE COMMAND TURNS ANY FOLDER INTO A CLAUDE CODE SECOND BRAIN Safi Shamsi built Graphify 48 hours after Karpathy posted his LLM wiki idea. It turns any folder, codebase, docs, PDFs, into a knowledge graph Claude reads instead of grepping. Up to 43x fewer tokens per query. The trick almost nobody is using yet: one flag exports the entire graph as a fully-linked Obsidian vault. Repo: /safishamsi/graphify Setup, end to end: 1. Install: uv tool install graphifyy (or pipx install graphifyy). Verify with graphify --version. 2. Install the skill in Claude Code: graphify claude install. This wires Graphify into Claude Code so you can call it as a skill. 3. Open the folder you want mapped in Claude Code. In the terminal: graphify . It extracts every concept and builds the graph in graphify-out/. 4. Export to Obsidian: graphify . --obsidian. Writes one note per concept, every relationship as a wikilink, every node linked back to its source. 5. Open the new vault in Obsidian (Manage vaults → Open folder as a vault), or drag it as a subfolder into your existing one. That's it. Your Claude Code instance now has a navigable map of the codebase that loads instantly instead of re-reading files every session. Full step-by-step build of the Claude + Obsidian second brain in the article below. Bookmark this

Yarchi

141,580 Aufrufe • vor 3 Monaten

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 ↓
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codila

22,815 Aufrufe • vor 1 Monat

PSYCHO CREATED A GOD-MODE SECOND BRAIN ON FABLE 5 THAT FILLS ITS OWN KNOWLEDGE GAPS WHILE YOU SLEEP. KARPATHY'S FILE STRUCTURE MAKES IT POSSIBLE. Everyone wires Claude into Obsidian, posts the graph, and stops. this guy added a third system and it quietly changes the whole game: The full build is in the article below: vault structure, the Claude wiring, the exact prompts, the skills. > Obsidian as the knowledge base, but any notes app works as long as Claude Code can see the files > Karpathy's LLM Wiki doc as the skeleton, so the vault is organized the way a model actually reads, not the way humans decorate folders > a self-learning skill on a weekly loop: scans the entire brain, finds the gaps, then researches YouTube and Google by itself to fill them Read the third one again, actually your notes only ever contain what you happened to write down, and the gap-finder breaks that ceiling: the brain starts knowing things you never told it. Most second brains know what you told them. a real one notices what you didn't. that's the difference between note-taking and compounding. one sits there. the other gets smarter every week whether you showed up or not. P.S. send the article straight to your Claude, tell it to interview you, and the brain starts filling itself tonight.
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PSYCHO CREATED A GOD-MODE SECOND BRAIN ON FABLE 5 THAT FILLS ITS OWN KNOWLEDGE GAPS WHILE YOU SLEEP. KARPATHY'S FILE STRUCTURE MAKES IT POSSIBLE. Everyone wires Claude into Obsidian, posts the graph, and stops. this guy added a third system and it quietly changes the whole game: The full build is in the article below: vault structure, the Claude wiring, the exact prompts, the skills. > Obsidian as the knowledge base, but any notes app works as long as Claude Code can see the files > Karpathy's LLM Wiki doc as the skeleton, so the vault is organized the way a model actually reads, not the way humans decorate folders > a self-learning skill on a weekly loop: scans the entire brain, finds the gaps, then researches YouTube and Google by itself to fill them Read the third one again, actually your notes only ever contain what you happened to write down, and the gap-finder breaks that ceiling: the brain starts knowing things you never told it. Most second brains know what you told them. a real one notices what you didn't. that's the difference between note-taking and compounding. one sits there. the other gets smarter every week whether you showed up or not. P.S. send the article straight to your Claude, tell it to interview you, and the brain starts filling itself tonight.

Miraqle

41,307 Aufrufe • vor 2 Monaten

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 Aufrufe • vor 1 Monat

THIS GUY REPLACED HIS ENTIRE CONTENT TEAM WITH A FREE AI AGENT - NOW GETS 340K VIEWS PER POST ALONE 😳 he uses hermes + obsidian and it's completely free - runs on the claude plan he already had you give it one idea and it splits into a team one part researches, one finds the angle, one writes the post, one turns it into a newsletter, one makes the captions all of that from a single sentence it already knows his voice, his audience, his business - so nothing it makes sounds generic he doesn't manage it, remind it, or babysit it 7,100 bookmarks, 340K views, 20 minutes per post, zero team, zero paid tools ↓ he wrote the full guide so you can copy the exact setup from zero:
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THIS GUY REPLACED HIS ENTIRE CONTENT TEAM WITH A FREE AI AGENT - NOW GETS 340K VIEWS PER POST ALONE 😳 he uses hermes + obsidian and it's completely free - runs on the claude plan he already had you give it one idea and it splits into a team one part researches, one finds the angle, one writes the post, one turns it into a newsletter, one makes the captions all of that from a single sentence it already knows his voice, his audience, his business - so nothing it makes sounds generic he doesn't manage it, remind it, or babysit it 7,100 bookmarks, 340K views, 20 minutes per post, zero team, zero paid tools ↓ he wrote the full guide so you can copy the exact setup from zero:

Isra

25,704 Aufrufe • vor 3 Monaten

I genuinely don't understand why everyone isn't using this yet. Andrej Karpathy, OpenAI co-founder, posted a simple idea that went massively viral: Stop using AI to write code. Use it to build a second brain. You point Claude Code at a folder. Drop in any source: an article, a transcript, a PDF. Claude reads it, links it, files it into a living wiki of everything you know. It compounds like interest. The more you feed it, the smarter it gets. Here's the whole thing: 1) Install Obsidian 2) Create a vault 3) Open it in Claude Code 4) Paste Karpathy's wiki idea and tell Claude to build it 5) Claude makes three folders: - raw (for sources) - wiki (for its pages) - CLAUDE. md (that runs it) 6) Drop any source into raw and say: "ingest this" 7) Ask questions across everything, forever Five minutes to set up and you never start from a blank chat again. Full step by step guide below.
1:48

I genuinely don't understand why everyone isn't using this yet. Andrej Karpathy, OpenAI co-founder, posted a simple idea that went massively viral: Stop using AI to write code. Use it to build a second brain. You point Claude Code at a folder. Drop in any source: an article, a transcript, a PDF. Claude reads it, links it, files it into a living wiki of everything you know. It compounds like interest. The more you feed it, the smarter it gets. Here's the whole thing: 1) Install Obsidian 2) Create a vault 3) Open it in Claude Code 4) Paste Karpathy's wiki idea and tell Claude to build it 5) Claude makes three folders: - raw (for sources) - wiki (for its pages) - CLAUDE. md (that runs it) 6) Drop any source into raw and say: "ingest this" 7) Ask questions across everything, forever Five minutes to set up and you never start from a blank chat again. Full step by step guide below.

CyrilXBT

330,664 Aufrufe • vor 3 Monaten

Obsidian + Claude Code turned a 4,300-note vault into a second brain that answers back. Most people use Obsidian as a graveyard. You save 40 highlights a week, link nothing, and reread maybe 2% of it. The knowledge is there the retrieval is dead. Claude Code fixes the retrieval, because your vault is just a folder of markdown files and Claude Code lives in folders. The whole setup takes 20 minutes: Open a terminal inside your Obsidian folder and run Claude Code no plugins, no API glue, no export step. It reads all 4,300 files natively. Then drop a CLAUDE.md at the vault root explaining your structure: where daily notes live, how you tag, what your MOCs are. Now every session starts with context instead of chaos. From there you just talk to your vault: > "find every note where I mentioned churn and write a summary with backlinks" > "read my last 30 daily notes and list the 5 ideas I keep circling but never ship" > "build a MOC for everything tagged startup-ideas and link the orphan notes into it" It writes new notes straight into the vault with proper [[wikilinks]], and they show up in your graph view 10 seconds later. The boring layer automates too one command cleans broken links, one merges duplicates, one turns 6 months of meeting notes into a single decisions log. Work that used to eat a Sunday now runs while you make coffee. Before: 4,300 notes, 900 orphans, search by keyword and prayer. After: an agent that read everything you ever wrote and drafts in your own voice, from your own sources, with receipts. People pay $30/month for AI note apps with 10% of this. You already own the other 90%. Your notes stopped being storage. They started being staff.
0:55

Obsidian + Claude Code turned a 4,300-note vault into a second brain that answers back. Most people use Obsidian as a graveyard. You save 40 highlights a week, link nothing, and reread maybe 2% of it. The knowledge is there the retrieval is dead. Claude Code fixes the retrieval, because your vault is just a folder of markdown files and Claude Code lives in folders. The whole setup takes 20 minutes: Open a terminal inside your Obsidian folder and run Claude Code no plugins, no API glue, no export step. It reads all 4,300 files natively. Then drop a CLAUDE.md at the vault root explaining your structure: where daily notes live, how you tag, what your MOCs are. Now every session starts with context instead of chaos. From there you just talk to your vault: > "find every note where I mentioned churn and write a summary with backlinks" > "read my last 30 daily notes and list the 5 ideas I keep circling but never ship" > "build a MOC for everything tagged startup-ideas and link the orphan notes into it" It writes new notes straight into the vault with proper [[wikilinks]], and they show up in your graph view 10 seconds later. The boring layer automates too one command cleans broken links, one merges duplicates, one turns 6 months of meeting notes into a single decisions log. Work that used to eat a Sunday now runs while you make coffee. Before: 4,300 notes, 900 orphans, search by keyword and prayer. After: an agent that read everything you ever wrote and drafts in your own voice, from your own sources, with receipts. People pay $30/month for AI note apps with 10% of this. You already own the other 90%. Your notes stopped being storage. They started being staff.

Spike 1%

18,223 Aufrufe • vor 2 Monaten

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.
0:20

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 Aufrufe • vor 1 Monat

Karpathy dropped a viral post about building a "second brain" with AI. We built it live in under 20 minutes. The system: 1. Three folders: raw/ (brain dump), wiki/ (AI organizes it), outputs/ (query results) 2. One-click install: npx skills add NicholasSpisak/second-brain 3. Obsidian Web Clipper to capture any article with one click 4. Run "ingest" and the AI reads everything in raw/, builds wiki pages, and maps connections automatically 5. Query your own knowledge base like talking to an expert 6. Monthly lint command catches contradictions and gaps before they compound Day 1 it's basic. Day 90 it's a company asset nobody else has. Free skill in the YouTube description that scaffolds the whole thing in 60 seconds. Video below, YouTube link in the replies.
22:20

Karpathy dropped a viral post about building a "second brain" with AI. We built it live in under 20 minutes. The system: 1. Three folders: raw/ (brain dump), wiki/ (AI organizes it), outputs/ (query results) 2. One-click install: npx skills add NicholasSpisak/second-brain 3. Obsidian Web Clipper to capture any article with one click 4. Run "ingest" and the AI reads everything in raw/, builds wiki pages, and maps connections automatically 5. Query your own knowledge base like talking to an expert 6. Monthly lint command catches contradictions and gaps before they compound Day 1 it's basic. Day 90 it's a company asset nobody else has. Free skill in the YouTube description that scaffolds the whole thing in 60 seconds. Video below, YouTube link in the replies.

Corey Ganim

128,845 Aufrufe • vor 5 Monaten

this is pure f*cking treasure 20 useful GitHub projects for a system that captures sources, connects ideas, remembers context, and helps agents act on it your second brain should get sharper after every session CAPTURE THE INPUT 01 Chubby Skills ▸ 02 OpenWiki ▸ 03 Agent Second Brain ▸ 04 DocMason ▸ 05 DocsAgent ▸ CONNECT THE IDEAS 06 claude-obsidian ▸ 07 SwarmVault ▸ 08 sage-wiki ▸ 09 Vault Curate ▸ 10 COG Second Brain ▸ REMEMBER WHAT MATTERS 11 Hindsight ▸ 12 memU ▸ 13 TencentDB Agent Memory ▸ 14 agentmemory ▸ 15 OpenViking ▸ PUT IT TO WORK 16 Open Second Brain ▸ 17 Second Brain Cloudflare ▸ 18 makerskills ▸ 19 Row-Bot ▸ 20 MateClaw ▸ the loop: capture the source → turn it into linked, checkable knowledge → recall the right context → act with it → write the correction back 3 builds I'd explore: creator: Chubby Skills → SwarmVault → Hindsight → makerskills researcher: DocsAgent → sage-wiki → memU → Row-Bot team: DocMason → COG Second Brain → TencentDB Agent Memory → MateClaw save this, then build a second brain for your business ⭣
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this is pure f*cking treasure 20 useful GitHub projects for a system that captures sources, connects ideas, remembers context, and helps agents act on it your second brain should get sharper after every session CAPTURE THE INPUT 01 Chubby Skills ▸ 02 OpenWiki ▸ 03 Agent Second Brain ▸ 04 DocMason ▸ 05 DocsAgent ▸ CONNECT THE IDEAS 06 claude-obsidian ▸ 07 SwarmVault ▸ 08 sage-wiki ▸ 09 Vault Curate ▸ 10 COG Second Brain ▸ REMEMBER WHAT MATTERS 11 Hindsight ▸ 12 memU ▸ 13 TencentDB Agent Memory ▸ 14 agentmemory ▸ 15 OpenViking ▸ PUT IT TO WORK 16 Open Second Brain ▸ 17 Second Brain Cloudflare ▸ 18 makerskills ▸ 19 Row-Bot ▸ 20 MateClaw ▸ the loop: capture the source → turn it into linked, checkable knowledge → recall the right context → act with it → write the correction back 3 builds I'd explore: creator: Chubby Skills → SwarmVault → Hindsight → makerskills researcher: DocsAgent → sage-wiki → memU → Row-Bot team: DocMason → COG Second Brain → TencentDB Agent Memory → MateClaw save this, then build a second brain for your business ⭣

beamnxw ./

78,460 Aufrufe • vor 1 Tag

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.
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FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.

kocer

31,162 Aufrufe • vor 28 Tagen

ANTHROPIC LEAKED A 5-AGENT SETUP THAT TURNS ONE QUESTION INTO A SOURCED REPORT you write one line and never open a tab - the fleet reads 40 sources and only the answer comes back. question → scope → 5 searchers → dedupe → writer → fact-check → report the scope agent turns a vague question into 5 angles that do not overlap - skip this and five agents fetch the same three articles. 5 searchers run in parallel with separate contexts - none of them sees what the others found, which is exactly why they do not converge on the same source. dedupe is code, not an agent - flatten, drop repeats, normalize URLs, zero tokens, instant. the writer only sees structured findings, never raw pages - it cannot cite something that was never verified because it never had access to it. the fact-checker reads the draft against the sources, not against itself - every claim without a matching source goes back, and only that section is rewritten. nothing reaches you until a second agent, with clean context, has tried to kill it. 12 minutes, 40 sources, one report where every line traces back to something real. save this and read the full graph engineering course below ↓
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ANTHROPIC LEAKED A 5-AGENT SETUP THAT TURNS ONE QUESTION INTO A SOURCED REPORT you write one line and never open a tab - the fleet reads 40 sources and only the answer comes back. question → scope → 5 searchers → dedupe → writer → fact-check → report the scope agent turns a vague question into 5 angles that do not overlap - skip this and five agents fetch the same three articles. 5 searchers run in parallel with separate contexts - none of them sees what the others found, which is exactly why they do not converge on the same source. dedupe is code, not an agent - flatten, drop repeats, normalize URLs, zero tokens, instant. the writer only sees structured findings, never raw pages - it cannot cite something that was never verified because it never had access to it. the fact-checker reads the draft against the sources, not against itself - every claim without a matching source goes back, and only that section is rewritten. nothing reaches you until a second agent, with clean context, has tried to kill it. 12 minutes, 40 sources, one report where every line traces back to something real. save this and read the full graph engineering course below ↓

Hanako

58,923 Aufrufe • vor 1 Monat

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below
1:05:19

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

rari

28,241 Aufrufe • vor 1 Monat

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 Aufrufe • vor 1 Monat

Today we're launching Jitsu MCP. This is an MCP server that lets your agent run your entire event pipeline directly in Claude Code, Claude Desktop, Cursor, or VS Code. Here's how it works: Add the server: claude mcp add --transport http jitsu Run this prompt: "Route my web stream into ClickHouse, but only purchase and signup events. Send a test event, check Live Events, and if anything errors, find the function in the path and fix it." Approve the OAuth prompt once, and all 26 tools are live Your agent wires the streams, watches Live Events, and rewrites a function when one of them throws. It runs the fix against a real event before shipping it, so nothing is persisted until it works. Every change lands in the audit log tagged mcp, so you can tell what the agent did from what a person did. It's all MIT licensed, and it runs against your own deployment. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Comment Jitsu and I'll DM you the exact prompts I run in Claude Code to build an event pipeline end to end.
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Today we're launching Jitsu MCP. This is an MCP server that lets your agent run your entire event pipeline directly in Claude Code, Claude Desktop, Cursor, or VS Code. Here's how it works: Add the server: claude mcp add --transport http jitsu Run this prompt: "Route my web stream into ClickHouse, but only purchase and signup events. Send a test event, check Live Events, and if anything errors, find the function in the path and fix it." Approve the OAuth prompt once, and all 26 tools are live Your agent wires the streams, watches Live Events, and rewrites a function when one of them throws. It runs the fix against a real event before shipping it, so nothing is persisted until it works. Every change lands in the audit log tagged mcp, so you can tell what the agent did from what a person did. It's all MIT licensed, and it runs against your own deployment. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Comment Jitsu and I'll DM you the exact prompts I run in Claude Code to build an event pipeline end to end.

Soham Mehta

281,726 Aufrufe • vor 19 Tagen