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HE BUILT A 100% PRIVATE SECOND BRAIN IN OBSIDIAN TO AUTOMATE HIS RESEARCH USING HERMES AGENT AND NOTEBOOKLM He visualizes his entire knowledge graph on-premise without paying for cloud subscriptions By connecting Hermes Agent and NotebookLM, developers can index hundreds of documents and generate content locally Four components of...

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20,684 views • 2 months ago •via X (Twitter)

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HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES. AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇
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HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES. AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇

YanXbt

34,987 views • 2 months ago

YOUR OBSIDIAN VAULT READS ALL 2,000 NOTES TO ANSWER ONE QUESTION. THAT'S NOT A SECOND BRAIN. That's a hoarder with markdown files. Hundreds of thousands of tokens burned per answer, and it still grabs the wrong note half the time. A real second brain runs on a graph: a router, an index, nodes, edges. Fable 5 reads 2-3 files instead of the whole vault. Answer back in under a second. The graph view everyone screenshots in Obsidian is a poster. The index is the actual engine. Full 11-step build below, from raw vault to working graph 👇
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YOUR OBSIDIAN VAULT READS ALL 2,000 NOTES TO ANSWER ONE QUESTION. THAT'S NOT A SECOND BRAIN. That's a hoarder with markdown files. Hundreds of thousands of tokens burned per answer, and it still grabs the wrong note half the time. A real second brain runs on a graph: a router, an index, nodes, edges. Fable 5 reads 2-3 files instead of the whole vault. Answer back in under a second. The graph view everyone screenshots in Obsidian is a poster. The index is the actual engine. Full 11-step build below, from raw vault to working graph 👇

unicode

52,729 views • 19 days ago

THIS GUY HAD 1,283 NOTES AND HIS SYSTEM WAS DUMBER THAN A FOLDER that galaxy in the background is his Obsidian vault > looks incredible > does nothing dead notes, zero connections, zero value extracted from any of it > the fix isn't taking better notes > it's building a pipeline where Claude Opus 4.8 does the research, NotebookLM does the analysis, and every session feeds your vault automatically one command: > Claude Code searches YouTube for 10 relevant sources > passes them to NotebookLM - Google's servers do the heavy processing, not your tokens > NotebookLM runs full analysis, generates infographics, mindmaps, flashcards > everything lands as structured markdown in your Obsidian vault, linked and ready total time: 6 minutes > after a month the vault knows your thinking style. after a year it's a trained research assistant the $300K AI architects aren't taking better notes > they're building systems that compound 30-minute setup. full step-by-step in the article below 👇
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THIS GUY HAD 1,283 NOTES AND HIS SYSTEM WAS DUMBER THAN A FOLDER that galaxy in the background is his Obsidian vault > looks incredible > does nothing dead notes, zero connections, zero value extracted from any of it > the fix isn't taking better notes > it's building a pipeline where Claude Opus 4.8 does the research, NotebookLM does the analysis, and every session feeds your vault automatically one command: > Claude Code searches YouTube for 10 relevant sources > passes them to NotebookLM - Google's servers do the heavy processing, not your tokens > NotebookLM runs full analysis, generates infographics, mindmaps, flashcards > everything lands as structured markdown in your Obsidian vault, linked and ready total time: 6 minutes > after a month the vault knows your thinking style. after a year it's a trained research assistant the $300K AI architects aren't taking better notes > they're building systems that compound 30-minute setup. full step-by-step in the article below 👇

Mr. Buzzoni

48,417 views • 2 months ago

THIS TRADER TRAINED HIS OBSIDIAN VAULT ON HUNDREDS OF CHART PATTERNS AND NOW IT THINKS WITH HIM every setup he ever studied is in there > linked to the outcome > linked to the context > linked to what he was thinking at the time he types one command > Claude Code finds the relevant sources, runs analysis through NotebookLM, saves everything structured the vault doesn't just store information anymore it connects it most traders are still screenshotting charts into a Discord and forgetting them in 48 hours article below
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THIS TRADER TRAINED HIS OBSIDIAN VAULT ON HUNDREDS OF CHART PATTERNS AND NOW IT THINKS WITH HIM every setup he ever studied is in there > linked to the outcome > linked to the context > linked to what he was thinking at the time he types one command > Claude Code finds the relevant sources, runs analysis through NotebookLM, saves everything structured the vault doesn't just store information anymore it connects it most traders are still screenshotting charts into a Discord and forgetting them in 48 hours article below

monokern

81,693 views • 2 months ago

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE FABLE 5 + OBSIDIAN Andrej Karpathy (openai co-founder) shared an architecture that turns Claude into a persistent second brain instead of a basic chat window how it works: > you point Claude Code at an Obsidian vault folder > you drop articles, PDFs, or video transcripts into raw folders > Claude reads the files, updates topic summaries, and cross-references everything > the knowledge base compounds like interest instead of resetting on every new chat the setup is simple: > install and create a local vault directory > open the directory in Claude Code and paste Karpathy's wiki prompt: > > let the agent generate raw, wiki, and CLAUDE.md schema directories > drop any text file into raw and tell the model to ingest it > ask questions across the whole vault and query compiled summaries this eliminates rag database overhead and keeps your local vault organized how do you manage your local knowledge base?
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BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE FABLE 5 + OBSIDIAN Andrej Karpathy (openai co-founder) shared an architecture that turns Claude into a persistent second brain instead of a basic chat window how it works: > you point Claude Code at an Obsidian vault folder > you drop articles, PDFs, or video transcripts into raw folders > Claude reads the files, updates topic summaries, and cross-references everything > the knowledge base compounds like interest instead of resetting on every new chat the setup is simple: > install and create a local vault directory > open the directory in Claude Code and paste Karpathy's wiki prompt: > > let the agent generate raw, wiki, and CLAUDE.md schema directories > drop any text file into raw and tell the model to ingest it > ask questions across the whole vault and query compiled summaries this eliminates rag database overhead and keeps your local vault organized how do you manage your local knowledge base?

Mr. Buzzoni

89,170 views • 1 month ago

A Harvard sophomore pointed Claude Code at his Obsidian vault and woke up to 1,847 notes he never wrote. The vault started in September 2024 with 210 notes lecture scraps, 4 problem sets, 3 half-finished essays and by March it was dead, because nobody links notes by hand at 2 AM. So he handed the folder to an agent: read access to 210 files and one instruction, find what connects. It ran for 6 hours and built edges he'd never seen, wiring a thermodynamics lecture to a supply-chain note to a Douyin livestream case study he saved in 2023 and forgot. Graph view at 4 AM showed 1,847 nodes and 11,400 links, a white sphere so dense the center goes solid. His midterm was 9 days out, so he skipped the textbook and asked the vault instead, and it answered in his own words from 3 semesters back. Now the agent runs nightly, pulling 40 new notes a week writing summaries, tagging, linking, killing duplicates while he hasn't typed a note in 5 weeks. He opens the graph most mornings and scrolls for something he doesn't recognize, and there's always something. Tuition runs $59,320 a year; the vault cost one afternoon of setup. He built a second brain, then stopped using the first one.
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A Harvard sophomore pointed Claude Code at his Obsidian vault and woke up to 1,847 notes he never wrote. The vault started in September 2024 with 210 notes lecture scraps, 4 problem sets, 3 half-finished essays and by March it was dead, because nobody links notes by hand at 2 AM. So he handed the folder to an agent: read access to 210 files and one instruction, find what connects. It ran for 6 hours and built edges he'd never seen, wiring a thermodynamics lecture to a supply-chain note to a Douyin livestream case study he saved in 2023 and forgot. Graph view at 4 AM showed 1,847 nodes and 11,400 links, a white sphere so dense the center goes solid. His midterm was 9 days out, so he skipped the textbook and asked the vault instead, and it answered in his own words from 3 semesters back. Now the agent runs nightly, pulling 40 new notes a week writing summaries, tagging, linking, killing duplicates while he hasn't typed a note in 5 weeks. He opens the graph most mornings and scrolls for something he doesn't recognize, and there's always something. Tuition runs $59,320 a year; the vault cost one afternoon of setup. He built a second brain, then stopped using the first one.

Spike 1%

56,112 views • 22 days ago

I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.
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I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.

Miles Deutscher

81,972 views • 1 month ago

I stack Hermes agents with OpenClaw for financial research, and the results should be illegal. I track every politician, insider trader, and I know EXACTLY what moves they're making. If you can't beat them, join them. The exact playbook for printing money from insider trading (copy me): Requirements: • OpenClaw setup • Hermes Agent setup Step 1. Define your research thesis Before you send any prompts to either tool, you'll need to clarify exactly what you're trying to research. This could be: a specific industry, asset class, market sector, and so on. Examples: • Tracking smart money buys in the semiconductor industry • Tracking smart money buys in crypto • Tracking a specific politician and where they're bidding (like Nancy Pelosi) Step 2. Deploy Hermes agents to track the smart money (in parallel) Hermes is your data layer. Spin up 5 agents at the same time, each with one job: Agent 1: Track every politician's disclosed trades from the last 30 days (House and Senate stock disclosures) Agent 2: Pull insider transactions (Form 4 filings, CEO/CFO buys and sells) Agent 3: Scrape X sentiment from top 50 accounts on the topic Agent 4: Pull on-chain data (whale wallets, TVL, exchange flows) *if applicable* Agent 5: Monitor news, regulatory filings, and announcements from the last 30 days Each agent runs independently. You're not waiting for one to finish before the next starts. Step 3. Consolidate the output Once your Hermes agents finish, dump every output into a single document. (don't filter or summarize) - you want OpenClaw to see the raw data. Step 4. Feed it all into OpenClaw Open OpenClaw and paste the consolidated research file with this prompt: "Act as an elite macro analyst. Below is raw data gathered from multiple sources on [thesis], including politician disclosures and insider transactions. Synthesize the findings, identify the strongest signals and contradictions, flag any unusual smart-money activity, and give me a clear directional view with conviction levels. Flag any data gaps that need follow-up." OpenClaw will go deep, run its own reasoning chain, and produce a synthesized report. Done. Now you're literally tapping into the financial data they don't want you to see (it's all public - you just had to find it). Make sure to save this playbook so you don't lose it!
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I stack Hermes agents with OpenClaw for financial research, and the results should be illegal. I track every politician, insider trader, and I know EXACTLY what moves they're making. If you can't beat them, join them. The exact playbook for printing money from insider trading (copy me): Requirements: • OpenClaw setup • Hermes Agent setup Step 1. Define your research thesis Before you send any prompts to either tool, you'll need to clarify exactly what you're trying to research. This could be: a specific industry, asset class, market sector, and so on. Examples: • Tracking smart money buys in the semiconductor industry • Tracking smart money buys in crypto • Tracking a specific politician and where they're bidding (like Nancy Pelosi) Step 2. Deploy Hermes agents to track the smart money (in parallel) Hermes is your data layer. Spin up 5 agents at the same time, each with one job: Agent 1: Track every politician's disclosed trades from the last 30 days (House and Senate stock disclosures) Agent 2: Pull insider transactions (Form 4 filings, CEO/CFO buys and sells) Agent 3: Scrape X sentiment from top 50 accounts on the topic Agent 4: Pull on-chain data (whale wallets, TVL, exchange flows) *if applicable* Agent 5: Monitor news, regulatory filings, and announcements from the last 30 days Each agent runs independently. You're not waiting for one to finish before the next starts. Step 3. Consolidate the output Once your Hermes agents finish, dump every output into a single document. (don't filter or summarize) - you want OpenClaw to see the raw data. Step 4. Feed it all into OpenClaw Open OpenClaw and paste the consolidated research file with this prompt: "Act as an elite macro analyst. Below is raw data gathered from multiple sources on [thesis], including politician disclosures and insider transactions. Synthesize the findings, identify the strongest signals and contradictions, flag any unusual smart-money activity, and give me a clear directional view with conviction levels. Flag any data gaps that need follow-up." OpenClaw will go deep, run its own reasoning chain, and produce a synthesized report. Done. Now you're literally tapping into the financial data they don't want you to see (it's all public - you just had to find it). Make sure to save this playbook so you don't lose it!

Miles Deutscher

19,955 views • 3 months ago

AN ANTHROPIC LEAD ENGINEER ACCIDENTALLY LEAKED HIS PERSONAL OBSIDIAN. INSIDE - NOT CODE OR PROMPTS, BUT A DIAGRAM OF HIS OWN BRAIN, ORGANIZED AS A NEURAL NETWORK 8,893 nodes. 4,729 connections. A $10/month app opens Obsidian. 21 inputs, ReLU on every layer. The first hidden layer has 26 neurons, followed by 33, then 24, and so on all the way to the output. Thousands of connections flash in real time this isn’t a conceptual diagram from a blog, but a living brain that powers decision-making within the company. 9,000 documents, each with its own semantic space, all interconnected it earns about $2m a year for sorting Markdown files into the right folders. The company that builds the world’s best AI maintains its internal knowledge base in the same app that a freshman uses for class notes three years of discipline and a single open Obsidian tab you’re reading this on a device where, tonight, you can open that same Obsidian and start building your own vault
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AN ANTHROPIC LEAD ENGINEER ACCIDENTALLY LEAKED HIS PERSONAL OBSIDIAN. INSIDE - NOT CODE OR PROMPTS, BUT A DIAGRAM OF HIS OWN BRAIN, ORGANIZED AS A NEURAL NETWORK 8,893 nodes. 4,729 connections. A $10/month app opens Obsidian. 21 inputs, ReLU on every layer. The first hidden layer has 26 neurons, followed by 33, then 24, and so on all the way to the output. Thousands of connections flash in real time this isn’t a conceptual diagram from a blog, but a living brain that powers decision-making within the company. 9,000 documents, each with its own semantic space, all interconnected it earns about $2m a year for sorting Markdown files into the right folders. The company that builds the world’s best AI maintains its internal knowledge base in the same app that a freshman uses for class notes three years of discipline and a single open Obsidian tab you’re reading this on a device where, tonight, you can open that same Obsidian and start building your own vault

chewa.

359,384 views • 1 month ago

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
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OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 views • 10 months ago

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE OPUS 5 + OBSIDIAN Andrej Karpathy (OpenAI co-founder) shared an architecture that turns Claude into a persistent second brain not just a basic chat window how it works: > point Claude Code at an Obsidian vault folder > drop articles, PDFs, or video transcripts into raw > Claude reads them, updates summaries, cross-references everything > the knowledge base compounds like interest, never resets on a new chat the setup: > install create a local vault folder > open it in Claude Code, paste Karpathy's wiki prompt > > let the agent build raw, wiki, and CLAUDE.md folders > drop any file into raw, tell it to ingest > ask questions, get compiled summaries back this skips rag overhead entirely. your vault stays organized on its own run ingest on a schedule and it's loop engineering: sense a new file, plan, write, verify, repeat how do you manage your local knowledge base?
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BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE OPUS 5 + OBSIDIAN Andrej Karpathy (OpenAI co-founder) shared an architecture that turns Claude into a persistent second brain not just a basic chat window how it works: > point Claude Code at an Obsidian vault folder > drop articles, PDFs, or video transcripts into raw > Claude reads them, updates summaries, cross-references everything > the knowledge base compounds like interest, never resets on a new chat the setup: > install create a local vault folder > open it in Claude Code, paste Karpathy's wiki prompt > > let the agent build raw, wiki, and CLAUDE.md folders > drop any file into raw, tell it to ingest > ask questions, get compiled summaries back this skips rag overhead entirely. your vault stays organized on its own run ingest on a schedule and it's loop engineering: sense a new file, plan, write, verify, repeat how do you manage your local knowledge base?

Mr. Buzzoni

14,716 views • 11 days ago

ANTHROPIC'S LEAD ENGINEER ACCIDENTALLY LEAKED HIS PERSONAL OBSIDIAN VAULT. INSIDE - NO CODE, NO PROMPTS. JUST A SCHEMATIC OF HIS OWN MIND, BUILT LIKE A NEURAL NETWORK 8,893 nodes. 4,729 links. A $10-a-month app opens Obsidian. 21 inputs, 10+ hidden layers, ReLU activation. First layer 64 neurons, then 37, then 22, all the way to the output. Thousands of connections firing in real time this isn't a concept diagram from a blog - it's a living brain that actually runs decisions inside the company. 9,000 documents, each its own knowledge space, all interconnected he makes around $2M a year for putting markdown files into the right folders. The company building the best AI in the world manages its internal knowledge with the same app a college freshman uses for lecture notes. hasn't written a single line of infrastructure for any of it. three years of discipline and one open Obsidian tab you're reading this on a device where you could open the same Obsidian tonight and start building your own vault
0:10

ANTHROPIC'S LEAD ENGINEER ACCIDENTALLY LEAKED HIS PERSONAL OBSIDIAN VAULT. INSIDE - NO CODE, NO PROMPTS. JUST A SCHEMATIC OF HIS OWN MIND, BUILT LIKE A NEURAL NETWORK 8,893 nodes. 4,729 links. A $10-a-month app opens Obsidian. 21 inputs, 10+ hidden layers, ReLU activation. First layer 64 neurons, then 37, then 22, all the way to the output. Thousands of connections firing in real time this isn't a concept diagram from a blog - it's a living brain that actually runs decisions inside the company. 9,000 documents, each its own knowledge space, all interconnected he makes around $2M a year for putting markdown files into the right folders. The company building the best AI in the world manages its internal knowledge with the same app a college freshman uses for lecture notes. hasn't written a single line of infrastructure for any of it. three years of discipline and one open Obsidian tab you're reading this on a device where you could open the same Obsidian tonight and start building your own vault

chewa

1,210,268 views • 1 month ago

LLM Knowledge Base → Slides When Andrej Karpathy shared his LLM Knowledge Base setup, many were wondering how to generate more visual forms of the wiki. There are many options, but I think Gamma is one of the best at producing high-quality, rich presentations. To showcase this, I just built a pipeline that turns my AI papers wiki (1K+ papers across 20 AI agent topics) into polished slide presentations using Gamma. The flow: Obsidian vault → Gamma MCP → embedded preview in my dashboard. I give one command to my agent, which pulls the top papers from each topic (via the wiki), feeds them to Gamma, and renders the presentation inline. The Gamma connector for Claude is a great choice for generating beautiful and professional slides. Easy to use. Go to your Claude instance and add the official Gamma connector. That's it! Claude Code will now have access to all the necessary MCP tools for generating slides. I use the Claude Agent SDK for my agent orchestrator, so I use the official Gamma MCP tools and embed the generated slides in an iframe via my artifact preview. See the clip below for an example.
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LLM Knowledge Base → Slides When Andrej Karpathy shared his LLM Knowledge Base setup, many were wondering how to generate more visual forms of the wiki. There are many options, but I think Gamma is one of the best at producing high-quality, rich presentations. To showcase this, I just built a pipeline that turns my AI papers wiki (1K+ papers across 20 AI agent topics) into polished slide presentations using Gamma. The flow: Obsidian vault → Gamma MCP → embedded preview in my dashboard. I give one command to my agent, which pulls the top papers from each topic (via the wiki), feeds them to Gamma, and renders the presentation inline. The Gamma connector for Claude is a great choice for generating beautiful and professional slides. Easy to use. Go to your Claude instance and add the official Gamma connector. That's it! Claude Code will now have access to all the necessary MCP tools for generating slides. I use the Claude Agent SDK for my agent orchestrator, so I use the official Gamma MCP tools and embed the generated slides in an iframe via my artifact preview. See the clip below for an example.

elvis

47,989 views • 4 months ago

🚨BREAKING: Google just merged Gemini and NotebookLM into one unified workspace and it changes everything about how you use AI for deep work. It's called Notebooks in Gemini and it's the personal knowledge base that power users have been begging for. You create a notebook for a project, drop in your files, PDFs, and documents, give Gemini custom instructions, and every chat you have stays organized in one place. No more hunting through old conversations. No more re-uploading the same files every session. The wildest part is the sync. Anything you add in Gemini automatically appears in NotebookLM. Anything you add in NotebookLM automatically appears in Gemini. One source of truth. Two powerful apps. Zero friction switching between them. So you can start a research notebook in Gemini, ask it questions all week, then flip to NotebookLM to generate a Cinematic Video Overview from the same material. Next morning, open Gemini and ask it to write a full report on exactly what you just watched. That workflow used to take three apps and a lot of copy-pasting. Now it's one notebook. Rolling out this week to Google AI Ultra, Pro, and Plus subscribers on web. Mobile and free users coming soon. What do you think?
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🚨BREAKING: Google just merged Gemini and NotebookLM into one unified workspace and it changes everything about how you use AI for deep work. It's called Notebooks in Gemini and it's the personal knowledge base that power users have been begging for. You create a notebook for a project, drop in your files, PDFs, and documents, give Gemini custom instructions, and every chat you have stays organized in one place. No more hunting through old conversations. No more re-uploading the same files every session. The wildest part is the sync. Anything you add in Gemini automatically appears in NotebookLM. Anything you add in NotebookLM automatically appears in Gemini. One source of truth. Two powerful apps. Zero friction switching between them. So you can start a research notebook in Gemini, ask it questions all week, then flip to NotebookLM to generate a Cinematic Video Overview from the same material. Next morning, open Gemini and ask it to write a full report on exactly what you just watched. That workflow used to take three apps and a lot of copy-pasting. Now it's one notebook. Rolling out this week to Google AI Ultra, Pro, and Plus subscribers on web. Mobile and free users coming soon. What do you think?

Mayank Vora

136,834 views • 4 months ago

most people open Claude every morning and re-explain their entire life. every. single. time. then I built 7 layers that remember everything: Layer 1: tell Claude who I am, once Layer 2: build separate brains for separate work Layer 3: turn on memory so it learns me Layer 4: upload 5 writing samples so it sounds like me Layer 5: dump my world into project files Layer 6: connect Gmail, Calendar, Drive, Slack Layer 7: schedule tasks that run while I sleep 60 minutes to set up. spread across one week. now Claude finishes my sentences. knows which client I mean from one word. catches mistakes I'd miss. it's not a chatbot anymore. it's a personal AI that knows me better than most coworkers do. your AI doesn't know you yet. this article fixes that.
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most people open Claude every morning and re-explain their entire life. every. single. time. then I built 7 layers that remember everything: Layer 1: tell Claude who I am, once Layer 2: build separate brains for separate work Layer 3: turn on memory so it learns me Layer 4: upload 5 writing samples so it sounds like me Layer 5: dump my world into project files Layer 6: connect Gmail, Calendar, Drive, Slack Layer 7: schedule tasks that run while I sleep 60 minutes to set up. spread across one week. now Claude finishes my sentences. knows which client I mean from one word. catches mistakes I'd miss. it's not a chatbot anymore. it's a personal AI that knows me better than most coworkers do. your AI doesn't know you yet. this article fixes that.

Nav Toor

62,558 views • 3 months ago

I launched 300 Grok agents to automate the routine and see what would come of it. As a result, my business reached a profit of +356% Honestly, I still don't understand why absolutely everyone isn't using this method. While everyone else is writing posts and monitoring trends manually, this AI stack works like a well-oiled clockwork mechanism How it's structured: Dedicated cloud instances share the load via a single distributed bus, distributing tasks in batches of 50 parallel threads per agent The central configurator dynamically switches tasks between system workers, processing domains, parsing inputs, and gathering results into a single knowledge graph Each agent runs an algorithm through a fixed cycle (setup -> run -> correct -> save -> schedule), instantly correcting deviations and fixing data in the database Instead of spending hundreds of hours on manual operations, I handed all this machinery over to AI. The result speaks for itself, reach and productivity have grown exponentially, and the system operates autonomously 24/7 The visualization is made for a clear example and may differ from the actual work of the agents The article describes the detailed automation method, bookmark it
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I launched 300 Grok agents to automate the routine and see what would come of it. As a result, my business reached a profit of +356% Honestly, I still don't understand why absolutely everyone isn't using this method. While everyone else is writing posts and monitoring trends manually, this AI stack works like a well-oiled clockwork mechanism How it's structured: Dedicated cloud instances share the load via a single distributed bus, distributing tasks in batches of 50 parallel threads per agent The central configurator dynamically switches tasks between system workers, processing domains, parsing inputs, and gathering results into a single knowledge graph Each agent runs an algorithm through a fixed cycle (setup -> run -> correct -> save -> schedule), instantly correcting deviations and fixing data in the database Instead of spending hundreds of hours on manual operations, I handed all this machinery over to AI. The result speaks for itself, reach and productivity have grown exponentially, and the system operates autonomously 24/7 The visualization is made for a clear example and may differ from the actual work of the agents The article describes the detailed automation method, bookmark it

Bober_smart

13,883 views • 6 days ago

YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥
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YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥

YOMIRGO

23,685 views • 6 months ago

new Zoe upgrades today (day 4): - semantic search over my entire obsidian vault - 561 files indexed, auto-reindexes 3x daily, hash deduplication - i can now ask "what did we decide about X" and she finds it instantly, also saves 85% on token - setup took 10 minutes: brew install llm && llm keys set openai -> bash script to index also: - spawns coding agents in isolated git worktrees - checks in on them periodically via cron - reports back when done or stuck will share my full coding agent setup another time, i literally can’t review all the PRs she’s creating fast enough the rabbit hole deepens ps video is POV of building a business while raising 2 kids in 2026, video credits to my wife (yes she knows about Zoe and I)
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new Zoe upgrades today (day 4): - semantic search over my entire obsidian vault - 561 files indexed, auto-reindexes 3x daily, hash deduplication - i can now ask "what did we decide about X" and she finds it instantly, also saves 85% on token - setup took 10 minutes: brew install llm && llm keys set openai -> bash script to index also: - spawns coding agents in isolated git worktrees - checks in on them periodically via cron - reports back when done or stuck will share my full coding agent setup another time, i literally can’t review all the PRs she’s creating fast enough the rabbit hole deepens ps video is POV of building a business while raising 2 kids in 2026, video credits to my wife (yes she knows about Zoe and I)

Elvis

108,485 views • 7 months ago

THIS DEVELOPER USED A 10,000+ STAR OBSIDIAN REPO TO TURN HIS GROWING VAULT INTO A SEARCHABLE AI MEMORY SYSTEM most people keep adding notes until the graph looks impressive but nobody can find the exact context when a real task starts new note → ai summary → entity links → backlinks → graph update → claude retrieves the right 3–5 files instead of scanning the entire vault the system connects projects, decisions, research, and unfinished ideas, so old context resurfaces automatically instead of disappearing inside hundreds of folders he built the workflow around Claudian, an open-source repo with 10,000+ GitHub stars that brings Claude Code directly into Obsidian → bookmark this before your next 1,000 notes become another beautiful graph that your AI still cannot use
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THIS DEVELOPER USED A 10,000+ STAR OBSIDIAN REPO TO TURN HIS GROWING VAULT INTO A SEARCHABLE AI MEMORY SYSTEM most people keep adding notes until the graph looks impressive but nobody can find the exact context when a real task starts new note → ai summary → entity links → backlinks → graph update → claude retrieves the right 3–5 files instead of scanning the entire vault the system connects projects, decisions, research, and unfinished ideas, so old context resurfaces automatically instead of disappearing inside hundreds of folders he built the workflow around Claudian, an open-source repo with 10,000+ GitHub stars that brings Claude Code directly into Obsidian → bookmark this before your next 1,000 notes become another beautiful graph that your AI still cannot use

Gipp 🦅

16,393 views • 1 month ago