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Claude Code cannot read 300 files at once. So someone built a system that lets it control NotebookLM from the terminal instead. The results are wild. Here is the full workflow nobody is talking about: The Setup → Claude Code connects to NotebookLM via a command line interface →...

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Dami-Defi

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252,693 次观看 • 5 个月前 •via X (Twitter)

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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 次观看 • 3 个月前

THIS GUY BUILT A SYSTEM THAT GETS SMARTER EVERY TIME HE USES IT AND IT LOOKS LIKE A GALAXY he types one command into Claude Code it finds sources, runs analysis through NotebookLM, saves everything to Obsidian automatically graph in the background is every idea he's ever researched - all linked together system handles the research so he only has to think about what to do with it most people are still copy-pasting into Notion and losing everything by next week full setup in the article
0:48

THIS GUY BUILT A SYSTEM THAT GETS SMARTER EVERY TIME HE USES IT AND IT LOOKS LIKE A GALAXY he types one command into Claude Code it finds sources, runs analysis through NotebookLM, saves everything to Obsidian automatically graph in the background is every idea he's ever researched - all linked together system handles the research so he only has to think about what to do with it most people are still copy-pasting into Notion and losing everything by next week full setup in the article

monokern

45,188 次观看 • 5 个月前

You’re using NotebookLM like a PDF reader. It’s actually a “second brain” builder. Do this: → Create separate notebooks per project → Upload sources one by one so you control what it learns → Ask questions and verify with built in citations → Pin the best answers as Notes for reuse Save this video, you’ll stop redoing research every week. Want the SOP? DM me. 💬
9:01

You’re using NotebookLM like a PDF reader. It’s actually a “second brain” builder. Do this: → Create separate notebooks per project → Upload sources one by one so you control what it learns → Ask questions and verify with built in citations → Pin the best answers as Notes for reuse Save this video, you’ll stop redoing research every week. Want the SOP? DM me. 💬

Julian Goldie SEO

28,693 次观看 • 10 个月前

NotebookLM is great for research. Claude’s browser agent is great for clicking. Combine them and you get a “researcher + executor” that works while you do something else. → Install Claude for Chrome and enable browser use. → Set “Ask before acting” for safety. → Prompt: “Open NotebookLM → create notebook → add sources → build a data table → export to Google Sheets → generate an audio overview.” → Let it run in the background while you work. This turns competitor research into a repeatable SOP instead of a one-time grind. Save this video, you’ll stop doing busywork. Want the SOP? DM me. 💬
1:30:40

NotebookLM is great for research. Claude’s browser agent is great for clicking. Combine them and you get a “researcher + executor” that works while you do something else. → Install Claude for Chrome and enable browser use. → Set “Ask before acting” for safety. → Prompt: “Open NotebookLM → create notebook → add sources → build a data table → export to Google Sheets → generate an audio overview.” → Let it run in the background while you work. This turns competitor research into a repeatable SOP instead of a one-time grind. Save this video, you’ll stop doing busywork. Want the SOP? DM me. 💬

Julian Goldie SEO

16,031 次观看 • 10 个月前

Nobody will see this but this is how I do research 10x faster now > I drop a topic into Claude Code > It finds 10 relevant YouTube sources automatically > Sends them to NotebookLM 6 minutes later I get a full structured analysis, an infographic, and a markdown file saved to my vault Before this I was spending 3 hours doing the same thing manually and ending up with messy notes I'd never look at again Now the system does it in one command and the output gets better every time I use it full setup guide in the article
0:47

Nobody will see this but this is how I do research 10x faster now > I drop a topic into Claude Code > It finds 10 relevant YouTube sources automatically > Sends them to NotebookLM 6 minutes later I get a full structured analysis, an infographic, and a markdown file saved to my vault Before this I was spending 3 hours doing the same thing manually and ending up with messy notes I'd never look at again Now the system does it in one command and the output gets better every time I use it full setup guide in the article

monokern

189,172 次观看 • 5 个月前

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
0:24

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

Yarchi

58,643 次观看 • 4 个月前

MOST "SECOND BRAINS" ARE JUST FOLDERS YOU FEEL GUILTY ABOUT. HERE'S HOW CLAUDE TURNS ONE INTO A BRAIN THAT ANSWERS YOU. that clip is the build: obsidian plus claude, structured like a filing cabinet. three drawers, one job each: raw: everything you dump in, docs, sheets, pdfs, transcripts, untouched new knowledge: what claude distills, links and rewrites into clean notes and the connections between them, drawn automatically the part most note apps miss: you are not the librarian. you drop the raw in. claude reads it once, files it, links it to what you already had, and updates the index. then you never dig through the raw pile again. you ask the brain, and it answers from the clean layer with sources. that graph on screen isn't decoration. every line is a connection you didn't have to make by hand. no re-reading the same pdf, no notes you wrote once and never opened, no app that guilts you for not organizing. the uncomfortable part isn't that note apps are messy. it's that a second brain was never about saving more, it was about something rereading you after you stopped. follow RetroChainer for systems that keep thinking after you close the tab, and bookmark this before your notes turn into another graveyard.
1:25

MOST "SECOND BRAINS" ARE JUST FOLDERS YOU FEEL GUILTY ABOUT. HERE'S HOW CLAUDE TURNS ONE INTO A BRAIN THAT ANSWERS YOU. that clip is the build: obsidian plus claude, structured like a filing cabinet. three drawers, one job each: raw: everything you dump in, docs, sheets, pdfs, transcripts, untouched new knowledge: what claude distills, links and rewrites into clean notes and the connections between them, drawn automatically the part most note apps miss: you are not the librarian. you drop the raw in. claude reads it once, files it, links it to what you already had, and updates the index. then you never dig through the raw pile again. you ask the brain, and it answers from the clean layer with sources. that graph on screen isn't decoration. every line is a connection you didn't have to make by hand. no re-reading the same pdf, no notes you wrote once and never opened, no app that guilts you for not organizing. the uncomfortable part isn't that note apps are messy. it's that a second brain was never about saving more, it was about something rereading you after you stopped. follow RetroChainer for systems that keep thinking after you close the tab, and bookmark this before your notes turn into another graveyard.

RetroChainer

13,775 次观看 • 2 个月前

Two big steps towards our vision for @NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.
0:34

Two big steps towards our vision for @NotebookLM as the ultimate research platform: • Integrating Deep Research, with a set of only-at-Notebook features that let you explore the retrieved sources • Launching a series of Featured Notebooks curated by Google Research These developments are designed to enhance the full life cycle of research and scholarship: using the power of AI to assemble the knowledge base you need to advance your understanding, and then making your work accessible and intelligible to a wider audience using all the explanatory tools that Notebook offers. If you've used DeepResearch in the Gemini app, you already know that it's a pioneering advance in assembling complex, grounded information on any topic imaginable—collecting an entire trove of material for you and writing a nuanced research report that summarizes the findings. But because NotebookLM is designed to manage and explore potentially hundreds of sources, the Deep Research report is only the beginning of your journey. In our integration, Deep Research gives you an overview all of the sources it found during its research phase, with annotated commentary explaining how each source related to your original query. You can then choose to import some or all of the sources to the notebook, along with the report itself, which you can then explore or transform using the full suite of tools that Notebook offers: grounded chat with citations, Mind Maps, Audio/Video overviews, and much more. And it's that suite of tools that make the Google Research Featured Notebooks so compelling as well. Each notebook contains a curated collection of articles on a specific topic, published by the Google Research team. Think of them as a kind of knowledge base of Google's best thinking on a series of compelling research questions: How do scientists link genetics to health? How will quantum computing be useful? If you're a specialist in these fields, you can read the original papers or ask nuanced questions in chat and advance your understanding of the latest developments. But these notebooks can also make the complex but important topics understandable to non-specialists or students. Each notebook comes with pre-generated audio and video overviews, flashcards, and other Studio artifacts designed to make the scientific and technological concepts accessible and interesting. And you can always explore the material with our new "Learning Guide" chat mode that effectively gives you a personal tutor to enhance your understanding. There's much more to come on this front, but you can see in these two announcements how we see Notebook as both a workbench for conducting research and a publishing platform for sharing the results of that research once you're ready to make it public. Deep Research is rolling out this week to all users. The first two Google Research notebooks are live now, both of them deep dives into our most recent discoveries involving genetics and health. (Links in the following tweets.) We'll be publishing new notebooks in the series every other week or so for the next few months.

Steven Johnson

104,939 次观看 • 11 个月前

Tried every note plugin before one Karpathy gist automated the whole vault Karpathy published llm-wiki.md. Seven repos already run it. About 255k stars between them. Obsidian is the IDE. The LLM is the programmer. The wiki is the codebase. Three operations run everything. 1. Ingest reads a raw source, writes a wiki summary, updates every related page in the vault 2. Query answers from the wiki and files good answers back as new pages 3. Lint runs periodic checks for contradictions, stale claims, orphan pages, missing cross-references 4. graphify turns codebases, docs and PDFs into a queryable knowledge graph, 137.9k stars 5. claude-obsidian files sources into linked cited notes and answers from the vault, 15.9k stars 6. mcp-obsidian connects one MCP server with 7 tools to read, search and write the whole vault, 4.5k stars Seven repos. ~255k stars. Three gist operations. The gist names a method, not a model. The model is a slot you fill with whatever runs your vault. Drop a source in. The wiki rewrites itself. Lint catches what drifted. Save this ↓
0:24

Tried every note plugin before one Karpathy gist automated the whole vault Karpathy published llm-wiki.md. Seven repos already run it. About 255k stars between them. Obsidian is the IDE. The LLM is the programmer. The wiki is the codebase. Three operations run everything. 1. Ingest reads a raw source, writes a wiki summary, updates every related page in the vault 2. Query answers from the wiki and files good answers back as new pages 3. Lint runs periodic checks for contradictions, stale claims, orphan pages, missing cross-references 4. graphify turns codebases, docs and PDFs into a queryable knowledge graph, 137.9k stars 5. claude-obsidian files sources into linked cited notes and answers from the vault, 15.9k stars 6. mcp-obsidian connects one MCP server with 7 tools to read, search and write the whole vault, 4.5k stars Seven repos. ~255k stars. Three gist operations. The gist names a method, not a model. The model is a slot you fill with whatever runs your vault. Drop a source in. The wiki rewrites itself. Lint catches what drifted. Save this ↓

meya.

12,564 次观看 • 5 天前

The AI power trio of NotebookLM, Perplexity, and Gemini 3 just killed manual research. No more spending hours digging through articles. No more manually building slide decks from scratch. Here’s the new play 👇 → Perplexity pulls real-time facts with verified source citations. → NotebookLM processes 1M tokens to synthesize massive data. → Gemini 3 adds multimodal reasoning for text, audio, and video. → Auto-generate full presentations and video overviews instantly. → Transform days of research into a 20-minute automated system. Save this workflow. Google is shipping these AI updates quietly and fast. Want the full guide? DM me.
8:46

The AI power trio of NotebookLM, Perplexity, and Gemini 3 just killed manual research. No more spending hours digging through articles. No more manually building slide decks from scratch. Here’s the new play 👇 → Perplexity pulls real-time facts with verified source citations. → NotebookLM processes 1M tokens to synthesize massive data. → Gemini 3 adds multimodal reasoning for text, audio, and video. → Auto-generate full presentations and video overviews instantly. → Transform days of research into a 20-minute automated system. Save this workflow. Google is shipping these AI updates quietly and fast. Want the full guide? DM me.

Julian Goldie SEO

29,434 次观看 • 10 个月前

Google just made your entire content creation workflow free and automated. No more manual research and 50 open tabs. No more paying for expensive content tools. Here’s the new play 👇 → Turn notes into podcasts, videos, and quizzes in 1 click. → Upload 300+ sources to build a custom AI knowledge base. → Deep Research: Automated web scanning with full citations. → Studio Mode: Generate slide decks and landing pages instantly. → Gemini 3 Power: Zero-hallucination outputs grounded in your data. Save this for your next project. Google is shipping quietly and fast. Want the full guide? DM me.
11:34

Google just made your entire content creation workflow free and automated. No more manual research and 50 open tabs. No more paying for expensive content tools. Here’s the new play 👇 → Turn notes into podcasts, videos, and quizzes in 1 click. → Upload 300+ sources to build a custom AI knowledge base. → Deep Research: Automated web scanning with full citations. → Studio Mode: Generate slide decks and landing pages instantly. → Gemini 3 Power: Zero-hallucination outputs grounded in your data. Save this for your next project. Google is shipping quietly and fast. Want the full guide? DM me.

Julian Goldie SEO

15,449 次观看 • 10 个月前

Karpathy sketched the second brain. these 7 open-source repos already built it for Obsidian + Claude Code 250,000+ GitHub stars between them, and every one is free to clone sorted by stars: 1. graphify - 134k stars turns your codebase, docs, SQL schemas and PDFs into one knowledge graph you can query ↳ 2. Understand-Anything - 88k stars maps any codebase into an interactive knowledge graph you can click through ↳ 3. claude-obsidian - 15.8k stars a second brain that organizes itself: drop in a source, Claude Code files it ↳ 4. obsidian-second-brain - 4.9k stars long-term memory for Claude Code, stored as plain markdown in your vault ↳ 5. mcp-obsidian - 4.5k stars one MCP server and Claude can read, search and write your whole vault ↳ 6. claudesidian - 2.6k stars a starter vault designed around Claude Code from the first note ↳ 7. second-brain - 756 stars an Obsidian wiki the model keeps up to date, built on Karpathy's LLM Wiki pattern ↳ where to start: > your knowledge lives in code -> graphify > your knowledge lives in Obsidian -> claude-obsidian > you just want Claude to remember -> obsidian-second-brain > you live in Claude Desktop -> mcp-obsidian the skills that sit on top of all seven are in the full map below ↓
0:24

Karpathy sketched the second brain. these 7 open-source repos already built it for Obsidian + Claude Code 250,000+ GitHub stars between them, and every one is free to clone sorted by stars: 1. graphify - 134k stars turns your codebase, docs, SQL schemas and PDFs into one knowledge graph you can query ↳ 2. Understand-Anything - 88k stars maps any codebase into an interactive knowledge graph you can click through ↳ 3. claude-obsidian - 15.8k stars a second brain that organizes itself: drop in a source, Claude Code files it ↳ 4. obsidian-second-brain - 4.9k stars long-term memory for Claude Code, stored as plain markdown in your vault ↳ 5. mcp-obsidian - 4.5k stars one MCP server and Claude can read, search and write your whole vault ↳ 6. claudesidian - 2.6k stars a starter vault designed around Claude Code from the first note ↳ 7. second-brain - 756 stars an Obsidian wiki the model keeps up to date, built on Karpathy's LLM Wiki pattern ↳ where to start: > your knowledge lives in code -> graphify > your knowledge lives in Obsidian -> claude-obsidian > you just want Claude to remember -> obsidian-second-brain > you live in Claude Desktop -> mcp-obsidian the skills that sit on top of all seven are in the full map below ↓

Mr. Buzzoni

30,515 次观看 • 9 天前

HERMES AGENT JUST GOT A RESEARCH BRAIN. connect it to NotebookLM and it becomes a 247 research system that builds knowledge over time. it resources. connects ideas across sources. uses that context in every real task. setup in 4 steps: 1. install Hermes with MCP enabled 2. download NotebookLM skill from GitHub 3. add endpoint to MCP servers in config 4. restart Hermes what your agent can now do: → query your notebooks instead of the open web → connect ideas across multiple sources → build knowledge session after session → use your curated context in every /goal → zero hallucinations from your own verified sources ask it anything — it answers from what YOU already know. full setup guide — MCP, models, use cases — in the article 👇
0:30

HERMES AGENT JUST GOT A RESEARCH BRAIN. connect it to NotebookLM and it becomes a 247 research system that builds knowledge over time. it resources. connects ideas across sources. uses that context in every real task. setup in 4 steps: 1. install Hermes with MCP enabled 2. download NotebookLM skill from GitHub 3. add endpoint to MCP servers in config 4. restart Hermes what your agent can now do: → query your notebooks instead of the open web → connect ideas across multiple sources → build knowledge session after session → use your curated context in every /goal → zero hallucinations from your own verified sources ask it anything — it answers from what YOU already know. full setup guide — MCP, models, use cases — in the article 👇

YanXbt

187,943 次观看 • 5 个月前

THE OBSIDIAN GRAPH IS NOT A PRETTY NOTE MAP, IT IS A SELF-MAINTAINING AI WIKI THAT CAN TURN 120 SAVED SOURCES INTO 700 LINKED PAGES WHILE YOU ONLY KEEP ADDING NEW MATERIAL 00:11 the graph opens and the trick becomes obvious: every dot is a saved idea, every cluster is a topic, and every line is context the AI no longer has to rebuild from scratch every morning most people use chatgpt like a disposable memory slot. they upload 1 pdf, ask 3 questions, close the tab, and the next day the model knows nothing unless they feed it the same context again karpathy’s LLM wiki pattern changes the game. the model reads each new source, pulls out claims, updates old pages, creates entity files, links concepts, and marks places where newer info breaks older notes with claude code and obsidian, a single clipped article can expand into 8-20 markdown files: source summary, concept pages, people, tools, comparisons, open questions, and a running index after 60 sources, this is no longer “note taking.” it becomes a private research machine that remembers your old thinking, catches repeated angles, and gives you cited answers from your own archive bookmark this before your notes turn into a graveyard again
0:35

THE OBSIDIAN GRAPH IS NOT A PRETTY NOTE MAP, IT IS A SELF-MAINTAINING AI WIKI THAT CAN TURN 120 SAVED SOURCES INTO 700 LINKED PAGES WHILE YOU ONLY KEEP ADDING NEW MATERIAL 00:11 the graph opens and the trick becomes obvious: every dot is a saved idea, every cluster is a topic, and every line is context the AI no longer has to rebuild from scratch every morning most people use chatgpt like a disposable memory slot. they upload 1 pdf, ask 3 questions, close the tab, and the next day the model knows nothing unless they feed it the same context again karpathy’s LLM wiki pattern changes the game. the model reads each new source, pulls out claims, updates old pages, creates entity files, links concepts, and marks places where newer info breaks older notes with claude code and obsidian, a single clipped article can expand into 8-20 markdown files: source summary, concept pages, people, tools, comparisons, open questions, and a running index after 60 sources, this is no longer “note taking.” it becomes a private research machine that remembers your old thinking, catches repeated angles, and gives you cited answers from your own archive bookmark this before your notes turn into a graveyard again

Gipp 🦅

41,728 次观看 • 4 个月前

Fable 5 and Karpathy's knowledge base trick are basically a cheat code. Someone just dropped a full walkthrough on building the most powerful second brain ever Andrej Karpathy shared a simple idea: use an LLM to build personal knowledge bases. Index your sources, let the model organize them, and use Obsidian as the front end. Someone just built the full thing with Claude Code and Fable 5, and walked through every step You drop your sources into a folder (YouTube transcripts, PDFs, articles, meeting notes) and Claude Code reads them, splits them into linked wiki pages, and builds a living second brain that connects everything. It grows on its own as you add more. Where Fable 5 pulls ahead: it found a connection buried across two sources you'd miss reading them separately. And asked to turn the messy web of links into something a beginner could understand, it produced clean, simple HTML in one shot. The same task took a full day on Opus 4.8 and came out worse. The best part: it's all just markdown files with routing. So it's not locked to Claude Code. Plug it into Codex or any agent you want. Give your AI a real knowledge base, and it stops guessing and starts knowing. Bookmark this.
14:34

Fable 5 and Karpathy's knowledge base trick are basically a cheat code. Someone just dropped a full walkthrough on building the most powerful second brain ever Andrej Karpathy shared a simple idea: use an LLM to build personal knowledge bases. Index your sources, let the model organize them, and use Obsidian as the front end. Someone just built the full thing with Claude Code and Fable 5, and walked through every step You drop your sources into a folder (YouTube transcripts, PDFs, articles, meeting notes) and Claude Code reads them, splits them into linked wiki pages, and builds a living second brain that connects everything. It grows on its own as you add more. Where Fable 5 pulls ahead: it found a connection buried across two sources you'd miss reading them separately. And asked to turn the messy web of links into something a beginner could understand, it produced clean, simple HTML in one shot. The same task took a full day on Opus 4.8 and came out worse. The best part: it's all just markdown files with routing. So it's not locked to Claude Code. Plug it into Codex or any agent you want. Give your AI a real knowledge base, and it stops guessing and starts knowing. Bookmark this.

Shadow Nick

47,331 次观看 • 4 个月前

This is freaking cool NotebookLM just launched its “Deep Research” mode - which can automatically browse hundreds of websites, synthesize the findings into a structured report, and include an annotated list of sources. This means that instead of piecemeal searches and manual organization, users can now let the tool take care of the heavy lifting for in-depth research. NotebookLM is a new version of the notebook. Richtig stark von Google, dass sie NotebookLM regelmäßig weiterentwickeln.
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This is freaking cool NotebookLM just launched its “Deep Research” mode - which can automatically browse hundreds of websites, synthesize the findings into a structured report, and include an annotated list of sources. This means that instead of piecemeal searches and manual organization, users can now let the tool take care of the heavy lifting for in-depth research. NotebookLM is a new version of the notebook. Richtig stark von Google, dass sie NotebookLM regelmäßig weiterentwickeln.

Chubby♨️

82,801 次观看 • 11 个月前

NotebookLM just ended manual AI paper reading. They’ve released a new version for arXiv papers and it’s insane. It doesn’t just summarize papers. - It studies thousands of related sources - Connects the research for you - Explains everything back in audio like a real mentor And yes… it’s completely free.
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NotebookLM just ended manual AI paper reading. They’ve released a new version for arXiv papers and it’s insane. It doesn’t just summarize papers. - It studies thousands of related sources - Connects the research for you - Explains everything back in audio like a real mentor And yes… it’s completely free.

Eyisha Zyer

224,317 次观看 • 1 年前

HOLY F*CKING SH*T you waste 20 minutes every morning re-explaining your project to Claude like it has never heard of you, and Karpathy showed you the fix a year ago Andrej Karpathy, co-founder of OpenAI, posted one idea that pulled 16 million views: stop using AI to write code. use it to build a second brain You point Claude Code to a folder, drop any source in there (article, transcript, PDF) and Claude reads it, links it, and saves it into a living wiki of everything you know. it compounds like interest: the more you feed it, the sharper every answer gets Here is the gist: → install Obsidian, create a vault, open it in Claude Code → paste Karpathy's wiki prompt and tell Claude to build it → Claude creates 3 folders: raw for sources, wiki for its pages, CLAUDE.md that runs everything → drop any source into raw and say "ingest this" → ask anything across all of it, forever 5 minutes to set up. and you never open a blank Claude chat again the full step-by-step is in the article below bookmark this before you waste another monday
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HOLY F*CKING SH*T you waste 20 minutes every morning re-explaining your project to Claude like it has never heard of you, and Karpathy showed you the fix a year ago Andrej Karpathy, co-founder of OpenAI, posted one idea that pulled 16 million views: stop using AI to write code. use it to build a second brain You point Claude Code to a folder, drop any source in there (article, transcript, PDF) and Claude reads it, links it, and saves it into a living wiki of everything you know. it compounds like interest: the more you feed it, the sharper every answer gets Here is the gist: → install Obsidian, create a vault, open it in Claude Code → paste Karpathy's wiki prompt and tell Claude to build it → Claude creates 3 folders: raw for sources, wiki for its pages, CLAUDE.md that runs everything → drop any source into raw and say "ingest this" → ask anything across all of it, forever 5 minutes to set up. and you never open a blank Claude chat again the full step-by-step is in the article below bookmark this before you waste another monday

mojesko

96,487 次观看 • 4 天前

NotebookLM just went from smart… to unstoppable. And nobody is talking about how big this update really is. Google just gave Notebook LM the ability to read your photos → handwritten notes, textbook pages, graphs, screenshots. Here’s how to use it: → Upload any image (notes, diagrams, whiteboards). → Notebook LM runs OCR and turns it into clean text. → Ask it anything about the image. → Use Deep Research to pull in verified web sources. → Build a complete research hub in minutes. This is the first time your messy notes become a searchable knowledge base. Save this video, you’ll need it. Full guide is inside AI Money Lab. DM me for the link. 💬
11:08

NotebookLM just went from smart… to unstoppable. And nobody is talking about how big this update really is. Google just gave Notebook LM the ability to read your photos → handwritten notes, textbook pages, graphs, screenshots. Here’s how to use it: → Upload any image (notes, diagrams, whiteboards). → Notebook LM runs OCR and turns it into clean text. → Ask it anything about the image. → Use Deep Research to pull in verified web sources. → Build a complete research hub in minutes. This is the first time your messy notes become a searchable knowledge base. Save this video, you’ll need it. Full guide is inside AI Money Lab. DM me for the link. 💬

Julian Goldie SEO

13,193 次观看 • 11 个月前

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.
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Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Simplifying AI

68,025 次观看 • 3 个月前