Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

What if you could Google your own brain? 🧠 🦾 (Use code TAAFT25 for 25% off until September 1, 2025) Recall lets you instantly chat across everything you've ever read, watched, listened to, or noted down. ⁠ Here's how you can upgrade your memory: • Chat directly with your...

theresanaiforit's profile picture

There's An AI For That

35,411 subscribers

46,590 Aufrufe • vor 1 Jahr •via X (Twitter)

Gesundheit & WellnessWissenschaft & TechnologieBildung
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Kuse AI: your second brain powered by AI Kuse AI provides a boundless canvas where you can organize all your knowledge, including PDFs, Word docs, text files, images, and YouTube videos, just like a powerful visual notebook. Chat directly with ANY content on the canvas, summarize, brainstorm, and organize. Traditional chatbots‘ conversations get longer and harder to navigate; instead, everything stays neatly arranged on an intuitive canvas. Simply circle content to set your conversation context instantly.
0:30

Kuse AI: your second brain powered by AI Kuse AI provides a boundless canvas where you can organize all your knowledge, including PDFs, Word docs, text files, images, and YouTube videos, just like a powerful visual notebook. Chat directly with ANY content on the canvas, summarize, brainstorm, and organize. Traditional chatbots‘ conversations get longer and harder to navigate; instead, everything stays neatly arranged on an intuitive canvas. Simply circle content to set your conversation context instantly.

Gorden Sun

39,457 Aufrufe • vor 1 Jahr

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

47,058 Aufrufe • vor 1 Monat

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 Aufrufe • vor 7 Monaten

Be Smart as Karpathy Andrej Karpathy with Teamily AI 🧠 Your Personal Knowledge Base: ✅ Built in One Chat. 📈 Compounded via Conversations. Karpathy’s insight is spot on ( It attracts 10 million views in a few days. The idea is simple: AI should build personal knowledge from everything you feed it, so it stops rediscovering things from scratch like a Retrieval-Augmented Generation (RAG). But here’s the reality — most people aren’t Stanford PhD-level geeks like Karpathy. For the rest of us, operating a hacky collection of scripts and tools (Obsidian Web Clipper, Marp, Dataview, etc.) as seen in Karpathy’s idea file is far too complex ( The Internet needs an intuitive product where a personal knowledge base is a persistent, compounding artifact — one that grows alongside the content you consume, the contexts you inhabit, and the questions you ask. Teamily AI ( is the answer. The conversation IS the knowledge base. It’s an AI-native messenger where AI teammates join your chats. They remember your past discussions, your preferences, and your team’s context — getting smarter the more you talk. No setup. No complicated workflows. Just text as you normally do. Whether you’re saving articles and videos, brainstorming at work, or collaborating with colleagues, your AI teammates are right there. They listen, remember, and help — not from scratch every time, but by building a personal knowledge graph of everything you’re involved in. In essence, your knowledge compounds automatically. ✨ The user experience is effortless. Whenever you need a well-organized view of your data, just ask the "Personal AI" at the top of the Teamily window: "Visualize my personal knowledge base" Want to customize the style or indexes? Just chat with it. You define how you manage your knowledge. Our co-founder Aiden has prepared a short video to show you just how easy it is. 📽️
2:41

Be Smart as Karpathy Andrej Karpathy with Teamily AI 🧠 Your Personal Knowledge Base: ✅ Built in One Chat. 📈 Compounded via Conversations. Karpathy’s insight is spot on ( It attracts 10 million views in a few days. The idea is simple: AI should build personal knowledge from everything you feed it, so it stops rediscovering things from scratch like a Retrieval-Augmented Generation (RAG). But here’s the reality — most people aren’t Stanford PhD-level geeks like Karpathy. For the rest of us, operating a hacky collection of scripts and tools (Obsidian Web Clipper, Marp, Dataview, etc.) as seen in Karpathy’s idea file is far too complex ( The Internet needs an intuitive product where a personal knowledge base is a persistent, compounding artifact — one that grows alongside the content you consume, the contexts you inhabit, and the questions you ask. Teamily AI ( is the answer. The conversation IS the knowledge base. It’s an AI-native messenger where AI teammates join your chats. They remember your past discussions, your preferences, and your team’s context — getting smarter the more you talk. No setup. No complicated workflows. Just text as you normally do. Whether you’re saving articles and videos, brainstorming at work, or collaborating with colleagues, your AI teammates are right there. They listen, remember, and help — not from scratch every time, but by building a personal knowledge graph of everything you’re involved in. In essence, your knowledge compounds automatically. ✨ The user experience is effortless. Whenever you need a well-organized view of your data, just ask the "Personal AI" at the top of the Teamily window: "Visualize my personal knowledge base" Want to customize the style or indexes? Just chat with it. You define how you manage your knowledge. Our co-founder Aiden has prepared a short video to show you just how easy it is. 📽️

Teamily AI

15,642 Aufrufe • vor 4 Monaten

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.
0:23

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 Aufrufe • vor 8 Tagen

Get Gemini up to speed on what matters most to you with memory import. Bring an understanding of your key preferences, relationships, and personal context directly into Gemini. Once you import these memories, Gemini will understand the same key facts you’ve shared with other AI apps, like your dietary restrictions, your sibling’s names, or where you grew up and securely save those details for your next chat. To get started: 1) Head to your Settings in Gemini and select “Import memory to Gemini.” 2) Ask your current AI: Copy our suggested prompt and paste it into your other AI app to generate a summary of your preferences and style. 3) Copy the response: Copy the personalized summary it creates for you. 4) Paste into Gemini: Go back to “Import memory to Gemini,” paste the text, and start a new chat.
0:29

Get Gemini up to speed on what matters most to you with memory import. Bring an understanding of your key preferences, relationships, and personal context directly into Gemini. Once you import these memories, Gemini will understand the same key facts you’ve shared with other AI apps, like your dietary restrictions, your sibling’s names, or where you grew up and securely save those details for your next chat. To get started: 1) Head to your Settings in Gemini and select “Import memory to Gemini.” 2) Ask your current AI: Copy our suggested prompt and paste it into your other AI app to generate a summary of your preferences and style. 3) Copy the response: Copy the personalized summary it creates for you. 4) Paste into Gemini: Go back to “Import memory to Gemini,” paste the text, and start a new chat.

Google Gemini

48,843 Aufrufe • vor 4 Monaten

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇
0:29

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇

Hugo

54,481 Aufrufe • vor 5 Monaten

HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇
14:28

HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇

YanXbt

30,734 Aufrufe • vor 2 Monaten

🚨BREAKING: Someone just built the open source Loom killer that turns your screen recordings into AI agents. It's called Bloom and it does something Loom never could. Your recordings don't just sit as files. They become queryable, searchable, agent-ready data the moment you stop recording. Here's what actually happens: You hit record. Bloom captures your screen, mic, and system audio locally. While you're still recording, it uploads chunks to the cloud in real time. The second you stop, it auto-generates a full transcript, creates visual embeddings, and indexes every spoken word. Then the wild part kicks in. You can query your recordings through APIs. You can hook Claude Code directly into your video library and ask it questions. Your screen recording just became a knowledge base. → Local-first, your files never get locked in → Cmd+Shift+R starts and stops from anywhere on your system → One-click shareable link for any recording → Chat directly with your video content through VideoDB Chat → Works with Claude Code and other agent frameworks out of the box Recordings are no longer files. They're inputs for AI. MIT License. 100% Opensource. Link in comments.
1:13

🚨BREAKING: Someone just built the open source Loom killer that turns your screen recordings into AI agents. It's called Bloom and it does something Loom never could. Your recordings don't just sit as files. They become queryable, searchable, agent-ready data the moment you stop recording. Here's what actually happens: You hit record. Bloom captures your screen, mic, and system audio locally. While you're still recording, it uploads chunks to the cloud in real time. The second you stop, it auto-generates a full transcript, creates visual embeddings, and indexes every spoken word. Then the wild part kicks in. You can query your recordings through APIs. You can hook Claude Code directly into your video library and ask it questions. Your screen recording just became a knowledge base. → Local-first, your files never get locked in → Cmd+Shift+R starts and stops from anywhere on your system → One-click shareable link for any recording → Chat directly with your video content through VideoDB Chat → Works with Claude Code and other agent frameworks out of the box Recordings are no longer files. They're inputs for AI. MIT License. 100% Opensource. Link in comments.

Rimsha Bhardwaj

128,775 Aufrufe • vor 4 Monaten

🚀 The team at Google DeepMind just released Gemini Embedding 2, a frontier embeddings model with 3072 dimensions and state-of-the-art semantic quality. 👩‍💻 We built a demo showing how to integrate it across the LlamaIndex ecosystem, from LlamaParse to LlamaAgents: 𝗮𝘂𝗱𝗶𝗼-𝗸𝗯, a knowledge base for your audio notes. With audio-kb, you can: 🔹 Upload an MP3 or record directly from your terminal 🔹 LlamaParse extracts the transcript from the audio 🔹 Gemini Embedding 2 generates embeddings 🔹 Metadata + vectors are stored in SurrealDB and indexed with HNSW 🔍 Once ingested, you can search all your audio notes directly from the terminal. 🎙️ Perfect for turning voice memos, meetings, or lectures into a searchable knowledge base. 📖 Full blog: 💻 GitHub: ⚡ Try LlamaParse:
0:21

🚀 The team at Google DeepMind just released Gemini Embedding 2, a frontier embeddings model with 3072 dimensions and state-of-the-art semantic quality. 👩‍💻 We built a demo showing how to integrate it across the LlamaIndex ecosystem, from LlamaParse to LlamaAgents: 𝗮𝘂𝗱𝗶𝗼-𝗸𝗯, a knowledge base for your audio notes. With audio-kb, you can: 🔹 Upload an MP3 or record directly from your terminal 🔹 LlamaParse extracts the transcript from the audio 🔹 Gemini Embedding 2 generates embeddings 🔹 Metadata + vectors are stored in SurrealDB and indexed with HNSW 🔍 Once ingested, you can search all your audio notes directly from the terminal. 🎙️ Perfect for turning voice memos, meetings, or lectures into a searchable knowledge base. 📖 Full blog: 💻 GitHub: ⚡ Try LlamaParse:

LlamaIndex 🦙

34,945 Aufrufe • vor 5 Monaten

I built a Claude skill that turns Claude Code into your personal coding tutor. The core insight: Claude Opus 4.5 is already the best tutor in the world. Anthropic cooked with this model! It has incredible emotional intelligence and deep coding knowledge. What this skill does is just provide a harness—a way for Claude to agentically build the right context about YOU so it can personalize the tutoring experience in exactly the right way. Here's what makes it work: Learner profile from day one. The first time you use it, Claude interviews you. It asks about your programming background, your goal (where do you want this to take you?), and who you are as a person. This gets saved and informs every single tutorial it ever writes for you. From the very first interaction, everything is 100% personalized. Tutorials that use YOUR code. When you ask to learn something, Claude doesn't give you generic examples from some blog post. It finds examples in the actual codebase you're working in. This makes concepts stick in a way abstract examples never do. Quiz mode with spaced repetition. You can run "/quiz-me" and Claude will test you on concepts you've learned. It tracks your understanding score for each tutorial. Then it uses spaced repetition to prioritize the next quiz—concepts you're shaky on come back in 2 days, concepts you've mastered fade to 55+ day intervals. It literally builds retention into the learning process. One central knowledge base across all your projects. Whether you're joining a new company and want to understand their codebase, learning from an open source project, or leveling up on your own vibe-coded project—all your tutorials live in one place (~/coding-tutor-tutorials/). So your personal coding-tutor accompanies you across all your coding adventures. The whole thing is a feedback loop: learn → quiz → retain → learn more → quiz → retain. Your tutorials evolve, your knowledge compounds, and Claude gets better at teaching YOU specifically over time. To install it in Claude Code: • Run /plugin to open the plugin manager • Add marketplace nityeshaga/claude-code-essentials • Enable coding-tutor plugin Here's the Github: And here's 20-mins of me walking you through how to use this plugin and how it works 👇🏽 Let me know if you use it to teach yourself something cool!
21:41

I built a Claude skill that turns Claude Code into your personal coding tutor. The core insight: Claude Opus 4.5 is already the best tutor in the world. Anthropic cooked with this model! It has incredible emotional intelligence and deep coding knowledge. What this skill does is just provide a harness—a way for Claude to agentically build the right context about YOU so it can personalize the tutoring experience in exactly the right way. Here's what makes it work: Learner profile from day one. The first time you use it, Claude interviews you. It asks about your programming background, your goal (where do you want this to take you?), and who you are as a person. This gets saved and informs every single tutorial it ever writes for you. From the very first interaction, everything is 100% personalized. Tutorials that use YOUR code. When you ask to learn something, Claude doesn't give you generic examples from some blog post. It finds examples in the actual codebase you're working in. This makes concepts stick in a way abstract examples never do. Quiz mode with spaced repetition. You can run "/quiz-me" and Claude will test you on concepts you've learned. It tracks your understanding score for each tutorial. Then it uses spaced repetition to prioritize the next quiz—concepts you're shaky on come back in 2 days, concepts you've mastered fade to 55+ day intervals. It literally builds retention into the learning process. One central knowledge base across all your projects. Whether you're joining a new company and want to understand their codebase, learning from an open source project, or leveling up on your own vibe-coded project—all your tutorials live in one place (~/coding-tutor-tutorials/). So your personal coding-tutor accompanies you across all your coding adventures. The whole thing is a feedback loop: learn → quiz → retain → learn more → quiz → retain. Your tutorials evolve, your knowledge compounds, and Claude gets better at teaching YOU specifically over time. To install it in Claude Code: • Run /plugin to open the plugin manager • Add marketplace nityeshaga/claude-code-essentials • Enable coding-tutor plugin Here's the Github: And here's 20-mins of me walking you through how to use this plugin and how it works 👇🏽 Let me know if you use it to teach yourself something cool!

Nityesh

64,563 Aufrufe • vor 7 Monaten

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 → Claude searches YouTube, finds relevant videos, uploads them as sources automatically → NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers → Everything syncs back into your Obsidian vault with passage-level citations you can click to verify Why This Changes Research Forever → No more 20 browser tabs you never close → No more copy-pasting outputs into random notes → No more hallucinated answers with no sources to back them up → 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data What Claude Can Do From the Terminal → Search YouTube for relevant videos on any topic and rank by relevance → Create a new NotebookLM notebook and add 20 sources in parallel automatically → Ask questions and export cited answers directly into Obsidian with wikilinks → Set custom personas per notebook - concise, no filler, no preamble → Generate audio overviews and save them as MP3 files into your vault → Build mind maps, flashcard decks, and research dashboards from your sources → Search arXiv for academic papers and feed them directly into NotebookLM → Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes The Obsidian Output → Every answer arrives with clickable citations that link to the exact passage in the source video or article → Graph view shows connections between all 20 sources and the topics they share → Q&A log tracks every question asked and the grounded response received → Source dashboard shows citation frequency, topics extracted, and which questions each source answered Use Cases Worth Building Today → Academic research with arXiv papers, full citation traceability → Competitor analysis from their YouTube channels and blog posts → Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates → Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week → Personal second brain, 300 daily notes uploaded and queryable in one notebook Before this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real. Now you type one prompt in the terminal and Claude does all of it for you. The research stack of 2026 is not a browser. It is a terminal connected to everything
32:50

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 → Claude searches YouTube, finds relevant videos, uploads them as sources automatically → NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers → Everything syncs back into your Obsidian vault with passage-level citations you can click to verify Why This Changes Research Forever → No more 20 browser tabs you never close → No more copy-pasting outputs into random notes → No more hallucinated answers with no sources to back them up → 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data What Claude Can Do From the Terminal → Search YouTube for relevant videos on any topic and rank by relevance → Create a new NotebookLM notebook and add 20 sources in parallel automatically → Ask questions and export cited answers directly into Obsidian with wikilinks → Set custom personas per notebook - concise, no filler, no preamble → Generate audio overviews and save them as MP3 files into your vault → Build mind maps, flashcard decks, and research dashboards from your sources → Search arXiv for academic papers and feed them directly into NotebookLM → Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes The Obsidian Output → Every answer arrives with clickable citations that link to the exact passage in the source video or article → Graph view shows connections between all 20 sources and the topics they share → Q&A log tracks every question asked and the grounded response received → Source dashboard shows citation frequency, topics extracted, and which questions each source answered Use Cases Worth Building Today → Academic research with arXiv papers, full citation traceability → Competitor analysis from their YouTube channels and blog posts → Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates → Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week → Personal second brain, 300 daily notes uploaded and queryable in one notebook Before this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real. Now you type one prompt in the terminal and Claude does all of it for you. The research stack of 2026 is not a browser. It is a terminal connected to everything

Dami-Defi

252,693 Aufrufe • vor 2 Monaten

Super Simple - Free $USDC Some ChatGPT & Claude AI users are seeing $100–$500 USDC available to claim through Paybox. I just checked... and some users are getting up to $27 instantly. Here's how to check yours: 1. Visit • Choose ChatGPT or Claude AI • Follow the setup guide • Connect Paybox (enable Developer Mode if prompted) 2. Open • Sign up with the same email • Connect your X account • Go to Credentials • Add your wallet • Import a new Solana wallet (don't use your main wallet) • Check your allocation • Claim your USDC (no fee) Rewards are distributed by MoonPay directly to your wallet. ⚠️ Servers are overloaded, so if you don't see your allocation, try again later. ⚠️ The more you've used ChatGPT or Claude and shared data with them, the higher your potential allocation may be. How much USDC did you get? Drop a screenshot below! 👇
1:02

Super Simple - Free $USDC Some ChatGPT & Claude AI users are seeing $100–$500 USDC available to claim through Paybox. I just checked... and some users are getting up to $27 instantly. Here's how to check yours: 1. Visit • Choose ChatGPT or Claude AI • Follow the setup guide • Connect Paybox (enable Developer Mode if prompted) 2. Open • Sign up with the same email • Connect your X account • Go to Credentials • Add your wallet • Import a new Solana wallet (don't use your main wallet) • Check your allocation • Claim your USDC (no fee) Rewards are distributed by MoonPay directly to your wallet. ⚠️ Servers are overloaded, so if you don't see your allocation, try again later. ⚠️ The more you've used ChatGPT or Claude and shared data with them, the higher your potential allocation may be. How much USDC did you get? Drop a screenshot below! 👇

COCO 🧭

167,447 Aufrufe • vor 15 Tagen

Today I'm soft launching , the world's first AI computer. Most people could be creating way more with their computers. Instead, they're stuck consuming content made by other people. Zo Computer is a new kind of computer in the cloud, with an AI that "lives" in your computer. You can choose between all the leading models, and just chat (or text) with your new computer to: • write • research • vibe code • generate images and videos • host websites and APIs • save articles • set reminders • automate your life and much more. And because Zo is *your* personal server, everything is stored and hosted on it – your files, your sites, and even the Zo app itself.
0:36

Today I'm soft launching , the world's first AI computer. Most people could be creating way more with their computers. Instead, they're stuck consuming content made by other people. Zo Computer is a new kind of computer in the cloud, with an AI that "lives" in your computer. You can choose between all the leading models, and just chat (or text) with your new computer to: • write • research • vibe code • generate images and videos • host websites and APIs • save articles • set reminders • automate your life and much more. And because Zo is *your* personal server, everything is stored and hosted on it – your files, your sites, and even the Zo app itself.

hraness 🎣

33,424 Aufrufe • vor 10 Monaten

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

186,667 Aufrufe • vor 2 Monaten

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!
3:46

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,403 Aufrufe • vor 2 Jahren

PhD Students – How to easily understand a complex research topic? Meet Ponder – a tool for understanding complex research. 𝐇𝐨𝐰 𝐏𝐨𝐧𝐝𝐞𝐫 𝐰𝐨𝐫𝐤𝐬? 1. Go to and log in 2. Enter your research topic or research question 3. Ponder will start building a knowledge map 4. This knowledge map breaks down complex ideas into structured cards 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐝𝐨 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞𝐬𝐞 𝐜𝐚𝐫𝐝𝐬? → You can add your own thoughts, questions, and insights. → Ask follow-up questions and deepen your exploration. → You can color the cards for better understanding → You can drag & organize them freely across the infinite canvas. 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐝𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐜𝐚𝐫𝐝𝐬? — You can search for relevant papers with built-in discovery. — Ponder will identify all relevant papers — You can then add or upload research papers — You can also attach papers to specific cards. 𝐀𝐟𝐭𝐞𝐫 𝐲𝐨𝐮𝐫 𝐩𝐨𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐢𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝: ➟ You can change the view to document, browser, or full screen. ➟ You can also download your knowledge map as a PDF ➟ You can ask further questions and refine with Ponder’s Agent. 𝐖𝐡𝐚𝐭 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐭𝐡𝐢𝐬 𝐰𝐚𝐲 𝐢𝐬 𝟏𝟎𝐱 𝐛𝐞𝐭𝐭𝐞𝐫? ↳ It brings discovery and analysis of research into one workspace ↳ It makes ideas branch and evolve naturally, just like your brain ↳ It helps you to easily identify research gaps ↳ It connects knowledge from all sources such as papers and web ↳ It enables you to export knowledge as maps, reports, or data. ↳ Designed for PhD students & researchers, who think deeply. Try Ponder here: Anything you'd like to add?
2:30

PhD Students – How to easily understand a complex research topic? Meet Ponder – a tool for understanding complex research. 𝐇𝐨𝐰 𝐏𝐨𝐧𝐝𝐞𝐫 𝐰𝐨𝐫𝐤𝐬? 1. Go to and log in 2. Enter your research topic or research question 3. Ponder will start building a knowledge map 4. This knowledge map breaks down complex ideas into structured cards 𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐝𝐨 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞𝐬𝐞 𝐜𝐚𝐫𝐝𝐬? → You can add your own thoughts, questions, and insights. → Ask follow-up questions and deepen your exploration. → You can color the cards for better understanding → You can drag & organize them freely across the infinite canvas. 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐝𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐜𝐚𝐫𝐝𝐬? — You can search for relevant papers with built-in discovery. — Ponder will identify all relevant papers — You can then add or upload research papers — You can also attach papers to specific cards. 𝐀𝐟𝐭𝐞𝐫 𝐲𝐨𝐮𝐫 𝐩𝐨𝐧𝐝𝐞𝐫𝐢𝐧𝐠 𝐢𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝: ➟ You can change the view to document, browser, or full screen. ➟ You can also download your knowledge map as a PDF ➟ You can ask further questions and refine with Ponder’s Agent. 𝐖𝐡𝐚𝐭 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐭𝐡𝐢𝐬 𝐰𝐚𝐲 𝐢𝐬 𝟏𝟎𝐱 𝐛𝐞𝐭𝐭𝐞𝐫? ↳ It brings discovery and analysis of research into one workspace ↳ It makes ideas branch and evolve naturally, just like your brain ↳ It helps you to easily identify research gaps ↳ It connects knowledge from all sources such as papers and web ↳ It enables you to export knowledge as maps, reports, or data. ↳ Designed for PhD students & researchers, who think deeply. Try Ponder here: Anything you'd like to add?

Faheem Ullah

12,175 Aufrufe • vor 1 Jahr

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.
8:17

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,598 Aufrufe • vor 2 Monaten

You can build an AI second brain in 15 minutes. No coding experience needed. no $1000 course [Here is how you can do it in 5 mins:] Step 1: Download Claude Desktop. Step 2: Download Obsidian Desktop. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect directly to your vault using Andrej Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude can now read all of it, find connections you missed, and surface insights from your own thinking that you forgot you had. Most people are using Claude as a search engine. The people building second brains with it are using it as an intelligence layer on top of everything they know. The gap between those two use cases is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight.
22:22

You can build an AI second brain in 15 minutes. No coding experience needed. no $1000 course [Here is how you can do it in 5 mins:] Step 1: Download Claude Desktop. Step 2: Download Obsidian Desktop. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect directly to your vault using Andrej Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude can now read all of it, find connections you missed, and surface insights from your own thinking that you forgot you had. Most people are using Claude as a search engine. The people building second brains with it are using it as an intelligence layer on top of everything they know. The gap between those two use cases is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight.

Avid

35,544 Aufrufe • vor 2 Monaten

You can build an AI second brain in 15 minutes. No coding experience needed. no $1000 course [Here is how you can do it in 5 mins:] Step 1: Download Claude Desktop. Step 2: Download Obsidian Desktop. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect directly to your vault using Andrej Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude can now read all of it, find connections you missed, and surface insights from your own thinking that you forgot you had. Most people are using Claude as a search engine. The people building second brains with it are using it as an intelligence layer on top of everything they know. The gap between those two use cases is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight.
1:18

You can build an AI second brain in 15 minutes. No coding experience needed. no $1000 course [Here is how you can do it in 5 mins:] Step 1: Download Claude Desktop. Step 2: Download Obsidian Desktop. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect directly to your vault using Andrej Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude can now read all of it, find connections you missed, and surface insights from your own thinking that you forgot you had. Most people are using Claude as a search engine. The people building second brains with it are using it as an intelligence layer on top of everything they know. The gap between those two use cases is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight.

Avid

469,325 Aufrufe • vor 1 Monat