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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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250,346 Aufrufe • vor 16 Tagen •via X (Twitter)

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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. 💬
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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. 💬

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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. 💬
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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

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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
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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

186,874 Aufrufe • vor 11 Tagen

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

Yarchi

55,472 Aufrufe • vor 4 Tagen

Finally: An agentic research companion You can now start a notebook by just entering your loose ideas and questions into the chat. NotebookLM will then guide you through building out your repository, even finding and adding (with your permission!) relevant sources from the web. As always your feedback is everything to us. Try it out and let us know what you think 💖
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Finally: An agentic research companion You can now start a notebook by just entering your loose ideas and questions into the chat. NotebookLM will then guide you through building out your repository, even finding and adding (with your permission!) relevant sources from the web. As always your feedback is everything to us. Try it out and let us know what you think 💖

NotebookLM

21,049 Aufrufe • vor 3 Tagen

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.
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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

104,774 Aufrufe • vor 7 Monaten

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.
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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 5 Monaten

NotebookLM has always been grounded in your sources. However, sometimes we all need a little more info. Now NBLM can help you discover new sources to expand your research. Just type in what you want to learn about and we'll scour the web for the best the internet has to offer 🕵️‍♀️
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NotebookLM has always been grounded in your sources. However, sometimes we all need a little more info. Now NBLM can help you discover new sources to expand your research. Just type in what you want to learn about and we'll scour the web for the best the internet has to offer 🕵️‍♀️

NotebookLM

267,122 Aufrufe • vor 1 Jahr

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.
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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

29,315 Aufrufe • vor 5 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 👇
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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

182,206 Aufrufe • vor 15 Tagen

The updates keep coming— you can now discover new sources in the NotebookLM mobile app! Whether you're expanding an existing notebook or deep diving into a new topic, just type what you want to learn about and NotebookLM will browse the web for the most relevant results.
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The updates keep coming— you can now discover new sources in the NotebookLM mobile app! Whether you're expanding an existing notebook or deep diving into a new topic, just type what you want to learn about and NotebookLM will browse the web for the most relevant results.

NotebookLM

119,050 Aufrufe • vor 7 Monaten

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 Aufrufe • vor 6 Monaten

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

223,809 Aufrufe • vor 7 Monaten

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. 💬
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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

13,157 Aufrufe • vor 6 Monaten

This beginner guide to Karpathy's LLM Wiki is one of the most useful AI videos i've shared this year Karpathy's idea: instead of asking AI questions every time, make it build you a permanent knowledge base that grows smarter over time > drop your documents into a folder > Claude reads them and builds a structured wiki in Obsidian > every time you add a new source, Claude Sonnet updates existing pages and links new ideas > ask complex questions - Claude Opus answers from the organized wiki, not raw files it's like having a researcher who never forgets and connects dots across everything you've ever given it full setup guide is in the video above
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This beginner guide to Karpathy's LLM Wiki is one of the most useful AI videos i've shared this year Karpathy's idea: instead of asking AI questions every time, make it build you a permanent knowledge base that grows smarter over time > drop your documents into a folder > Claude reads them and builds a structured wiki in Obsidian > every time you add a new source, Claude Sonnet updates existing pages and links new ideas > ask complex questions - Claude Opus answers from the organized wiki, not raw files it's like having a researcher who never forgets and connects dots across everything you've ever given it full setup guide is in the video above

Mr. Buzzoni

58,787 Aufrufe • vor 27 Tagen

I just built a Claude skill that acts as a second brain for DTC brands 🤯 Drop your ad exports, customer reviews, competitor screenshots, and brand docs into a folder → Claude compiles it all into an organized wiki you can ask questions against. All inside Claude Cowork. Perfect for DTC brands and agencies whose knowledge is scattered across Google Drive, Notion, Meta Ads Manager, Figma, and 47 spreadsheets nobody has opened in 3 months. If every strategic question takes 2 hours to answer because the data lives in 8 different places ... This skill eliminates the entire loop: → Claude scaffolds a DTC folder structure: ads, customers, competitors, brand, performance, notes → You drop every file you have into those folders — messy, unorganized, exactly how you have them now → Claude reads everything and compiles a wiki: hooks-that-work, customer-pains, competitor-angles, brand-voice, performance-patterns, creative-brief-library → Every article is cross-linked and traceable back to the source file → You ask questions against the wiki — "what hooks are actually working?" "what objections come up most?" "where are my competitors weak?" → Claude answers, grounded entirely in your own data → Save the answers back in and the system gets smarter every time you use it No more hunting through 12 tools. No more "where did I save that brief?" No more answering the same question twice. What you get: → A complete DTC brand brain scaffold in 60 seconds → Six core wiki articles Claude populates automatically from your raw files → A schema file that tells Claude exactly how to maintain the wiki for DTC use cases → Monthly health checks that catch contradictions and flag gaps before errors compound → A knowledge base that compounds — every question you ask makes the next answer better Built on a methodology Andrej Karpathy shared for personal knowledge bases, I rebuilt the entire thing for DTC operators: folder structure, schema rules, wiki articles, and question frameworks all tuned for brands and agencies. I put together the full skill file plus a playbook walking through the exact setup and 5 real questions to ask your brand brain. Want it for free? > Like this post > Comment "BRAIN" And I'll send it over (must be following so I can DM)
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I just built a Claude skill that acts as a second brain for DTC brands 🤯 Drop your ad exports, customer reviews, competitor screenshots, and brand docs into a folder → Claude compiles it all into an organized wiki you can ask questions against. All inside Claude Cowork. Perfect for DTC brands and agencies whose knowledge is scattered across Google Drive, Notion, Meta Ads Manager, Figma, and 47 spreadsheets nobody has opened in 3 months. If every strategic question takes 2 hours to answer because the data lives in 8 different places ... This skill eliminates the entire loop: → Claude scaffolds a DTC folder structure: ads, customers, competitors, brand, performance, notes → You drop every file you have into those folders — messy, unorganized, exactly how you have them now → Claude reads everything and compiles a wiki: hooks-that-work, customer-pains, competitor-angles, brand-voice, performance-patterns, creative-brief-library → Every article is cross-linked and traceable back to the source file → You ask questions against the wiki — "what hooks are actually working?" "what objections come up most?" "where are my competitors weak?" → Claude answers, grounded entirely in your own data → Save the answers back in and the system gets smarter every time you use it No more hunting through 12 tools. No more "where did I save that brief?" No more answering the same question twice. What you get: → A complete DTC brand brain scaffold in 60 seconds → Six core wiki articles Claude populates automatically from your raw files → A schema file that tells Claude exactly how to maintain the wiki for DTC use cases → Monthly health checks that catch contradictions and flag gaps before errors compound → A knowledge base that compounds — every question you ask makes the next answer better Built on a methodology Andrej Karpathy shared for personal knowledge bases, I rebuilt the entire thing for DTC operators: folder structure, schema rules, wiki articles, and question frameworks all tuned for brands and agencies. I put together the full skill file plus a playbook walking through the exact setup and 5 real questions to ask your brand brain. Want it for free? > Like this post > Comment "BRAIN" And I'll send it over (must be following so I can DM)

Mike Futia

15,054 Aufrufe • vor 2 Monaten

Also, you can now customize your video overviews in the NotebookLM mobile app! We're all watching videos on our phones anyway... might as well make them educational, grounded in your sources, (and kawaii!) 💕
4:40

Also, you can now customize your video overviews in the NotebookLM mobile app! We're all watching videos on our phones anyway... might as well make them educational, grounded in your sources, (and kawaii!) 💕

NotebookLM

83,924 Aufrufe • vor 3 Monaten

everyone's talking about building a second brain with claude code + obsidian right now. so i made a full video walkthrough showing exactly how to set it up from scratch. drop an article into a folder. claude code reads it, creates wiki pages, builds relationships between concepts, and links everything automatically. by 20 sources your wiki knows more about your domain than you can hold in your head. here's the full setup:
13:22

everyone's talking about building a second brain with claude code + obsidian right now. so i made a full video walkthrough showing exactly how to set it up from scratch. drop an article into a folder. claude code reads it, creates wiki pages, builds relationships between concepts, and links everything automatically. by 20 sources your wiki knows more about your domain than you can hold in your head. here's the full setup:

J.B.

54,534 Aufrufe • vor 2 Monaten

Think of it as the Shopify of Claude Code, but for agents, commands, and MCPs 🚀 Pick what you need, “add to cart,” and… it’s 100% FREE. Just copy the command, paste it in your terminal, and instantly get the perfect Claude Code stack for your project. Built as an open-source project so the community can spin up, share, and install Claude Code stacks in seconds, no setup headaches, no paywalls. Here’s the video showing how I built this post… and yes, if you share the command, anyone can clone the exact same stack you’re using. Enjoy, share, and help others build faster with Claude Code ✨
0:59

Think of it as the Shopify of Claude Code, but for agents, commands, and MCPs 🚀 Pick what you need, “add to cart,” and… it’s 100% FREE. Just copy the command, paste it in your terminal, and instantly get the perfect Claude Code stack for your project. Built as an open-source project so the community can spin up, share, and install Claude Code stacks in seconds, no setup headaches, no paywalls. Here’s the video showing how I built this post… and yes, if you share the command, anyone can clone the exact same stack you’re using. Enjoy, share, and help others build faster with Claude Code ✨

Daniel San

124,913 Aufrufe • vor 10 Monaten

We’re entering the 10x speed of research publication workflow with AI. SciSpace (SciSpace), the first AI Agent built exclusively for the scientific community, is releasing so many inredibly useful features. 🎯 This is the AI Agent that can use 150+ tools, 59 databases, and 280M+ papers A few weeks back they launched BioMed Agent - It can design entire molecular biology workflows and even create publication-ready illustrations in a single prompt. This is its new domain-specialized AI co-scientist that sits on top of the existing SciSpace Agent and automates full biomedical workflows, from raw data and papers to analysis, decisions, and the final production-grade illustrations. You just need to give it 1 prompt. And today the added the following - Library Search, so it can search and analyze the PDFs already sitting in My Library, letting people ask questions across their own paper pile while keeping it private. - Now connects directly to Zotero, so the Agent can pull and work with the papers you already saved there without manual uploads. - For bigger prompts, it auto-triggers a Report Writing Sub-Agent that turns the chat into a structured research-style report, which is way cleaner for literature reviews and long summaries. - And when you get something worth keeping, Save to Notebook lets you store the output as .md notes with citations in My notebooks, so the work becomes reusable research notes instead of disappearing into chat. Behind the scenes, it indexes the PDF text, pulls a few relevant chunks for the question, then writes an answer grounded on those chunks.
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We’re entering the 10x speed of research publication workflow with AI. SciSpace (SciSpace), the first AI Agent built exclusively for the scientific community, is releasing so many inredibly useful features. 🎯 This is the AI Agent that can use 150+ tools, 59 databases, and 280M+ papers A few weeks back they launched BioMed Agent - It can design entire molecular biology workflows and even create publication-ready illustrations in a single prompt. This is its new domain-specialized AI co-scientist that sits on top of the existing SciSpace Agent and automates full biomedical workflows, from raw data and papers to analysis, decisions, and the final production-grade illustrations. You just need to give it 1 prompt. And today the added the following - Library Search, so it can search and analyze the PDFs already sitting in My Library, letting people ask questions across their own paper pile while keeping it private. - Now connects directly to Zotero, so the Agent can pull and work with the papers you already saved there without manual uploads. - For bigger prompts, it auto-triggers a Report Writing Sub-Agent that turns the chat into a structured research-style report, which is way cleaner for literature reviews and long summaries. - And when you get something worth keeping, Save to Notebook lets you store the output as .md notes with citations in My notebooks, so the work becomes reusable research notes instead of disappearing into chat. Behind the scenes, it indexes the PDF text, pulls a few relevant chunks for the question, then writes an answer grounded on those chunks.

Rohan Paul

11,516 Aufrufe • vor 4 Monaten