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Today, I am launching Paper Breakdown. - PBD gets you academic paper recommendations and lets you study CS/ML/AI research with LLM agents. - It highlights relevant sections directly in the actual PDF - generates flowcharts/illustrations too - we provide an in-build screenshot tool to send images to the agent...

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14,579 görüntüleme • 7 ay önce •via X (Twitter)

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How to extract data from papers for literature review in seconds? 1. Go to 2. Upload 2-3 papers you already know are relevant 3. Start with clicking on one paper 4. ResearchCollab do the following for the paper ✦ Provide a short overview of the paper ✦ Identify weaknesses in the paper ✦ Share papers that contrast the existing paper ✦ Extract meta data about the paper ✦ Evaluate each part (e.g., methodology) of the paper ✦ Provide AI chat to extract any other data 5. Now click on the Related Papers button at top right 6. You will get papers related to the seed paper 7. You can click on each paper and read its abstract 8. If you find it relevant, just add it to your list of papers 9. Do the same for the other 2 seed papers 10. This way you will collect all relevant papers 11. And extract data from those papers After this, analyse the data and report the findings. Try ResearchCollab today:
0:47

How to extract data from papers for literature review in seconds? 1. Go to 2. Upload 2-3 papers you already know are relevant 3. Start with clicking on one paper 4. ResearchCollab do the following for the paper ✦ Provide a short overview of the paper ✦ Identify weaknesses in the paper ✦ Share papers that contrast the existing paper ✦ Extract meta data about the paper ✦ Evaluate each part (e.g., methodology) of the paper ✦ Provide AI chat to extract any other data 5. Now click on the Related Papers button at top right 6. You will get papers related to the seed paper 7. You can click on each paper and read its abstract 8. If you find it relevant, just add it to your list of papers 9. Do the same for the other 2 seed papers 10. This way you will collect all relevant papers 11. And extract data from those papers After this, analyse the data and report the findings. Try ResearchCollab today:

Faheem Ullah

26,494 görüntüleme • 6 ay önce

How to read a research paper 10x faster? PhD Students are often under time pressure. Many research papers but little time to read. Meet Bohrium – a tool that can 10x your paper reading. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐁𝐨𝐡𝐫𝐢𝐮𝐦 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐫𝐞𝐚𝐝 𝐬𝐦𝐚𝐫𝐭𝐞𝐫? 1. Go to and log in. 2. Click on 𝑁𝑒𝑤 𝐶ℎ𝑎𝑡 and then on 𝐿𝑖𝑡𝑇𝑎𝑙𝑘 3. Upload the paper you want to read 4. Click on 𝑆𝑢𝑚𝑚𝑎𝑟𝑖𝑧𝑒 from the list. 5. Bohirum will summarize the paper for you. 6. This will help you understand the gist of the paper. 7. Now click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝐹𝑖𝑛𝑑𝑖𝑛𝑔𝑠 8. Bohrium will extract key findings from the paper. 9. Go through each of the key finding. 10. Bohrium links each finding to respective part 11. This way you can see the links to the paper. 12. In the similar manner, you can also ↳ Extract information about research methods ↳ Academic concepts used in the paper 13. Bohrium can also help you in your writing 14. For example, you can use it to create diagrams 15. You can use Bohrium to create ✓ Graphical abstract ✓ Mechanism diagram ✓ Flowchart ✓ Concept cover 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐚𝐛𝐨𝐮𝐭 𝐁𝐨𝐡𝐫𝐢𝐮𝐦? → You can upload and chat with multiple papers together → Instead of only summarizing, it helps you in deep understanding → It links key findings to exact sections of the paper → It works like research-focused but free NotebookLLM Try it today:
1:10

How to read a research paper 10x faster? PhD Students are often under time pressure. Many research papers but little time to read. Meet Bohrium – a tool that can 10x your paper reading. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐁𝐨𝐡𝐫𝐢𝐮𝐦 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐫𝐞𝐚𝐝 𝐬𝐦𝐚𝐫𝐭𝐞𝐫? 1. Go to and log in. 2. Click on 𝑁𝑒𝑤 𝐶ℎ𝑎𝑡 and then on 𝐿𝑖𝑡𝑇𝑎𝑙𝑘 3. Upload the paper you want to read 4. Click on 𝑆𝑢𝑚𝑚𝑎𝑟𝑖𝑧𝑒 from the list. 5. Bohirum will summarize the paper for you. 6. This will help you understand the gist of the paper. 7. Now click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝐹𝑖𝑛𝑑𝑖𝑛𝑔𝑠 8. Bohrium will extract key findings from the paper. 9. Go through each of the key finding. 10. Bohrium links each finding to respective part 11. This way you can see the links to the paper. 12. In the similar manner, you can also ↳ Extract information about research methods ↳ Academic concepts used in the paper 13. Bohrium can also help you in your writing 14. For example, you can use it to create diagrams 15. You can use Bohrium to create ✓ Graphical abstract ✓ Mechanism diagram ✓ Flowchart ✓ Concept cover 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐚𝐛𝐨𝐮𝐭 𝐁𝐨𝐡𝐫𝐢𝐮𝐦? → You can upload and chat with multiple papers together → Instead of only summarizing, it helps you in deep understanding → It links key findings to exact sections of the paper → It works like research-focused but free NotebookLLM Try it today:

Faheem Ullah

27,133 görüntüleme • 8 ay önce

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:
1:56

Our first short course with Anthropic! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here:

Andrew Ng

170,425 görüntüleme • 1 yıl önce

Read 100 paywalled research papers for free every month! You don't even need a university account to do this. Here's how to read paywalled papers on JSTOR for free: 1. Go to jstor(dot)org and click on "Register" in the top-right corner. You can register with your personal Google or Outlook account. Or, you can create a JSTOR account manually. 2. Once you've logged in to your JSTOR account, click on "Workspace" in the menu bar. Then click on "Create folder." Choose a name for your folder and click on "Create."Creating folders in Workspace is a great way to keep your papers organized. 3. Type in the keywords in the search bar to find relevant papers. JSTOR willl give you a list of papers. To read a paper for free, click on "Read online." You will see a preview of the paper. Scroll down a bit and click on "Read Online" again. 4. If you find the paper super-relevant to your project, click on "Save" on the top of the article. Choose the folder you just created in your Workspace and save the paper in it. If you go to your Workspace, the paper will show up in the relevant folder. 5. You can also take notes on papers in your Workspace. To do so, click on the "Add Note" button under a paper and start typing. Click on "Save" to your save your note. 6. If you already have a paper and you want to related to it, you can use Text Analyzer. To do so, click on "Tools" and select "Text Analyzer." Upload the paper you have and JSTOR will give you a list of papers related to you original paper. 7. Text Analyzer also lets you callibrate your search parameters. Adjust the priority for different terms by moving the priority scale left or right. You can more related terms and adjust their priority. Text Analyzer will update the results accordingly. 8. If you find a paper interesting, simply click on it and then select "Read Online." 9. You can also add papers to your Zotero library. Open the paper you want to add and click on the Zotero Connector in the top-right corner of your browser. Choose the Zotero collection you want to save the paper in and click on "Done." The paper will show up in your Zotero. Found this post on JSTOR helpful? • Repost to share it with your friends and colleagues. •Follow me for more posts on academic writing.
3:14

Read 100 paywalled research papers for free every month! You don't even need a university account to do this. Here's how to read paywalled papers on JSTOR for free: 1. Go to jstor(dot)org and click on "Register" in the top-right corner. You can register with your personal Google or Outlook account. Or, you can create a JSTOR account manually. 2. Once you've logged in to your JSTOR account, click on "Workspace" in the menu bar. Then click on "Create folder." Choose a name for your folder and click on "Create."Creating folders in Workspace is a great way to keep your papers organized. 3. Type in the keywords in the search bar to find relevant papers. JSTOR willl give you a list of papers. To read a paper for free, click on "Read online." You will see a preview of the paper. Scroll down a bit and click on "Read Online" again. 4. If you find the paper super-relevant to your project, click on "Save" on the top of the article. Choose the folder you just created in your Workspace and save the paper in it. If you go to your Workspace, the paper will show up in the relevant folder. 5. You can also take notes on papers in your Workspace. To do so, click on the "Add Note" button under a paper and start typing. Click on "Save" to your save your note. 6. If you already have a paper and you want to related to it, you can use Text Analyzer. To do so, click on "Tools" and select "Text Analyzer." Upload the paper you have and JSTOR will give you a list of papers related to you original paper. 7. Text Analyzer also lets you callibrate your search parameters. Adjust the priority for different terms by moving the priority scale left or right. You can more related terms and adjust their priority. Text Analyzer will update the results accordingly. 8. If you find a paper interesting, simply click on it and then select "Read Online." 9. You can also add papers to your Zotero library. Open the paper you want to add and click on the Zotero Connector in the top-right corner of your browser. Choose the Zotero collection you want to save the paper in and click on "Done." The paper will show up in your Zotero. Found this post on JSTOR helpful? • Repost to share it with your friends and colleagues. •Follow me for more posts on academic writing.

Mushtaq Bilal, PhD

31,810 görüntüleme • 2 yıl önce

Now you can have real-time conversations with research papers. And it's not written chat. It's as if you're talking to a colleague. Go to callpdf(dot)ai and upload the PDF you want to talk to. Wait fot it to process the paper. Then click on the call button and start talking. Here CallPDF and I are discussing one of my own papers. This will be especially helpful for folks with neurodivergence.
2:31

Now you can have real-time conversations with research papers. And it's not written chat. It's as if you're talking to a colleague. Go to callpdf(dot)ai and upload the PDF you want to talk to. Wait fot it to process the paper. Then click on the call button and start talking. Here CallPDF and I are discussing one of my own papers. This will be especially helpful for folks with neurodivergence.

Mushtaq Bilal, PhD

74,507 görüntüleme • 2 yıl önce

🔹STORM🔹 Stanford University’s free, public app automates comprehensive research and report generation using AI to create Wikipedia-style articles with citations from web sources. STORM is incredible. I asked it to write a research paper about the use of AI in DNA analysis and predicting genetic traits - got a comprehensive article with citations. It takes about 3 minutes and you can download the PDF:
0:48

🔹STORM🔹 Stanford University’s free, public app automates comprehensive research and report generation using AI to create Wikipedia-style articles with citations from web sources. STORM is incredible. I asked it to write a research paper about the use of AI in DNA analysis and predicting genetic traits - got a comprehensive article with citations. It takes about 3 minutes and you can download the PDF:

Heather Cooper

176,245 görüntüleme • 1 yıl önce

PhD Students – How to extract data from papers for your literature review in seconds? Extracting data from papers takes a lot of time. You can automate this process with Bohrium 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐞𝐱𝐭𝐫𝐚𝐜𝐭 𝐝𝐚𝐭𝐚 𝐟𝐫𝐨𝐦 𝐩𝐚𝐩𝐞𝐫𝐬? 1. Go to and log in 2. Click on 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝐵𝑎𝑠𝑒 from the left menu 3. Upload the papers you selected for literature review 4. You will see the following option against each paper - Read PDF - Key Takeaway - AI Poster 5. Click on 𝑅𝑒𝑎𝑑 𝑃𝐷𝐹 for the first paper in your list 6. Write a prompt for the data you want to extract 7. For example, you can enter datasets, methodology etc. 8. It will extract the required data from the paper 9. If you want to extract Key Takeaways from the paper 10. Go back and click on 𝐾𝑒𝑦 𝑇𝑎𝑘𝑒𝑎𝑤𝑎𝑦𝑠 11. Bohrium will extract Key Takeaways from the paper 12. In addition to this, you also have 2 more options - AI Poster - Podcast 13. Click on 𝐴𝐼 𝑃𝑜𝑠𝑡𝑒𝑟 and it will create a poster for you 14. This is the poster based on the given research paper 15. If you click on 𝑃𝑜𝑑𝑐𝑎𝑠𝑡, it will convert the paper to audio 16. You can listen to the paper instead of reading it Repeat this cycle for all the papers in your pool. You will end up with the required data. You can use this data to write your literature review Try Bohrium today for FREE: Anything you’d like to add?
0:59

PhD Students – How to extract data from papers for your literature review in seconds? Extracting data from papers takes a lot of time. You can automate this process with Bohrium 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐞𝐱𝐭𝐫𝐚𝐜𝐭 𝐝𝐚𝐭𝐚 𝐟𝐫𝐨𝐦 𝐩𝐚𝐩𝐞𝐫𝐬? 1. Go to and log in 2. Click on 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝐵𝑎𝑠𝑒 from the left menu 3. Upload the papers you selected for literature review 4. You will see the following option against each paper - Read PDF - Key Takeaway - AI Poster 5. Click on 𝑅𝑒𝑎𝑑 𝑃𝐷𝐹 for the first paper in your list 6. Write a prompt for the data you want to extract 7. For example, you can enter datasets, methodology etc. 8. It will extract the required data from the paper 9. If you want to extract Key Takeaways from the paper 10. Go back and click on 𝐾𝑒𝑦 𝑇𝑎𝑘𝑒𝑎𝑤𝑎𝑦𝑠 11. Bohrium will extract Key Takeaways from the paper 12. In addition to this, you also have 2 more options - AI Poster - Podcast 13. Click on 𝐴𝐼 𝑃𝑜𝑠𝑡𝑒𝑟 and it will create a poster for you 14. This is the poster based on the given research paper 15. If you click on 𝑃𝑜𝑑𝑐𝑎𝑠𝑡, it will convert the paper to audio 16. You can listen to the paper instead of reading it Repeat this cycle for all the papers in your pool. You will end up with the required data. You can use this data to write your literature review Try Bohrium today for FREE: Anything you’d like to add?

Faheem Ullah

13,433 görüntüleme • 11 ay önce

Today we introduced Gemini Enterprise, built with our most advanced Gemini models. It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context. Have a look at how it helps you build an agent for almost anything:
0:37

Today we introduced Gemini Enterprise, built with our most advanced Gemini models. It allows you to chat with your company’s documents, data and apps as well as build and deploy AI agents, all grounded in your information and context. Have a look at how it helps you build an agent for almost anything:

Sundar Pichai

1,105,854 görüntüleme • 10 ay önce

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this DAIR.AI) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!
0:27

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scaling your intelligence stack. To showcase this, I wanted to share an LLM Wiki I have built over the last couple of months. It's called PaperWiki, and I use it across all my research workflows, along with my research agents. In fact, I also use it to curate papers I share with my communities, newsletter, and on X. The PaperWiki is updated regularly with automations, so I basically have agents on a loop maintaining it. All the entries are ingested from different sources and stored in a vault (Obsidian) and further indexed using qmd. And then further presented via an HTML artifact. So all of it is easily accessible to all my agents and easily searchable through full-text search and rich semantic search. The structure of the wiki has proven significantly useful to start interesting and exciting cutting-edge research projects with my research agents (from building tiny and more efficient gpt/difussion llms to building out SoTA harnesses and memory systems). It turns out that agents love markdown files and can more easily navigate the papers given the rich metadata structure of the wiki. I am just getting started on this, but it's clear to me that we should all be experimenting with LLM Wikis. Here's why: Building LLM knowledge bases gets you into the habit of leveraging AI outputs in all kinds of creative ways. It's the good kind of tokenmaxxing we should all be pushing for. LLM Wikis can be maintained automatically in a loop. I use an automation that updates the wiki every day based on papers I curate. The curation is another automation I run in a loop (with a bit of human in the loop), so I get to build on all my previous knowledge and expertise, and all of it compounds the deeper the integration/layers. One interesting result of this process is that I feel like I can better spot high-quality papers and remove noise more easily. Social media could never solve that. And most paper aggregators use metrics I simply don't trust. I like that agents can help with the noise vs. signal problem. This is important for research. Lots of people consider agents to produce mostly slop. But it doesn't have to be that way. Careful curations, prompts, automations, verifiers, and human-in-the-loop can produce some astonishing results. And you really don't need frontier models for this. I use a combination of frontier models (opus-4.8) and open-weight models (deepseek-v4-flash) to maintain this. An exciting future work (we are working on this DAIR.AI) is to tune specialized models on top of this to allow LLMs to quickly understand cutting-edge research ideas and can better conceptualize research strategies that further accelerate scientific research agents. I plan to open-source a bunch of this work, including the artifact, but this is currently work in progress, and I was excited to share some thoughts as I continue working on it. Sharing more as I go. Stay tuned!

elvis

55,566 görüntüleme • 1 ay önce

Three years ago, I started working on an easy-to-use tool for interpretable machine learning in science. I wanted it to do for symbolic regression what Theano did for deep learning. Today, I am beyond excited to share with you the paper describing it! 1.
1:33

Three years ago, I started working on an easy-to-use tool for interpretable machine learning in science. I wanted it to do for symbolic regression what Theano did for deep learning. Today, I am beyond excited to share with you the paper describing it! 1.

Miles Cranmer

240,402 görüntüleme • 3 yıl önce

The founder and CEO of Solana, Toly, personally replied to our DM after seeing our project. He praised Grokene, the room temperature superconductor discovered by $DEAR DAO, saying it looked amazing, and even asked if we had a paper. We already have a draft and a GitHub repository prepared, but we want to send him a truly professional research paper. Our team is now working day and night to officially publish the Grokene paper on arXiv, as an intellectual property of $DEAR DAO. Toly, as the CEO of Solana, rarely DMs anyone so the fact that he personally replied and complimented our project means a lot. We believe he recognized our genuine effort and vision. Thank you again, Toly, for your words. We’ll soon show you an amazing arXiv paper made with pure passion, science, and the spirit of DeSci.
0:42

The founder and CEO of Solana, Toly, personally replied to our DM after seeing our project. He praised Grokene, the room temperature superconductor discovered by $DEAR DAO, saying it looked amazing, and even asked if we had a paper. We already have a draft and a GitHub repository prepared, but we want to send him a truly professional research paper. Our team is now working day and night to officially publish the Grokene paper on arXiv, as an intellectual property of $DEAR DAO. Toly, as the CEO of Solana, rarely DMs anyone so the fact that he personally replied and complimented our project means a lot. We believe he recognized our genuine effort and vision. Thank you again, Toly, for your words. We’ll soon show you an amazing arXiv paper made with pure passion, science, and the spirit of DeSci.

DEAR

18,201 görüntüleme • 9 ay önce

One of my best use cases for Google NotebookLM is using it to break down AI research papers. I asked it to create a video overview of DeepSeek's new mHC (mHC: Manifold-Constrained Hyper-Connections) paper, and it made the concepts incredibly simple to understand.
7:21

One of my best use cases for Google NotebookLM is using it to break down AI research papers. I asked it to create a video overview of DeepSeek's new mHC (mHC: Manifold-Constrained Hyper-Connections) paper, and it made the concepts incredibly simple to understand.

AshutoshShrivastava

52,360 görüntüleme • 7 ay önce

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.
0:22

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

11,574 görüntüleme • 6 ay önce

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.
7:37

It takes 5 minutes to create a world-class web research agent that runs 24/7 and returns structured search results. 1. You can limit the sources it's allowed to use 2. You can specify the goals it needs to complete 3. You can design the format structure of the results 4. You can configure how much computing power it will use I show you how to set up a Web Search Agent in the video. A few benefits here: 1. This is great for complex research tasks 2. It's really easy to add this like a LEGO piece to a more complex workflow 3. Create an agent once, and reuse it with different inputs 4. The agent adapts to your use case as you work with it By the way, Nimble published a whole library of templates and research agents you can reuse and modify. You can start sending queries to one of these existing agents, and it will begin to adapt to Go here to try it out: Thanks to the Nimble team for partnering with me on this post.

Santiago

20,943 görüntüleme • 20 gün önce

🐪camelAI is the deep research agent for your structured data, and the last BI tool you will ever need. It connects directly to your database, turning natural language questions into in-depth reports complete with beautiful visualizations in seconds.
2:25

🐪camelAI is the deep research agent for your structured data, and the last BI tool you will ever need. It connects directly to your database, turning natural language questions into in-depth reports complete with beautiful visualizations in seconds.

Y Combinator

29,743 görüntüleme • 1 yıl önce

My Researcher Agent - {Xinstein} was a HIT. It is performing very well. But now I want to take Xinstein to the next level. I am implementing the "Chain of Density" Summarization Technique. To gather the best context for the Agent to explain the concept even better. Here are a few ideas to build your next AI Agent in ChatGPT: 1. Add Writer Agent prompt structure after the summarization step. And you'll have your Blog Writer Agent. 2. Add "Doc Maker A+" Plugin in the end to save all your Research in Google Doc or PDF format. 3. Wait till DALL-E 3 and you'll be able to design infographics for your research, posts, blogs. Xinstein was 1 of 100 weird experiments I did with ChatGPT. Results: I think I have found a technique that erases hallucinations & assumptions from AI Agents. I am experimenting with this technique. Once I'll format it correctly, I might publish a paper on that. I call it the "Agent Benchmark Technique." This week was crazy! ✦ I have cracked the best offer for AI Automation Agency. ✦ I got solid breakthrough with HypeGenius. ✦ I got invited to speak in one of the biggest LLM Firm. ✦ I got another viral post after 25 days of gap. ✦ 100 days left in New Year: I have an exciting ANNOUNCEMENT for AI Hustlers, I will share that in Monday's Newsletter Edition. If you haven't tried Xinstein Agent in ChatGPT, try it. It'll sort your research with just ONE prompt. Here's a link: --- That's a wrap for today. Get Ready for Monday's Newsletter Edition. *Something coming to push you forward and make you take Action! Follow me CJ for daily AI breakdowns and Crazy AI experiments. Peace.
1:24

My Researcher Agent - {Xinstein} was a HIT. It is performing very well. But now I want to take Xinstein to the next level. I am implementing the "Chain of Density" Summarization Technique. To gather the best context for the Agent to explain the concept even better. Here are a few ideas to build your next AI Agent in ChatGPT: 1. Add Writer Agent prompt structure after the summarization step. And you'll have your Blog Writer Agent. 2. Add "Doc Maker A+" Plugin in the end to save all your Research in Google Doc or PDF format. 3. Wait till DALL-E 3 and you'll be able to design infographics for your research, posts, blogs. Xinstein was 1 of 100 weird experiments I did with ChatGPT. Results: I think I have found a technique that erases hallucinations & assumptions from AI Agents. I am experimenting with this technique. Once I'll format it correctly, I might publish a paper on that. I call it the "Agent Benchmark Technique." This week was crazy! ✦ I have cracked the best offer for AI Automation Agency. ✦ I got solid breakthrough with HypeGenius. ✦ I got invited to speak in one of the biggest LLM Firm. ✦ I got another viral post after 25 days of gap. ✦ 100 days left in New Year: I have an exciting ANNOUNCEMENT for AI Hustlers, I will share that in Monday's Newsletter Edition. If you haven't tried Xinstein Agent in ChatGPT, try it. It'll sort your research with just ONE prompt. Here's a link: --- That's a wrap for today. Get Ready for Monday's Newsletter Edition. *Something coming to push you forward and make you take Action! Follow me CJ for daily AI breakdowns and Crazy AI experiments. Peace.

CJ Zafir

44,626 görüntüleme • 2 yıl önce

You can create a Grok Project, have it search for relevant research papers, and attach the good ones to your project. Because Grok 4 can parse PDFs, you can interact with the model, and it will have direct knowledge of your research.
1:26

You can create a Grok Project, have it search for relevant research papers, and attach the good ones to your project. Because Grok 4 can parse PDFs, you can interact with the model, and it will have direct knowledge of your research.

tetsuo

1,662,794 görüntüleme • 10 ay önce

The tide has turned. In the last week, this is what Sandra Bullock, Reese Witherspoon, Steven Soderbergh, and Christopher Nolan said about AI: "It’s here. We have to observe it. We have to understand it. We have to lean into it. We have to use it in a really constructive and creative way, make it our friend rather than — I mean, we have to be incredibly cautious and aware of it because there are people who will use it for evil and not good. But I do feel that there’s a place for it… it’s here. We have to just be friends in some dark way." - Sandra bullock “The AI revolution has begun, and I need to learn as much as I possibly can about AI and share it with all of you. Also, FYI: the jobs women hold are 3x more likely to be automated by AI, yet women are using AI at a rate 25% lower than men on average. We don’t want to be left behind. So…do you want to learn with me?” - Reese Witherspoon “Five years from now, we all may be going, ‘That was a fun phase.’ We may end up not using it as much as we thought we were going to. There are some people that I have absolute love and respect for that refuse to engage with it. That’s their privilege. But I’m not built that way. You show me a new tool. I want to get my hands on it and see what’s going on.” - Steven Soderbergh “I think any tool, whether AI generated, whether it's computer based, or whatever, it's all another tool for filmmakers to create with. And so as long as we have faith in our in our human beings, creating these tools now, I think the medium of film will continue to develop in exciting ways.” - Christopher Nolan
2:36

The tide has turned. In the last week, this is what Sandra Bullock, Reese Witherspoon, Steven Soderbergh, and Christopher Nolan said about AI: "It’s here. We have to observe it. We have to understand it. We have to lean into it. We have to use it in a really constructive and creative way, make it our friend rather than — I mean, we have to be incredibly cautious and aware of it because there are people who will use it for evil and not good. But I do feel that there’s a place for it… it’s here. We have to just be friends in some dark way." - Sandra bullock “The AI revolution has begun, and I need to learn as much as I possibly can about AI and share it with all of you. Also, FYI: the jobs women hold are 3x more likely to be automated by AI, yet women are using AI at a rate 25% lower than men on average. We don’t want to be left behind. So…do you want to learn with me?” - Reese Witherspoon “Five years from now, we all may be going, ‘That was a fun phase.’ We may end up not using it as much as we thought we were going to. There are some people that I have absolute love and respect for that refuse to engage with it. That’s their privilege. But I’m not built that way. You show me a new tool. I want to get my hands on it and see what’s going on.” - Steven Soderbergh “I think any tool, whether AI generated, whether it's computer based, or whatever, it's all another tool for filmmakers to create with. And so as long as we have faith in our in our human beings, creating these tools now, I think the medium of film will continue to develop in exciting ways.” - Christopher Nolan

Minh Do

23,410 görüntüleme • 4 ay önce

While building SEV0, I ran into moments where natural language prompting just wasn't enough - too vague, too indirect. So I started using a method that bridges that gap. I call it Bridged Prompting - a technique where you temporarily step out of the prompt-response loop to manipulate something directly using an AI-generated UI, then step back in. Think of it like GenUI, but more user-driven and transient. AI generates a UI on-the-fly, tailored to your prompt, and lets you manipulate the artifact directly before resuming the conversation. Sure, you could build a full tool, switch tabs, wire it up and round-trip your data. But with Bridged Prompting, the AI just spins up a temporary interface right in the flow. In the video below, I used it to construct the hallway system to closely match the layout of the severed floor. This is something I needed to do for this one project, this one time - I didn't need to make a whole separate app. Bridged Prompting lets you: - Make precise edits visually or structurally - "Hit save" to persist changes to local storage or a backend - Return to your natural language prompt
0:05

While building SEV0, I ran into moments where natural language prompting just wasn't enough - too vague, too indirect. So I started using a method that bridges that gap. I call it Bridged Prompting - a technique where you temporarily step out of the prompt-response loop to manipulate something directly using an AI-generated UI, then step back in. Think of it like GenUI, but more user-driven and transient. AI generates a UI on-the-fly, tailored to your prompt, and lets you manipulate the artifact directly before resuming the conversation. Sure, you could build a full tool, switch tabs, wire it up and round-trip your data. But with Bridged Prompting, the AI just spins up a temporary interface right in the flow. In the video below, I used it to construct the hallway system to closely match the layout of the severed floor. This is something I needed to do for this one project, this one time - I didn't need to make a whole separate app. Bridged Prompting lets you: - Make precise edits visually or structurally - "Hit save" to persist changes to local storage or a backend - Return to your natural language prompt

Chris Tate

42,558 görüntüleme • 1 yıl önce