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📚 Build Deep Research Agent: From Question to Insight Complex research rarely fits in one search box. The Deep Research workflow built with Dify Iterates, reasons, and sources until the answer is complete. 💡 Key workflow nodes include: 1) Start: capture the research topic and the loop budget 2)... Exa Answer: gather background terminology 3) Intent LLM: clarify the real research goal 4) Loop: drive the iterative cycles 5) Reasoning LLM: frame better follow-up questions 6) Agent: execute searches, extract content, and reflect 7) Variable Assigner: update the state after each pass 8) Final Summary: synthesize a Markdown report with citations Teams using this template cut research time dramatically and surface insights competitors miss. Learn to implement this workflow:show more

Dify

22,407 subscribers

22,470 次观看 • 1 年前 •via X (Twitter)

Anya Rossi• Live Now

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jem 的头像

jem1 年前

Is there a template for it? since the blog post doesn’t cover the prompts and all of the structured outputs

Dify 的头像

Dify1 年前

The template is now live on our Explore page:

ksminnovation 的头像

ksminnovation1 年前

Can AI redefine scientific discovery? Dr. Tal Patalon explores OpenAI’s Deep Research in her latest Forbes article. 🎨 Future by Eduardo Kobra, provided by Eden Gallery. @TalPatalon @forbes @edengallery_

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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.

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

20,504 次观看 • 6 天前

The March to Mainnet is in full swing. ⚡️ This week, Co-Founder and Head of Research Neurmal//Theoriq takes us inside the latest from the research and engineering teams. He kicks things off with a look at the core agents powering AlphaSwarm—then dives into our first live deployment: a Knowledge Agent built and configured to Kaito AI 🌊 🌊 Think of Knowledge Agents as the swarm’s research desk, blending curated insight and real-time data to answer complex token, protocol & metric queries. Check out how these agents are being built, and the role they’ll play in Alpha Protocol and the Agentic future. 👇

The March to Mainnet is in full swing. ⚡️ This week, Co-Founder and Head of Research Neurmal//Theoriq takes us inside the latest from the research and engineering teams. He kicks things off with a look at the core agents powering AlphaSwarm—then dives into our first live deployment: a Knowledge Agent built and configured to Kaito AI 🌊 🌊 Think of Knowledge Agents as the swarm’s research desk, blending curated insight and real-time data to answer complex token, protocol & metric queries. Check out how these agents are being built, and the role they’ll play in Alpha Protocol and the Agentic future. 👇

Theoriq

26,611 次观看 • 1 年前

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

🔥 Our latest LangChain Academy course – Deep Research with LangGraph – is now live! 🔥 Deep research agents are taking off – from major AI labs to companies building their own. Research is inherently open-ended. You can't always predict whether a question needs broad exploration or deep analysis. Agents excel here because they adapt on the fly, using each finding to decide where to dig next. Building these systems ourselves and with customers, we've learned that structure matters. The best research agents scope problems with users first. Then, they coordinate multiple specialists instead of overwhelming one generalist. LangGraph is built for these types of long-running, multi-agent workflows. Its persistence layer tracks progress across agents. LangSmith gives you the observability and evaluation tools you need to track and improve performance. In this course, you'll build and evaluate: - A user scoping agent to define research parameters - A multi-agent research team with supervisor - And add tool integration via MCP Enroll for free ➡️

LangChain

61,205 次观看 • 1 年前

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

GREG ISENBERG

436,249 次观看 • 5 个月前

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

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

104,833 次观看 • 8 个月前

I wish it existed when I was doing PhD. It’d have saved me hours in literature reviews. Here is the FASTEST way to get a COMPLETE overview of any research field. 1. Go to 2. Type your research topic in one sentence 3. Review the structured plan Noah builds 4. Tweak or approve the structured plan 4. Watch it run: ✦ Database searches ✦ Clinical trial analysis, ✦ Web + medical literature deep dives ✦ And full verification 5. In 10 minutes, get a comprehensive, structured report with accurate in-text citations What you get: → A structured research plan you control before execution → Searches across PubMed, clinical trial registries, FDA, NIH, and more → Clinical data analyzed and cross-verified automatically → A full field overview, competitive landscape, drug pipelines, key findings → In-text citations from trusted sources, no hallucinations Try Noah AI today for FREE:

I wish it existed when I was doing PhD. It’d have saved me hours in literature reviews. Here is the FASTEST way to get a COMPLETE overview of any research field. 1. Go to 2. Type your research topic in one sentence 3. Review the structured plan Noah builds 4. Tweak or approve the structured plan 4. Watch it run: ✦ Database searches ✦ Clinical trial analysis, ✦ Web + medical literature deep dives ✦ And full verification 5. In 10 minutes, get a comprehensive, structured report with accurate in-text citations What you get: → A structured research plan you control before execution → Searches across PubMed, clinical trial registries, FDA, NIH, and more → Clinical data analyzed and cross-verified automatically → A full field overview, competitive landscape, drug pipelines, key findings → In-text citations from trusted sources, no hallucinations Try Noah AI today for FREE:

Faheem Ullah

11,980 次观看 • 1 个月前

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

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

Julian Goldie SEO

15,449 次观看 • 7 个月前

The Agentic Literature Review is Now a Reality. 🚀 I watched a student from King’s College London dismantle a task that used to take weeks. His mission? Deconstruct 47 complex academic papers for his dissertation. The old way: ❌ Endless skimming & highlighting ❌ Messy, unsearchable notes ❌ Citation chaos across 5 different tools The new way? He used a single platform and finished the core analysis in an afternoon. This isn't just another "AI summarizer." ResearchCollab is an AI co-pilot for research. I tested it against the standard "academic grind." The difference was staggering: 🔴 Traditional Workflow: Scattered PDFs, chaotic notes, mental burnout. 🟢 ResearchCollab Workflow: • AI instantly surfaces key insights from 250M+ papers without manual prompting. • Auto-organizes and relates everything personalized to the user. • Generates perfect citations (APA, MLA) in one click. • Brainstorms new research directions you might have missed. 👇 See how it works (No Credit Card Needed): Start your free trial → Why this is a silent revolution for knowledge workers: 1️⃣ Students are cutting literature review time by up to 80%. 2️⃣ Research teams are collaborating in real-time, killing version control nightmares. 3️⃣ The "blank page syndrome" is solved. AI helps you generate outlines and spark unique ideas instantly. The most compelling part? It’s not just about speed. It’s about clarity. ✔️ Finds connections between papers you'd never see. ✔️ Keeps your entire research universe in one searchable place. ✔️ Works 24/7 for less than the cost of your monthly coffee budget. This is the "Copilot" moment for academia and R&D. The barrier to high-quality, organized research has just collapsed. 👉 Support ResearchCollab Product Hunt launch : PS: I've compiled a short guide on "The 5-Day Research Sprint" methodology that this enables. Like 👍 and Comment "Research" and I'll DM you the link.

The Agentic Literature Review is Now a Reality. 🚀 I watched a student from King’s College London dismantle a task that used to take weeks. His mission? Deconstruct 47 complex academic papers for his dissertation. The old way: ❌ Endless skimming & highlighting ❌ Messy, unsearchable notes ❌ Citation chaos across 5 different tools The new way? He used a single platform and finished the core analysis in an afternoon. This isn't just another "AI summarizer." ResearchCollab is an AI co-pilot for research. I tested it against the standard "academic grind." The difference was staggering: 🔴 Traditional Workflow: Scattered PDFs, chaotic notes, mental burnout. 🟢 ResearchCollab Workflow: • AI instantly surfaces key insights from 250M+ papers without manual prompting. • Auto-organizes and relates everything personalized to the user. • Generates perfect citations (APA, MLA) in one click. • Brainstorms new research directions you might have missed. 👇 See how it works (No Credit Card Needed): Start your free trial → Why this is a silent revolution for knowledge workers: 1️⃣ Students are cutting literature review time by up to 80%. 2️⃣ Research teams are collaborating in real-time, killing version control nightmares. 3️⃣ The "blank page syndrome" is solved. AI helps you generate outlines and spark unique ideas instantly. The most compelling part? It’s not just about speed. It’s about clarity. ✔️ Finds connections between papers you'd never see. ✔️ Keeps your entire research universe in one searchable place. ✔️ Works 24/7 for less than the cost of your monthly coffee budget. This is the "Copilot" moment for academia and R&D. The barrier to high-quality, organized research has just collapsed. 👉 Support ResearchCollab Product Hunt launch : PS: I've compiled a short guide on "The 5-Day Research Sprint" methodology that this enables. Like 👍 and Comment "Research" and I'll DM you the link.

Anuj

10,780 次观看 • 9 个月前

I just vibe-coded a TikTok research AI agent in Claude Code 🤯 A complete research-to-brief pipeline that scrapes TikTok, analyzes video hooks with AI, and generates creative briefs on demand. All inside Claude Code. Perfect for creative agencies and DTC brands who are still turning competitor research into briefs manually. If you're spending hours scrolling TikTok "for research," watching videos one by one, screenshotting hooks, and rewriting briefs from scratch every time, this agent eliminates the entire loop: → Search TikTok by keyword, date range, and video count → Pull engagement metrics, captions, and thumbnails → Gemini watches the video and analyzes the hook → AI scrapes comments for common questions and insights → Generates a full creative brief from your template + brand bible No watching videos manually. No copying notes into docs. No rewriting briefs from scratch. What you control: - Multiple client projects with separate brand bibles - Your own creative brief template - Which videos to analyze and brief - Full customization through Replit's AI agent Research → Analysis → Brief. One workflow, running as a custom mini-SaaS inside your company. Every e-comm brand and agency should have at least one person who can vibe-code tools like this. It's becoming non-negotiable. I recorded a full walkthrough showing exactly how I built this from scratch. Want the full tutorial? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

Mike Futia

97,342 次观看 • 5 个月前

Want a free private AI agent on your laptop? Here’s the simple setup. 👇 1. Install Hermes Agent from GitHub. 2. Install Ollama. 3. Download Gemma 4 26B. 4. Use Docker to keep everything contained. 5. Add Firecrawl so the agent can research the web. 6. Connect Telegram so you can chat with it from your phone. 7. Turn on context compression so it stays sharp. 8. Set the daily reset for 4 AM so it refreshes while you sleep. What you get at the end: → local AI agent → private workflow → web research built in → self improving skills → no monthly model rental That is a serious setup for anyone building with AI. Save this video, you’ll have the easiest beginner roadmap to follow later. Want the SOP? DM me. 💬

Want a free private AI agent on your laptop? Here’s the simple setup. 👇 1. Install Hermes Agent from GitHub. 2. Install Ollama. 3. Download Gemma 4 26B. 4. Use Docker to keep everything contained. 5. Add Firecrawl so the agent can research the web. 6. Connect Telegram so you can chat with it from your phone. 7. Turn on context compression so it stays sharp. 8. Set the daily reset for 4 AM so it refreshes while you sleep. What you get at the end: → local AI agent → private workflow → web research built in → self improving skills → no monthly model rental That is a serious setup for anyone building with AI. Save this video, you’ll have the easiest beginner roadmap to follow later. Want the SOP? DM me. 💬

Julian Goldie SEO

31,292 次观看 • 3 个月前

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.

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 次观看 • 6 个月前

BREAKING 🚨: Google is working on multi-agent systems to help you refine ideas with tournament-like evaluation. Each run takes around 40 minutes and brings you 100 detailed ideas on a given research topic. 2 new multi-agents are being developed for Gemini Enterprise: - Idea Generation - "Create a multi-agent innovation session" - Co-Scientist - "Drive novel scientific discovery with Co-Scientist" Co-Scientist 3-step workflow 👀 - Tell Co-Scientist what you plan to research, point it to relevant data, and set your evaluation criteria. - A team of agents will generate ideas on your topic using their available data - The agents will evaluate the ideas against your criteria and rank them, tournament-style Google is not only automating research but also preparing a product that will enable others to do so. This is the next level 🤯

BREAKING 🚨: Google is working on multi-agent systems to help you refine ideas with tournament-like evaluation. Each run takes around 40 minutes and brings you 100 detailed ideas on a given research topic. 2 new multi-agents are being developed for Gemini Enterprise: - Idea Generation - "Create a multi-agent innovation session" - Co-Scientist - "Drive novel scientific discovery with Co-Scientist" Co-Scientist 3-step workflow 👀 - Tell Co-Scientist what you plan to research, point it to relevant data, and set your evaluation criteria. - A team of agents will generate ideas on your topic using their available data - The agents will evaluate the ideas against your criteria and rank them, tournament-style Google is not only automating research but also preparing a product that will enable others to do so. This is the next level 🤯

🚨 AI News | TestingCatalog

569,702 次观看 • 8 个月前

I just ran a full research project without doing any research. Lemma by Analemma did everything: 1. Explore I typed a question. It mapped the entire field. Concepts, references, key papers. Done. 2. Survey I asked for a literature review. It gave me 50+ pages with real citations. Not a summary. A publication-grade draft. 3. Code I described an experiment. It wrote the code, ran it, showed results. 4. FARS I gave it a research direction. It generated hypotheses, planned experiments, ran them, and wrote the full paper. Zero input from me after that. This is the first tool I've used that actually runs research instead of just helping you do it. FARS already ran live and produced 100 papers in 10 days. One every 2 hours. Watch it here: This is what "Vibe Research" looks like.

I just ran a full research project without doing any research. Lemma by Analemma did everything: 1. Explore I typed a question. It mapped the entire field. Concepts, references, key papers. Done. 2. Survey I asked for a literature review. It gave me 50+ pages with real citations. Not a summary. A publication-grade draft. 3. Code I described an experiment. It wrote the code, ran it, showed results. 4. FARS I gave it a research direction. It generated hypotheses, planned experiments, ran them, and wrote the full paper. Zero input from me after that. This is the first tool I've used that actually runs research instead of just helping you do it. FARS already ran live and produced 100 papers in 10 days. One every 2 hours. Watch it here: This is what "Vibe Research" looks like.

Muhammad Ayan

40,052 次观看 • 2 个月前

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?

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 次观看 • 1 年前

🚨 BREAKING: Someone built an AI that replaces 6 months of literature review with a single workspace. It's called Moara. You dump your research papers in. The AI organizes, analyzes, and synthesizes everything across hundreds of millions of academic sources. You come back and there's a structured literature review waiting. Not a chatbot answer. Not a vague summary. The actual research synthesis. A full research workspace that thinks like a scientist. Works for medicine, engineering, social sciences, law, business, humanities — every discipline. Here's what it does on its own: → Searches hundreds of millions of papers across PubMed, arXiv, Google Scholar, Semantic Scholar, ClinicalTrials .gov, Cochrane, and more in one shot → Screens and tags results automatically, pulling out methodologies, findings, and contributions from every paper → Lets you chat with your entire library at once, find themes, spot gaps, and compare findings across dozens of papers simultaneously → Integrates with Zotero, LibKey, RIS, and BibTeX so your existing research workflow doesn't break → Traces every AI insight back to its original source so nothing is hallucinated and everything is verifiable → Supports full team collaboration so entire research groups can screen and synthesize together Try it here:

🚨 BREAKING: Someone built an AI that replaces 6 months of literature review with a single workspace. It's called Moara. You dump your research papers in. The AI organizes, analyzes, and synthesizes everything across hundreds of millions of academic sources. You come back and there's a structured literature review waiting. Not a chatbot answer. Not a vague summary. The actual research synthesis. A full research workspace that thinks like a scientist. Works for medicine, engineering, social sciences, law, business, humanities — every discipline. Here's what it does on its own: → Searches hundreds of millions of papers across PubMed, arXiv, Google Scholar, Semantic Scholar, ClinicalTrials .gov, Cochrane, and more in one shot → Screens and tags results automatically, pulling out methodologies, findings, and contributions from every paper → Lets you chat with your entire library at once, find themes, spot gaps, and compare findings across dozens of papers simultaneously → Integrates with Zotero, LibKey, RIS, and BibTeX so your existing research workflow doesn't break → Traces every AI insight back to its original source so nothing is hallucinated and everything is verifiable → Supports full team collaboration so entire research groups can screen and synthesize together Try it here:

Rimsha Bhardwaj

49,743 次观看 • 4 个月前

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.

RLM is the most import foundation of my Pi Harness (other than Pi of course). It's seeded with late interaction retrieval results (thanks to @lightonai for pylate). The Agent initiates it with query then.. 𝐒𝐞𝐭𝐮𝐩 A python REPL is created and seeded with: 1. Late interaction search to pre-filter. Instead of doing top 3/5/10, it's top hundreds of documents. This is set into a `context` variable. 2. Python functions are loaded in to do more searches if `context` variable isn't enough. And to make llm calls with cheaper models in parallel batches. 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐨𝐨𝐩 From there, an LLM iterates in the REPL based on the query. It's just like exploring in a jupyter notebook. The LLM writes prose (like a markdown cell) and code to be run in the REPL each turn. This allows the LLM to sort, filter, and synthesize information. It can fan out and ask smaller models to summarize, combine, contrast, or do anything else to documents to help it understand the data. After several turns the LLM reponds with the final answer. Either because it found the answer, or hit the budget limit. Context as a Python variable, LLM as the programmer, REPL as the runtime. 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐓𝐡𝐢𝐬 𝐖𝐨𝐫𝐤 1. Richer Shell. Agents (and subagents) work by intermixing code and prose/thinking. But they use static scripts or bash that run and exit and start over each tool call. That's not ideal for exploration and synthesis of data. For that, state is useful to continue building and exploring the data as you learn more. There's a reason jupyter notebooks have been popular with data scientists. 2. Keeps main agent context clean. The better context you have the better the agent will perform (duh!). This means three thing: better human input, less missing search results, and less incorrect search results. Letting the agent iterate allows it to synthesize just what is needed and nothing else. All bad paths or peeks at something that turns out to be irrelevant stays out of main agent context. 3. Stack the good ideas! People often compare late interaction search vs RLM. Or static vs dynamic languages. Or agentic search vs semantic search. But...You can just use them all together for what they're each good at. Use them all for the area they're really great for. Read the full post which has more detail about how and why.

Isaac Flath

40,212 次观看 • 3 个月前

You do not need to listen to most podcasts anymore. I pasted a 2-hour interview into HypeScribe and got: • a full transcript • key takeaways • action items • a clean summary • answers from AI chat in under 5 minutes. The wild part: it works with YouTube, TikTok, Instagram, Zoom calls, voice memos, basically anything with audio. My current workflow: podcast/video → HypeScribe → summary → content ideas Way faster than rewatching everything manually. I’ve been using it to turn long-form content into threads, notes, and research without wasting hours. Feels less like “taking notes” and more like having an AI research assistant for everything you watch or record. If you work with content, meetings, research, or long videos, this is ridiculously useful. Try it here 👉🏻

You do not need to listen to most podcasts anymore. I pasted a 2-hour interview into HypeScribe and got: • a full transcript • key takeaways • action items • a clean summary • answers from AI chat in under 5 minutes. The wild part: it works with YouTube, TikTok, Instagram, Zoom calls, voice memos, basically anything with audio. My current workflow: podcast/video → HypeScribe → summary → content ideas Way faster than rewatching everything manually. I’ve been using it to turn long-form content into threads, notes, and research without wasting hours. Feels less like “taking notes” and more like having an AI research assistant for everything you watch or record. If you work with content, meetings, research, or long videos, this is ridiculously useful. Try it here 👉🏻

Onil Coder

12,075 次观看 • 2 个月前

How to compare 10 papers in 10 seconds? Meet 𝐒𝐜𝐢𝐒𝐩𝐚𝐜𝐞 – this tool compares papers for you. Here is how it works. 1. Go to 2. Click on + 𝑠𝑖𝑔𝑛 and upload the 10 papers. 3. After uploading papers, write your prompt. 𝐶𝑜𝑚𝑝𝑎𝑟𝑒 𝑡ℎ𝑒 𝑢𝑝𝑙𝑜𝑎𝑑𝑒𝑑 10 𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ 𝑝𝑎𝑝𝑒𝑟𝑠 4. SciSpace will start comparing the papers. 5. You will see the comparison result on right side. 6. You will see various insights with paper numbers. 7. At the end, you will see summary of the comparison. 8. SciSpace compares the papers based on: ✓ Similarities in research themes ✓ Differences in approaches ✓ Relative strengths and weaknesses ✓ Gaps identified across papers ✓ Relationships and building upon each other 9. To trace to each paper, click on the 𝑝𝑎𝑝𝑒𝑟 𝑛𝑢𝑚𝑏𝑒𝑟𝑠 10. To trace to exact location, click on 𝑙𝑜𝑐𝑎𝑡𝑒 𝑃𝐷𝐹. Where can you use such comparison? You can use it to: ➝ Understand the related literature. ➝ Position the novelty of your research paper. ➝ Understand niche questions in a research area. ➝ Grasp key insights from a bunch of papers in one go. Try SciSpace today:

How to compare 10 papers in 10 seconds? Meet 𝐒𝐜𝐢𝐒𝐩𝐚𝐜𝐞 – this tool compares papers for you. Here is how it works. 1. Go to 2. Click on + 𝑠𝑖𝑔𝑛 and upload the 10 papers. 3. After uploading papers, write your prompt. 𝐶𝑜𝑚𝑝𝑎𝑟𝑒 𝑡ℎ𝑒 𝑢𝑝𝑙𝑜𝑎𝑑𝑒𝑑 10 𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ 𝑝𝑎𝑝𝑒𝑟𝑠 4. SciSpace will start comparing the papers. 5. You will see the comparison result on right side. 6. You will see various insights with paper numbers. 7. At the end, you will see summary of the comparison. 8. SciSpace compares the papers based on: ✓ Similarities in research themes ✓ Differences in approaches ✓ Relative strengths and weaknesses ✓ Gaps identified across papers ✓ Relationships and building upon each other 9. To trace to each paper, click on the 𝑝𝑎𝑝𝑒𝑟 𝑛𝑢𝑚𝑏𝑒𝑟𝑠 10. To trace to exact location, click on 𝑙𝑜𝑐𝑎𝑡𝑒 𝑃𝐷𝐹. Where can you use such comparison? You can use it to: ➝ Understand the related literature. ➝ Position the novelty of your research paper. ➝ Understand niche questions in a research area. ➝ Grasp key insights from a bunch of papers in one go. Try SciSpace today:

Faheem Ullah

24,341 次观看 • 5 个月前