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Introducing Wide Research Tackle complex research in a single step. With Wide Research, Manus breaks massive, large-scale tasks into multiple sub-tasks — and runs them all at once — without missing a beat. - Research a long list of sales leads - Profile hundreds of companies across multiple metrics...

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Manus

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1,248,982 次观看 • 1 年前 •via X (Twitter)

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With “Wide Research,” is bringing about a fundamental change in agentic work. I instructed the tool to scan 100 scientific articles – including titles, abstracts, authors, journals, year, number of citations, institution, and country. Everything was automatically written into a table, sorted by relevance and impact. The result: an immediately usable overview of the global research landscape. Ideal for science journalism, strategies in health tech – or simply to have more informed discussions. Wide Research is rethinking research: – Parallel scanning instead of individual queries – Data directly usable for Notion, Excel, or video scripts – Saves time, reduces costs – without compromising on depth Here is the prompt:
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With “Wide Research,” is bringing about a fundamental change in agentic work. I instructed the tool to scan 100 scientific articles – including titles, abstracts, authors, journals, year, number of citations, institution, and country. Everything was automatically written into a table, sorted by relevance and impact. The result: an immediately usable overview of the global research landscape. Ideal for science journalism, strategies in health tech – or simply to have more informed discussions. Wide Research is rethinking research: – Parallel scanning instead of individual queries – Data directly usable for Notion, Excel, or video scripts – Saves time, reduces costs – without compromising on depth Here is the prompt:

Chubby♨️

59,115 次观看 • 1 年前

Manus is joining Lenny Rachitsky Product Pass. Lenny subscribers now get 1 year of complimentary access to Manus, a general AI agent that delivers research, slides, websites, images, videos, and more. To our users: thank you for pushing the boundaries. To new builders: welcome. We can't wait to see what you create.
1:35

Manus is joining Lenny Rachitsky Product Pass. Lenny subscribers now get 1 year of complimentary access to Manus, a general AI agent that delivers research, slides, websites, images, videos, and more. To our users: thank you for pushing the boundaries. To new builders: welcome. We can't wait to see what you create.

Manus

49,961 次观看 • 6 个月前

Use case: Prompt ➝ polished slides in minutes. No manual research. No formatting headaches. Just instant insights, visuals, and narratives ready for decision-making. We asked Manus to analyze a movie star’s reputation across movie review platforms. Manus transformed the prompt into a fully-designed slide deck, complete with sentiment charts, comparisons to past projects, and PR-ready insights. This is what bridging the gap between thought and action looks like
0:21

Use case: Prompt ➝ polished slides in minutes. No manual research. No formatting headaches. Just instant insights, visuals, and narratives ready for decision-making. We asked Manus to analyze a movie star’s reputation across movie review platforms. Manus transformed the prompt into a fully-designed slide deck, complete with sentiment charts, comparisons to past projects, and PR-ready insights. This is what bridging the gap between thought and action looks like

ManusAI

15,713 次观看 • 11 个月前

Many people still remember when ManusAI was released. It was astonishing to see what AI agents are already capable of today, and its in-depth research was impressive. But what else can ManusAI actually do? Unlike traditional chatbots, Manus can independently plan, execute, and optimize complex, multi-step tasks from start to finish—without any ongoing user intervention. In short: Manus AI is an autonomous AI agent that can independently research, plan, analyze, design, and program—without you having to manually accompany every step. For example: • Create market analyses and generate structured reports from online data • Evaluate Excel or Google Sheets and derive data-driven recommendations • Automatically design websites, fill them with content, and publish them online • Write applications, emails, or social media posts in the desired style • Write Python code, test it, and use it to solve technical tasks Below is a brief overview of the user interface. Prompt used: Create a presentation with 5 slides on the state of the AI industry in 2025. Focus: investments, areas of application, risks, regulation, outlook, everything in english
1:09

Many people still remember when ManusAI was released. It was astonishing to see what AI agents are already capable of today, and its in-depth research was impressive. But what else can ManusAI actually do? Unlike traditional chatbots, Manus can independently plan, execute, and optimize complex, multi-step tasks from start to finish—without any ongoing user intervention. In short: Manus AI is an autonomous AI agent that can independently research, plan, analyze, design, and program—without you having to manually accompany every step. For example: • Create market analyses and generate structured reports from online data • Evaluate Excel or Google Sheets and derive data-driven recommendations • Automatically design websites, fill them with content, and publish them online • Write applications, emails, or social media posts in the desired style • Write Python code, test it, and use it to solve technical tasks Below is a brief overview of the user interface. Prompt used: Create a presentation with 5 slides on the state of the AI industry in 2025. Focus: investments, areas of application, risks, regulation, outlook, everything in english

Chubby♨️

51,219 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 ➡️
2:47

🔥 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 • 11 个月前

PhD Students – How to identify a research gap in seconds? Use this FREE tool to identify research gap in seconds. 1. Go to 2. Click on 𝑇𝑜𝑝𝑖𝑐 𝐸𝑥𝑝𝑙𝑜𝑟𝑒𝑟 ---> 𝐶𝑟𝑒𝑎𝑡𝑖𝑣𝑒 𝑇𝑜𝑜𝑙 3. Enter your topic e.g., deep learning for healthcare 4. Now click on 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝑖𝑑𝑒𝑎𝑠 5. ResearchCollab will give you the following 7 gaps ➝ Research that combines multiple existing research ➝ Research that adapts a new approach/technology ➝ Research that modifies existing variables in research ➝ Research that incorporates new variables ➝ Research that eliminates variables from research ➝ Research that re-arrange existing variables Pick the gap you think has the most potential. Then click on 𝑡𝑜𝑝𝑖𝑐 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 will dive deeper into the selected topic It gives you different angles of the chosen research gap. It is very helpful and at the same time very easy to use Try 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 today:
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PhD Students – How to identify a research gap in seconds? Use this FREE tool to identify research gap in seconds. 1. Go to 2. Click on 𝑇𝑜𝑝𝑖𝑐 𝐸𝑥𝑝𝑙𝑜𝑟𝑒𝑟 ---> 𝐶𝑟𝑒𝑎𝑡𝑖𝑣𝑒 𝑇𝑜𝑜𝑙 3. Enter your topic e.g., deep learning for healthcare 4. Now click on 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝑖𝑑𝑒𝑎𝑠 5. ResearchCollab will give you the following 7 gaps ➝ Research that combines multiple existing research ➝ Research that adapts a new approach/technology ➝ Research that modifies existing variables in research ➝ Research that incorporates new variables ➝ Research that eliminates variables from research ➝ Research that re-arrange existing variables Pick the gap you think has the most potential. Then click on 𝑡𝑜𝑝𝑖𝑐 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 will dive deeper into the selected topic It gives you different angles of the chosen research gap. It is very helpful and at the same time very easy to use Try 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐂𝐨𝐥𝐥𝐚𝐛 today:

Faheem Ullah

106,224 次观看 • 4 个月前

Released by Reality Labs at Meta Research at #ECCV2024, Nymeria is a large-scale multimodal egocentric dataset for full-body motion understanding with potential applications in VR/MR headsets, smart glasses and more. More on this work + access to the dataset ➡️
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Released by Reality Labs at Meta Research at #ECCV2024, Nymeria is a large-scale multimodal egocentric dataset for full-body motion understanding with potential applications in VR/MR headsets, smart glasses and more. More on this work + access to the dataset ➡️

AI at Meta

52,408 次观看 • 1 年前

This is the best AI tool I have seen so far this year, and everyone can use it. Normally, when you research a topic using tools like Perplexity, Gemini, or ChatGPT, they just give you a wall of text. Try Superagent, a newly launched product by Airtable. It literally builds you an interactive Super Report with high-quality data from a single prompt, ready to present as-is. You can click into research sections, scroll through dynamic graphs, and see visualizations that make complex topics easy to understand. 👉 Use code KUNAL2FREE and get free 2 month access: It’s not just a chatbot. It spins up a team of multiple specialized AI agents that build a research plan, divide the tasks, and execute them simultaneously. You can build presentations, university reports, websites, dashboards, you name it! Shoutout to them for sponsoring this post.
5:09

This is the best AI tool I have seen so far this year, and everyone can use it. Normally, when you research a topic using tools like Perplexity, Gemini, or ChatGPT, they just give you a wall of text. Try Superagent, a newly launched product by Airtable. It literally builds you an interactive Super Report with high-quality data from a single prompt, ready to present as-is. You can click into research sections, scroll through dynamic graphs, and see visualizations that make complex topics easy to understand. 👉 Use code KUNAL2FREE and get free 2 month access: It’s not just a chatbot. It spins up a team of multiple specialized AI agents that build a research plan, divide the tasks, and execute them simultaneously. You can build presentations, university reports, websites, dashboards, you name it! Shoutout to them for sponsoring this post.

Kunal Kushwaha

15,150 次观看 • 6 个月前

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀
1:32

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,052 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:
1:07

🚨 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

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

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

Steven Johnson

104,833 次观看 • 8 个月前

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
24:21

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 • 4 个月前

Today we’re launching the newest version of Paradigm When we started Paradigm, the goal was never to tack AI onto existing spreadsheets. It was to build a new type of interface that does the work for you. Now we’re pushing that vision much further. Workflows turn Paradigm into a system that runs research processes for you. Connect your CRM, existing spreadsheets, Slack, email, and internal data, and let Paradigm continuously run the research workflows your team already does. Same intuitive interface. But now a system of action. If you tried Paradigm before, try it again. Manual research is now a competitive liability.
3:44

Today we’re launching the newest version of Paradigm When we started Paradigm, the goal was never to tack AI onto existing spreadsheets. It was to build a new type of interface that does the work for you. Now we’re pushing that vision much further. Workflows turn Paradigm into a system that runs research processes for you. Connect your CRM, existing spreadsheets, Slack, email, and internal data, and let Paradigm continuously run the research workflows your team already does. Same intuitive interface. But now a system of action. If you tried Paradigm before, try it again. Manual research is now a competitive liability.

Anna Monaco

244,626 次观看 • 4 个月前

The verdict is in! Frontier models can pass the bar, yet they struggle on comprehensive legal research Today we're releasing Legal Research Bench, a benchmark that measures models’ ability to solve realistic legal research tasks across eight areas of U.S. law Instead of awarding partial credit, Legal Research Bench measures whether a model can conduct exhaustive legal analysis. We grade against a strict, all-pass rubric written by practicing lawyers. A model only receives full credit if every required legal element is correct Claude Opus 4.8 leads with 43.8% all-pass accuracy, followed by GPT 5.5 (40.4%) and Claude Sonnet 4.6 (38.5%). While top models score around 80% with partial credit, none exceed 44% when every required legal element must be correct The gap between partial and all-pass accuracy shows how difficult it remains for AI to produce complete, reliable legal research. We hope that Legal Research Bench helps better measure, and ultimately close that gap Lots of exciting work happening in Legal AI from Harvey and Crosby. Excited for the legal research benchmarks ahead!
1:33

The verdict is in! Frontier models can pass the bar, yet they struggle on comprehensive legal research Today we're releasing Legal Research Bench, a benchmark that measures models’ ability to solve realistic legal research tasks across eight areas of U.S. law Instead of awarding partial credit, Legal Research Bench measures whether a model can conduct exhaustive legal analysis. We grade against a strict, all-pass rubric written by practicing lawyers. A model only receives full credit if every required legal element is correct Claude Opus 4.8 leads with 43.8% all-pass accuracy, followed by GPT 5.5 (40.4%) and Claude Sonnet 4.6 (38.5%). While top models score around 80% with partial credit, none exceed 44% when every required legal element must be correct The gap between partial and all-pass accuracy shows how difficult it remains for AI to produce complete, reliable legal research. We hope that Legal Research Bench helps better measure, and ultimately close that gap Lots of exciting work happening in Legal AI from Harvey and Crosby. Excited for the legal research benchmarks ahead!

Vals AI

17,351 次观看 • 1 个月前

Openmart (Openmart) is teaming up with Clay to give sales teams intelligence on 20M+ SMBs. They specialize in small account research at scale—pulling in millions of live signals from public filings, websites, reviews, and even calls—and stitching them into a living database of over 20M local businesses worldwide. They're also launching a free version:
1:29

Openmart (Openmart) is teaming up with Clay to give sales teams intelligence on 20M+ SMBs. They specialize in small account research at scale—pulling in millions of live signals from public filings, websites, reviews, and even calls—and stitching them into a living database of over 20M local businesses worldwide. They're also launching a free version:

Y Combinator

56,443 次观看 • 11 个月前

Introducing Scheduled Tasks 2.0 — a major upgrade to recurring work in Manus. Scheduling is no longer just about running something at the right time. It is about running it in the right place, with the right context. Now scheduled work can continue in the same task, power background actions in Manus-built web apps, and give you clearer visibility into upcoming and past runs. Available now to all users.
0:41

Introducing Scheduled Tasks 2.0 — a major upgrade to recurring work in Manus. Scheduling is no longer just about running something at the right time. It is about running it in the right place, with the right context. Now scheduled work can continue in the same task, power background actions in Manus-built web apps, and give you clearer visibility into upcoming and past runs. Available now to all users.

Manus

94,187 次观看 • 2 个月前

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

20,504 次观看 • 3 天前

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹
1:37:23

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

12,091 次观看 • 2 个月前

AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as evidence accumulates. Because real research is not a to do list of tasks. It is a living search process. Leads emerge, failures matter, teams form around what works, and priorities shift when evidence changes. Much like how a lab of scientists would work on cutting edge research together. Across GPT training optimization, biomedical ML, and protein fitness prediction, this decentralized structure consistently does better research. Learn more 👇 Shanghua Gao Marinka Zitnik Kempner Institute at Harvard University DBMI at Harvard Med Harvard University
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AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as evidence accumulates. Because real research is not a to do list of tasks. It is a living search process. Leads emerge, failures matter, teams form around what works, and priorities shift when evidence changes. Much like how a lab of scientists would work on cutting edge research together. Across GPT training optimization, biomedical ML, and protein fitness prediction, this decentralized structure consistently does better research. Learn more 👇 Shanghua Gao Marinka Zitnik Kempner Institute at Harvard University DBMI at Harvard Med Harvard University

Ada Fang

73,119 次观看 • 2 个月前