正在加载视频...

视频加载失败

返回首页

1. Scientific work is often grueling: poring over papers, structuring data, citing literature, recording ideas. And then starting all over again. What if there was an AI agent designed specifically for this task? There aren't many AI tools that I find truly convincing and consider to be of great...

kimmonismus's profile picture

Chubby♨️

81,291 subscribers

56,955 次观看 • 1 年前 •via X (Twitter)

科学技术教育
LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Systematic literature reviews take 12-18 months to complete. Looks like AI is going to fully automate systematic reviews sooner than later. SciSpace ( SciSpace) just launched an autonomous AI agent that conducts a systematic literature review with a single prompt. Go to scispace[.]com and run the following prompt: "Conduct a systematic literature review on [your topic]" SciSpace agent will generate research questions based on the PICO framework. You can review these questions and edit them according to your specific requirements. The agent will also draft screening criteria that you can edit according to your needs. Then the agent asks you to select the databases you want to use and the date range for paper. After this step, everything is fully automated. The agent will search for papers in the relevant databases, it will combine and rerank the papers. Then it will start the title and abstract screening and include the papers that meet the include criteria. In the next step, it will download the full text of included papers and screen them followed by data extraction. Based on the extracted data, it generates a complete systematic literature review and also a PRISMA diagram. It will also give you a table of papers included along with the rational for including them. The only thing that is keeping AI agents to fully automate systematic literature reviews fields is the papers behind paywalls. Check out the agent at scispace[.]com and see if you find its review useful.
0:47

Systematic literature reviews take 12-18 months to complete. Looks like AI is going to fully automate systematic reviews sooner than later. SciSpace ( SciSpace) just launched an autonomous AI agent that conducts a systematic literature review with a single prompt. Go to scispace[.]com and run the following prompt: "Conduct a systematic literature review on [your topic]" SciSpace agent will generate research questions based on the PICO framework. You can review these questions and edit them according to your specific requirements. The agent will also draft screening criteria that you can edit according to your needs. Then the agent asks you to select the databases you want to use and the date range for paper. After this step, everything is fully automated. The agent will search for papers in the relevant databases, it will combine and rerank the papers. Then it will start the title and abstract screening and include the papers that meet the include criteria. In the next step, it will download the full text of included papers and screen them followed by data extraction. Based on the extracted data, it generates a complete systematic literature review and also a PRISMA diagram. It will also give you a table of papers included along with the rational for including them. The only thing that is keeping AI agents to fully automate systematic literature reviews fields is the papers behind paywalls. Check out the agent at scispace[.]com and see if you find its review useful.

Mushtaq Bilal, PhD

42,003 次观看 • 4 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.
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 次观看 • 6 个月前

“A recent study found five or six cancers associated with the Covid vaccine.” The conspiracy theorists were right—again. Dr. Andrew Huff just REVEALED that a new study confirms the link between Covid-19 shots and cancer: “The writing’s on the wall for mRNA technology.” “A lot of corporations in the pharmaceutical or biotech industry have banked on mRNA being the future of vaccine technology.” “If you look at the rise in cancers associated with mRNA technology in the [Covid] vaccine, there’s a recent study that came out in Korea, which is a massive cohort study with a lot of statistical power, found five or six cancers that were associated with the vaccine.” “Many of these other studies looked at one type of cancer—for example, lymphoma in Sweden, they didn’t find an association—but this Korean study looked at all types of cancers, and they’re now finding these associations.” Andrew G. Huff, Ph.D., M.S. Tucker Carlson
1:31

“A recent study found five or six cancers associated with the Covid vaccine.” The conspiracy theorists were right—again. Dr. Andrew Huff just REVEALED that a new study confirms the link between Covid-19 shots and cancer: “The writing’s on the wall for mRNA technology.” “A lot of corporations in the pharmaceutical or biotech industry have banked on mRNA being the future of vaccine technology.” “If you look at the rise in cancers associated with mRNA technology in the [Covid] vaccine, there’s a recent study that came out in Korea, which is a massive cohort study with a lot of statistical power, found five or six cancers that were associated with the vaccine.” “Many of these other studies looked at one type of cancer—for example, lymphoma in Sweden, they didn’t find an association—but this Korean study looked at all types of cancers, and they’re now finding these associations.” Andrew G. Huff, Ph.D., M.S. Tucker Carlson

Holden Culotta

149,400 次观看 • 9 个月前

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.
2:14

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

Aaron Levie

24,618 次观看 • 4 个月前

Professor Angus Dalgleish, a distinguished virologist, oncologist, immunologist, and Professor Emeritus at St George's University of London, drew on his extensive expertise to highlight the significant adverse effects associated with mRNA vaccines. He is the author or co-author of over 500 peer reviewed scientific papers and over 70 chapters in medical books. He is the co-editor of five medical books and has been principal of the Institute for Cancer vaccines and Immunotherapy(ICVI) since 2000.
55:02

Professor Angus Dalgleish, a distinguished virologist, oncologist, immunologist, and Professor Emeritus at St George's University of London, drew on his extensive expertise to highlight the significant adverse effects associated with mRNA vaccines. He is the author or co-author of over 500 peer reviewed scientific papers and over 70 chapters in medical books. He is the co-editor of five medical books and has been principal of the Institute for Cancer vaccines and Immunotherapy(ICVI) since 2000.

NZDSOS - NZ Doctors Speaking Out with Science

47,131 次观看 • 10 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:
0:40

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

Epidemiologist Nicolas Hulscher cites data pointing to 138,000 excess cancer deaths in the United States alone since the rollout of Covid mRNA injections. He references "over a hundred studies showing that the mRNA shots induce... or promote cancer via 17 distinct mechanisms". "Over 70% of the global population was injected with a carcinogenic biological agent. And that biological agent is the mRNA gene transfer injections."
2:27

Epidemiologist Nicolas Hulscher cites data pointing to 138,000 excess cancer deaths in the United States alone since the rollout of Covid mRNA injections. He references "over a hundred studies showing that the mRNA shots induce... or promote cancer via 17 distinct mechanisms". "Over 70% of the global population was injected with a carcinogenic biological agent. And that biological agent is the mRNA gene transfer injections."

Wide Awake Media

43,913 次观看 • 1 个月前

Introducing PaperQA2, the first AI agent that conducts entire scientific literature reviews on its own. PaperQA2 is also the first agent to beat PhD and Postdoc-level biology researchers on multiple literature research tasks, as measured both by accuracy on objective benchmarks and assessments by human experts. We are publishing a paper and open-sourcing the code. This is the first example of AI agents exceeding human performance on a major portion of scientific research, and will be a game-changer for the way humans interact with the scientific literature. Paper and code are below, and congratulations in particular to Michael Skarlinski, Sam Cox, Jon Laurent, James Braza, Michaela Hinks, Michael Hammerling, Manvitha Ponnapati, Andrew White 🐦‍⬛, and others who pulled this off. 1/
0:46

Introducing PaperQA2, the first AI agent that conducts entire scientific literature reviews on its own. PaperQA2 is also the first agent to beat PhD and Postdoc-level biology researchers on multiple literature research tasks, as measured both by accuracy on objective benchmarks and assessments by human experts. We are publishing a paper and open-sourcing the code. This is the first example of AI agents exceeding human performance on a major portion of scientific research, and will be a game-changer for the way humans interact with the scientific literature. Paper and code are below, and congratulations in particular to Michael Skarlinski, Sam Cox, Jon Laurent, James Braza, Michaela Hinks, Michael Hammerling, Manvitha Ponnapati, Andrew White 🐦‍⬛, and others who pulled this off. 1/

Sam Rodriques

555,852 次观看 • 1 年前

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!
2:55

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

fxnction

27,570 次观看 • 2 个月前

BREAKING: The Prime Minister has just attacked and lied about me in Parliament 1. I did NOT state that ‘vaccines are linked to cancer’ I was presenting information about the possible link of the covid mRNA vaccine and cancer from US HHS data and quoting oncologist Professor Angus Dalgleish ‘it’s highly likely that the covid vaccines have been a significant factor in cancer of members of the royal family’ 2. Conflating legitimate safety concerns of a novel mRNA technology with all vaccines is preposterous and dangerous 3. Research reveals that behaviour such as Starmer’s today is what is fuelling the concerning reduced uptake of vital traditional vaccines. It’s no wonder trust in this Labour government is very low. We need to do better
1:13

BREAKING: The Prime Minister has just attacked and lied about me in Parliament 1. I did NOT state that ‘vaccines are linked to cancer’ I was presenting information about the possible link of the covid mRNA vaccine and cancer from US HHS data and quoting oncologist Professor Angus Dalgleish ‘it’s highly likely that the covid vaccines have been a significant factor in cancer of members of the royal family’ 2. Conflating legitimate safety concerns of a novel mRNA technology with all vaccines is preposterous and dangerous 3. Research reveals that behaviour such as Starmer’s today is what is fuelling the concerning reduced uptake of vital traditional vaccines. It’s no wonder trust in this Labour government is very low. We need to do better

Dr Aseem Malhotra

1,102,341 次观看 • 11 个月前

Linus explains to Charlie Brown the real meaning of Christmas. “And in the same region there were shepherds out in the field, keeping watch over their flock by night. And an angel of the Lord appeared to them, and the glory of the Lord shone around them, and they were filled with great fear. And the angel said to them, “Fear not, for behold, I bring you good news of great joy that will be for all the people. For unto you is born this day in the city of David a Savior, who is Christ the Lord. And this will be a sign for you: you will find a baby wrapped in swaddling cloths and lying in a manger.” And suddenly there was with the angel a multitude of the heavenly host praising God and saying, “Glory to God in the highest, and on earth peace among those with whom he is pleased!” — Luke 2:8-14
1:47

Linus explains to Charlie Brown the real meaning of Christmas. “And in the same region there were shepherds out in the field, keeping watch over their flock by night. And an angel of the Lord appeared to them, and the glory of the Lord shone around them, and they were filled with great fear. And the angel said to them, “Fear not, for behold, I bring you good news of great joy that will be for all the people. For unto you is born this day in the city of David a Savior, who is Christ the Lord. And this will be a sign for you: you will find a baby wrapped in swaddling cloths and lying in a manger.” And suddenly there was with the angel a multitude of the heavenly host praising God and saying, “Glory to God in the highest, and on earth peace among those with whom he is pleased!” — Luke 2:8-14

Terri Green

29,628 次观看 • 7 个月前

Maple is preparing for the release of a co-working agent. You install it locally and it works with your files, whether it's office work or building websites and apps. It's a turnkey solution, as easy as Claude Code, that keeps your data secure and private, no data sharing with closed AI labs. This is THE sovereign AI app for individuals and businesses who want powerful AI while retaining ownership of their information. Why build an agent into the Maple app when other agents already exist? Easy, we want to give you control over your work. We don't have a business plan that incorporates making money off our users' data. In the age of AI, your information, whether it's personal or company trade secrets, is the single thing that differentiates you from everyone else. We all have access to AI that can build a professional website for selling shoes. But your strategy and network for how you sell shoes should not be shared with your competitors. Sovereignty is the path to protecting what makes you, you. Maple sits at the intersection of Usability and Sovereignty. Maple gives you the best tools that are both easy to use and maintain your data sovereignty. Sovereign for one, sovereign for all. It has been a journey to get here. We brought to market the very first personal chatbot with end-to-end encryption using TEEs in late 2024. Prior to that there were proofs of concept but no full product offerings. Every other AI chat product on the market handled your data in plain text, either selling you a service to get your data or asking you to trust that they won't snoop on you. Quickly people found Maple and latched onto its open-source code and verifiable encryption. We didn't stop there. You may remember earlier this year we teased a product called "Maple Agent" and opened up a waiting list. That product is a mobile app that acts as your AI "friend", maintaining one long continuous chat, and getting to know you over time. I dislike using the word "friend" there, but it's the best way to convey the UX in a few words. AI is a tool, always has been, always will be. Any kind of friendly personality on top is just synthetic. In our testing, the UX of Maple Agent is really powerful for what it does. Think about the many short AI chats you have in your favorite app, whether it's looking up a historical fact or asking advice about a topic. With Maple Agent, those all go away in favor of the long-running chat with the friendly agent. It's like you have your own personal assistant who knows you so well and can look up anything for you. When I ask AI certain questions, I want to ask an expert who already understands my situation so I'm not repeating myself for the 100th time. That's the amazing value the personal agent brings to the table. We still see great utility for a personal agent like the "Maple Agent". Thousands of people on the waiting list, hoping to get their hands on it, agree that the concept is worth exploring and trying out. We were constrained in launching it due to a few circumstances, one of them being access to the scale of compute needed to power it. We have a clear path laid out for how to get there, but today is not the day to execute on that. It will be in the near future. Instead we have a different agent ready to go that we think is also incredible. We now have an agentic harness inside of the Maple Research app. This thing is a powerhouse. It even builds and publishes its own software releases. The agent in Maple Research works with your local filesystem, speaks to the largest open models running in TEEs, utilizes local models for certain tasks, is compatible with MCP tools, has an API for connecting to anything you need, and also supports the ACP protocol, which means it can be extended in the future to speak to other tools like Claude Code, Codex, and local models running on your own hardware. A big unlock for us was the Goose Development Kit, which powers the core of our agent harness. More on that to come as we publish articles and documentation later about the agent. The agent inside Maple Research doesn't have a name. At least not yet, not sure if it ever will. For now we call it "Chat Mode" and "Agent Mode". Think of this as the workhorse, the truck, the heavy lifter. Our other "Agent", the phone app, is your sidekick in your pocket, ready to help with quick things and ongoing conversations about life. I am incredibly excited about the Maple Research Agent. While I'm already seeing great results using it for internal work items, I'm especially thrilled about the personal health and wellness work it's doing for me. I know there are plenty of apps out there for compiling wellness data, but I'm having it build a tool tailored specifically for what I need, without the extra fluff. And none of my health data is being donated to the closed AI labs or sent to advertisers. I know that the AI logic is not being silently adjusted to fit the whims of a large corporation that has paid for product placement. It's me, state of the art AI, and my data. That's how I want it. Maple's new agent makes that possible. We can't wait for you to try it out. If you want early access, comment here, email us, reach out in some way. To those on the other agent waitlist, you're already in the queue. Thanks for reading this lengthy update. :)
0:49

Maple is preparing for the release of a co-working agent. You install it locally and it works with your files, whether it's office work or building websites and apps. It's a turnkey solution, as easy as Claude Code, that keeps your data secure and private, no data sharing with closed AI labs. This is THE sovereign AI app for individuals and businesses who want powerful AI while retaining ownership of their information. Why build an agent into the Maple app when other agents already exist? Easy, we want to give you control over your work. We don't have a business plan that incorporates making money off our users' data. In the age of AI, your information, whether it's personal or company trade secrets, is the single thing that differentiates you from everyone else. We all have access to AI that can build a professional website for selling shoes. But your strategy and network for how you sell shoes should not be shared with your competitors. Sovereignty is the path to protecting what makes you, you. Maple sits at the intersection of Usability and Sovereignty. Maple gives you the best tools that are both easy to use and maintain your data sovereignty. Sovereign for one, sovereign for all. It has been a journey to get here. We brought to market the very first personal chatbot with end-to-end encryption using TEEs in late 2024. Prior to that there were proofs of concept but no full product offerings. Every other AI chat product on the market handled your data in plain text, either selling you a service to get your data or asking you to trust that they won't snoop on you. Quickly people found Maple and latched onto its open-source code and verifiable encryption. We didn't stop there. You may remember earlier this year we teased a product called "Maple Agent" and opened up a waiting list. That product is a mobile app that acts as your AI "friend", maintaining one long continuous chat, and getting to know you over time. I dislike using the word "friend" there, but it's the best way to convey the UX in a few words. AI is a tool, always has been, always will be. Any kind of friendly personality on top is just synthetic. In our testing, the UX of Maple Agent is really powerful for what it does. Think about the many short AI chats you have in your favorite app, whether it's looking up a historical fact or asking advice about a topic. With Maple Agent, those all go away in favor of the long-running chat with the friendly agent. It's like you have your own personal assistant who knows you so well and can look up anything for you. When I ask AI certain questions, I want to ask an expert who already understands my situation so I'm not repeating myself for the 100th time. That's the amazing value the personal agent brings to the table. We still see great utility for a personal agent like the "Maple Agent". Thousands of people on the waiting list, hoping to get their hands on it, agree that the concept is worth exploring and trying out. We were constrained in launching it due to a few circumstances, one of them being access to the scale of compute needed to power it. We have a clear path laid out for how to get there, but today is not the day to execute on that. It will be in the near future. Instead we have a different agent ready to go that we think is also incredible. We now have an agentic harness inside of the Maple Research app. This thing is a powerhouse. It even builds and publishes its own software releases. The agent in Maple Research works with your local filesystem, speaks to the largest open models running in TEEs, utilizes local models for certain tasks, is compatible with MCP tools, has an API for connecting to anything you need, and also supports the ACP protocol, which means it can be extended in the future to speak to other tools like Claude Code, Codex, and local models running on your own hardware. A big unlock for us was the Goose Development Kit, which powers the core of our agent harness. More on that to come as we publish articles and documentation later about the agent. The agent inside Maple Research doesn't have a name. At least not yet, not sure if it ever will. For now we call it "Chat Mode" and "Agent Mode". Think of this as the workhorse, the truck, the heavy lifter. Our other "Agent", the phone app, is your sidekick in your pocket, ready to help with quick things and ongoing conversations about life. I am incredibly excited about the Maple Research Agent. While I'm already seeing great results using it for internal work items, I'm especially thrilled about the personal health and wellness work it's doing for me. I know there are plenty of apps out there for compiling wellness data, but I'm having it build a tool tailored specifically for what I need, without the extra fluff. And none of my health data is being donated to the closed AI labs or sent to advertisers. I know that the AI logic is not being silently adjusted to fit the whims of a large corporation that has paid for product placement. It's me, state of the art AI, and my data. That's how I want it. Maple's new agent makes that possible. We can't wait for you to try it out. If you want early access, comment here, email us, reach out in some way. To those on the other agent waitlist, you're already in the queue. Thanks for reading this lengthy update. :)

Mark

44,707 次观看 • 21 天前

Real estate agent and brokers. The recent ruling in Missouri is wrong, will only benefit litigators, ambulance, tracers, and I believe will be appealed. More importantly, this ruling will not negatively impact housing or buyers agents unless you allowed to iMessage to all homebuyers I have never as a professional real estate investor not used a buyers agent to represent me in a transaction. Even when the deal was off market, I hired an agent to represent me to make sure that all my rights were protected and that I got the best price. If you pay a bar’s agent is nothing compared to what they can get done for you as a buyer, I will never ask for a discount. I will always use the agent to go get that discount for me more importantly, or as importantly, I will always use the agent to keep the deal together. The purpose of the buyers agent is to find the right or best property in the right or best location at the right time at the best price. If you’re not using your buyer’s agent to achieve that or you’re hiring the wrong agent well, that’s on you. Hire the right agent pay them well and make them do their job. It will be well worth the money. #RealEstate #Buyers #BuyerAgent #RealEstateRuling #Commission
1:30

Real estate agent and brokers. The recent ruling in Missouri is wrong, will only benefit litigators, ambulance, tracers, and I believe will be appealed. More importantly, this ruling will not negatively impact housing or buyers agents unless you allowed to iMessage to all homebuyers I have never as a professional real estate investor not used a buyers agent to represent me in a transaction. Even when the deal was off market, I hired an agent to represent me to make sure that all my rights were protected and that I got the best price. If you pay a bar’s agent is nothing compared to what they can get done for you as a buyer, I will never ask for a discount. I will always use the agent to go get that discount for me more importantly, or as importantly, I will always use the agent to keep the deal together. The purpose of the buyers agent is to find the right or best property in the right or best location at the right time at the best price. If you’re not using your buyer’s agent to achieve that or you’re hiring the wrong agent well, that’s on you. Hire the right agent pay them well and make them do their job. It will be well worth the money. #RealEstate #Buyers #BuyerAgent #RealEstateRuling #Commission

Grant Cardone

51,494 次观看 • 2 年前

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 次观看 • 6 个月前

PhD Students – How to identify related papers for your research paper? 1. Go to 2. Paste your DOI or title of your research paper 3. ResearchCollab will display the network of papers 4. Explore the network in following ways → Filter the network of papers based on research field → Filter the network based on thematic clusters → Filter the network based on keywords → and much more As you find related papers, keep saving them. This use case has multiple advantages. ➟ You won’t miss a related paper to cite in your paper ➟ You can identify related papers for your lit review ➟ You can explore new research directions and trends ➟ You can visualize the entire collection of papers As a researcher, work smarter not just harder. For this, try ResearchCollab:
0:52

PhD Students – How to identify related papers for your research paper? 1. Go to 2. Paste your DOI or title of your research paper 3. ResearchCollab will display the network of papers 4. Explore the network in following ways → Filter the network of papers based on research field → Filter the network based on thematic clusters → Filter the network based on keywords → and much more As you find related papers, keep saving them. This use case has multiple advantages. ➟ You won’t miss a related paper to cite in your paper ➟ You can identify related papers for your lit review ➟ You can explore new research directions and trends ➟ You can visualize the entire collection of papers As a researcher, work smarter not just harder. For this, try ResearchCollab:

Faheem Ullah

46,809 次观看 • 2 个月前

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.
4:16

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 次观看 • 1 个月前

This thread reminded me of Thiel's take on biology and the excuses of biologists for why we aren't solving hard problems (I have a biology background and work as a scientist and still agree with him). My take is that AI won't solve biology, but it will help us understand it at a deeper level. Saying that it is impossible is a lazy take. Saying that AI will solve all problems is a lazy take too. There is an in-between where AI will support and help scientists move things forward. I am really tired of the excuses of the gatekeeper boomer scientists and the hype of the AI bros.
1:42

This thread reminded me of Thiel's take on biology and the excuses of biologists for why we aren't solving hard problems (I have a biology background and work as a scientist and still agree with him). My take is that AI won't solve biology, but it will help us understand it at a deeper level. Saying that it is impossible is a lazy take. Saying that AI will solve all problems is a lazy take too. There is an in-between where AI will support and help scientists move things forward. I am really tired of the excuses of the gatekeeper boomer scientists and the hype of the AI bros.

Linden

67,241 次观看 • 1 个月前

1. Go to openread(dot)academy and sign up for a free account. Once you've logged in, simply type in the title of an article or keywords related to your project. OpenRead will give you a list articles along with an AI-generated summary. Click on the article you find interested and OpenRead will give you its abstract. Click on the "Basic Information" button at the top and select "Related Paper Graph" to create a graphy of relevant literature.
0:28

1. Go to openread(dot)academy and sign up for a free account. Once you've logged in, simply type in the title of an article or keywords related to your project. OpenRead will give you a list articles along with an AI-generated summary. Click on the article you find interested and OpenRead will give you its abstract. Click on the "Basic Information" button at the top and select "Related Paper Graph" to create a graphy of relevant literature.

Mushtaq Bilal, PhD

15,860 次观看 • 2 年前