Загрузка видео...

Не удалось загрузить видео

На главную

Meet Jurist. Your AI legal assistant. 📝Draft. Tweak docs with targeted prompts. 🔎Review/redline. Perform contract reviews and redlines based on company preferences. 📚Research. Conduct research from various legal sources and reference legal knowledge.

ironclad_inc's profile picture

Ironclad

2,870 subscribers

14,131 просмотров • 1 год назад •via X (Twitter)

Наука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

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 просмотров • 1 месяц назад

AI isn’t replacing lawyers — it’s helping them work smarter. From drafting documents to legal research, AI is cutting down hours of rote work, allowing lawyers to focus on higher-value strategy and better serve their clients. But as with all enterprise customers handling sensitive data and complex workflows, selling to the legal profession means taking great care to build a great UX, prioritize security, and understand the field inside and out. For more, listen to Aatish Nayak, head of product at Harvey and a16z’s Kimberly Tan on the AI + a16z podcast:
0:44

AI isn’t replacing lawyers — it’s helping them work smarter. From drafting documents to legal research, AI is cutting down hours of rote work, allowing lawyers to focus on higher-value strategy and better serve their clients. But as with all enterprise customers handling sensitive data and complex workflows, selling to the legal profession means taking great care to build a great UX, prioritize security, and understand the field inside and out. For more, listen to Aatish Nayak, head of product at Harvey and a16z’s Kimberly Tan on the AI + a16z podcast:

a16z

31,065 просмотров • 1 год назад

Someone created the Hyper Research skill for Claude Code It turns the model into a strong team of researchers that goes through 16 stages of search Here is what the AI agents do: > Break down the task into a topic coverage matrix and conduct deep research > Find and save hundreds of sources while identifying contradictions between them > Actively search for evidence against their own conclusions to avoid mistakes > Create a report draft, then review it for weak points, improve readability, and remove filler content > Deliver a complete report with a source base and save all materials to a local knowledge store Advanced Deep Research tool here:
0:56

Someone created the Hyper Research skill for Claude Code It turns the model into a strong team of researchers that goes through 16 stages of search Here is what the AI agents do: > Break down the task into a topic coverage matrix and conduct deep research > Find and save hundreds of sources while identifying contradictions between them > Actively search for evidence against their own conclusions to avoid mistakes > Create a report draft, then review it for weak points, improve readability, and remove filler content > Deliver a complete report with a source base and save all materials to a local knowledge store Advanced Deep Research tool here:

beamnxw ./

62,986 просмотров • 1 месяц назад

Use this FREE tool to generate the first draft of ANY type of literature review. Meet AnswerThis — a tool that makes literature review faster and easier. Here’s how it works. 1. Visit and log in. 2. Select the 𝐿𝑖𝑡𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑅𝑒𝑣𝑖𝑒𝑤 option from the menu. 3. From the prompt helper, select 𝑊𝑟𝑖𝑡𝑒 𝑎 𝑙𝑖𝑡𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑟𝑒𝑣𝑖𝑒𝑤 𝑜𝑛. 4. Enter your research topic in the blank text field ➝ For example, Vulnerabilities in Big Data Systems 5. Click 𝐶𝑟𝑒𝑎𝑡𝑒 to generate the initial search prompt. 6. Press Enter to see research filter options. 7. Choose your response type based on your needs. ➝ Structured Literature Review: Citation-rich and detailed. ➝ Dynamic Research Assistant: To explore research gaps. ➝ AI Only: Fast, but unreliable with no citations. 8. Set the minimum number of citations for the review. ➝ Choose at least 10 for comprehensive results. 9. Decide whether to enable 𝑇𝑢𝑟𝑏𝑜 𝑀𝑜𝑑𝑒 for faster results. ➝ Disabling it gives you more comprehensive answers. 10. Select the sources for search results. ➝ Choose both web and databases for thorough results. 11. Specify the date range to get recent papers. 12. Enable 𝑑𝑜𝑢𝑏𝑙𝑒-𝑐ℎ𝑒𝑐𝑘 𝑐𝑖𝑡𝑎𝑡𝑖𝑜𝑛𝑠 for accurate results. 13. Once filters are set, click 𝑆𝑢𝑏𝑚𝑖𝑡 𝑆𝑒𝑎𝑟𝑐ℎ to proceed. 14. After a while, your literature review will be generated. ➝ Sources and citations will be listed on the right. 15. Review the results and assess the paper sources carefully. 16. Add relevant papers to your library for easy access later. 17. Export citations in formats like BibTeX or CSV as needed. 18. You can also download the review as a Word or PDF file. Treat this literature review as an initial draft. Refine it and build your review on the top of it. Ready to make literature review effortless? Try AnswerThis ( today and see the difference!
0:57

Use this FREE tool to generate the first draft of ANY type of literature review. Meet AnswerThis — a tool that makes literature review faster and easier. Here’s how it works. 1. Visit and log in. 2. Select the 𝐿𝑖𝑡𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑅𝑒𝑣𝑖𝑒𝑤 option from the menu. 3. From the prompt helper, select 𝑊𝑟𝑖𝑡𝑒 𝑎 𝑙𝑖𝑡𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑟𝑒𝑣𝑖𝑒𝑤 𝑜𝑛. 4. Enter your research topic in the blank text field ➝ For example, Vulnerabilities in Big Data Systems 5. Click 𝐶𝑟𝑒𝑎𝑡𝑒 to generate the initial search prompt. 6. Press Enter to see research filter options. 7. Choose your response type based on your needs. ➝ Structured Literature Review: Citation-rich and detailed. ➝ Dynamic Research Assistant: To explore research gaps. ➝ AI Only: Fast, but unreliable with no citations. 8. Set the minimum number of citations for the review. ➝ Choose at least 10 for comprehensive results. 9. Decide whether to enable 𝑇𝑢𝑟𝑏𝑜 𝑀𝑜𝑑𝑒 for faster results. ➝ Disabling it gives you more comprehensive answers. 10. Select the sources for search results. ➝ Choose both web and databases for thorough results. 11. Specify the date range to get recent papers. 12. Enable 𝑑𝑜𝑢𝑏𝑙𝑒-𝑐ℎ𝑒𝑐𝑘 𝑐𝑖𝑡𝑎𝑡𝑖𝑜𝑛𝑠 for accurate results. 13. Once filters are set, click 𝑆𝑢𝑏𝑚𝑖𝑡 𝑆𝑒𝑎𝑟𝑐ℎ to proceed. 14. After a while, your literature review will be generated. ➝ Sources and citations will be listed on the right. 15. Review the results and assess the paper sources carefully. 16. Add relevant papers to your library for easy access later. 17. Export citations in formats like BibTeX or CSV as needed. 18. You can also download the review as a Word or PDF file. Treat this literature review as an initial draft. Refine it and build your review on the top of it. Ready to make literature review effortless? Try AnswerThis ( today and see the difference!

Faheem Ullah

12,694 просмотров • 6 месяцев назад

Introducing Sider 5.0: Meet Deep Research Agent and Wisebase! Deep Research delivers well-cited, in-depth, interactive reports on any topic within minutes, mimicking a human researcher by intelligently curating top sources, highlighting key insights, and organizing everything in Wisebase—your personal AI knowledge hub to revisit, interact with, and expand your insights over time. Discover more at our newly refreshed website: Or, go straight to and get a taste of the new features! #SiderAI #DeepResearch
0:43

Introducing Sider 5.0: Meet Deep Research Agent and Wisebase! Deep Research delivers well-cited, in-depth, interactive reports on any topic within minutes, mimicking a human researcher by intelligently curating top sources, highlighting key insights, and organizing everything in Wisebase—your personal AI knowledge hub to revisit, interact with, and expand your insights over time. Discover more at our newly refreshed website: Or, go straight to and get a taste of the new features! #SiderAI #DeepResearch

Sider

457,225 просмотров • 1 год назад

Leverage your company knowledge with Grok Grok can securely access your internal knowledge through Apps, starting with Google Drive, turning your documents into a searchable intelligence layer for your team Key highlights: ➝ Permission-awareness by design: Grok respects your existing Google Drive permissions. If a file isn't shared with you in Drive, you won't see it in Grok ➝ Trusted citations: Every answer links directly to source documents with quote previews Grok can also perform agentic search using its industry-leading Collections API via Projects, and reason over large document stores for legal, financial, and research workflows This isn’t chat over files.... It’s enterprise-grade reasoning grounded in your own data
1:00

Leverage your company knowledge with Grok Grok can securely access your internal knowledge through Apps, starting with Google Drive, turning your documents into a searchable intelligence layer for your team Key highlights: ➝ Permission-awareness by design: Grok respects your existing Google Drive permissions. If a file isn't shared with you in Drive, you won't see it in Grok ➝ Trusted citations: Every answer links directly to source documents with quote previews Grok can also perform agentic search using its industry-leading Collections API via Projects, and reason over large document stores for legal, financial, and research workflows This isn’t chat over files.... It’s enterprise-grade reasoning grounded in your own data

X Freeze

4,714,515 просмотров • 7 месяцев назад

PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:
0:53

PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:

Faheem Ullah

15,270 просмотров • 1 год назад

Excited to share we’ve raised $2.75M for Cubby Law from Ludlow Ventures, Sam Hinkie, and Positive Sum. We’re building the first AI teaching assistant for law students, powered by our own legal intelligence model trained on thousands of professors’ syllabi, past exams, outlines, and grading rubrics. Cubby started as a horizontal AI research tool, but when law students began adopting it fast, we went all in. Law school prep is broken: • Curve-based grading • 100+ hrs building outlines • Legacy tools students still pay thousands for Cubby brings it all into one connected workspace: case briefs, outlines, and practice exams, calibrated to how your professor teaches and tests. Law school is our first step toward a new era in legal tech.
0:37

Excited to share we’ve raised $2.75M for Cubby Law from Ludlow Ventures, Sam Hinkie, and Positive Sum. We’re building the first AI teaching assistant for law students, powered by our own legal intelligence model trained on thousands of professors’ syllabi, past exams, outlines, and grading rubrics. Cubby started as a horizontal AI research tool, but when law students began adopting it fast, we went all in. Law school prep is broken: • Curve-based grading • 100+ hrs building outlines • Legacy tools students still pay thousands for Cubby brings it all into one connected workspace: case briefs, outlines, and practice exams, calibrated to how your professor teaches and tests. Law school is our first step toward a new era in legal tech.

Truman Sacks

55,597 просмотров • 1 год назад

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️
2:36

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️

LangChain

105,461 просмотров • 1 год назад

Anthropic's Claude Opus 5 can reason through legal work where the answer depends on the substance of each document, who received it, when it was created, and how it relates to the rest of the production set. In this Box Agent demo, Opus 5 reviews 14 documents against a privilege protocol and discovery requests, catches a possible waiver, separates a privileged email from its producible attachment, identifies one sentence that should be redacted, and saves a cited review and draft privilege log back to Box for supervising counsel. Customers can use Claude Opus 5 in Box AI Studio to build custom agents for their own document-heavy legal workflows.
0:43

Anthropic's Claude Opus 5 can reason through legal work where the answer depends on the substance of each document, who received it, when it was created, and how it relates to the rest of the production set. In this Box Agent demo, Opus 5 reviews 14 documents against a privilege protocol and discovery requests, catches a possible waiver, separates a privileged email from its producible attachment, identifies one sentence that should be redacted, and saves a cited review and draft privilege log back to Box for supervising counsel. Customers can use Claude Opus 5 in Box AI Studio to build custom agents for their own document-heavy legal workflows.

Box

1,693,466 просмотров • 26 дней назад

🧵🚨 🚨 Restaurant Notice Sent 🚨 AFLF has formally contacted the restaurant connected to the recent “ChudTheBuilder” incident after livestream viewers were reportedly encouraged to leave negative reviews targeting the business. We advised the restaurant to preserve evidence, document review activity, and consult legal counsel regarding potential civil remedies tied to coordinated online harm and reputational damage. We have also preserved our communications with @pumpdotfun and GiveSendGo GiveSendGo GiveSendGo CoFounder Jacob Wells regarding prior platform notices and escalation concerns tied to this ongoing conduct. Businesses should not become collateral damage from escalation-based livestream content and financial backing. Read the rest of the email in the comments. ———————————————————————— Support the American Freedmen Legal Fund with a Donation. Donate: CashApp donation to Donate at
1:54

🧵🚨 🚨 Restaurant Notice Sent 🚨 AFLF has formally contacted the restaurant connected to the recent “ChudTheBuilder” incident after livestream viewers were reportedly encouraged to leave negative reviews targeting the business. We advised the restaurant to preserve evidence, document review activity, and consult legal counsel regarding potential civil remedies tied to coordinated online harm and reputational damage. We have also preserved our communications with @pumpdotfun and GiveSendGo GiveSendGo GiveSendGo CoFounder Jacob Wells regarding prior platform notices and escalation concerns tied to this ongoing conduct. Businesses should not become collateral damage from escalation-based livestream content and financial backing. Read the rest of the email in the comments. ———————————————————————— Support the American Freedmen Legal Fund with a Donation. Donate: CashApp donation to Donate at

American Freedmen Legal Fund

41,939 просмотров • 3 месяцев назад

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 просмотров • 4 месяцев назад

🇺🇸🇺🇦🦠Another US-linked biolab was found in liberated Rubezhnoye The "Farmbiotest" laboratory was found in a building in the city polyclinic. As it turns out, they tested "medicines" on local residents under the guise of safe research. It became clear that the company conducting the "research" was interested in patients with cardiovascular diseases, coronavirus and children with congenital anomalies (medical staff took screenings from newborn infants). American medical documentation was found in the building as well. All the evedences point to the fact that tests of subspecies of bioweapons were conducted in Rubezhnoye under the guise of legal research.
1:38

🇺🇸🇺🇦🦠Another US-linked biolab was found in liberated Rubezhnoye The "Farmbiotest" laboratory was found in a building in the city polyclinic. As it turns out, they tested "medicines" on local residents under the guise of safe research. It became clear that the company conducting the "research" was interested in patients with cardiovascular diseases, coronavirus and children with congenital anomalies (medical staff took screenings from newborn infants). American medical documentation was found in the building as well. All the evedences point to the fact that tests of subspecies of bioweapons were conducted in Rubezhnoye under the guise of legal research.

Ignorance, the root and stem of all evil

41,656 просмотров • 2 месяцев назад

🚨 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 просмотров • 4 месяцев назад

We’ve had biosensors connected to our bodies since the 1950s. Today, they use 5G and your WiFi router to literally get under your skin with👏a👏remote👏control👏. They harvest your energy. They manipulate your biology. And they lie about your anatomy. Don’t believe it? Check the sources yourself: 🪖 Pentagon directives 🏫 Cambridge 🗄️ MDPI 📄 Whitepapers 🩺 Medical journals 📚 College textbooks 🗂️ Legal documents Everywhere, and it’s hiding in plain sight. Follow Sabrina’s extensive body of research at Psinergy-solafide Stay free friends, Dr. Edward Group, DC #truthseeker #truther #whistleblower #spiritualawakening
2:38

We’ve had biosensors connected to our bodies since the 1950s. Today, they use 5G and your WiFi router to literally get under your skin with👏a👏remote👏control👏. They harvest your energy. They manipulate your biology. And they lie about your anatomy. Don’t believe it? Check the sources yourself: 🪖 Pentagon directives 🏫 Cambridge 🗄️ MDPI 📄 Whitepapers 🩺 Medical journals 📚 College textbooks 🗂️ Legal documents Everywhere, and it’s hiding in plain sight. Follow Sabrina’s extensive body of research at Psinergy-solafide Stay free friends, Dr. Edward Group, DC #truthseeker #truther #whistleblower #spiritualawakening

Dr. Edward Group, DC

43,082 просмотров • 6 месяцев назад

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:
1:15

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 просмотров • 1 месяц назад

Today we're announcing the Legora aOS™. It's something we've been building toward for three years, and I think it's the most important thing we've ever shipped. The legal industry has had AI that assists with individual tasks. What it hasn't had is AI that drives entire work products from start to finish. The Legora aOS changes that. It's a single connected system – matter intake, research, drafting, review, service delivery – orchestrated by the new Legora Agent, running continuously, grounded in your organization’s own knowledge. The legal teams who use it won't just be faster. They'll operate at a scale that simply wasn't possible before. We've spent three years being told the legal industry moves too slowly to change. We've also spent three years watching it change faster than almost anyone predicted. The best time in history to be a lawyer starts today. Legora is built to be the partner that makes it possible. Read the full announcement:
0:46

Today we're announcing the Legora aOS™. It's something we've been building toward for three years, and I think it's the most important thing we've ever shipped. The legal industry has had AI that assists with individual tasks. What it hasn't had is AI that drives entire work products from start to finish. The Legora aOS changes that. It's a single connected system – matter intake, research, drafting, review, service delivery – orchestrated by the new Legora Agent, running continuously, grounded in your organization’s own knowledge. The legal teams who use it won't just be faster. They'll operate at a scale that simply wasn't possible before. We've spent three years being told the legal industry moves too slowly to change. We've also spent three years watching it change faster than almost anyone predicted. The best time in history to be a lawyer starts today. Legora is built to be the partner that makes it possible. Read the full announcement:

Max Junestrand

238,594 просмотров • 3 месяцев назад

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion
41:50

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion

Charlie O'Neill

91,088 просмотров • 29 дней назад