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Black boxes in PDFs don’t delete the data underneath. That’s how some of the biggest redaction failures happened: “redacted” text, copy-pasted right back out. Redactable fixes this with AI: • Flags every instance of sensitive data automatically • Deletes it — data and metadata — for real, not just... show more

Alamin

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40,979 views • 11 days ago •via X (Twitter)

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PhD Students – Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs for data analysis. 5. AnswerThis will generate variety of graphs for you. 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE. AnswerThis link:

PhD Students – Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs for data analysis. 5. AnswerThis will generate variety of graphs for you. 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE. AnswerThis link:

Faheem Ullah

206,524 views • 4 months ago

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. For example, generate graphs from data. 5. AnswerThis will generate variety of graphs 6. It also generates an insightful data analysis report. You can download the graphs and the report. Try AnswerThis today. It’s FREE.

Faheem Ullah

26,459 views • 6 months ago

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

Use this FREE tool to analyze data in 10 sec. This tool is used by more than 500,000 researchers. 1. Go to and log in 2. Click on Data Analysis and upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data analysis. 5. Liner will generate a variety of graphs for you. 6. It also generates an insightful data analysis report. ✦ You can edit the report. For example, you can: ✦ Insert citations in the report ✦ Make changes to the text ✦ Add/remove any graphs After making changes, you can download the report. Try Liner today. It’s FREE. Liner link:

Faheem Ullah

11,959 views • 4 months ago

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Andrew Bolis

36,504 views • 11 months ago

Thunderbit is probably the most powerful web scraper right now. It can scrape any website in just 2 clicks, no code, no setup. It’s smart enough to understand complex page layouts and nested structures. Normally, scraping this kind of data manually takes hours of tedious copy-paste work. With Thunderbit, you click twice, and it auto-builds the table for you, scraping in seconds. More details and examples below 👇 #Thunderbit #WebScraping #AITools #BlackFridayDeals

Thunderbit is probably the most powerful web scraper right now. It can scrape any website in just 2 clicks, no code, no setup. It’s smart enough to understand complex page layouts and nested structures. Normally, scraping this kind of data manually takes hours of tedious copy-paste work. With Thunderbit, you click twice, and it auto-builds the table for you, scraping in seconds. More details and examples below 👇 #Thunderbit #WebScraping #AITools #BlackFridayDeals

AshutoshShrivastava

17,857 views • 8 months ago

FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: “The internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, there’s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not they’re using it to train, I don’t know. I’ve heard from insiders they’re not allowed to yet and would have to redo the terms of service.” Source: AIFinInsights david friedberg

FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: “The internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, there’s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not they’re using it to train, I don’t know. I’ve heard from insiders they’re not allowed to yet and would have to redo the terms of service.” Source: AIFinInsights david friedberg

Mario Nawfal

15,855 views • 8 months ago

This April AMA is here. In this session, the team breaks down how biodata (like sleep and brain data) can be standardized, valued, and turned into real-world assets (RWA). What you’ll learn: 1. The 4 key milestones for Biodata RWA in 2026 2. Why sleep data is the first step in building a data economy 3. How data becomes structured, scored, and monetizable 4. The role of MAN token in rewarding and settling data usage 5. How privacy is protected with distributed storage & computation 6. Ownership models: sell vs license your data 7. Why institutions (AI, pharma, insurance) need verified biodata In the future, your data won’t just exist, it will work for you.

This April AMA is here. In this session, the team breaks down how biodata (like sleep and brain data) can be standardized, valued, and turned into real-world assets (RWA). What you’ll learn: 1. The 4 key milestones for Biodata RWA in 2026 2. Why sleep data is the first step in building a data economy 3. How data becomes structured, scored, and monetizable 4. The role of MAN token in rewarding and settling data usage 5. How privacy is protected with distributed storage & computation 6. Ownership models: sell vs license your data 7. Why institutions (AI, pharma, insurance) need verified biodata In the future, your data won’t just exist, it will work for you.

Matrix AI Network

15,296 views • 3 months ago

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”

Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”

David Fields

13,639 views • 2 years ago

A Jane Street quant earning $7M a year just showed the fund's actual code on camera The library that generated $20,500,000,000 in a single year Running for 16 years. Nobody talked about it until now. 33 minutes. Every millisecond of market data processed. The same architecture underneath everything I broke down in the neural networks article: > real-time data ingestion > signal computation > position sizing > execution Watch it like your financial future depends on it Full implementation guide for Polymarket below

A Jane Street quant earning $7M a year just showed the fund's actual code on camera The library that generated $20,500,000,000 in a single year Running for 16 years. Nobody talked about it until now. 33 minutes. Every millisecond of market data processed. The same architecture underneath everything I broke down in the neural networks article: > real-time data ingestion > signal computation > position sizing > execution Watch it like your financial future depends on it Full implementation guide for Polymarket below

Atlas

92,927 views • 2 months ago

We introduce Tagger Phase 2, bringing manual #data labeling and #data review into the #DeCorp ecosystem. With this iteration, we have completed the final components necessary for a fully integrated and decentralized cycle: 1) Data processing from scratch 2) Peer-to-peer data validation 3) Automated pre-training of AI copilot models 4) Quasi-autonomous active learning It is not long now before you may directly engage with this unprecedented and comprehensive system. See you soon.

We introduce Tagger Phase 2, bringing manual #data labeling and #data review into the #DeCorp ecosystem. With this iteration, we have completed the final components necessary for a fully integrated and decentralized cycle: 1) Data processing from scratch 2) Peer-to-peer data validation 3) Automated pre-training of AI copilot models 4) Quasi-autonomous active learning It is not long now before you may directly engage with this unprecedented and comprehensive system. See you soon.

Tagger

104,265 views • 1 year ago

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:

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 views • 6 months ago

Open-sourcing Introspect: MIT-licensed Deep-Research for your internal data! Works with spreadsheets, databases, PDFs, and web search. Has a remarkably simple architecture – Sonnet agent armed with recursive tool calling and 3 default tools. Best for use-cases where you want to combine insights from SQL with unstructured data + data from the web. Launching a bunch of products built on top of of this soon, but wanted to open-source our core engine to give back to the community! 2 min demo below, links in comments

Open-sourcing Introspect: MIT-licensed Deep-Research for your internal data! Works with spreadsheets, databases, PDFs, and web search. Has a remarkably simple architecture – Sonnet agent armed with recursive tool calling and 3 default tools. Best for use-cases where you want to combine insights from SQL with unstructured data + data from the web. Launching a bunch of products built on top of of this soon, but wanted to open-source our core engine to give back to the community! 2 min demo below, links in comments

Rishabh Srivastava

71,563 views • 1 year ago

instead of watching 2 hours of Netflix tonight, watch this Stanford lecture it's the clearest explanation I've seen of how ChatGPT and Claude actually work useful whether you've never touched AI in your life or have been using it every day for the past year i took the key ideas and turned them into a practical guide on how to actually get 100% out of AI you can find it below with ready-to-copy prompts and solutions

instead of watching 2 hours of Netflix tonight, watch this Stanford lecture it's the clearest explanation I've seen of how ChatGPT and Claude actually work useful whether you've never touched AI in your life or have been using it every day for the past year i took the key ideas and turned them into a practical guide on how to actually get 100% out of AI you can find it below with ready-to-copy prompts and solutions

Anatoli Kopadze

2,755,981 views • 9 days ago

I don't think people understand that one of the biggest real-world use cases for Crypto it is happening - right now. FedEx, just sat down with Mance Harmon of $HBAR and explained why they chose Hedera to track millions of packages a day, to 220 countries. Dale Christie, Blockchain Strategist at FedEx said in 2018, FedEx tried building its own blockchain... But it wouldn’t scale so they chose Hedera. Why? Every package generates hundreds - sometimes thousands - of data points, and Hedera enables the key facts to move through customs at the speed of data, not paper. “When you are a trusted shipper and I am a trusted receiver… that data can move through customs at the speed of data and keep it moving.” One of the largest logistics companies in the world just publicly said Hedera is the foundation they’re building on. Hedera is the most undervalued token imo. and this is one of the biggest use cases Crypto's ever seen.

I don't think people understand that one of the biggest real-world use cases for Crypto it is happening - right now. FedEx, just sat down with Mance Harmon of $HBAR and explained why they chose Hedera to track millions of packages a day, to 220 countries. Dale Christie, Blockchain Strategist at FedEx said in 2018, FedEx tried building its own blockchain... But it wouldn’t scale so they chose Hedera. Why? Every package generates hundreds - sometimes thousands - of data points, and Hedera enables the key facts to move through customs at the speed of data, not paper. “When you are a trusted shipper and I am a trusted receiver… that data can move through customs at the speed of data and keep it moving.” One of the largest logistics companies in the world just publicly said Hedera is the foundation they’re building on. Hedera is the most undervalued token imo. and this is one of the biggest use cases Crypto's ever seen.

Mark

44,373 views • 5 months ago

Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇

Introducing the Future of AI-Powered Sports Data with $FXI.⚡️ We’re not just talking about innovation, we’re building it. 🚀 From #AI-driven data capture to on-chain validation and data monetization, the FX1 ecosystem is rewriting the rules of the sports and data economy. Our platform empowers leagues, teams, and creators to unlock the full potential of their data with cutting-edge AI solutions. 🧠 Why FX1? ➡️ AI-Powered Precision: Transform raw sports data into actionable insights. ➡️ Data Validation on Chain: Building trust in the data economy through blockchain. ➡️ AI-Powered Revenue Streams: Use sports data to drive predictive analytics, odds modeling, and other high-value solutions that unlock new ways to monetize. FX1 is leading the charge in AI innovation, creating a seamless bridge between sports, blockchain, and the data economy. This isn’t just evolution - it’s a revolution. 💡 The future of sports is here. And it’s powered by AI. 🤖 Watch the full video and see how we’re turning vision into reality. 👇

FX1

23,913 views • 1 year ago

PhD Students – How to automatically extract data from papers for your literature review? Extracting relevant data from papers is challenging. However, this process can be automated. Meet AnswerThis – a tool that extracts data in seconds. Here is how it works. 1. Go to and log in. 2. After logging in, click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 3. Then click on 𝑈𝑝𝑙𝑜𝑎𝑑 𝑃𝐷𝐹 and upload your papers. 4. These are the papers from which you want to extract data. 5. After uploading papers, select data you want to extract. 6. The predefined options are - Key findings - Research gaps - Methodology - Limitations - Future work - Contributions - Practical implications 7. You can also extract custom data e.g., dataset used. 8. For example, I want to extract methodology used in these papers. 9. I selected 𝑀𝑒𝑡ℎ𝑜𝑑𝑜𝑙𝑜𝑔𝑦 and clicked on 𝐴𝑑𝑑 𝐶𝑜𝑙𝑢𝑚𝑛. 10. AnswerThis extract data about methodology used in the papers. 11. You can change data view from normal to Table View. 12. For this, scroll back to top and click on 𝑇𝑎𝑏𝑙𝑒 𝑉𝑖𝑒𝑤. 13. Now for instance, you want to extract more data from these papers. 14. Go back to the top and click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 15. Select the data type you want to extract. 16. For example, I want to extract data about future work. 17. So I click on 𝐹𝑢𝑡𝑢𝑟𝑒 𝑊𝑜𝑟𝑘 and then clicked on 𝐴𝑑𝑑 𝑐𝑜𝑙𝑢𝑚𝑛. 18. AnswerThis extracted data about future work from the papers. 19. After extracting the desired data, you can export it. 20. Select the data you want to extract. 21. Then click on 𝐸𝑥𝑝𝑜𝑟𝑡 𝑑𝑎𝑡𝑎. 22. Your data will be exported in CSV format. You can then analyze this data for your literature review. Try AnswerThis today: Anything you'd like to add?

PhD Students – How to automatically extract data from papers for your literature review? Extracting relevant data from papers is challenging. However, this process can be automated. Meet AnswerThis – a tool that extracts data in seconds. Here is how it works. 1. Go to and log in. 2. After logging in, click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 3. Then click on 𝑈𝑝𝑙𝑜𝑎𝑑 𝑃𝐷𝐹 and upload your papers. 4. These are the papers from which you want to extract data. 5. After uploading papers, select data you want to extract. 6. The predefined options are - Key findings - Research gaps - Methodology - Limitations - Future work - Contributions - Practical implications 7. You can also extract custom data e.g., dataset used. 8. For example, I want to extract methodology used in these papers. 9. I selected 𝑀𝑒𝑡ℎ𝑜𝑑𝑜𝑙𝑜𝑔𝑦 and clicked on 𝐴𝑑𝑑 𝐶𝑜𝑙𝑢𝑚𝑛. 10. AnswerThis extract data about methodology used in the papers. 11. You can change data view from normal to Table View. 12. For this, scroll back to top and click on 𝑇𝑎𝑏𝑙𝑒 𝑉𝑖𝑒𝑤. 13. Now for instance, you want to extract more data from these papers. 14. Go back to the top and click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 15. Select the data type you want to extract. 16. For example, I want to extract data about future work. 17. So I click on 𝐹𝑢𝑡𝑢𝑟𝑒 𝑊𝑜𝑟𝑘 and then clicked on 𝐴𝑑𝑑 𝑐𝑜𝑙𝑢𝑚𝑛. 18. AnswerThis extracted data about future work from the papers. 19. After extracting the desired data, you can export it. 20. Select the data you want to extract. 21. Then click on 𝐸𝑥𝑝𝑜𝑟𝑡 𝑑𝑎𝑡𝑎. 22. Your data will be exported in CSV format. You can then analyze this data for your literature review. Try AnswerThis today: Anything you'd like to add?

Faheem Ullah

21,390 views • 9 months ago

We just launched a major new Data Engineering Professional Certificate on Coursera! Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. If you're interested in learning this skill, please check out this 4-course sequence, which is designed to make you job-ready to be a Data Engineer. This is a new specialization taught by Joe Reis, the co-author of the best-selling book “Fundamentals of Data Engineering," in collaboration with AWS. (Disclosure, I serve on Amazon's board.) For many AI systems, data engineering is 80% of the work, and modeling is 20%. But people’s attention on these two topics is often flipped. This makes the job of the data engineer particularly important. In this professional certificate, you'll learn foundational data engineering skills while implementing modern data architectures using open-source tools: - Learn the key steps of the data lifecycle, to generate, ingest, store, transform, and serve data. - Learn to align with organizational goals to design the data pipeline right for your business' needs. - Understand how to make necessary trade-offs between speed, scalability, security, and cost. Joe has distilled into this specialization decades of experience helping startups and large companies with data infrastructure. He is also joined by 17 other industry leaders in the data field, who will help you learn in-demand skills for the growing field of data engineering. Please sign up here:

We just launched a major new Data Engineering Professional Certificate on Coursera! Data underlies all modern AI systems, and engineers who know how to build systems to store and serve it are in high demand. If you're interested in learning this skill, please check out this 4-course sequence, which is designed to make you job-ready to be a Data Engineer. This is a new specialization taught by Joe Reis, the co-author of the best-selling book “Fundamentals of Data Engineering," in collaboration with AWS. (Disclosure, I serve on Amazon's board.) For many AI systems, data engineering is 80% of the work, and modeling is 20%. But people’s attention on these two topics is often flipped. This makes the job of the data engineer particularly important. In this professional certificate, you'll learn foundational data engineering skills while implementing modern data architectures using open-source tools: - Learn the key steps of the data lifecycle, to generate, ingest, store, transform, and serve data. - Learn to align with organizational goals to design the data pipeline right for your business' needs. - Understand how to make necessary trade-offs between speed, scalability, security, and cost. Joe has distilled into this specialization decades of experience helping startups and large companies with data infrastructure. He is also joined by 17 other industry leaders in the data field, who will help you learn in-demand skills for the growing field of data engineering. Please sign up here:

Andrew Ng

118,937 views • 1 year ago

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Sam Rodriques

61,934 views • 8 months ago

I don’t think people realize the scale of data behind the JBP Search Engine. Every single episode since Episode 1 has been transcribed and converted into embeddings for semantic search. This isn’t keyword matching. It understands the context of your query and returns the exact moment you’re looking for…not just a list of episodes where something was mentioned. 1,415 episodes. ~3 hours each. Over 600GB of locally stored audio + additional metadata powering search quality. Could you do it? Maybe, but just know, I’ve personally spent thousands of dollars in compute to make this possible. There’s no way around it when dealing with this much data.

I don’t think people realize the scale of data behind the JBP Search Engine. Every single episode since Episode 1 has been transcribed and converted into embeddings for semantic search. This isn’t keyword matching. It understands the context of your query and returns the exact moment you’re looking for…not just a list of episodes where something was mentioned. 1,415 episodes. ~3 hours each. Over 600GB of locally stored audio + additional metadata powering search quality. Could you do it? Maybe, but just know, I’ve personally spent thousands of dollars in compute to make this possible. There’s no way around it when dealing with this much data.

burner account

130,807 views • 2 months ago