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PhD Students - Use this FREE tool to analyze data in 10 seconds. This tool is used by more than 500,000 researchers. 1. Go to 2. Click on attach file to upload your data file. 3. Write your prompt for data analysis. 4. E.g., generate graphs for exploratory data... show more

Faheem Ullah

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10,427 次观看 • 13 天前 •via X (Twitter)

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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 次观看 • 6 个月前

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

11,927 次观看 • 3 个月前

PhD Students - How to analyze data in seconds? 1. Go to 2. Upload your data 3. Describe how you want the data to be analyzed 4. Novix will handle the entire analysis It will perform the following data analysis tasks. - Load and validate the dataset - Generate graphs for exploratory analysis - Create histograms for numeric variables - Build a correlation heatmap - Create bar charts for categorical features - Generate box plots for score distributions - Create scatter plots for relationships - Generate a final report Download the report in PDF or Word format.

PhD Students - How to analyze data in seconds? 1. Go to 2. Upload your data 3. Describe how you want the data to be analyzed 4. Novix will handle the entire analysis It will perform the following data analysis tasks. - Load and validate the dataset - Generate graphs for exploratory analysis - Create histograms for numeric variables - Build a correlation heatmap - Create bar charts for categorical features - Generate box plots for score distributions - Create scatter plots for relationships - Generate a final report Download the report in PDF or Word format.

Faheem Ullah

16,061 次观看 • 2 个月前

PhD Students – How to analyze data in minutes? Use the following FREE tool to analyze your data. 𝐌𝐞𝐞𝐭 DataLumio – 𝐚 𝐭𝐨𝐨𝐥 𝐟𝐨𝐫 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬. It works in 3 simple steps. 1. Go to 2. Upload your data and ask your research question 3. DataLumio will analyze data and generate report. 𝐓𝐡𝐞 𝐫𝐞𝐩𝐨𝐫𝐭 𝐰𝐢𝐥𝐥 𝐜𝐨𝐧𝐭𝐚𝐢𝐧: → Insightful graphs and charts → Tables and illustrations → Concrete answer to your research question → Examples from the analyzed reports 𝐃𝐚𝐭𝐚𝐋𝐮𝐦𝐢𝐨 𝐚𝐧𝐚𝐥𝐲𝐳𝐞𝐬 𝐭𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐭𝐲𝐩𝐞𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚. ↳ Text data ↳ Interview transcripts ↳ Observational data ↳ Video/audio transcripts ↳ Any qualitative data Try Datalumio here: Anything you'd like to add?

PhD Students – How to analyze data in minutes? Use the following FREE tool to analyze your data. 𝐌𝐞𝐞𝐭 DataLumio – 𝐚 𝐭𝐨𝐨𝐥 𝐟𝐨𝐫 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬. It works in 3 simple steps. 1. Go to 2. Upload your data and ask your research question 3. DataLumio will analyze data and generate report. 𝐓𝐡𝐞 𝐫𝐞𝐩𝐨𝐫𝐭 𝐰𝐢𝐥𝐥 𝐜𝐨𝐧𝐭𝐚𝐢𝐧: → Insightful graphs and charts → Tables and illustrations → Concrete answer to your research question → Examples from the analyzed reports 𝐃𝐚𝐭𝐚𝐋𝐮𝐦𝐢𝐨 𝐚𝐧𝐚𝐥𝐲𝐳𝐞𝐬 𝐭𝐡𝐞 𝐟𝐨𝐥𝐥𝐨𝐰𝐢𝐧𝐠 𝐭𝐲𝐩𝐞𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚. ↳ Text data ↳ Interview transcripts ↳ Observational data ↳ Video/audio transcripts ↳ Any qualitative data Try Datalumio here: Anything you'd like to add?

Faheem Ullah

14,307 次观看 • 11 个月前

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 次观看 • 8 个月前

How to analyze data for literature review in seconds? 𝐅𝐢𝐫𝐬𝐭, 𝐥𝐞𝐭’𝐬 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐈 𝐦𝐞𝐚𝐧 𝐛𝐲 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬. In a literature review, we study data in the papers: ✓ To understand the overall field of study ✓ To learn about trends and patterns in the field ✓ To identify gaps for future research For this, we collect a pool of papers say 100 papers. 𝐁𝐮𝐭 𝐰𝐡𝐚𝐭 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞𝐬𝐞 𝟏𝟎𝟎 𝐩𝐚𝐩𝐞𝐫𝐬? Like the following data: ↳ Publication year of these papers ↳ Citations of each paper ↳ Top authors in these papers ↳ Key terms in these papers ↳ Citation impact of these papers ↳ Authors’ impact of these papers This data about the papers also needs to be analyzed. It can reveal interesting patterns about the field. 𝐇𝐨𝐰 𝐭𝐨 𝐝𝐨 𝐭𝐡𝐢𝐬 𝐦𝐞𝐭𝐚-𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐧 𝐬𝐞𝐜𝐨𝐧𝐝𝐬? 1. Go to 2. Upload the PDF of papers to the library 3. Select all papers and click on bibliometric analysis 4. Create your canvas for meta-analysis ➟ This canvas contains all types of meta-analysis. ➟ You can download the graphs ➟ You can include them in your literature review. Try AnswerThis Canvas today: Anything you'd like to add?

How to analyze data for literature review in seconds? 𝐅𝐢𝐫𝐬𝐭, 𝐥𝐞𝐭’𝐬 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐈 𝐦𝐞𝐚𝐧 𝐛𝐲 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬. In a literature review, we study data in the papers: ✓ To understand the overall field of study ✓ To learn about trends and patterns in the field ✓ To identify gaps for future research For this, we collect a pool of papers say 100 papers. 𝐁𝐮𝐭 𝐰𝐡𝐚𝐭 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞𝐬𝐞 𝟏𝟎𝟎 𝐩𝐚𝐩𝐞𝐫𝐬? Like the following data: ↳ Publication year of these papers ↳ Citations of each paper ↳ Top authors in these papers ↳ Key terms in these papers ↳ Citation impact of these papers ↳ Authors’ impact of these papers This data about the papers also needs to be analyzed. It can reveal interesting patterns about the field. 𝐇𝐨𝐰 𝐭𝐨 𝐝𝐨 𝐭𝐡𝐢𝐬 𝐦𝐞𝐭𝐚-𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐧 𝐬𝐞𝐜𝐨𝐧𝐝𝐬? 1. Go to 2. Upload the PDF of papers to the library 3. Select all papers and click on bibliometric analysis 4. Create your canvas for meta-analysis ➟ This canvas contains all types of meta-analysis. ➟ You can download the graphs ➟ You can include them in your literature review. Try AnswerThis Canvas today: Anything you'd like to add?

Faheem Ullah

12,068 次观看 • 7 个月前

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

61,860 次观看 • 8 个月前

PhD Students – How to extract data from papers for your literature review in seconds? Extracting data from papers takes a lot of time. You can automate this process with Bohrium 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐞𝐱𝐭𝐫𝐚𝐜𝐭 𝐝𝐚𝐭𝐚 𝐟𝐫𝐨𝐦 𝐩𝐚𝐩𝐞𝐫𝐬? 1. Go to and log in 2. Click on 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝐵𝑎𝑠𝑒 from the left menu 3. Upload the papers you selected for literature review 4. You will see the following option against each paper - Read PDF - Key Takeaway - AI Poster 5. Click on 𝑅𝑒𝑎𝑑 𝑃𝐷𝐹 for the first paper in your list 6. Write a prompt for the data you want to extract 7. For example, you can enter datasets, methodology etc. 8. It will extract the required data from the paper 9. If you want to extract Key Takeaways from the paper 10. Go back and click on 𝐾𝑒𝑦 𝑇𝑎𝑘𝑒𝑎𝑤𝑎𝑦𝑠 11. Bohrium will extract Key Takeaways from the paper 12. In addition to this, you also have 2 more options - AI Poster - Podcast 13. Click on 𝐴𝐼 𝑃𝑜𝑠𝑡𝑒𝑟 and it will create a poster for you 14. This is the poster based on the given research paper 15. If you click on 𝑃𝑜𝑑𝑐𝑎𝑠𝑡, it will convert the paper to audio 16. You can listen to the paper instead of reading it Repeat this cycle for all the papers in your pool. You will end up with the required data. You can use this data to write your literature review Try Bohrium today for FREE: Anything you’d like to add?

PhD Students – How to extract data from papers for your literature review in seconds? Extracting data from papers takes a lot of time. You can automate this process with Bohrium 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐞𝐱𝐭𝐫𝐚𝐜𝐭 𝐝𝐚𝐭𝐚 𝐟𝐫𝐨𝐦 𝐩𝐚𝐩𝐞𝐫𝐬? 1. Go to and log in 2. Click on 𝐾𝑛𝑜𝑤𝑙𝑒𝑑𝑔𝑒 𝐵𝑎𝑠𝑒 from the left menu 3. Upload the papers you selected for literature review 4. You will see the following option against each paper - Read PDF - Key Takeaway - AI Poster 5. Click on 𝑅𝑒𝑎𝑑 𝑃𝐷𝐹 for the first paper in your list 6. Write a prompt for the data you want to extract 7. For example, you can enter datasets, methodology etc. 8. It will extract the required data from the paper 9. If you want to extract Key Takeaways from the paper 10. Go back and click on 𝐾𝑒𝑦 𝑇𝑎𝑘𝑒𝑎𝑤𝑎𝑦𝑠 11. Bohrium will extract Key Takeaways from the paper 12. In addition to this, you also have 2 more options - AI Poster - Podcast 13. Click on 𝐴𝐼 𝑃𝑜𝑠𝑡𝑒𝑟 and it will create a poster for you 14. This is the poster based on the given research paper 15. If you click on 𝑃𝑜𝑑𝑐𝑎𝑠𝑡, it will convert the paper to audio 16. You can listen to the paper instead of reading it Repeat this cycle for all the papers in your pool. You will end up with the required data. You can use this data to write your literature review Try Bohrium today for FREE: Anything you’d like to add?

Faheem Ullah

13,413 次观看 • 10 个月前

How much of your manuscript is AI-written? Use this FREE tool to find out in 10 seconds. 1. Go to 2. Click on 𝐴𝐼 𝑤𝑟𝑖𝑡𝑒𝑟 3. Copy and paste specific text or upload manuscript 4. Click on 𝐴𝑛𝑎𝑙𝑦𝑠𝑖𝑠 and then on 𝑃𝑙𝑎𝑔𝑖𝑎𝑟𝑖𝑠𝑚 5. You will get a complete plagiarism report. 6. Now click on 𝐴𝐼 𝐷𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛 7. You will get an AI plagiarism report Here you will see how much % of your text is AI written You will also see the exact sentences A manuscript with plagiarized content is not acceptable What to do next? Remove plagiarized content from your manuscript Also, remove AI patterns in your writing Once done, go ahead and submit. Try AnswerThis for FREE today:

How much of your manuscript is AI-written? Use this FREE tool to find out in 10 seconds. 1. Go to 2. Click on 𝐴𝐼 𝑤𝑟𝑖𝑡𝑒𝑟 3. Copy and paste specific text or upload manuscript 4. Click on 𝐴𝑛𝑎𝑙𝑦𝑠𝑖𝑠 and then on 𝑃𝑙𝑎𝑔𝑖𝑎𝑟𝑖𝑠𝑚 5. You will get a complete plagiarism report. 6. Now click on 𝐴𝐼 𝐷𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛 7. You will get an AI plagiarism report Here you will see how much % of your text is AI written You will also see the exact sentences A manuscript with plagiarized content is not acceptable What to do next? Remove plagiarized content from your manuscript Also, remove AI patterns in your writing Once done, go ahead and submit. Try AnswerThis for FREE today:

Faheem Ullah

46,447 次观看 • 4 个月前

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:

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 • 5 个月前

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,284 次观看 • 2 年前

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.

Here is an open-source tool to generate a complete dataset. 1. Describe the data you want 2. An orchestrator agent searches the web 3. Sub-agents run in parallel to fetch the data 4. You get a structured dataset you can download For example, you can run Bigset with the query "all leica lenses being sold on amazon", or "leica stores in kyoto with their opening hours and ratings". Bigset uses TinyFish's free Search and Fetch APIs in the background. You can configure it to refresh the data on a schedule. You can self-host it with your own keys. Here is the GitHub repository: You can get free TinyFish API keys here: Thanks to the TinyFish team for partnering with me on this post.

Santiago

20,744 次观看 • 1 个月前

Our integration with Walrus 🦭/acc is now live! 1. Upload a CSV or Parquet file to Baselight 2. Flip it from “Private” to “Public” to share it with the community 3. Your file is stored on Walrus, structured by Baselight, and ready for AI-powered analysis Start publishing data that thinks with you 👇

Our integration with Walrus 🦭/acc is now live! 1. Upload a CSV or Parquet file to Baselight 2. Flip it from “Private” to “Public” to share it with the community 3. Your file is stored on Walrus, structured by Baselight, and ready for AI-powered analysis Start publishing data that thinks with you 👇

Baselight

30,406 次观看 • 8 个月前

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 次观看 • 10 个月前

$BTC Statistical Study using Claude - A Beginner's Workflow Here's an example of a z-score study on $BTC - still tinkering so don't take this as overtly useful information but the creation of a dashboard for the visualization of statistical data is phenomenal. my current workflow: > import $BTC time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.

$BTC Statistical Study using Claude - A Beginner's Workflow Here's an example of a z-score study on $BTC - still tinkering so don't take this as overtly useful information but the creation of a dashboard for the visualization of statistical data is phenomenal. my current workflow: > import $BTC time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.

Stoic

19,757 次观看 • 1 年前

This is a big leap forward to doing real data analysis with GPT-4o. It still can't quite handle the way many people use spreadsheets (Excel formulas, etc.) but it is pretty impressive. You can zoom into spreadsheets and ask about cells, modify graphs, etc. I trimmed 45 seconds.

This is a big leap forward to doing real data analysis with GPT-4o. It still can't quite handle the way many people use spreadsheets (Excel formulas, etc.) but it is pretty impressive. You can zoom into spreadsheets and ask about cells, modify graphs, etc. I trimmed 45 seconds.

Ethan Mollick

1,746,072 次观看 • 2 年前

Today we launched OpenClaw for Sales. It combines Openmart’s data, LinkedIn data, and top data sources to do the work for you. Used by teams like Whatnot, DoorDash, Alibaba, and many others. Try for free: or DM me for an invite code.

Today we launched OpenClaw for Sales. It combines Openmart’s data, LinkedIn data, and top data sources to do the work for you. Used by teams like Whatnot, DoorDash, Alibaba, and many others. Try for free: or DM me for an invite code.

Kathryn Wu

208,877 次观看 • 2 个月前

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:

Santiago

125,928 次观看 • 9 个月前