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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...

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Sam Rodriques

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61,934 просмотров • 8 месяцев назад •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:
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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:

Faheem Ullah

206,524 просмотров • 4 месяцев назад

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:
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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 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 просмотров • 4 месяцев назад

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.
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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.

Faheem Ullah

26,459 просмотров • 6 месяцев назад

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
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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 просмотров • 11 месяцев назад

Jupyter Agents - LLMs running data analysis directly in a notebook! The agent can load data, execute code, plot results and following your guidance and ideas! A very natural way to collaborate with an LLM over data and it's just scratching the surface of what's possible soon!
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Jupyter Agents - LLMs running data analysis directly in a notebook! The agent can load data, execute code, plot results and following your guidance and ideas! A very natural way to collaborate with an LLM over data and it's just scratching the surface of what's possible soon!

Leandro von Werra

200,291 просмотров • 1 год назад

Placing trades with our no-code Polymarket agent is giving me butterflies. → Live data piped from our Dome agents → An agent then runs statistical analysis on each market → The agent then autonomously decides which bet to make, and places it for me. Zero. code.
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Placing trades with our no-code Polymarket agent is giving me butterflies. → Live data piped from our Dome agents → An agent then runs statistical analysis on each market → The agent then autonomously decides which bet to make, and places it for me. Zero. code.

Tim

29,304 просмотров • 9 месяцев назад

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk
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What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk

Dogtor

16,127 просмотров • 9 месяцев назад

Big step forward for root cause analysis in real-world applications! There’s a new method that will help identify the causes of a problem or event. It uses causal discovery, boosting trees together with TDA. This is crucial to enable root cause analysis in tasks like the following: • Fraud detection • Drug discovery • Customer behavior analysis • Energy and sustainability • Financial analysis and risk management • Failure analysis in engineering systems Topological data analysis (TDA), on the other hand, studies the topological properties of data sets. You can use TDA for clustering, classification, and anomaly detection. The team DataRefiner developed a new approach to integrating causal discovery with TDA segmentation, and they are getting the best results from anything in the market right now. For the first time, there's a tool where users can choose clusters and get a focused causal dependency graph. They use boosting trees to estimate causal effects in complex systems. They complement this approach with TDA by offering insights into potential causal pathways. With TDA, users can visualize and understand the relationship between variables. Most open-source systems struggle with categorical parameters or values with different scales. This new approach doesn’t have those problems. Look at the attached video to understand what you can do with this. Here is a link to a post with all of the details. It contains three detailed examples that will drive the idea home: Thanks to DataRefiner for sponsoring this post.
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Big step forward for root cause analysis in real-world applications! There’s a new method that will help identify the causes of a problem or event. It uses causal discovery, boosting trees together with TDA. This is crucial to enable root cause analysis in tasks like the following: • Fraud detection • Drug discovery • Customer behavior analysis • Energy and sustainability • Financial analysis and risk management • Failure analysis in engineering systems Topological data analysis (TDA), on the other hand, studies the topological properties of data sets. You can use TDA for clustering, classification, and anomaly detection. The team DataRefiner developed a new approach to integrating causal discovery with TDA segmentation, and they are getting the best results from anything in the market right now. For the first time, there's a tool where users can choose clusters and get a focused causal dependency graph. They use boosting trees to estimate causal effects in complex systems. They complement this approach with TDA by offering insights into potential causal pathways. With TDA, users can visualize and understand the relationship between variables. Most open-source systems struggle with categorical parameters or values with different scales. This new approach doesn’t have those problems. Look at the attached video to understand what you can do with this. Here is a link to a post with all of the details. It contains three detailed examples that will drive the idea home: Thanks to DataRefiner for sponsoring this post.

Santiago

234,796 просмотров • 2 лет назад

$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 $BTC time data - .csv file (can get this information from multiple venues - Binance is where I got mine) > creating bins - if you have a larger data set - you can use recent data to prevent overt bias in the long direction or filter consolidation and trending regime data into separate bins for statistical analysis - however, you will have to define thresholds and determine what that entails. > defining metrics in Claude that you want to use for statistical analysis e.g. for z-score what is it based on and what type of calculation? make sure you understand the calculations being performed for any metrics that you are doing a study for and modify them accordingly. > prompting Claude to do a statistical analysis with specific instructions and then tell it to create visualization for this. I've been messing around with this and I'm seriously impressed by the output.
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$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 просмотров • 1 год назад

Meet Data Scout. It's a $500k data scientist for every employee. One of those agents every business needs. > Has Snowflake access (+ Notion, Slack, Github) > Runs deep analysis on all user and account data > Can be called by other agents for recurring reports Data Scout is one of the most used agents at Notion. And you can build one.
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Meet Data Scout. It's a $500k data scientist for every employee. One of those agents every business needs. > Has Snowflake access (+ Notion, Slack, Github) > Runs deep analysis on all user and account data > Can be called by other agents for recurring reports Data Scout is one of the most used agents at Notion. And you can build one.

Hurley

33,300 просмотров • 4 месяцев назад

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.
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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 просмотров • 3 месяцев назад

DocETL is a system we’ve been building at Berkeley for the past two years to make large-scale unstructured data analysis reliable and efficient. It powers our broader stack—used by journalists, public defenders, and researchers—to extract, transform, and reason over messy documents with LLMs. As part of making the DocETL ecosystem easier to use, we’re introducing a natural language–to–pipeline generator! Our hosted version is free to use BUT we're collecting the data so we can build better tools.
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DocETL is a system we’ve been building at Berkeley for the past two years to make large-scale unstructured data analysis reliable and efficient. It powers our broader stack—used by journalists, public defenders, and researchers—to extract, transform, and reason over messy documents with LLMs. As part of making the DocETL ecosystem easier to use, we’re introducing a natural language–to–pipeline generator! Our hosted version is free to use BUT we're collecting the data so we can build better tools.

Shreya Shankar

14,428 просмотров • 9 месяцев назад

I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):
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I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):

Matt Dancho (Business Science)

12,736 просмотров • 1 год назад

How to Create a Professional Data Analysis Report—Even If You’re Not a Data Specialist Today on Agent 101—MuleRun’s first review series where real users test AI agents in real work scenarios—we introduce “Smart Q,” a data analysis expert agent designed to turn anyone into a data-savvy reporter. 1. Team Expertise Smart Q was developed by a team with over 10 years of data analysis experience at a giant corporation. This background ensures that the agent delivers insights and reports that meet professional standards. 2. The Traditional Approach & Its Pain Points Traditionally, creating a data analysis report required deep expertise in tools like Excel, SQL, or Python. You’d need to clean the data, run calculations, generate visualizations, and summarize findings—all of which is time-consuming and prone to human error. For non-specialists, this process is often inaccessible and intimidating. 3. How Smart Q Uses AI—and What Problems It Solves With Smart Q, the entire reporting process is simplified into three steps: upload your raw data, ask a question, and receive charts, key insights, and a polished report—all generated by AI expert. Its key advantages include: ✅Accessibility:No technical background required. ✅Speed:Get a complete analysis in minutes, not hours. ✅Clarity:Receive expert-level conclusions presented in clear, actionable language. Want to become a tester for future AI agents? Engage with this video—comment, like, or share—and we’ll be sure to notice your support! 🎥 See Smart Q in action. #mulerun #mulerun4U #SmartQ
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How to Create a Professional Data Analysis Report—Even If You’re Not a Data Specialist Today on Agent 101—MuleRun’s first review series where real users test AI agents in real work scenarios—we introduce “Smart Q,” a data analysis expert agent designed to turn anyone into a data-savvy reporter. 1. Team Expertise Smart Q was developed by a team with over 10 years of data analysis experience at a giant corporation. This background ensures that the agent delivers insights and reports that meet professional standards. 2. The Traditional Approach & Its Pain Points Traditionally, creating a data analysis report required deep expertise in tools like Excel, SQL, or Python. You’d need to clean the data, run calculations, generate visualizations, and summarize findings—all of which is time-consuming and prone to human error. For non-specialists, this process is often inaccessible and intimidating. 3. How Smart Q Uses AI—and What Problems It Solves With Smart Q, the entire reporting process is simplified into three steps: upload your raw data, ask a question, and receive charts, key insights, and a polished report—all generated by AI expert. Its key advantages include: ✅Accessibility:No technical background required. ✅Speed:Get a complete analysis in minutes, not hours. ✅Clarity:Receive expert-level conclusions presented in clear, actionable language. Want to become a tester for future AI agents? Engage with this video—comment, like, or share—and we’ll be sure to notice your support! 🎥 See Smart Q in action. #mulerun #mulerun4U #SmartQ

MuleRun

30,549 просмотров • 8 месяцев назад

Thrilled to announce a partnership with Anthropic! Together, we are combining Planet’s deep stack of daily satellite data with Anthropic’s Claude LLM to improve analysis of complex visual information at global scale. This is a win-win: AI helps unlock the value of satellite data — helping analysis at scale for present users and making analysis easier, opening it to a broader set of users. Meanwhile, satellite data helps AI models learn how to tackle real world problems. I recently interviewed Dario Amodei, CEO of Anthropic. Teaser with this post; full interview here: Partnership details:
1:58

Thrilled to announce a partnership with Anthropic! Together, we are combining Planet’s deep stack of daily satellite data with Anthropic’s Claude LLM to improve analysis of complex visual information at global scale. This is a win-win: AI helps unlock the value of satellite data — helping analysis at scale for present users and making analysis easier, opening it to a broader set of users. Meanwhile, satellite data helps AI models learn how to tackle real world problems. I recently interviewed Dario Amodei, CEO of Anthropic. Teaser with this post; full interview here: Partnership details:

Will Marshall

19,444 просмотров • 1 год назад

New from the Anthropic Economic Index: the first comprehensive analysis of how AI is used in every US state and country we serve. We've produced a detailed report, and you can explore our data yourself on our new interactive website.
0:50

New from the Anthropic Economic Index: the first comprehensive analysis of how AI is used in every US state and country we serve. We've produced a detailed report, and you can explore our data yourself on our new interactive website.

Anthropic

303,950 просмотров • 11 месяцев назад

One of the major challenges of Traders is inability to do a well-detailed analysis on the Trade they want to take. Therefore, I will release the full video of this tomorrow where I did a well-detailed analysis on EURUSD and also ended it with a live analysis of what I expect next on EURUSD. You will be doing a lot of people a great good by sharing this. Subscribe and on your notifications for my YouTube channel where I will drop it (Link in the comment section below)
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One of the major challenges of Traders is inability to do a well-detailed analysis on the Trade they want to take. Therefore, I will release the full video of this tomorrow where I did a well-detailed analysis on EURUSD and also ended it with a live analysis of what I expect next on EURUSD. You will be doing a lot of people a great good by sharing this. Subscribe and on your notifications for my YouTube channel where I will drop it (Link in the comment section below)

HENDRAY

35,701 просмотров • 1 год назад

🚨 IMPORTANT GIVEAWAY 🚨 Very excited to announce that in conjunction with Professional Football Analysis and In-Play Sports it's time for a big football analysis giveaway! £400+ worth of value ... for FREE More details in the video but a great chance to undertake a professional analysis course, and grab yourself some software 💻 A great way to become more employable as we approach a busy time for new jobs being posted. To enter, all you need to to is: - Follow Chris Gill, Professional Football Analysis & Professional Football Analysis - Like this post - Retweet this post We will draw the winner in 2 weeks time. Good luck 👍🏼
2:40

🚨 IMPORTANT GIVEAWAY 🚨 Very excited to announce that in conjunction with Professional Football Analysis and In-Play Sports it's time for a big football analysis giveaway! £400+ worth of value ... for FREE More details in the video but a great chance to undertake a professional analysis course, and grab yourself some software 💻 A great way to become more employable as we approach a busy time for new jobs being posted. To enter, all you need to to is: - Follow Chris Gill, Professional Football Analysis & Professional Football Analysis - Like this post - Retweet this post We will draw the winner in 2 weeks time. Good luck 👍🏼

Chris Gill

24,745 просмотров • 2 лет назад

I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):
13:04

I'm excited to introduce my AI Exploratory Data Analysis (EDA) Agent that built EDA reports, performs correlation analysis, and missing data analysis in 20 seconds. Today, I'll share with you how to automate creating EDA reports with the AI EDA Agent, which is available on GitHub. We'll create an EDA Agent focusing on a Customer Churn Problem. I'll guide you through setting up the EDA Agent, creating EDA reports, and analyzing the findings. This AI agent is a huge time-saver! Table of Contents: 00:00 Introduction to EDA Tools Agent 01:20 Get the AI Data Science Team 04:49 Create the EDA Tools Agent 06:35 Using the EDA Tools Agent for Missing Data and Correlation Funnels 10:00 Get SweetViz EDA Reports with AI 12:00 8-Week AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: Business Intelligence with AI Register here (1570+ registered):

Matt Dancho (Business Science)

31,079 просмотров • 1 год назад