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Andrej Karpathy released a project scoring all 342 U.S. occupations on how exposed they are to AI using an LLM & BLS data. The college premium that defined American aspiration for decades may get repriced as AI starts doing knowledge work for free & a skilled plumber can out-earn...

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84,175 görüntüleme • 4 ay önce •via X (Twitter)

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Andrej Karpathy just dropped a project scoring every job in America on how likely an AI will replace it from 0-10 > Scraped all 342 occupations from the Bureau of Labor > Fed each one to an LLM with a detailed scoring rubric > Built an interactive treemap where rectangle size = number of jobs and color = how exposed that job is to AI The key signal in his scoring: if the work product is fundamentally digital and the job can be done entirely from a home office, exposure is inherently high. The scale: 0-1: Roofers, janitors 4-5: Nurses, retail, physicians 8-9: Software devs, paralegals, data analysts 10: Medical transcriptionists Average across all 342 occupations: 5.3/10. The entire pipeline is open source. BLS scraping, LLM scoring, the visualization. All of it. Much respect for the sensei this is scary and awesome
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Andrej Karpathy just dropped a project scoring every job in America on how likely an AI will replace it from 0-10 > Scraped all 342 occupations from the Bureau of Labor > Fed each one to an LLM with a detailed scoring rubric > Built an interactive treemap where rectangle size = number of jobs and color = how exposed that job is to AI The key signal in his scoring: if the work product is fundamentally digital and the job can be done entirely from a home office, exposure is inherently high. The scale: 0-1: Roofers, janitors 4-5: Nurses, retail, physicians 8-9: Software devs, paralegals, data analysts 10: Medical transcriptionists Average across all 342 occupations: 5.3/10. The entire pipeline is open source. BLS scraping, LLM scoring, the visualization. All of it. Much respect for the sensei this is scary and awesome

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For me, our Analyst agent in Microsoft 365 Copilot is like having a skilled data analyst on hand — all the time. It generates insights and helps you visualize complex data. Here's a quick demo on how it can make sense of marketing campaign data and help you get more done at work.
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For me, our Analyst agent in Microsoft 365 Copilot is like having a skilled data analyst on hand — all the time. It generates insights and helps you visualize complex data. Here's a quick demo on how it can make sense of marketing campaign data and help you get more done at work.

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CHINA: China is illegally using American Ai technology made by $NVDA to build a powerful Ai model called DeepSeek R1. The LLM was released it for free using an open source license. This is a massive blow to US Ai companies like OpenAi and Antropic.
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CHINA: China is illegally using American Ai technology made by $NVDA to build a powerful Ai model called DeepSeek R1. The LLM was released it for free using an open source license. This is a massive blow to US Ai companies like OpenAi and Antropic.

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THE ALPHA YOU WERE LOOKING FOR! Perle Labs a crypto project built on Solana with the aim to help and train AI models using real human knowledge and data. Perle Labs creates a system where experts do AI tasks, all on chain & transparent! Join here :

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What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership
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What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

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Exploring Andrej Karpathy Nanochat through a Knowledge Graph Love how this project combines simplicity with power. Want to navigate and understand the entire repo? Check it out here: Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository. Powered by Deep Graph MCP AI at Meta and Groq Inc
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Exploring Andrej Karpathy Nanochat through a Knowledge Graph Love how this project combines simplicity with power. Want to navigate and understand the entire repo? Check it out here: Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository. Powered by Deep Graph MCP AI at Meta and Groq Inc

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Agentic AI will transform every enterprise–but only if agents are trusted experts. The key: Evaluation & tuning on specialized, expert data. I’m excited to announce two new products to support this–Snorkel AI Evaluate & Expert Data-as-a-Service–along w/ our $100M Series D! --- Snorkel Evaluate is our new data-centric agentic AI evaluation platform for specialized, mission-critical enterprise settings where vibe checks and out-of-the-box metrics driven by simple LLM prompts are not enough. Snorkel Expert Data-as-a-Service is our white glove service for expert-level AI datasets, powering frontier LLM developers in areas like expert knowledge, reasoning, agentic action and tool use, and more! Both built on top of Snorkel AI’s Data Development Platform, using our programmatic technology to drive higher-quality expert data, faster– for getting specialized AI to real production value. If you’re building enterprise AI and want to partner around the key ingredient in AI today–the data–book a demo and let's talk! Finally, see thread for details on 🧵👇 - 📽️ A walkthrough of Snorkel Evaluate and Expert Data-as-a-Service on an agentic AI enterprise task - 📅 An upcoming event on Enterprise Agentic AI with innovators from Accenture @BNY Comcast Stanford University QBE & others - 📊 An upcoming series of benchmark datasets and model artifact releases 👀 Want early access to the full agentic AI dataset? Retweet this post and we'll send you the link!

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As AI agents get better at computer and tool use, or writing code on the fly for a task, we're going to be able to solve much broader domains of knowledge work. Here's an example of Box AI with the new Claude Skills to generate a clean powerpoint file from existing data.

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Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

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OMG Larry Ellison just exposed the plan to put every American citizen's financial and health data into a single AI database for total control. They are openly bragging about running a psychological operation on the American people to force us to be on our best behavior.
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OMG Larry Ellison just exposed the plan to put every American citizen's financial and health data into a single AI database for total control. They are openly bragging about running a psychological operation on the American people to force us to be on our best behavior.

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The Genie is out of the bottle! ✨ Meet Genie, Databox’s AI analyst. Ask your data a question. Get an answer, instantly. Use it to: - Analyze performance. - Build dashboards. - Create metrics. All from a simple prompt. Comment "Genie" and we’ll DM you a free month of our Pro plan + our AI Analyst Prompt Playbook.
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The Genie is out of the bottle! ✨ Meet Genie, Databox’s AI analyst. Ask your data a question. Get an answer, instantly. Use it to: - Analyze performance. - Build dashboards. - Create metrics. All from a simple prompt. Comment "Genie" and we’ll DM you a free month of our Pro plan + our AI Analyst Prompt Playbook.

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I made an AI data science team with Python. I'm excited to share my new project, the AI Data Science Team, a Python package for creating AI co-pilots and agents to assist in common data science tasks. I walk you through the GitHub page, showcasing the progress and potential of this project. I'll explain how these agents can streamline tasks like code generation and modeling pipeline creation, making data science projects more efficient. Introduction to AI Data Science Team Project Integrating Human-in-the-Loop Example: Accessing Agents for Feature Engineering Setting up Feature Engineering Agent Code On GitHub: Python AI-Tips Newsletter (stay updated as I build this project in the open):

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Can Eli Lilly become the largest company in the world? Jordi Visser thinks so. "The reason I believe Eli Lilly has a chance to be the largest company in the world, and the number one AI company in the world within 5 years, is because they are building a specialized model." "They have their own data center with 1,000 GPUs with all the data that $LLY has had [as] a 150 year old company." "They are creating an innovation hub of using data all for human software."
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Can Eli Lilly become the largest company in the world? Jordi Visser thinks so. "The reason I believe Eli Lilly has a chance to be the largest company in the world, and the number one AI company in the world within 5 years, is because they are building a specialized model." "They have their own data center with 1,000 GPUs with all the data that $LLY has had [as] a 150 year old company." "They are creating an innovation hub of using data all for human software."

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MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.
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MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

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How to automate data cleaning with AI. Today, I'll show you how to automate data cleaning using my free AI co-pilot available on GitHub. We'll create an AI co-pilot to automate data-cleaning tasks, focusing on cleaning a customer churn dataset. I'll guide you through setting up the co-pilot and using it effectively. This AI agent is a big help. Table of Contents: 00:00 Introduction 02:52 Setting Up Libraries 06:31 Setting Up LLMs 08:31 Creating Data Cleaner Code 11:02 Data Cleaning Process 13:02 Examining Data Cleaner Function 15:02 Final Thoughts Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter:

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With the boom in AI driving the growth of digital data at an unprecedented rate, traditional magnetic tapes that we have been using for 50 years as a storage option may not be enough, according to a UK start-up who are developing a low-energy replacement
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With the boom in AI driving the growth of digital data at an unprecedented rate, traditional magnetic tapes that we have been using for 50 years as a storage option may not be enough, according to a UK start-up who are developing a low-energy replacement

Reuters

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Andrej Karpathy beautifully explains the fundamental difference of learning between a human and an LLM. > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” many of the best minds in this field think LLMs are not really learning anything, and therefore are incapable of surpassing human-level intelligence.
7:01

Andrej Karpathy beautifully explains the fundamental difference of learning between a human and an LLM. > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” many of the best minds in this field think LLMs are not really learning anything, and therefore are incapable of surpassing human-level intelligence.

ℏεsam

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1/3 Zesh AI Layer is an advanced AI solution on SUI, powered by a library of data models that act as its engine. Zesh layer is designed for building cutting-edge AI products, giving every project on Zesh access to its expansive and ever-evolving library of data models. As more products, users, and activity takes place in the Zesh ecosystem, the data model library continually learns and grows. This is AI that evolves with you.
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1/3 Zesh AI Layer is an advanced AI solution on SUI, powered by a library of data models that act as its engine. Zesh layer is designed for building cutting-edge AI products, giving every project on Zesh access to its expansive and ever-evolving library of data models. As more products, users, and activity takes place in the Zesh ecosystem, the data model library continually learns and grows. This is AI that evolves with you.

Zesh AI Layer

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