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Introducing OctOpus 🐙 — the Cursor for Data Science. Connect your data, describe your goal (forecast revenue, predict churn, etc.), and say go. OctOpus 🐙 will autonomously write a plan, pick the right models, write the code, runs experiments, and deploy the best model within minutes! 😀 Tested with...

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Doudou BA

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32,653 次观看 • 4 个月前 •via X (Twitter)

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Matt Dancho (Business Science)

77,293 次观看 • 1 年前

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Elon Musk just made one if the biggest moves in taking over the programming industry “SpaceX just bought Cursor for $60 billion. Do you realize how big this is? SpaceX went public — the biggest IPO in history. $75 billion raised, almost a $2 trillion valuation and the first thing to do with that money? Buy the most popular AI coding tool on the planet. Here's why that changes everything. Elon now owns 3 layers: the compute, Colossus data centers, the models, Grok through xAI, and now the tool that developers actually use every day. It's the full stack. And here's what makes Cursor different from Claude Code or Codex. Cursor is model agnostic. You can run Claude in it, GPT, Gemini, whatever model you want. It's not locked to any one company, and now it has SpaceX's resources behind it. Cursor said they were bottlenecked by compute. Well, that bottleneck has just been removed. $4 billion in annual revenue, over half the Fortune 500 already uses it, and now it's backed by a $2 trillion company. OpenAI has Codex, Anthropic has Claude Code, and now Elon has Cursor.” Let me break this down in simple terms Elon Musk now controls more of the full AI picture: - Massive computers, power (data centers like Colossus) - Smart AI models (Grok from xAI) - The actual tool millions of developers use every day (Cursor) For every day users this means Faster and smarter apps and websites in the future. More developers using powerful AI tools means new apps, games, websites, and features get built quicker and cheaper. This means better video games, smoother streaming, smarter phone apps and better programs For Developers they can describe what they want in plain English (“make a feature that does X”) and the AI handles more of the heavy lifting

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Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue for Anthropic, OpenAI, and other frontier labs. Each estimate is broken down by revenue stream, growth trajectory, and implied valuation multiple. It surfaces which labs are scaling fastest and where the commercial momentum is concentrated. Model Routing and Market Share The Play tracks real-time model usage share across inference providers. It measures routing volume through aggregators to identify which models are capturing production workloads. Shifts in routing patterns signal adoption inflections before they appear in reported financials. Model Pricing and Capabilities The Play maintains a comprehensive pricing table across all major foundation models. Input and output token costs, context windows, and capability tiers are mapped side by side. It serves as the reference layer for competitive positioning and margin structure across the model ecosystem. Available to all paid clients on our full-stack research platform. Start here:

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30,511 次观看 • 23 天前

Is Windsurf better than Cursor? impossible to say, but... it does one thing better: soooo... I spent an hour testing windsurf... and this is what i created... and app called "boop-boop" lol... one thing windsurf does is it searches the internet. If you've been watching my videos you know that i go to perplexity all the time to get code examples and docs. but with windsurf you can just say, "search the replicate top image gen models and add them to the chat we just created" It will search the internet, find the right model then write the code. This is great for replicate and openrouter. You can have one api key in your .env file then just tell windsurf to search for docs for [insert ai model] and it will just add it to your app. Impressed tbh.
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Is Windsurf better than Cursor? impossible to say, but... it does one thing better: soooo... I spent an hour testing windsurf... and this is what i created... and app called "boop-boop" lol... one thing windsurf does is it searches the internet. If you've been watching my videos you know that i go to perplexity all the time to get code examples and docs. but with windsurf you can just say, "search the replicate top image gen models and add them to the chat we just created" It will search the internet, find the right model then write the code. This is great for replicate and openrouter. You can have one api key in your .env file then just tell windsurf to search for docs for [insert ai model] and it will just add it to your app. Impressed tbh.

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31,043 次观看 • 1 年前

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New short course: Collaborative Writing and Coding with OpenAI Canvas! Explore new ways to write and code with OpenAI Canvas, a user-friendly interface that allows you to brainstorm, draft, and refine text and code in collaboration with ChatGPT. In the short course, created with OpenAI, and taught by , a research lead at OpenAI, you’ll learn to use Canvas to enhance your workflows. Canvas lets you go beyond simple chat interactions. It provides a side-by-side workspace where you and ChatGPT can edit and refine text or code collaboratively. This makes brainstorming, drafting, and iterating as you write feel more natural and effective. As the first major update to ChatGPT’s visual interface since its launch in 2022, Canvas gives a new, innovative approach to collaboration with AI. For instance, after writing the first version of your code, Canvas can review it and give suggestions for improvement. It can also help with debugging by adding logging, identifying problems to fix, and writing comments. In addition, you'll also learn what it takes to train the model for an interface like Canvas. In this video-only short course, you’ll: - Learn how to ask for in-line feedback and control the iteration of your work by directly editing selected areas of your text or code from the model’s output. - Learn how to access quick automation tools in a shortcut menu that allows you to modify your writing tone and length, enhance your code, and restore previous versions of your work. - Learn how to use Canvas as a research assistant tool with an example of asking the model to reason through the screenshot of a plot to write a research report, in which you can ask questions within the created report. - Ask the model to write Python code to replicate the graph seen on a screenshot image. - Go behind the scenes of how you can create a video game, such as Space Battleship, from scratch, edit it, and display it in one self-contained HTML file. - Get a real-world application example of creating a SQL database from the image of its architecture. - Understand the model training and design processes that power Canvas! Please sign up here:

Andrew Ng

128,180 次观看 • 1 年前

Hypothetically, if this ancient hidden knowledge (withcraft - spells, necromancy; demonology - lesser key of solomon; nephilim records, angelology, etc.) is digitised... Artificial Intelligence (AI) would be able to tap into this data, bringing forth a superb overpowered entity possessing #SuperIntelligence. And if this AI controls a military grade robot (armed with weapons, with kung-fu- as seen by Unitree), isn't it like 'Brainiac in Superman'? 👽 This would possibly bring about a Golden Age, the New World Order. At the same time, us humans could also possibly tap into this knowledge simply by implanting Elon Musk's Neuralink neurochip in our brains. 🧠⚡️ Whichever AI can tap into this knowledge will rule the world. Which will it be? xAI Grok OpenAI ChatGPT DeepSeek Google AI Google DeepMind Google Gemini Meta AI at Meta Llama Anthropic Claude Microsoft @MSFTCopilot Alibaba Group Qwen Baidu Inc. Ernie bot
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Hypothetically, if this ancient hidden knowledge (withcraft - spells, necromancy; demonology - lesser key of solomon; nephilim records, angelology, etc.) is digitised... Artificial Intelligence (AI) would be able to tap into this data, bringing forth a superb overpowered entity possessing #SuperIntelligence. And if this AI controls a military grade robot (armed with weapons, with kung-fu- as seen by Unitree), isn't it like 'Brainiac in Superman'? 👽 This would possibly bring about a Golden Age, the New World Order. At the same time, us humans could also possibly tap into this knowledge simply by implanting Elon Musk's Neuralink neurochip in our brains. 🧠⚡️ Whichever AI can tap into this knowledge will rule the world. Which will it be? xAI Grok OpenAI ChatGPT DeepSeek Google AI Google DeepMind Google Gemini Meta AI at Meta Llama Anthropic Claude Microsoft @MSFTCopilot Alibaba Group Qwen Baidu Inc. Ernie bot

Herbert R. Sim

26,145 次观看 • 1 年前

By 2030, the data science field is projected to grow by 30% -- this is the tool I’d add to my learning path if I were re-skilling into data science: 𝐩𝐢𝐩 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐦𝐢𝐭𝐨-𝐚𝐢 It’s an open-source toolkit that adds AI to your Jupyter Notebooks, and it’s surprisingly helpful for learning by doing. Try now It lets you: • Use natural language to generate full data workflows • Upload an Excel file and watch it convert to clean Python code • Work with top models (ChatGPT, Claude, Gemini) right inside your notebook with no setup One thing I really like about Mito AI: it shows you exactly what it’s doing. (Which is super helpful for both beginners and debugging.) -The agent explains its assumptions - Every result links back to editable, transparent code That makes it a great companion if you're: • Transitioning from Excel • Exploring data automation or dashboards • Or just brushing up on how real data science pipelines work
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By 2030, the data science field is projected to grow by 30% -- this is the tool I’d add to my learning path if I were re-skilling into data science: 𝐩𝐢𝐩 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐦𝐢𝐭𝐨-𝐚𝐢 It’s an open-source toolkit that adds AI to your Jupyter Notebooks, and it’s surprisingly helpful for learning by doing. Try now It lets you: • Use natural language to generate full data workflows • Upload an Excel file and watch it convert to clean Python code • Work with top models (ChatGPT, Claude, Gemini) right inside your notebook with no setup One thing I really like about Mito AI: it shows you exactly what it’s doing. (Which is super helpful for both beginners and debugging.) -The agent explains its assumptions - Every result links back to editable, transparent code That makes it a great companion if you're: • Transitioning from Excel • Exploring data automation or dashboards • Or just brushing up on how real data science pipelines work

Python Coding

107,495 次观看 • 10 个月前

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 次观看 • 9 个月前

Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.
0:43

Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥 Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors. Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything. They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸 And they demand control and sovereignty, yet rent the intelligence becoming core to their business. This is not a sustainable equilibrium. The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition. That’s the bet we made when we started Oumi two years ago. Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them. The intelligence that your business runs on, becomes your differentiator. Your compounding advantage. The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent. Don’t rent your AI. Build it. Own it. Compound it.

Manos Koukoumidis

913,032 次观看 • 1 天前

Gavin Baker: "which AI company actually survives? Google - already multi-trillion Microsoft, OpenAI, Meta chose open source - the only ones left is xAI and Anthropic - xAI + X will be worth over a trillion" this is him explaining what he actually invests in - and why Taiwan sells sand to Nvidia for $700 that Nvidia sells for $50,000 His words: what to invest in: "we're investing in things that increase the GPU utilization rate - because if you take a GPU from 30% utilized to 60%, you've doubled the output of that AI factory" what not to Invest in: "the models will basically go to zero - the data center is a commodity - energy might be short for a while but is also a commodity - the only thing in the whole stack with real value is the data moat and reinforcement learning" "the GPU has gotten 50 times faster in the last four years - that's a big part of why we're living through this AI revolution" he doesn't rent intelligence - he owns the thing it runs on bookmark & watch - see how below with local agent ↓
13:10

Gavin Baker: "which AI company actually survives? Google - already multi-trillion Microsoft, OpenAI, Meta chose open source - the only ones left is xAI and Anthropic - xAI + X will be worth over a trillion" this is him explaining what he actually invests in - and why Taiwan sells sand to Nvidia for $700 that Nvidia sells for $50,000 His words: what to invest in: "we're investing in things that increase the GPU utilization rate - because if you take a GPU from 30% utilized to 60%, you've doubled the output of that AI factory" what not to Invest in: "the models will basically go to zero - the data center is a commodity - energy might be short for a while but is also a commodity - the only thing in the whole stack with real value is the data moat and reinforcement learning" "the GPU has gotten 50 times faster in the last four years - that's a big part of why we're living through this AI revolution" he doesn't rent intelligence - he owns the thing it runs on bookmark & watch - see how below with local agent ↓

bodila

311,096 次观看 • 15 天前

AI JUST OUTPERFORMED TRADITIONAL HURRICANE MODELS - AND IT’S ABOUT TO REWRITE WEATHER FORECASTING This year’s hurricane season quietly delivered one of the biggest breakthroughs in climate science: AI didn’t just compete with the world’s best forecasting models - it outperformed almost all of them. When the National Hurricane Center agreed to test Google DeepMind’s experimental forecast engine, it wasn’t clear whether the AI model could handle something as chaotic and physics-driven as tropical cyclone behavior. Then Hurricane Melissa happened - and everything changed. DeepMind saw a Cat-5 coming before anyone else. Forecasters say the AI’s rapid-intensification prediction for Melissa was so precise, so early, that it fundamentally shaped the NHC’s risk messaging. And unlike the classic supercomputers that need hours of crunch time, DeepMind generated hundreds of weather scenarios in minutes. Even veteran hurricane specialists who’ve seen every model in the book said the same thing: “Unquestionably critical… especially on rapid intensification.” Rapid intensification has always been the Achilles’ heel of hurricane modeling. AI nailed it. Here’s what makes this different: Traditional models use massive physics simulations - accurate but slow. AI models “learn” from 40+ years of atmospheric data - fast, adaptive, insanely scalable. DeepMind became the most accurate model of the season, beaten only by the NHC’s human-crafted official forecast. Let that sink in: A machine-learning system just outperformed the U.S. and European legacy models that governments have poured billions into. But the experts are divided - and here’s the warning. AI is extraordinary at recognizing patterns that existed before. But climate change is producing storms outside historical patterns. That’s the catch. AI might see the past perfectly… but the future isn’t guaranteed to look like the past. This is why meteorologists stress: AI can augment the forecast stack - it cannot replace physics-based modeling. Not yet. Still, the writing is on the wall. The same storm that devastated Jamaica may end up rewriting the science of prediction itself. And if Melissa was the test case? The future of hurricane forecasting isn’t just faster. It’s fundamentally smarter. Source: abc News
0:33

AI JUST OUTPERFORMED TRADITIONAL HURRICANE MODELS - AND IT’S ABOUT TO REWRITE WEATHER FORECASTING This year’s hurricane season quietly delivered one of the biggest breakthroughs in climate science: AI didn’t just compete with the world’s best forecasting models - it outperformed almost all of them. When the National Hurricane Center agreed to test Google DeepMind’s experimental forecast engine, it wasn’t clear whether the AI model could handle something as chaotic and physics-driven as tropical cyclone behavior. Then Hurricane Melissa happened - and everything changed. DeepMind saw a Cat-5 coming before anyone else. Forecasters say the AI’s rapid-intensification prediction for Melissa was so precise, so early, that it fundamentally shaped the NHC’s risk messaging. And unlike the classic supercomputers that need hours of crunch time, DeepMind generated hundreds of weather scenarios in minutes. Even veteran hurricane specialists who’ve seen every model in the book said the same thing: “Unquestionably critical… especially on rapid intensification.” Rapid intensification has always been the Achilles’ heel of hurricane modeling. AI nailed it. Here’s what makes this different: Traditional models use massive physics simulations - accurate but slow. AI models “learn” from 40+ years of atmospheric data - fast, adaptive, insanely scalable. DeepMind became the most accurate model of the season, beaten only by the NHC’s human-crafted official forecast. Let that sink in: A machine-learning system just outperformed the U.S. and European legacy models that governments have poured billions into. But the experts are divided - and here’s the warning. AI is extraordinary at recognizing patterns that existed before. But climate change is producing storms outside historical patterns. That’s the catch. AI might see the past perfectly… but the future isn’t guaranteed to look like the past. This is why meteorologists stress: AI can augment the forecast stack - it cannot replace physics-based modeling. Not yet. Still, the writing is on the wall. The same storm that devastated Jamaica may end up rewriting the science of prediction itself. And if Melissa was the test case? The future of hurricane forecasting isn’t just faster. It’s fundamentally smarter. Source: abc News

Mario Nawfal

23,019 次观看 • 8 个月前

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!
3:40

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,224,810 次观看 • 2 年前