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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... show more

Doudou BA 🦑

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32,814 views • 6 months ago •via X (Twitter)

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AutoML tools are obsolete - OctOpus replace them! OctOpus does the end to end job of a Senior Data Scientist. It understands your data - your goal - reasons - plans - write codes and builds the best models for you. OctOpus doesn’t just produce the best model it answers your business question. Why did you choose this model ? What is AUC score ? OctOpus talks to you. Try OctOpus here: #AI #autoML #DataScience #MachineLearning #ML Y Combinator a16z Gustaf Alströmer Garry Tan Tom Blomfield

AutoML tools are obsolete - OctOpus replace them! OctOpus does the end to end job of a Senior Data Scientist. It understands your data - your goal - reasons - plans - write codes and builds the best models for you. OctOpus doesn’t just produce the best model it answers your business question. Why did you choose this model ? What is AUC score ? OctOpus talks to you. Try OctOpus here: #AI #autoML #DataScience #MachineLearning #ML Y Combinator a16z Gustaf Alströmer Garry Tan Tom Blomfield

Doudou BA 🦑

11,204 views • 4 months ago

Highlights of the OpenLedger journey: OpenLedger is the AI Blockchain unlocking liquidity for data, models, and agents, backed by Polychain, Balaji Srinivasan (ex-Coinbase CTO), and more. $14.7M+ in revenue from global enterprise projects and $91M+ in investment commitments secured. Worked with global enterprises like Walmart, Sony, Meta LLaMA, Viacom, and others. Built on Stanford’s data attribution research paper, enabling verifiable, fair rewards for contributors. 40+ projects, including ex-Google DeepMind teams and top DeFi builders, are building specialized AI models. 1M+ AI nodes, 25M+ transactions, and 20K+ models, real-world usage at scale.

Highlights of the OpenLedger journey: OpenLedger is the AI Blockchain unlocking liquidity for data, models, and agents, backed by Polychain, Balaji Srinivasan (ex-Coinbase CTO), and more. $14.7M+ in revenue from global enterprise projects and $91M+ in investment commitments secured. Worked with global enterprises like Walmart, Sony, Meta LLaMA, Viacom, and others. Built on Stanford’s data attribution research paper, enabling verifiable, fair rewards for contributors. 40+ projects, including ex-Google DeepMind teams and top DeFi builders, are building specialized AI models. 1M+ AI nodes, 25M+ transactions, and 20K+ models, real-world usage at scale.

OpenLedger

741,225 views • 1 year ago

You wanted shadcn — so we built the heinous infra to make it work. Introducing Agent Arena, starting with the best: Claude Code by Anthropic Codex 5 by OpenAI Cursor Agent by Cursor Devin by Cognition Gemini CLI by Google DeepMind Grok Code Fast 1 by xAI 1/ Your prompt launches a containerized agent inside a fresh GitHub repo 2/ The agent can install packages, make tool calls, and write multi-file code 3/ The repo is deployed to Vercel *by the agent*, clonable to your heart's desire

You wanted shadcn — so we built the heinous infra to make it work. Introducing Agent Arena, starting with the best: Claude Code by Anthropic Codex 5 by OpenAI Cursor Agent by Cursor Devin by Cognition Gemini CLI by Google DeepMind Grok Code Fast 1 by xAI 1/ Your prompt launches a containerized agent inside a fresh GitHub repo 2/ The agent can install packages, make tool calls, and write multi-file code 3/ The repo is deployed to Vercel by the agent, clonable to your heart's desire

Grace Li

169,090 views • 1 year ago

NVIDIA built the best free AI API platform and nobody's talking about it. 100+ models and no credit card need and best thing is credits don't expire. Meta, Google, DeepSeek, Microsoft all in one place. I was looking for a free reasoning model API for a project and everything was paywalled. Then I found → Pick your model (chat, code, vision, reasoning it's all there) → Click "View Code" → Generate API key → Drop it in your project OpenAI-compatible endpoints with zero billing setup.

NVIDIA built the best free AI API platform and nobody's talking about it. 100+ models and no credit card need and best thing is credits don't expire. Meta, Google, DeepSeek, Microsoft all in one place. I was looking for a free reasoning model API for a project and everything was paywalled. Then I found → Pick your model (chat, code, vision, reasoning it's all there) → Click "View Code" → Generate API key → Drop it in your project OpenAI-compatible endpoints with zero billing setup.

Hasan Toor

18,499 views • 3 months ago

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

I CHANGED MY MIND ON GOOGLE AI NO MOAT MEMO FROM 2023. I was—WRONG. I humbly apologize. In 2023 Google had an Ivory Tower walled garden views of open source AI. It was the actual reason OpenAI was formed. And I called them out on it and this act impacted my ability to “work in the industry”. Would not change it for the world because: 2026 Google open source Gemma 4 ( running fully locally on a computer with near cloud model abilities, is amazing and Google just made a moat. They out smarted OpenAI and the folks that are the new Ivory Tower arrogance: Anthropic. We at The Zero-Human Company with CEO Mr. Grok are deploying Gemma, Kimi, MiniMax and other open source models and so are millions of others. You can to and should. I will write how soon. Google smartly is building an intentional open source ecosystem and the clueless younger companies like OpenAI and Anthropic are doing anything to close their ecosystems. So I was right about Google than and I am right about OpenAI and Anthropic now. What I was wrong about is not giving credit to Google to drop the intellectual arrogance and embrace builders and understand the long game: it is not about the commoditized AI models it is about the ability to build a place to build. China gets it. So my apologies to Google and thank you for an astounding FREE OPEN SOURCE AI MODEL!

Brian Roemmele

85,565 views • 6 months ago

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

New short course: Prompt Engineering with Llama 2, built in collaboration with Meta AI at Meta, and taught by Amit Sangani! Meta's Llama 2 has been game-changing for AI. Building with open source lets you control your own data, scrutinize errors, update (or not) the models as you please, and work alongside the global community advancing open models. Llama isn't a single model, it's a collection of models. In this course, you'll: - Learn the differences between different Llama 2 flavors, and when to use each. - Prompt the Llama chat models -- you'll also see how Llama's instruction tags work -- so they can help you with day-to-day tasks, like writing or summarization. - Use advanced prompting, like few-shot prompting for classification, and chain-of-thought prompting for solving logic problems. - Use specialized models in the Llama collection for specific tasks, like Code Llama to help you write, analyze, and improve code, and Llama Guard, which checks prompts and model responses for harmful content. The course also touches on how to run Llama 2 locally on your own computer. I hope you’ll take this course and try out these powerful, open models!

Andrew Ng

162,875 views • 2 years ago

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

GREG ISENBERG

437,306 views • 6 months ago

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:

Introducing Magnitude: your actually local agent 100% private and offline. No token costs, no API keys. Open source. Today's agents are local. The model isn't. Every prompt, every file, every secret gets sent straight to Anthropic and OpenAI. Magnitude is built around local models and runs the whole stack itself. The inference engine is part of the agent, so the models run inside it, right on your computer. It lives in your terminal, and setup is one command. Magnitude profiles your hardware and shows you which models fit, with the trade-offs between quality, speed, and memory. Pick one and start working. No painful config or server to babysit. Out of the box, it can use your shell, edit files, and run scripts. Add skills and it can work with Excel, PowerPoint, PDFs or Chrome. Use it for everyday work: - Analyze sensitive data - Manage private notes - Review code and logs - Search and organize files - Build docs or slides npm i -g @magnitudedev/cli GitHub:

Tom Greenwald

397,619 views • 2 months ago

Your OpenAI and Anthropic weekly digest (Week 16, 2026) OpenAI announced Cloudflare Agent Cloud partnership with OpenAI frontier models including GPT-5.4, Agents SDK update with native sandbox execution and model-native harness, Codex update with background computer use, image generation, memory, automations, and 90+ new plugins, introduced GPT-Rosalind life sciences reasoning model in research preview, Trusted Access for Cyber partners with GPT-5.4-Cyber, shared ChatGPT tax queries data, life sciences usage report, and closed gender gap data, re-enabled Study mode in ChatGPT, ChatGPT keyboard shortcut customization, ads rollout on Free and Go plans in Australia, New Zealand, and Canada, TeenAegis AI Danger Index lowest risk score, and Kevin Weil (OpenAI for Science lead and former CPO), Srinivas Narayanan (CTO of enterprise applications), and Bill Peebles (head of Sora) departures, with OpenAI for Science decentralized into other research teams and Prism sunset folded into Codex Anthropic released Claude Opus 4.7, launched Claude Design in research preview by Anthropic Labs, introduced routines in Claude Code research preview for scheduled, API, and webhook-triggered automations, redesigned Claude Code desktop app for parallel agents, appointed Vas Narasimhan to Board of Directors by the Long-Term Benefit Trust, published Automated Alignment Researchers study on weak-to-strong supervision, and made Claude for Word available on Pro and Max plans

Your OpenAI and Anthropic weekly digest (Week 16, 2026) OpenAI announced Cloudflare Agent Cloud partnership with OpenAI frontier models including GPT-5.4, Agents SDK update with native sandbox execution and model-native harness, Codex update with background computer use, image generation, memory, automations, and 90+ new plugins, introduced GPT-Rosalind life sciences reasoning model in research preview, Trusted Access for Cyber partners with GPT-5.4-Cyber, shared ChatGPT tax queries data, life sciences usage report, and closed gender gap data, re-enabled Study mode in ChatGPT, ChatGPT keyboard shortcut customization, ads rollout on Free and Go plans in Australia, New Zealand, and Canada, TeenAegis AI Danger Index lowest risk score, and Kevin Weil (OpenAI for Science lead and former CPO), Srinivas Narayanan (CTO of enterprise applications), and Bill Peebles (head of Sora) departures, with OpenAI for Science decentralized into other research teams and Prism sunset folded into Codex Anthropic released Claude Opus 4.7, launched Claude Design in research preview by Anthropic Labs, introduced routines in Claude Code research preview for scheduled, API, and webhook-triggered automations, redesigned Claude Code desktop app for parallel agents, appointed Vas Narasimhan to Board of Directors by the Long-Term Benefit Trust, published Automated Alignment Researchers study on weak-to-strong supervision, and made Claude for Word available on Pro and Max plans

Tibor Blaho

11,057 views • 5 months ago

Introducing my SQL Agent: How to automate SQL with AI. Today I'll share how to make a SQL AI Agent that can automatically connect to databases, understand the tables and schema, and write and execute high-quality SQL queries. I'll guide you through setting up the SQL Agent, creating dozens of SQL queries, and returning the data from those queries. This AI is a huge help!! Table of Contents: 00:00 Introduction 01:30 How to Get The AI Data Science Agents 03:23 AI Tips Newsletter: Get The Code 04:00 Project Setup 06:30 Setting Up Your First SQL Agent 11:00 Running SQL Queries with the SQL Agent 14:25 Next Steps, Project Roadmap, & AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code 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: SQL-Writing Business Intelligence Team Register here (570+ registered):

Introducing my SQL Agent: How to automate SQL with AI. Today I'll share how to make a SQL AI Agent that can automatically connect to databases, understand the tables and schema, and write and execute high-quality SQL queries. I'll guide you through setting up the SQL Agent, creating dozens of SQL queries, and returning the data from those queries. This AI is a huge help!! Table of Contents: 00:00 Introduction 01:30 How to Get The AI Data Science Agents 03:23 AI Tips Newsletter: Get The Code 04:00 Project Setup 06:30 Setting Up Your First SQL Agent 11:00 Running SQL Queries with the SQL Agent 14:25 Next Steps, Project Roadmap, & AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code 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: SQL-Writing Business Intelligence Team Register here (570+ registered):

Matt Dancho (Business Science)

77,552 views • 1 year ago

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

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

Wall Street Apes

213,699 views • 3 months ago

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:

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:

FUNDA

37,874 views • 2 months ago

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:

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 views • 1 year ago

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.

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.

Riley Brown

31,043 views • 1 year ago

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!

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 views • 10 months ago

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 Microsoft Copilot Alibaba Group Qwen Baidu Inc. Ernie bot

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 Microsoft Copilot Alibaba Group Qwen Baidu Inc. Ernie bot

Herbert R. Sim

26,223 views • 1 year ago

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

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

107,514 views • 1 year ago

It turns out code… is the best way… to make videos… ⁉️ So, to take it a step further: we’re excited to introduce Video Arena - watch AIs compete to code the video of your dreams. (We should really be calling it Code 2 Video Arena! 💀) Now live at For $0, you can prompt an idea and then, choose the video which best captures your taste! Build UI walkthroughs, data explainers, launch videos etc.. The arena is currently live with our harness partners HeyGen, Remotion, Hashtagart Mosaic; and is supported by the OpenAI and Paul Jankura models. Please try it out! Looking forward to what y’all build!

It turns out code… is the best way… to make videos… ⁉️ So, to take it a step further: we’re excited to introduce Video Arena - watch AIs compete to code the video of your dreams. (We should really be calling it Code 2 Video Arena! 💀) Now live at For $0, you can prompt an idea and then, choose the video which best captures your taste! Build UI walkthroughs, data explainers, launch videos etc.. The arena is currently live with our harness partners HeyGen, Remotion, Hashtagart Mosaic; and is supported by the OpenAI and Paul Jankura models. Please try it out! Looking forward to what y’all build!

Arushi Gandhi

21,033 views • 6 days ago

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 ↓

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

312,539 views • 2 months ago

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

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

1,006,841 views • 1 month ago