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Reflex (Reflex) is an AI app builder for creating production-grade web apps entirely in Python, powered by its own open-source framework. Just connect your data and enter a prompt. It generates production-ready apps that integrate any data source, API, or Python package. Reflex has over 1M apps created, 27k...

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Y Combinator

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18,079 次观看 • 8 个月前 •via X (Twitter)

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Today, we’re excited to announce and launch Reflex Cloud on Product Hunt! Reflex is an open-source framework for building and deploying data and AI web apps in pure Python. Frontend and Backend in Pure Python: No JavaScript required! With Reflex Cloud, you can now deploy, manage, and scale your Python apps with just a single command! If you're a Python developer, an upvote or a share would mean a lot to us :)💪❤️
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Today, we’re excited to announce and launch Reflex Cloud on Product Hunt! Reflex is an open-source framework for building and deploying data and AI web apps in pure Python. Frontend and Backend in Pure Python: No JavaScript required! With Reflex Cloud, you can now deploy, manage, and scale your Python apps with just a single command! If you're a Python developer, an upvote or a share would mean a lot to us :)💪❤️

Reflex

55,024 次观看 • 1 年前

🚀Excited to announce Superblocks AI – the AI coding assistant for internal apps! ⚡ Superblocks AI combines the power of the Superblocks drag-and-drop App Builder, with the speed of AI code generation, performance optimization, code explanation, mock data generation, and API call generation across SQL, Python, JavaScript, JSON and HTML. 🐨Our goal at Superblocks has always been to help developers ship mission-critical apps faster to elevate internal operations, and our deeply integrated AI coding assistant does exactly that! Blog:
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🚀Excited to announce Superblocks AI – the AI coding assistant for internal apps! ⚡ Superblocks AI combines the power of the Superblocks drag-and-drop App Builder, with the speed of AI code generation, performance optimization, code explanation, mock data generation, and API call generation across SQL, Python, JavaScript, JSON and HTML. 🐨Our goal at Superblocks has always been to help developers ship mission-critical apps faster to elevate internal operations, and our deeply integrated AI coding assistant does exactly that! Blog:

Brad Menezes

73,242 次观看 • 3 年前

Open Lovable is live and now has 6,000+ Github stars 💜 💙 It's an Next.js app I built that instantly reimagines any website and generates full React apps in seconds. Powered by , Groq Inc, E2B and more. Here's a complete breakdown of the project in 4 minutes👇
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Open Lovable is live and now has 6,000+ Github stars 💜 💙 It's an Next.js app I built that instantly reimagines any website and generates full React apps in seconds. Powered by , Groq Inc, E2B and more. Here's a complete breakdown of the project in 4 minutes👇

Developers Digest

49,922 次观看 • 11 个月前

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

Matt Dancho (Business Science)

12,736 次观看 • 1 年前

New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:
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New JavaScript short course: Build a full-stack web application that uses RAG in JavaScript RAG Web Apps with LlamaIndex, taught by Laurie Voss, VP of Developer Relations at LlamaIndex 🦙 and npm co-founder. - Build a RAG application for querying your own data - Develop tools to interact with multiple data sources using an agent that intelligently selects the right tool for your queries - Create a full-stack web app that can chat with your data - Dig further into production-ready techniques, like how to persist your data so you aren’t constantly reindexing, and try the create-llama command line tool from LlamaIndex You can sign up here:

Andrew Ng

218,284 次观看 • 2 年前

🚀 Exciting news! The GeoAI Python package just got a major upgrade. It now supports interactive geospatial data visualization powered by AI Agents. 🌍✨ GitHub: Full tutorial: 🌍 In this tutorial, you’ll learn how to build interactive maps powered by AI agents that respond to natural language queries. Watch how you can fly to any city, add raster or vector layers, visualize 3D buildings, and explore geospatial data—all without writing code. Perfect for developers, researchers, and anyone curious about AI, GIS, and interactive mapping. #geospatial #mapping #python #GeoAI #AI
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🚀 Exciting news! The GeoAI Python package just got a major upgrade. It now supports interactive geospatial data visualization powered by AI Agents. 🌍✨ GitHub: Full tutorial: 🌍 In this tutorial, you’ll learn how to build interactive maps powered by AI agents that respond to natural language queries. Watch how you can fly to any city, add raster or vector layers, visualize 3D buildings, and explore geospatial data—all without writing code. Perfect for developers, researchers, and anyone curious about AI, GIS, and interactive mapping. #geospatial #mapping #python #GeoAI #AI

Qiusheng Wu

12,831 次观看 • 9 个月前

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
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New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,010 次观看 • 1 年前

We’re excited to share the beta release of our tool use API! Now developers can easily equip any LLM with hosted open-source tools for web search, web crawling, and maps data (+ more coming soon). Under the hood, it's powered by MCP — but that's just an implementation detail ↓
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We’re excited to share the beta release of our tool use API! Now developers can easily equip any LLM with hosted open-source tools for web search, web crawling, and maps data (+ more coming soon). Under the hood, it's powered by MCP — but that's just an implementation detail ↓

OpenTools

125,965 次观看 • 1 年前

A toolkit for building agents that watch, listen, and understand video. Low latency by design. Open source. Production ready. Vision Agents lets you build real time video AI that works with your models and your edge layer. Supports YOLO, Moondream, Cartesia, Deepgram, ElevenLabs, HeyGen, Gemini, OpenAI, and more. Quick model switching. Easy to use API. Perfect for coaching tools, collaboration apps, avatars, and robotics.
0:43

A toolkit for building agents that watch, listen, and understand video. Low latency by design. Open source. Production ready. Vision Agents lets you build real time video AI that works with your models and your edge layer. Supports YOLO, Moondream, Cartesia, Deepgram, ElevenLabs, HeyGen, Gemini, OpenAI, and more. Quick model switching. Easy to use API. Perfect for coaching tools, collaboration apps, avatars, and robotics.

GitHub Projects Community

30,274 次观看 • 7 个月前

AI is insecure by default. The open secret is that most AI apps launch with serious flaws. It takes MORE time to secure these apps than it does to build them. I learned this firsthand from shipping LLM agents to 200M users at Discord and supporting LLM evals for hundreds of companies. It doesn’t have to be this way. Today, we’re launching promptfoo, an open-source company that helps find and fix vulnerabilities in AI powered apps before they ship, and announcing a $5M seed round led by a16z.
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AI is insecure by default. The open secret is that most AI apps launch with serious flaws. It takes MORE time to secure these apps than it does to build them. I learned this firsthand from shipping LLM agents to 200M users at Discord and supporting LLM evals for hundreds of companies. It doesn’t have to be this way. Today, we’re launching promptfoo, an open-source company that helps find and fix vulnerabilities in AI powered apps before they ship, and announcing a $5M seed round led by a16z.

Ian Webster

28,356 次观看 • 2 年前

Python is the best language in the world. Today, you can build AI and data applications using Python alone! No HTML or JavaScript necessary. Take a look at Taipy, an open-source Python library to build end-to-end production applications. Star their repository: I recorded a quick video showing how to build a simple chat interface to talk to OpenAI GPT-3.5 using Taipy alone. Something important to keep in mind: Taipy's goal is not to replace web developers but to provide an alternative to those who need to build applications without web experience. If you are a data scientist or someone dealing with data, Taipy will simplify your life considerably. Thanks to the team behind Taipy for collaborating with me on this post. Here is a link to the full demo. Download this code and run it. It takes 30 seconds:
5:13

Python is the best language in the world. Today, you can build AI and data applications using Python alone! No HTML or JavaScript necessary. Take a look at Taipy, an open-source Python library to build end-to-end production applications. Star their repository: I recorded a quick video showing how to build a simple chat interface to talk to OpenAI GPT-3.5 using Taipy alone. Something important to keep in mind: Taipy's goal is not to replace web developers but to provide an alternative to those who need to build applications without web experience. If you are a data scientist or someone dealing with data, Taipy will simplify your life considerably. Thanks to the team behind Taipy for collaborating with me on this post. Here is a link to the full demo. Download this code and run it. It takes 30 seconds:

Santiago

359,020 次观看 • 2 年前

Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.
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Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

LayerAI | AI2Earn

27,136 次观看 • 2 年前

Microsoft has launched a powerful new data analysis tool! Introducing Data Formulator, a 100% open-source LLM-powered, no-code tool that transforms data in a snap and creates stunning visualizations. Key features include: 🤖 AI-powered data transformation 🖱️ Interactive drag-and-drop UI for visualizations 💬 Seamless blend of UI & natural language inputs But that’s not all: You can even create charts beyond your initial dataset. Data Formulator automatically identifies extra computation needs, generates fields for you, and outputs the final visualization. Find the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on AI and Machine Learning.
0:24

Microsoft has launched a powerful new data analysis tool! Introducing Data Formulator, a 100% open-source LLM-powered, no-code tool that transforms data in a snap and creates stunning visualizations. Key features include: 🤖 AI-powered data transformation 🖱️ Interactive drag-and-drop UI for visualizations 💬 Seamless blend of UI & natural language inputs But that’s not all: You can even create charts beyond your initial dataset. Data Formulator automatically identifies extra computation needs, generates fields for you, and outputs the final visualization. Find the GitHub repo in the next tweet! _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials on AI and Machine Learning.

Akshay 🚀

280,449 次观看 • 1 年前

Forget about HTML, JavaScript, and CSS. You can build AI and data applications using Python alone! Python is the best programming language out there. You can now use it across the stack. Take a look at Taipy, an open-source Python library to build end-to-end production applications. Star their repository: I recorded a quick video showing how to build a simple chat interface to talk to OpenAI GPT-3.5 using Taipy alone. Something important to keep in mind: Taipy's goal is not to replace web developers but to provide an alternative to those who need to build applications without web experience. If you are a data scientist or someone dealing with data, Taipy will simplify your life considerably. Thanks to the team behind Taipy for collaborating with me on this post. Here is a link to the full demo. Download this code and run it. It takes 30 seconds:
4:22

Forget about HTML, JavaScript, and CSS. You can build AI and data applications using Python alone! Python is the best programming language out there. You can now use it across the stack. Take a look at Taipy, an open-source Python library to build end-to-end production applications. Star their repository: I recorded a quick video showing how to build a simple chat interface to talk to OpenAI GPT-3.5 using Taipy alone. Something important to keep in mind: Taipy's goal is not to replace web developers but to provide an alternative to those who need to build applications without web experience. If you are a data scientist or someone dealing with data, Taipy will simplify your life considerably. Thanks to the team behind Taipy for collaborating with me on this post. Here is a link to the full demo. Download this code and run it. It takes 30 seconds:

Santiago

233,878 次观看 • 2 年前

What if you could build production-ready apps in minutes with the skill of a senior engineer… for just $3/month? I plugged GLM-4.5 into my Python platform, and within minutes, it began shipping production-ready apps at a level that typically requires an entire development team. Here’s what it built instantly: ✅ A complete Next.js project with authentication and dashboards ✅ SQLite + Prisma with seeded data ✅ Professional, structured git commits ✅ Clear explanations of senior-level refactors You’ve been invited to join the GLM Coding Plan! Enjoy full support for Claude Code, Cline, and 10+ top coding tools — starting at just $3/month. Subscribe now and grab the limited-time deal! Link： The AI coding revolution is here today. The builders who adopt it first will lead tomorrow.
1:33

What if you could build production-ready apps in minutes with the skill of a senior engineer… for just $3/month? I plugged GLM-4.5 into my Python platform, and within minutes, it began shipping production-ready apps at a level that typically requires an entire development team. Here’s what it built instantly: ✅ A complete Next.js project with authentication and dashboards ✅ SQLite + Prisma with seeded data ✅ Professional, structured git commits ✅ Clear explanations of senior-level refactors You’ve been invited to join the GLM Coding Plan! Enjoy full support for Claude Code, Cline, and 10+ top coding tools — starting at just $3/month. Subscribe now and grab the limited-time deal! Link： The AI coding revolution is here today. The builders who adopt it first will lead tomorrow.

Vipin Gautam (Viipin I Gautam)

88,964 次观看 • 9 个月前

The cool thing about local-first web apps is you don't have to choose anymore, which was one of the goals for Thymer: real-time collaboration, E2EE sync, and web access, while your data also lives locally and the app works offline. That means tools like Claude (or local LLMs) can get fast access to your workspace data. There's already an MCP plugin and here's a quick prototype where Claude Code talks directly to offline workspace data stored in the browser DB.
0:26

The cool thing about local-first web apps is you don't have to choose anymore, which was one of the goals for Thymer: real-time collaboration, E2EE sync, and web access, while your data also lives locally and the app works offline. That means tools like Claude (or local LLMs) can get fast access to your workspace data. There's already an MCP plugin and here's a quick prototype where Claude Code talks directly to offline workspace data stored in the browser DB.

Wim Cools

31,006 次观看 • 6 个月前

Very excited to announce that Flowsta, the authentication system made possible by Holochain, is feature complete and close to public release. Here's a video with a sneak peek at the dashboard that every Flowsta user gets access to. All user data on Flowsta is stored on Holochain. 1 public hApp (Holochain App) for public data like Agent ID, DID (W3C Decentralized ID), Display Name and Thumbnail. Each user has their own private hApp that encrypts all their private data, like password, email and usage meta data. Nobody can access this data without your password and/or recovery keys. It is mathematically impossible for Flowsta staff or anybody else to access this data without you explicitly granting access. This is game-changing. True decentralized censorship resistant authentication and data. All made possible by Holochain. Our next video will give a sneak peek into the Developer Dashboard for adding Flowsta to any Web app. Not just Holochain apps. AMA.. 🎉
0:59

Very excited to announce that Flowsta, the authentication system made possible by Holochain, is feature complete and close to public release. Here's a video with a sneak peek at the dashboard that every Flowsta user gets access to. All user data on Flowsta is stored on Holochain. 1 public hApp (Holochain App) for public data like Agent ID, DID (W3C Decentralized ID), Display Name and Thumbnail. Each user has their own private hApp that encrypts all their private data, like password, email and usage meta data. Nobody can access this data without your password and/or recovery keys. It is mathematically impossible for Flowsta staff or anybody else to access this data without you explicitly granting access. This is game-changing. True decentralized censorship resistant authentication and data. All made possible by Holochain. Our next video will give a sneak peek into the Developer Dashboard for adding Flowsta to any Web app. Not just Holochain apps. AMA.. 🎉

Eric Doriean 🌱

37,049 次观看 • 7 个月前

Semrush now has an official app in ChatGPT by OpenAI enabling users to access Semrush data through natural-language prompts without leaving the chat. What this means for users: -Get on-demand SEO, traffic, and competitor insights powered by Semrush data. -Generate quick performance summaries and reports directly in ChatGPT. -Ask for keyword, backlink, or traffic breakdowns in natural language. -Track competitor trends in real time and spot new growth opportunities faster. Try it in ChatGPT Settings → apps.
0:23

Semrush now has an official app in ChatGPT by OpenAI enabling users to access Semrush data through natural-language prompts without leaving the chat. What this means for users: -Get on-demand SEO, traffic, and competitor insights powered by Semrush data. -Generate quick performance summaries and reports directly in ChatGPT. -Ask for keyword, backlink, or traffic breakdowns in natural language. -Track competitor trends in real time and spot new growth opportunities faster. Try it in ChatGPT Settings → apps.

Semrush

35,146 次观看 • 6 个月前

Vibe coders and creators can now tap into Pi Network’s massive user base of over 60 million Engaged Pioneers by easily bringing their external AI-created apps to Pi’s real distribution network and utility ecosystem through Pi App Studio! Creators, who may be technical developers or non-technical product people, can build apps using platforms such as Codex, Claude Code, Replit, Cursor, Lovable or other AI-assisted coding tools, and then use Pi App Studio to convert those apps into Pi Apps. That changes the equation for creators. AI can already help people generate apps quickly; the harder part is distribution and turning those apps into products people actually use. Pi helps close that gap. Instead of rebuilding infrastructure from scratch, creators can connect their apps to an existing ecosystem with users, payment capabilities, identity verification, decentralized human infrastructure and platform-level tools already in place. This feature makes Idea → App → Users that much faster. Because your ideas are too good not to be seen. Try the Pi App Studio Now! If you’re a vibe coder or know someone building apps with AI who needs distribution, head to Pi App Studio today to convert AI-created apps into Pi Apps and access the Pi ecosystem’s users, infrastructure, and monetization opportunities.
2:50

Vibe coders and creators can now tap into Pi Network’s massive user base of over 60 million Engaged Pioneers by easily bringing their external AI-created apps to Pi’s real distribution network and utility ecosystem through Pi App Studio! Creators, who may be technical developers or non-technical product people, can build apps using platforms such as Codex, Claude Code, Replit, Cursor, Lovable or other AI-assisted coding tools, and then use Pi App Studio to convert those apps into Pi Apps. That changes the equation for creators. AI can already help people generate apps quickly; the harder part is distribution and turning those apps into products people actually use. Pi helps close that gap. Instead of rebuilding infrastructure from scratch, creators can connect their apps to an existing ecosystem with users, payment capabilities, identity verification, decentralized human infrastructure and platform-level tools already in place. This feature makes Idea → App → Users that much faster. Because your ideas are too good not to be seen. Try the Pi App Studio Now! If you’re a vibe coder or know someone building apps with AI who needs distribution, head to Pi App Studio today to convert AI-created apps into Pi Apps and access the Pi ecosystem’s users, infrastructure, and monetization opportunities.

Pi Network

178,182 次观看 • 2 个月前

Introducing Superblocks 2.0: AI-generated enterprise apps – finally under IT control. Vibe-coded apps just became the #1 attack vector in the enterprise. Business teams are building on production data, while IT has zero visibility. No reviews. No audits. No permissions. No control. AI hackers are about to get 100x better. Anthropic proved it with Mythos. Superblocks 2.0 is the only platform to take back control: > Business teams build AI-powered apps with permissions baked in. > IT and Security can audit everything and lock down anything, instantly. > Engineering sets the standards. Every app follows them. Instacart, SoFi, and LinkedIn run Superblocks in production today. And larger organizations we can't yet name are too: A Fortune 500 just shut down 2,500 Replit users to standardize on Superblocks, running the platform air-gapped in their AWS environment. A 150,000-employee global services firm replaced Lovable with Superblocks to unlock AI-built apps on restricted internal systems. Every IT leader we’ve demoed to using Replit, Lovable or v0 asked for early access. Today we open access to the world. The genie is out of the bottle on employee vibe coding. Let it run wild, or take back control –
2:13

Introducing Superblocks 2.0: AI-generated enterprise apps – finally under IT control. Vibe-coded apps just became the #1 attack vector in the enterprise. Business teams are building on production data, while IT has zero visibility. No reviews. No audits. No permissions. No control. AI hackers are about to get 100x better. Anthropic proved it with Mythos. Superblocks 2.0 is the only platform to take back control: > Business teams build AI-powered apps with permissions baked in. > IT and Security can audit everything and lock down anything, instantly. > Engineering sets the standards. Every app follows them. Instacart, SoFi, and LinkedIn run Superblocks in production today. And larger organizations we can't yet name are too: A Fortune 500 just shut down 2,500 Replit users to standardize on Superblocks, running the platform air-gapped in their AWS environment. A 150,000-employee global services firm replaced Lovable with Superblocks to unlock AI-built apps on restricted internal systems. Every IT leader we’ve demoed to using Replit, Lovable or v0 asked for early access. Today we open access to the world. The genie is out of the bottle on employee vibe coding. Let it run wild, or take back control –

Brad Menezes

4,638,870 次观看 • 3 个月前