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I refuse to publish a tutorial unless I am impressed by it, here is a sneak peek of the upcoming "Cursor clone" 🙏 - Agent mode Inngest - Live preview StackBlitz - Shadow AI suggestion - Quick AI edit - Realtime database Convex - Sync engine - Collapsible sidebars... show more

Code With Antonio

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62,424 görüntüleme • 7 ay önce •via X (Twitter)

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💥 Introducing Polaris, an AI-powered code editor like Cursor or Replit. Stop using AI coding tools. Start building them. 😎 ✏️ Full code editor with syntax highlighting & minimap 🤖 AI-powered code suggestions & quick edit 🧠 AI agent with file manipulation tools 📚 Live docs scraping with Firecrawl ▶️ In-browser code execution with WebContainers 🖼️ Live preview with terminal output 🐙 GitHub import & export ⚡ Background job execution with Inngest 🗄️ Real-time database with Convex 🔐 Authentication with Clerk 🐛 Error tracking, monitoring & logs with Sentry 🐰 AI PR reviews with CodeRabbit 🌐 Next.js 16 + TypeScript 🎨 shadcn UI + Tailwind CSS v4

💥 Introducing Polaris, an AI-powered code editor like Cursor or Replit. Stop using AI coding tools. Start building them. 😎 ✏️ Full code editor with syntax highlighting & minimap 🤖 AI-powered code suggestions & quick edit 🧠 AI agent with file manipulation tools 📚 Live docs scraping with Firecrawl ▶️ In-browser code execution with WebContainers 🖼️ Live preview with terminal output 🐙 GitHub import & export ⚡ Background job execution with Inngest 🗄️ Real-time database with Convex 🔐 Authentication with Clerk 🐛 Error tracking, monitoring & logs with Sentry 🐰 AI PR reviews with CodeRabbit 🌐 Next.js 16 + TypeScript 🎨 shadcn UI + Tailwind CSS v4

Code With Antonio

86,243 görüntüleme • 6 ay önce

AIDesigner is officially live! I think we can all agree that vibe coded apps look horrible, so I built an AI UI designer that creates actually beautiful UI in seconds. • Stunning designs from the simplest of prompts. • Design, edit, iterate all on a single canvas. • Copy styles from any URL. • Publish in a single click (subdomain or custom). • Designs for both web and mobile. • Export to code and image. Great UI design doesn't have to be difficult. Components, mobile apps, websites, AIDesigner does it all. Try it for free:

AIDesigner is officially live! I think we can all agree that vibe coded apps look horrible, so I built an AI UI designer that creates actually beautiful UI in seconds. • Stunning designs from the simplest of prompts. • Design, edit, iterate all on a single canvas. • Copy styles from any URL. • Publish in a single click (subdomain or custom). • Designs for both web and mobile. • Export to code and image. Great UI design doesn't have to be difficult. Components, mobile apps, websites, AIDesigner does it all. Try it for free:

Wiz

17,068 görüntüleme • 6 ay önce

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

118,124 görüntüleme • 1 ay önce

The money printer is live 🤯 Take old webpages → Make them modern ✨ Still work in progress, but my inbox is exploding for people who wants to try this. 🔥 This was made for the Norwegian market, so still some sentences that i need to clean up, but here it is. ◆ Input URL of a website ◆ Scrape the site and make a schema from AI ◆ Have a template to populate a siteConfig Gonna push this trough the weekend, if you want to contribute come and join ✨ Repo is 100% Free and Open Source Tech Stack: shadcn Vercel GitHub Prisma Postgres Better Auth #opensource #buildinpublic

The money printer is live 🤯 Take old webpages → Make them modern ✨ Still work in progress, but my inbox is exploding for people who wants to try this. 🔥 This was made for the Norwegian market, so still some sentences that i need to clean up, but here it is. ◆ Input URL of a website ◆ Scrape the site and make a schema from AI ◆ Have a template to populate a siteConfig Gonna push this trough the weekend, if you want to contribute come and join ✨ Repo is 100% Free and Open Source Tech Stack: shadcn Vercel GitHub Prisma Postgres Better Auth #opensource #buildinpublic

CodeHagen

23,469 görüntüleme • 1 yıl önce

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 görüntüleme • 1 yıl önce

Everyone is talking about Vibe Coding (Using AI to Create Apps Only using AI) This is the most Comprehensive Guide for Vibe Coding with Cursor (By Far) 250 Minutes, All the vibe code basics of cursor, plus 4 Projects in one video! This is how I, as someone who has never written a line of code, approach building apps (every day). Part 1A Intro to Cursor, Composer, and some basics --------------------- 00:00 Intro 03:41 Downloading Cursor 06:09 What the hell is Composer? 10:47 A Note on Context and Keeping Composer Threads Small 11:38 Simple Desings with Cursor Composer From Blank Project 14:04 Editing a Simple Animation With Cursor Composer 16:35 Setting Up The Voice to Talk to Cursor Composer Whispr Flow 17:54 Lets an Early 2000's Landing Page Part 1B AI Image Generator --------------------- 23:59 Using the GitHub Template to Create a NextJS App 26:43 Template is Open, Let's Edit it 28:55 Drawing Out My Idea With Whimsical 30:11 First Prompt Using Place Holders For Image Generation 32:10 Accept All Vs Save All and Restoring in Composer (Saving your work) 33:54 Adding AI Feature (Brief Teaser, Deep Dive Later) 35:15 What is an API 37:22 Perplexity the best place to learn about API's 40:21 Api keys and running prompt for first AI Feature 42:48 Debugging, Woohoo! Learn to love this :) 43:20 Inspect - Console, In Browser Debugging Hack 48:02 AI Image Generation Works! Lets add more Part 2: Landing Page ---------------------- 51:03 Pause and Reflect, What have we done so far? 53:41 Plan for rest of video 54:34 Ok Let's Talk about (1) Designs 56:19 GitHub is like --sref for those who do image gen 58:20 Starting Cursor project from a GitHub Repo we found on Perplexity 01:00:48 Yolo Mode... Wtf is that? 01:02:38 Inspecting GitHub Repo's Examples, to use in our landing page 01:02:58 The Project We're making - A landing page 01:03:56 Landing Page from Screenshot 01:06:17 Making Changes to Landing Page 01:11:42 Making a more epic section 01:13:42 The Essence of Vibe Coding 01:15:17 Creating Cool Testimonials Section From Screenshot 01:18:18 Deploy to Vercel! But First New Repo on GitHub 01:20:45 Ok it's on GitHub... Now lets do vercel 01:21:17 Untechnical Explanation of what Vercel is Lol 01:24:18 Connecting Custom Domain (Bought on Name Cheap) To Vercel Deployment Part 3: App With Database and Authentication ---------------------- 01:27:59 Recap and Prep For The Bigger Project! 01:35:13 Getting Started from Template (Again) 01:38:52 Setting Up Database and Authentication (Firebase) 01:44:01 Back To Cursor, Let's Set up The Auth in the app 01:48:35 Switching to mermaid because compatibility issues 01:51:13 Using AI (Claude) to Generate Mermaid Diagrams 01:52:19 Adding Docs to Cursor to use AI Features over and over again 01:54:38 Let's Troubleshoot 01:56:10 Adding View Button and EDIT WITH AI 02:01:45 AI Diagram Edit Feature is DOPE 02:03:17 Using Search Feature on Cursor to find text in Codebase 02:05:55 Lets add ability to save these to Database 02:09:33 What does saved to Google Firebase even mean? 02:13:00 We can Export as PDF! 02:15:48 GitHub and Vercel Again! 02:17:27 Vercel with CLI From Cursor 02:20:52 Setting Vercel Domain as an Authorized Domain 02:27:34 How To Learn More

Everyone is talking about Vibe Coding (Using AI to Create Apps Only using AI) This is the most Comprehensive Guide for Vibe Coding with Cursor (By Far) 250 Minutes, All the vibe code basics of cursor, plus 4 Projects in one video! This is how I, as someone who has never written a line of code, approach building apps (every day). Part 1A Intro to Cursor, Composer, and some basics --------------------- 00:00 Intro 03:41 Downloading Cursor 06:09 What the hell is Composer? 10:47 A Note on Context and Keeping Composer Threads Small 11:38 Simple Desings with Cursor Composer From Blank Project 14:04 Editing a Simple Animation With Cursor Composer 16:35 Setting Up The Voice to Talk to Cursor Composer Whispr Flow 17:54 Lets an Early 2000's Landing Page Part 1B AI Image Generator --------------------- 23:59 Using the GitHub Template to Create a NextJS App 26:43 Template is Open, Let's Edit it 28:55 Drawing Out My Idea With Whimsical 30:11 First Prompt Using Place Holders For Image Generation 32:10 Accept All Vs Save All and Restoring in Composer (Saving your work) 33:54 Adding AI Feature (Brief Teaser, Deep Dive Later) 35:15 What is an API 37:22 Perplexity the best place to learn about API's 40:21 Api keys and running prompt for first AI Feature 42:48 Debugging, Woohoo! Learn to love this :) 43:20 Inspect - Console, In Browser Debugging Hack 48:02 AI Image Generation Works! Lets add more Part 2: Landing Page ---------------------- 51:03 Pause and Reflect, What have we done so far? 53:41 Plan for rest of video 54:34 Ok Let's Talk about (1) Designs 56:19 GitHub is like --sref for those who do image gen 58:20 Starting Cursor project from a GitHub Repo we found on Perplexity 01:00:48 Yolo Mode... Wtf is that? 01:02:38 Inspecting GitHub Repo's Examples, to use in our landing page 01:02:58 The Project We're making - A landing page 01:03:56 Landing Page from Screenshot 01:06:17 Making Changes to Landing Page 01:11:42 Making a more epic section 01:13:42 The Essence of Vibe Coding 01:15:17 Creating Cool Testimonials Section From Screenshot 01:18:18 Deploy to Vercel! But First New Repo on GitHub 01:20:45 Ok it's on GitHub... Now lets do vercel 01:21:17 Untechnical Explanation of what Vercel is Lol 01:24:18 Connecting Custom Domain (Bought on Name Cheap) To Vercel Deployment Part 3: App With Database and Authentication ---------------------- 01:27:59 Recap and Prep For The Bigger Project! 01:35:13 Getting Started from Template (Again) 01:38:52 Setting Up Database and Authentication (Firebase) 01:44:01 Back To Cursor, Let's Set up The Auth in the app 01:48:35 Switching to mermaid because compatibility issues 01:51:13 Using AI (Claude) to Generate Mermaid Diagrams 01:52:19 Adding Docs to Cursor to use AI Features over and over again 01:54:38 Let's Troubleshoot 01:56:10 Adding View Button and EDIT WITH AI 02:01:45 AI Diagram Edit Feature is DOPE 02:03:17 Using Search Feature on Cursor to find text in Codebase 02:05:55 Lets add ability to save these to Database 02:09:33 What does saved to Google Firebase even mean? 02:13:00 We can Export as PDF! 02:15:48 GitHub and Vercel Again! 02:17:27 Vercel with CLI From Cursor 02:20:52 Setting Vercel Domain as an Authorized Domain 02:27:34 How To Learn More

Riley Brown

367,311 görüntüleme • 1 yıl önce

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

134,714 görüntüleme • 3 ay önce

Yes here is my 10 minute breathless rant about why I'm so excited about Notion Workers + Custom Agents... Context: I spent this afternoon building a custom agent to help me manage Shiori (a side project I shipped last weekend). I gave the custom agent everything it needs to understand what's happening in my product (email, log drain, sentry alerts, stripe payments, etc) and to do work on my behalf (access to coding agents). In an afternoon of tinkering, this agent can: - Diagnose bug reports proactively by looking through past email conversations, system logs, and database records - Draft replies to user questions with the correct answer based on past email threads, or help me proactively reach out to churning paid users - Self-construct a database of feature requests with an understanding of who is requesting the feature and how they're using the product today - Answer any question I have about how people use the app and what I should be thinking about next - Initiate Claude Code workflows to open PRs proactively in the background when someone sends a bug report or feature request This custom agent is now my "Side Project Chief of Staff" (I don't really know what a chief of staff does but this sounds right). I didn't write a single line of the worker code because I didn't need to: models are so good that I can link to the Workers readme, yap my desired outcome into a microphone, and I get a super-personal and highly-capable AI agent out the other side. So fucking cool. The future is now! I'm excited to see what everyone makes.

Yes here is my 10 minute breathless rant about why I'm so excited about Notion Workers + Custom Agents... Context: I spent this afternoon building a custom agent to help me manage Shiori (a side project I shipped last weekend). I gave the custom agent everything it needs to understand what's happening in my product (email, log drain, sentry alerts, stripe payments, etc) and to do work on my behalf (access to coding agents). In an afternoon of tinkering, this agent can: - Diagnose bug reports proactively by looking through past email conversations, system logs, and database records - Draft replies to user questions with the correct answer based on past email threads, or help me proactively reach out to churning paid users - Self-construct a database of feature requests with an understanding of who is requesting the feature and how they're using the product today - Answer any question I have about how people use the app and what I should be thinking about next - Initiate Claude Code workflows to open PRs proactively in the background when someone sends a bug report or feature request This custom agent is now my "Side Project Chief of Staff" (I don't really know what a chief of staff does but this sounds right). I didn't write a single line of the worker code because I didn't need to: models are so good that I can link to the Workers readme, yap my desired outcome into a microphone, and I get a super-personal and highly-capable AI agent out the other side. So fucking cool. The future is now! I'm excited to see what everyone makes.

Brian Lovin

181,384 görüntüleme • 4 ay önce

Here is a quick walkthrough of everything new in Stitch: 🌊 Streaming: Stitch now streams its work straight to the canvas. You can see Stitch working and start steering before the final product is done! 📄 Existing Designs: Stitch can help you start with your design by creating a DESIGN.md from your codebase, .fig files, or a live website ⏩ In-place AI Edits: Stitch can quickly edit specific parts of your screens just by asking or pointing and clicking 🎥 Motion & HTML canvas: Javascript, shaders and more now render natively on an HTML canvas so you can immediately see animation, hover states and effects 🚀 Portability and Integrations: We've added a big list of import and export additions to fit right into your workflow, including import your .fig files and one-click export to Netlify, Loveable and Bolt 🌟 Bonus: New skills to IMPORT your screens directly into Stitch via MCP and sync changes back to your codebase Walkthrough video👇 Details for each improvement in 🧵

Here is a quick walkthrough of everything new in Stitch: 🌊 Streaming: Stitch now streams its work straight to the canvas. You can see Stitch working and start steering before the final product is done! 📄 Existing Designs: Stitch can help you start with your design by creating a DESIGN.md from your codebase, .fig files, or a live website ⏩ In-place AI Edits: Stitch can quickly edit specific parts of your screens just by asking or pointing and clicking 🎥 Motion & HTML canvas: Javascript, shaders and more now render natively on an HTML canvas so you can immediately see animation, hover states and effects 🚀 Portability and Integrations: We've added a big list of import and export additions to fit right into your workflow, including import your .fig files and one-click export to Netlify, Loveable and Bolt 🌟 Bonus: New skills to IMPORT your screens directly into Stitch via MCP and sync changes back to your codebase Walkthrough video👇 Details for each improvement in 🧵

Stitch by Google

21,597 görüntüleme • 2 ay önce

Deep Research is the most fun I've had with an AI tool since NotebookLM was launched! You can't imagine how incredible this agent feels for someone like me that loves doing research. I took it for a spin here and added my thoughts: I am impressed by the reasoning capabilities of the o3 model which operated under the hood. This is RL at its best once again! Synthesizing large amounts of information from different sources is no easy feat. I can tell you all about it from my experience building and scaling RAG systems. I have so many ways I plan to use this. More on this soon.

Deep Research is the most fun I've had with an AI tool since NotebookLM was launched! You can't imagine how incredible this agent feels for someone like me that loves doing research. I took it for a spin here and added my thoughts: I am impressed by the reasoning capabilities of the o3 model which operated under the hood. This is RL at its best once again! Synthesizing large amounts of information from different sources is no easy feat. I can tell you all about it from my experience building and scaling RAG systems. I have so many ways I plan to use this. More on this soon.

elvis

26,459 görüntüleme • 1 yıl önce

Got introduced to , an AI code assistant specialised for Unity by Julian Park and after dabbling with it for a day, I am really impressed with how good it is for vibe coding on the Unity Game Engine. Bezi early impressions: - Bezi works direcly on Unity with a Plugin, this means that I don't need a Unity MCP - Because Bezi is built for Unity, the success rate of feature implementations is visibly higher than general coding models - It was able to integrate Unity store assets like a Weather system to this game pretty seamlessly - I was able to add a fishing database, lighting and weather systems into this game fairly quickly I'm very impressed with the results with Bezi so far. I feel that vibe coding a full indie game on Unity and releasing it on Steam, Mobile and Web is within reach. This also validates one thing that I have been suspecting: A specialised model designed for a specific use case can perform better than a general model at the particular domain. Very exciting!

Got introduced to , an AI code assistant specialised for Unity by Julian Park and after dabbling with it for a day, I am really impressed with how good it is for vibe coding on the Unity Game Engine. Bezi early impressions: - Bezi works direcly on Unity with a Plugin, this means that I don't need a Unity MCP - Because Bezi is built for Unity, the success rate of feature implementations is visibly higher than general coding models - It was able to integrate Unity store assets like a Weather system to this game pretty seamlessly - I was able to add a fishing database, lighting and weather systems into this game fairly quickly I'm very impressed with the results with Bezi so far. I feel that vibe coding a full indie game on Unity and releasing it on Steam, Mobile and Web is within reach. This also validates one thing that I have been suspecting: A specialised model designed for a specific use case can perform better than a general model at the particular domain. Very exciting!

Danny Limanseta

48,991 görüntüleme • 4 ay önce

Everyone is sleeping on MiniMax Agent. It's like Clawdbot + Claude Cowork + Agent Skills combined. But more polished. I tested it by feeding an AI paper and asking it to create a presentation. I expected something basic. The output was actually usable. Good structure, relevant visuals, clean export. Watch the demo below. But presentations are just the start. Here's what else you can do with it: - Deep research via search, browser use, and MCPs - Build Full-stack apps with auth, DB, and Stripe - Connect to GitHub, Figma, Slack out of the box - Custom MCPs for your specific workflows - Multimodal, supports videos, audio, and images Just describe what you need, and it gets it done for you. One feature I really liked is the ability to roll back and re-run from any step using checkpoint restore if you make a wrong turn. Link to download the desktop app in the next tweet.

Everyone is sleeping on MiniMax Agent. It's like Clawdbot + Claude Cowork + Agent Skills combined. But more polished. I tested it by feeding an AI paper and asking it to create a presentation. I expected something basic. The output was actually usable. Good structure, relevant visuals, clean export. Watch the demo below. But presentations are just the start. Here's what else you can do with it: - Deep research via search, browser use, and MCPs - Build Full-stack apps with auth, DB, and Stripe - Connect to GitHub, Figma, Slack out of the box - Custom MCPs for your specific workflows - Multimodal, supports videos, audio, and images Just describe what you need, and it gets it done for you. One feature I really liked is the ability to roll back and re-run from any step using checkpoint restore if you make a wrong turn. Link to download the desktop app in the next tweet.

Akshay 🚀

23,124 görüntüleme • 5 ay önce

🌐 New website is now live! First up is the light mode, with dark mode coming soon. We're thrilled about what's next— ready to scream for these upcoming updates? 🌀 Brand New Roadmap (some product timelines may be adjusted based on development progress) 🌀 Kingnet AI V2 Official Release – A completely revamped dApp experience! Next week, we’ll officially open access to the Animation Generator and Agent, making interactions smarter and simpler than the first-gen version 🌀 Public Docs & GitHub Code Open-Source 🌀 Official Deployment on Solana & BNB Chain – Users will be able to pay on-chain to generate custom assets and export them (FBX format for direct download and use). TON integration will follow. 🌀$KNET’s First Utility Feature As always, we’ve included a surprise at the end of our video, showcasing the full workflow of AI text-to-3D model to in-game motion model: 1️⃣ Enter the Agent virtual assistant, use text prompts to generate a 3D character motion model. 2️⃣ The Agent generates a 3D base model, automatically bakes and saves textures. 3️⃣ Auto-rigging, joint adjustments, and natural motion generation. 4️⃣ Users can pay to download the FBX file with animations. 5️⃣ The file can be directly imported into Unity or Unreal Engine, with options to import animations, set animation cuts & loops—ready for in-game use! Continuous Delivery.

Kingnet AI

31,023 görüntüleme • 1 yıl önce

My upcoming release of "Cinematic AI" has made me think about how bodies of work are formed. I think some are manifested and some are revealed. I think "Cinematic AI" falls into the latter category. It was not something I had a clear vision for and then created, but it was something that was revealed to me over time. I thought I saw a glimmer of it early on, but only after some time had past, could I see the body of work emerge. With a bit more context and watching great artists and how they work and think, it finally came to me that this series of 15-20 pieces that I had created could be a worthy collection to mint. AI art has been going through so many transformations from the early GAN work to Collaborative AI to now AI being widely accessible through platforms like MidJourney, Stable Diffusion, and many others. But one area that I have seen explode in recent months is cinematic AI. I distinguish this from animated AI, which has been around for a while, but cinematic AI is where the movements created by AI are getting closer to what you'd see in a movie or captured on a video camera. It still has a long way to go but it is getting more real than surreal as the technology develops. And this is where I seem to have found my groove, my home, my little corner in the artistic landscape. After Runway launched their image-to-video tool, it just blew my mind and I went down a deep rabbit hole and have created a new cinematic AI piece almost every other day for the past few months. I initially saw many of these pieces as just experiments, but with some time to reflect, I am seeing them as having the potential of being relevant pieces of artwork to mark this time in the development of AI. In some cases, I'm not sure if I'll ever be able to create a similar piece again, since the tools I use are not in my control, but in the control of the AI platforms, who are constantly improving and evolving the tools. Given all of this, there is no better way to mark my place in time than on the blockchain. I truly believe that this body of work has the potential to be an important artifact of this era in AI and AI art. It is always hard to judge ones own work, but what I can do is permanently etch in time on an immutable public database saying that I created this. Only time will tell if the work has any value or is of any significance, but who created it and what was created cannot be disputed. I hope this gives you and especially collectors some perspective on the work I'll be releasing next week. I'm still very early in my artistic journey, but hopefully some of you will see promise in what I'm doing and maybe even put in a early bet on my art practice by bidding on a piece next week. Thanks to all of you who have supported me, taught me, advised me, been a friend to me. Much love and respect.🙏 ------------------------------------- CINEMATIC AI October 25, 2023 Marking on the blockchain, establishing historical provenance for a cinematic AI body of work. Minting on Transient Labs ERC-721TL Listing on SuperRare

My upcoming release of "Cinematic AI" has made me think about how bodies of work are formed. I think some are manifested and some are revealed. I think "Cinematic AI" falls into the latter category. It was not something I had a clear vision for and then created, but it was something that was revealed to me over time. I thought I saw a glimmer of it early on, but only after some time had past, could I see the body of work emerge. With a bit more context and watching great artists and how they work and think, it finally came to me that this series of 15-20 pieces that I had created could be a worthy collection to mint. AI art has been going through so many transformations from the early GAN work to Collaborative AI to now AI being widely accessible through platforms like MidJourney, Stable Diffusion, and many others. But one area that I have seen explode in recent months is cinematic AI. I distinguish this from animated AI, which has been around for a while, but cinematic AI is where the movements created by AI are getting closer to what you'd see in a movie or captured on a video camera. It still has a long way to go but it is getting more real than surreal as the technology develops. And this is where I seem to have found my groove, my home, my little corner in the artistic landscape. After Runway launched their image-to-video tool, it just blew my mind and I went down a deep rabbit hole and have created a new cinematic AI piece almost every other day for the past few months. I initially saw many of these pieces as just experiments, but with some time to reflect, I am seeing them as having the potential of being relevant pieces of artwork to mark this time in the development of AI. In some cases, I'm not sure if I'll ever be able to create a similar piece again, since the tools I use are not in my control, but in the control of the AI platforms, who are constantly improving and evolving the tools. Given all of this, there is no better way to mark my place in time than on the blockchain. I truly believe that this body of work has the potential to be an important artifact of this era in AI and AI art. It is always hard to judge ones own work, but what I can do is permanently etch in time on an immutable public database saying that I created this. Only time will tell if the work has any value or is of any significance, but who created it and what was created cannot be disputed. I hope this gives you and especially collectors some perspective on the work I'll be releasing next week. I'm still very early in my artistic journey, but hopefully some of you will see promise in what I'm doing and maybe even put in a early bet on my art practice by bidding on a piece next week. Thanks to all of you who have supported me, taught me, advised me, been a friend to me. Much love and respect.🙏 ------------------------------------- CINEMATIC AI October 25, 2023 Marking on the blockchain, establishing historical provenance for a cinematic AI body of work. Minting on Transient Labs ERC-721TL Listing on SuperRare

Chikai

21,488 görüntüleme • 2 yıl önce

6 months ago, building an app required: - 6 months of development - $300K budget - Team of 5 developers - Constant manual debugging Today, I'm helping a 17-year-old build one in a week. Workflow: ChatGPT -> Lovable -> Supabase -> Cursor Step-by-step how: 1/ Write your PRD first. This is non-negotiable. Clear requirements on paper = 10x faster execution with AI. We spent 30 minutes documenting exactly what PostPal needed to do. Then gave it to ChatGPT: "Create a Lovable prompt from this PRD." Copy. Paste. Done. 2/ Your first prompt sets everything. That initial Lovable prompt? It's your foundation. Give it the full high-level vision. Every feature. Every flow. Lovable uses this context for everything that follows. Get this wrong and you'll rebuild from scratch. 3/ Database first, frontend second. Biggest mistake I see: Building the entire UI, then trying to connect data. Set up Supabase immediately. Create your tables. Configure role-level security. Build backend and frontend together, not separately. 4/ Go feature by feature. Don't attack all screens at once. Pick one core feature. Build it completely. Connect it to your database. Test it. Then move to the next. Each feature should be fully functional before moving on. 5/ Chat mode is your debugging superpower. When something breaks (it will): → Use chat mode to diagnose → Let it explain the issue → Switch to agent mode to fix Chat mode for understanding. Agent mode for implementing. This combo saved us hours. 6/ Security isn't optional. Before deploying: → Enable row-level security → Secure your edge functions → Check your API endpoints → Run Lovable's security check Takes 5 minutes. Saves you from disasters. The result? A fully functional app. Not a prototype. Not a demo. A real product with authentication, database, and payments. The game has completely changed. While others debate if AI will replace developers... We're shipping products before lunch. The tools are here. The playbook is proven. The only question is: what will you build? Time to ship.

6 months ago, building an app required: - 6 months of development - $300K budget - Team of 5 developers - Constant manual debugging Today, I'm helping a 17-year-old build one in a week. Workflow: ChatGPT -> Lovable -> Supabase -> Cursor Step-by-step how: 1/ Write your PRD first. This is non-negotiable. Clear requirements on paper = 10x faster execution with AI. We spent 30 minutes documenting exactly what PostPal needed to do. Then gave it to ChatGPT: "Create a Lovable prompt from this PRD." Copy. Paste. Done. 2/ Your first prompt sets everything. That initial Lovable prompt? It's your foundation. Give it the full high-level vision. Every feature. Every flow. Lovable uses this context for everything that follows. Get this wrong and you'll rebuild from scratch. 3/ Database first, frontend second. Biggest mistake I see: Building the entire UI, then trying to connect data. Set up Supabase immediately. Create your tables. Configure role-level security. Build backend and frontend together, not separately. 4/ Go feature by feature. Don't attack all screens at once. Pick one core feature. Build it completely. Connect it to your database. Test it. Then move to the next. Each feature should be fully functional before moving on. 5/ Chat mode is your debugging superpower. When something breaks (it will): → Use chat mode to diagnose → Let it explain the issue → Switch to agent mode to fix Chat mode for understanding. Agent mode for implementing. This combo saved us hours. 6/ Security isn't optional. Before deploying: → Enable row-level security → Secure your edge functions → Check your API endpoints → Run Lovable's security check Takes 5 minutes. Saves you from disasters. The result? A fully functional app. Not a prototype. Not a demo. A real product with authentication, database, and payments. The game has completely changed. While others debate if AI will replace developers... We're shipping products before lunch. The tools are here. The playbook is proven. The only question is: what will you build? Time to ship.

Jacob Klug

40,338 görüntüleme • 1 yıl önce

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 görüntüleme • 8 ay önce

someone is going to make millions with this in 2026 99% of people think this is a real human (or they fail to notice it’s an ai-generated video) but this video is completely ai-generated, including the background music. let me teach you how to create this in a few minutes follow this workflow step by step: first, create the base image of your ai influencer using nb pro. this is currently the best tool for character consistency i used a json prompt to generate the base image then i turn it into video (i will share the exact json prompt with you in this thread) now, to generate the video paste this prompt into google veo using the “frame to video” option “a man in his 40s sits on a 1980s living room couch, looking directly at the camera with a serious expression. he gestures naturally with one hand as he speaks in vintage tv broadcast aesthetic: "today is october 12th, 1985. what i'm about to tell you will sound impossible... but mark my words, these three predictions will come true. no background music, no sound effects” next, i gave it a chunk of script, i only changed the script dialogue each time and kept the rest of the prompt exactly the same using this method, you also get a little consintent voiceover here is the format for you to use: [character description] + [visual style] + [dialogue of your script for under 8 seconds] for example, for the next part dialogue; [A man in his 40s sits on a 1980s living room couch, looking directly at the camera with a serious expression. He gestures naturally with one hand as he speaks in Vintage TV broadcast aesthetic: "but mark my words, these three predictions will come true... ONE: You will carry a device no bigger than a playing card that holds ten thousand songs" ] rest things i adjusted in the editing, now how to clone the audio for these several clips we just generated? i got the best audio from the very first clip i generated using veo 3 but here is the trick: - export that clip to a video editor - detach the audio of it - duplicate it to make it 10+ seconds long - clone the voice over using elevenlabs (go to 11Labs-> click on voices-> click on "create or clone a voice" button in the top right side) (i named mine “1985 ai influencer” inside 11labs) then, finally export all your video clips into your video editor detach the audios of all clips, and export it to 11Labs to clone it with that "1985 AI Influencer" voice once you dubbed it, import it back to your editor that’s it. there are endless use cases where you can use such an ai influencer like this to promote your biz: - skincare - weight loss & nutrition - psychology and mental health - marketing and sales - predictions (like this video) - making money & career growth - dating, parenting, and so on… there are a few people already started using such ai influencers, you are just behind them, you can find their pages on instagram don't be lazy, create one such an ai influencer for your targeted biz 2026 is going to be yours

someone is going to make millions with this in 2026 99% of people think this is a real human (or they fail to notice it’s an ai-generated video) but this video is completely ai-generated, including the background music. let me teach you how to create this in a few minutes follow this workflow step by step: first, create the base image of your ai influencer using nb pro. this is currently the best tool for character consistency i used a json prompt to generate the base image then i turn it into video (i will share the exact json prompt with you in this thread) now, to generate the video paste this prompt into google veo using the “frame to video” option “a man in his 40s sits on a 1980s living room couch, looking directly at the camera with a serious expression. he gestures naturally with one hand as he speaks in vintage tv broadcast aesthetic: "today is october 12th, 1985. what i'm about to tell you will sound impossible... but mark my words, these three predictions will come true. no background music, no sound effects” next, i gave it a chunk of script, i only changed the script dialogue each time and kept the rest of the prompt exactly the same using this method, you also get a little consintent voiceover here is the format for you to use: [character description] + [visual style] + [dialogue of your script for under 8 seconds] for example, for the next part dialogue; [A man in his 40s sits on a 1980s living room couch, looking directly at the camera with a serious expression. He gestures naturally with one hand as he speaks in Vintage TV broadcast aesthetic: "but mark my words, these three predictions will come true... ONE: You will carry a device no bigger than a playing card that holds ten thousand songs" ] rest things i adjusted in the editing, now how to clone the audio for these several clips we just generated? i got the best audio from the very first clip i generated using veo 3 but here is the trick: - export that clip to a video editor - detach the audio of it - duplicate it to make it 10+ seconds long - clone the voice over using elevenlabs (go to 11Labs-> click on voices-> click on "create or clone a voice" button in the top right side) (i named mine “1985 ai influencer” inside 11labs) then, finally export all your video clips into your video editor detach the audios of all clips, and export it to 11Labs to clone it with that "1985 AI Influencer" voice once you dubbed it, import it back to your editor that’s it. there are endless use cases where you can use such an ai influencer like this to promote your biz: - skincare - weight loss & nutrition - psychology and mental health - marketing and sales - predictions (like this video) - making money & career growth - dating, parenting, and so on… there are a few people already started using such ai influencers, you are just behind them, you can find their pages on instagram don't be lazy, create one such an ai influencer for your targeted biz 2026 is going to be yours

ViralOps

14,290 görüntüleme • 7 ay önce