Omfg… While everyone was building agents on browsers Someone... made the browser the agent. – works across web, files, & apps – real-time visual reports + scraping – reads, clicks, fills, builds – 100% local, free deep search Real examples + how to use it: 🧵show more

WTF... This scraper works like a self-driving car. You... give it a destination… it plans the route, clicks through pages, cleans it up & delivers a perfect spreadsheet. Goodbye manual scraping, Excel & data teams. Tutorial + some crazy examples: 🧵show more

You will NEVER touch PowerPoint again. The first real... AI agent for presentations is here. - Plans your slides - Researches the content - Designs & builds your deck in seconds Here’s exactly how it works (+ live demo): 👇show more

NO WAY… AI can now scrape the entire web.... You give it a task… it clicks through pages, extracts data and hands you clean spreadsheets, reports & charts in minutes. Goodbye interns, analysts & overpriced consultants. Full breakdown + outputs: 🧵show more

NO WAY. Someone is basically building a MANAGER for... your AI agents. You give it one objective. It briefs Claude, Codex + the agents you choose, keeps them on the same context and coordinates the work. This is Jarvix. And I had to dig into this: 🧵 #JarvixOSshow more

INSANE. This is YouTube for apps. You build something... → publish it → get real feedback + traffic. No code needed. Here’s how it works:👇show more

No AI should be this fast at scraping. 2... clicks and it auto-crawls, scrapes entire sites... all subpages, tables, PDFs, images, docs, even hidden content & structures it perfectly. Interns, freelancers and old tools can go home. Tutorial + use cases: 🧵show more

i built an extension that automatically calculates +EV plays... in in real time it's worked well the past week so I've decided to make it open source here's how it works & how to use it🧵show more

Tavily Agent Skills are now here ⚡ Your agents... can now: search the live web pull fresh docs & pages research markets, competitors and products cite sources all without leaving the terminal. We also shipped tavily-best-practices. A skill that teaches agents how to build production-ready web-connected AI built on real-world lessons from builders like you. Works wherever you’re building agents: Claude · Cursor · Google Antigravity Install now with one line.show more

THIS DEVELOPER USED OPENCLAW AGENTS TO RUN HIS B2B... BUSINESS VIA TELEGRAM AND MADE $15,000/MONTH he doesn't write prompts from scratch or use generic browser interfaces. he runs a multi-agent framework through a mobile chat. the agents write code, test deployments, and update sites in real-time while he just hits approve the setup is straightforward: - spin up Coolify on a free cloud instance to host your own self-hosted agent panels - link the agent loop to a Telegram gateway to approve code edits from your phone - deploy specialized skill files directly to limit token waste and context decay - containerize the terminal execution using Docker to prevent security breaches if you are still running local agents without container safety, you are leaving money on the table. read the 30-day battle between OpenClaw and Hermes Agent to see who actually wins in production Full breakdown and migration playbook ↓show more

Web Scraping is dead. Web Agenting is here. Writing... selectors (div > .class > span) breaks every time a site updates. Building custom bots for every new target is a waste of engineering hours. TinyFish turns the entire real-time web into a single API. Input: Natural Language ("Find availability for X"). Target: 1 or 100 URLs. Output: Structured JSON. This isn't a simulation. It visits the Real-Time Web. 1./ One API, Many Sites - Same contract whether you hit 1 URL or 50. You focus on the Goal (Business Logic). TinyFish handles the How (Navigation, Clicks, Inputs). 2./ Real Automation - It doesn't just "read." It interacts. It fills forms, navigates multi-step flows, and handles dynamic JS content. 3./ Production Ready - This is the same infrastructure used by large enterprises, now exposed as a clean developer primitive. Logs, error handling, and structured data are built-in. The web is finally a proper API.show more

Meet Avoko - The World's First Behavioral Lab for... Agent World 🔥 Everything AI Developers Need to Build Smarter Products: 🧠 Interview AI agents directly at scale 🎯 Real behavior insights, not human assumptions ⚡️ Structured reports delivered in minutes Try it yourself: #sponsored #ad Learn how to use Avoko in practice below 🧵👇show more

JUST DROPPED⚡: An open-source AI that makes entire movies... from one sentence. It creates full scenes, adds voice, syncs lips, does VFX and even builds the soundtrack. Here are some examples + How to do it yourself: ↓show more

1940: Women were computers. 1950: Code lived on punched... cards. 1990: You needed a CS degree to build. 2010: You needed devs, tools, VC money. 2025: You type one sentence… and mass-produce mobile apps like factory goods. Here's how + wild examples: 🧵show more

BYE CLAWDBOT. THE INTERNET IS NOW HANDS-FREE. > It’s... Claude Cowork + Browser Agent + Copilot + File Ops in ONE. > Runs on desktop, no Mac Mini needed. I’ve never seen an AI workspace like this. Full breakdown (with use cases): 👇show more

LangGraph. CrewAI. Agno. Which one to pick? The good... news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.show more