🚨 Perplexity dropped "Computer" and nobody is talking about... it. An AI system that runs 19 models at once and builds entire projects by itself. It researches, designs, codes, and deploys live websites in minutes. No subscriptions to 5 different tools. No switching tabs. Here's how to use it:show more

China released an AI employee that works 24x7 on... its own and runs 100% locally It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. 100% Open Source.show more

🚨 China has released an AI employee that runs... 100% locally. It can do research, code, build websites, create slide decks, and generate videos.. all by itself. And it comes with its own computer. 100% Open Source.show more

🚨BREAKING: China just released an AI employee that runs... on your own computer. It can search the web, write code, build websites, make slides, and create videos. All by itself. It even comes with its own built-in computer. 100% Open Source.show more

I no longer think you should use 3 different... tools to handle routine tasks it’s outdated, fragile, and takes hours to keep working use this instead.. it builds itself and just works. 5 things you can do: 1. personalized cold email campaignsshow more

🛠️ What if a robot could invent its own... tools. And teach itself how to use them? That’s exactly what VLMgineer does: a new framework that lets Vision Language Models (VLMs) design physical tools and the actions to use them, entirely on their own. No templates. No human demonstrations. Just raw, AI-driven creativity. Why it matters ✅ Co-designs tools and actions together using VLMs, ensuring tight coupling between form and function ✅ Uses VLM-guided evolution (not random search) to refine designs intelligently ✅ Outperforms human-designed tools by +64.7% in task success across 12 RoboToolBench challenges ✅ Produces better-than-everyday tools for real manipulation tasks—measured in success rate and elegance It builds on the emerging trend of large-model-guided evolutionary design (like Eureka and AlphaEvolve) and brings it into physical robotics. It opens the door to general-purpose, automated hardware design, no strong priors needed. Code & paper: —- Weekly robotics and AI insights. Subscribe free:show more

Perplexity Computer is massively multi-model. Computer orchestrates models to... run agents in parallel, leveraging Opus to match each task to the model best suited for it. In total, Computer can route work across 19 different models.show more

Type what you want. Watch Zapier build it. Debuted... live at ZapConnect: an AI copilot that works across Canvas, Tables, Interfaces, Zaps, Chatbots, and Agents. No more stitching tools together by hand. Just describe your idea, and Copilot delivers a production-ready workflow. It knows when to use automation, when to spin up an agent, and how to connect everything end-to-end. This is AI transformation through people. Everyone on your team can now go from idea → system in minutes. Zapier doesn’t just connect apps. Zapier makes your whole team a builder. And it’s here now. Try it:show more

wow this is literally ChatGPT + PowerPoint + McKinsey... in one tab it researches, analyzes, and builds full decks in minutes 8 wild use cases + link to access: 1. cloud to edge deckshow more

THIS APP IS CRAZY. Coursiv is generating $200K/month by... teaching how to use AI. It turns learning complex AI tools into an income-generating journey. It uses deep personalization, interactive learning, and smart monetization. Breaking it down 👇🧵 (1/19)show more

🚨 BREAKING: China just dropped an AI employee that... works 24/7 — and it's 100% open source. Meet DeerFlow. It researches, writes code, builds websites, creates slide decks, generates videos, and executes tasks entirely on your computer. Not a chatbot. Not an AI copilot. An AI worker with its own computer. Here's what makes it wild: • Creates a plan and spins up multiple sub-agents in parallel • Writes code, tests it, fixes errors, and keeps going autonomously • Builds websites, reports, dashboards, and presentations from scratch • Learns your preferences and improves over time • Reads your files and delivers finished outputs, not drafts • Searches the web, runs commands, and uses external tools One prompt → Lead agent plans → Specialized agents fan out → Results merge → Finished deliverable. A single task can launch dozens of agents working simultaneously while you sleep. The craziest part? DeerFlow 2.0 was completely rebuilt from scratch after users pushed it far beyond its original purpose. Now it has: ⭐ 22.7K GitHub stars 🔀 2.7K forks 🏆 #1 on GitHub Trending 🏢 Built by ByteDance 🆓 100% Open Source (MIT) We're entering the era where AI doesn't just answer questions. It does the work.show more

It's official... Emergent just dropped an AI Agent that... can build full-stack products, games, websites, and Chrome extensions—all by itself. No humans. No limits. (Example #5 will blow your mind)show more

Fixing plastic AI generated skin is a $100k/month problem.... Apps like Enhancor AI use open-source models and ComfyUI workflows as a backend. How do I know this? I built a workflow that clones their core feature and I'm dropping it below for FREE. Same results, no subscriptions. Use it yourself OR build your own app around it. Why pay for open-source tools? 🧵show more

How it feels using the latest AI tools to... summarize earnings transcripts and create excel models from scratch in minutes (your fund is down 26% YTD tho)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

ByteDance just open sourced an AI SuperAgent that can... research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.show more

Perplexity Computer orchestrates 20 different AI models - and... now you can embed them directly inside apps you create. To prove it, we built CEO Chat. Text Elon, Jensen, Zuck, and other tech CEOs. They text back.show more

Adobe for Creativity + Claude 🤯 just dropped! Photoshop.... Lightroom. Illustrator. Firefly. Premiere. Express. InDesign. Stock. Firefly Boards (my favorite). All inside Claude. No app switching. Just plain English. Here's an example how to connect it and use with Boards:show more

In 2025, the smartest thing you can do is... this: → Pick a painful problem → Train an AI agent to solve it → Charge people for using it Here's what I'd use to start and scale it to $5k/month, with no team, no funding, no code 👇show more

19-year-old from china makes $9,000/month designing product sites and... ships each one in an afternoon. here's his exact setup the whole thing runs on two tools that each do one job: > brief written by hand: 5 min > Moonchild builds the design system, then every screen from it: 20 min > MCP hands the design to Claude as real structure, not a screenshot: instant > Claude Code reads those exact tokens and builds the live app: 20 min > second Claude session reviews the build for drift: 10 min total: about an hour. screen five still matches screen one. no agency, no dev, no design team the trick is MCP. the design tool passes Claude the actual colors, components and layout, so it builds from the source instead of guessing from a picture. full pipeline, every prompt, in the article above.show more

🚨 JUST IN: CHINA just released an AI EMPLOYEE... that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.show more