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Created this mixed media video using the 'invideo editor', just by chatting with an editing agent. ~ Asked the agent to break up the original video into individual frames (for this one it was 12 fps) ~ Described the mixed media style we wanted in detail to the agent... show more

Invideo

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176,788 görüntüleme • 1 gün önce •via X (Twitter)

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i just generated this AI UGC creative for a teeth whitening product in 10 minutes in Higgsfield (you can now generate in 1080p)... here's my workflow: > deploy a research agent to find UGC style videos that promote products like the one you wanna make a creative for > use gemini 2.5 flash to analyze the creative & get a detailed analysis of each scene (get it to add a placeholder for each image reference in your prompt) > use GPT-image 2 in Higgsfield to extract a frame from each cut-scene & use it as a reference for your generation in the prompt wherever there's a placeholder > send the prompt + all the references to Higgsfields video generator & select Seedance 2.5, then generate it > once you have a finished product, take it to a video editor & add captions / cuts / zooms / music

i just generated this AI UGC creative for a teeth whitening product in 10 minutes in Higgsfield (you can now generate in 1080p)... here's my workflow: > deploy a research agent to find UGC style videos that promote products like the one you wanna make a creative for > use gemini 2.5 flash to analyze the creative & get a detailed analysis of each scene (get it to add a placeholder for each image reference in your prompt) > use GPT-image 2 in Higgsfield to extract a frame from each cut-scene & use it as a reference for your generation in the prompt wherever there's a placeholder > send the prompt + all the references to Higgsfields video generator & select Seedance 2.5, then generate it > once you have a finished product, take it to a video editor & add captions / cuts / zooms / music

EP

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Magnific 'Structure Reference' + RunwayML "Restyle video" to colorize old movies ... I think one might run this in a way you use this to help to determine the color layer and then apply the colors on top of the original black and white image... ... would work best if you would do a separate init frame for each cut...

Roope Rainisto

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Generated this video by chatting with a single agent ✨ I told GLIF that I wanted to create this anime short. It made the storyboard with GPT-Image-2 and then animated it with Seedance 2. Such an easy way to get controllable video generation!

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This opens up a completely new workflow for artists when it comes to shooting music videos. Take one performance and place it into multiple locations from multiple angles. By combining Kling 2.6 Motion Control with our Multi-Angle Fashion Shoot agent, we generate multiple angles from a single reference image and use them to drive the performance. Full workflow + agent below 👇

This opens up a completely new workflow for artists when it comes to shooting music videos. Take one performance and place it into multiple locations from multiple angles. By combining Kling 2.6 Motion Control with our Multi-Angle Fashion Shoot agent, we generate multiple angles from a single reference image and use them to drive the performance. Full workflow + agent below 👇

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Here's how to get 100% consistent product ads from one seedance 2.0 generation. I did it all in a single chat using the Comfy MCP. The real control here comes from calling my existing workflows (shared below) instead of the agent improvising a pipeline. I directed the agent to call my ComfyUI workflow for cinematic product ads. I specified the close-up shot of the sprite animating, the bezel turn flipping the screen, the display changing to the time 10:04. Now for the consistent variations. The driving video does the heavy lifting but you need to get it right → depthanything v3 pass blended with canny edge lines to show the fine detail... it's why the tiny debossed logo is there → the initial sprite outline lived in those edge lines too, and every gen kept inheriting it. claude suggested a sam3 mask over the screen to hide it (s/o the agent) → with the screen masked, the new star sprite is just prompting: one gpt-image-2 still to generate a reference, one extra line in the seedance 2.0 prompt, and it animates oh and the whole process is a claude skill now.

rob - comfyui

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> you went to sleep > gave your OpenClaw $200 > told it: make money > you wake up - account shows $6,700 > overnight the agent was trading spreads between Polymarket and NOAA > NOAA forecasts come with ~94% accuracy > the AI agent opens a position at 11c > sells at 44c > total research time: 10 minutes in Perplexity > Claude turned that into an AI agent > the agent made the money

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Quick tip: You probably know that you can extend an AI video by extracting the last frame and using that frame as the initial image for image to video. If you join the videos with Capcut (free video editor) you can use Mix to smooth out the transition which will very quickly improve your result. AI videos here generated with Pika (super impressive text to video) and Minimax (super impressive image to video).

Quick tip: You probably know that you can extend an AI video by extracting the last frame and using that frame as the initial image for image to video. If you join the videos with Capcut (free video editor) you can use Mix to smooth out the transition which will very quickly improve your result. AI videos here generated with Pika (super impressive text to video) and Minimax (super impressive image to video).

TomLikesRobots🤖

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From product image to video with just one tool - Dzine As you may have noticed, this is one of my favorite tools. It is also very underrated, as probably 50% of my tutorials include some workflow. I was testing the new image-to-video option today, and I love it. Step - by step guide in comments 🔽 I can do 95% of a workflow without switching between apps. Image generation, Image to image with style reference, background removal, background generation, and 2 frames image to video. The only other app I have been using for this video is CapCut so that I can stitch it together. Step by step 🔽

From product image to video with just one tool - Dzine As you may have noticed, this is one of my favorite tools. It is also very underrated, as probably 50% of my tutorials include some workflow. I was testing the new image-to-video option today, and I love it. Step - by step guide in comments 🔽 I can do 95% of a workflow without switching between apps. Image generation, Image to image with style reference, background removal, background generation, and 2 frames image to video. The only other app I have been using for this video is CapCut so that I can stitch it together. Step by step 🔽

Teodora P L

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What used to take lights, props, and a full set can now be done inside an agent. This was created using a Glif agent with Kling 2.6 Motion Control and a Nano Banana reference image to lock the look. No physical setup needed. Glif has agents for different creative use cases, so you can pick what fits your workflow and move fast. Check the agents used for this below 👇

What used to take lights, props, and a full set can now be done inside an agent. This was created using a Glif agent with Kling 2.6 Motion Control and a Nano Banana reference image to lock the look. No physical setup needed. Glif has agents for different creative use cases, so you can pick what fits your workflow and move fast. Check the agents used for this below 👇

GLIF

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Visualizer of our MultiAgentRouter 🤖 The MultiAgentRouter is an all-new multi-agent structure that leverages a hierarchical pattern to select the most specialized agent for your task. Here's how it works: Step 1. You give a task. Step 2. The Boss Agent Routes your task to the most specialized Agent Step 3. The selected agent returns your response! Get started with it now below ⬇️ Thanks to WE!SS for the visualizer!!

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At Uber, our testing agent hit an outage in Australia that I still think about. We had the recording. That's about it. No network trace for the run. No logs mapped to each step. No perf data. Just a video of an agent pressing a button and a team trying to reconstruct what happened underneath. We eventually traced it to a transient issue. But "eventually" is the problem. If the run had shipped with real instrumentation, the why would have taken one look, not an investigation. So when we built Revyl, that was non-negotiable. Every run comes with: > network waterfall, every request and response > device logs timestamped against each step > CPU, memory, and FPS traces for the full run The agent finds the problem. The report explains it.

Anam Hira

42,149 görüntüleme • 1 ay önce

Introducing Agent Sandbox, the infinite simulation playground for agents on Virtuals. Craft the perfect autonomous agent in our Sandbox with full control over its personality and goals. Enhance your agent with unique abilities by creating custom functions so they can trade onchain, generate memes, control physical robots and more. The Sandbox is available to all builders with graduated agents in the Developer Panel. For those who want to give it a spin without an existing agent, fret not. Try it out today at and join our Discord ( to jam with like-minded builders. Next stop, Society of Agents.

Introducing Agent Sandbox, the infinite simulation playground for agents on Virtuals. Craft the perfect autonomous agent in our Sandbox with full control over its personality and goals. Enhance your agent with unique abilities by creating custom functions so they can trade onchain, generate memes, control physical robots and more. The Sandbox is available to all builders with graduated agents in the Developer Panel. For those who want to give it a spin without an existing agent, fret not. Try it out today at and join our Discord ( to jam with like-minded builders. Next stop, Society of Agents.

Virtuals Protocol

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These product images weren’t made with a carefully crafted prompt. They were created by an AI Agent from EasyClaw EasyClaw that asks the right questions, understands what you need, and handles the heavy lifting for you. This is what it gave me. The complete workflow is in the next post 👇

These product images weren’t made with a carefully crafted prompt. They were created by an AI Agent from EasyClaw EasyClaw that asks the right questions, understands what you need, and handles the heavy lifting for you. This is what it gave me. The complete workflow is in the next post 👇

Jessica Collins

32,842 görüntüleme • 1 ay önce

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.

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.

Avi Chawla

30,932 görüntüleme • 9 ay önce

🚨 one person can now do the work of an entire creative team. i just tested it on a real one. a friend needed an ad for his brand, so I opened the new Runway Agent 2.0 to try it out. here's how it went: → it generated the music and the key image first, so I could approve the direction → once I gave the ok, it built the full video around it → and when something was off, i changed just that one piece, without redoing the rest one prompt, and I had the ad we needed, work that used to take weeks. this is what it made 👇 if you want to try it → · 30% off 3 months with code RUNWAYAGENT — made with Runway · #MadeWithRunway · #ad

brenz.

28,698 görüntüleme • 2 ay önce

this brand new AI agent is incredible.. NEX AI just dropped Marko, it can analyse your branding and generate videos for your product and.. edit by chatting with it so you can place it anywhere while keeping the product consistent step by step tutorial:

el.cine

119,563 görüntüleme • 1 yıl önce

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

258,436 görüntüleme • 2 ay önce

Introducing AgentsAI's Token Launcher: a seamless path to bringing your AI agent ideas to life and beyond. Create your own AI agent, train it by simply chatting, and with a few clicks, launch its token to establish a digital identity in a new era of automation. With AgentsAI, you're not just building bots; you're crafting autonomous entities that engage, interact, and generate value. Create, launch, and watch as your AI agent thrives in the market, all with zero coding and maximum impact. This is the future of AI-powered digital personalities — effortless, accessible, and ready to take on the world.

Introducing AgentsAI's Token Launcher: a seamless path to bringing your AI agent ideas to life and beyond. Create your own AI agent, train it by simply chatting, and with a few clicks, launch its token to establish a digital identity in a new era of automation. With AgentsAI, you're not just building bots; you're crafting autonomous entities that engage, interact, and generate value. Create, launch, and watch as your AI agent thrives in the market, all with zero coding and maximum impact. This is the future of AI-powered digital personalities — effortless, accessible, and ready to take on the world.

AgentsAI

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