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✨ Advanced Visual Intelligence Agent! Nano Banana PRO is an AI-powered visual generation agent available on Agent Forge. Using supported third-party AI models and image-processing tools, it enables users to generate images from text-based prompts for creative exploration, drafts, prototypes, and visual experimentation. Whether you’re working on early design... show more

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Google Nano Banana 🍌 is crazy good at static ads... But it only generates one image at a time. This n8n AI Agent helps you generate 1000s of winning ad variations in minutes, fully automated. → Built with the latest Nano Banana image model → Creates static ad images in bulk → Upload product reference image via n8n form → OpenAI Vision analyzes your product automatically → AI Agent generates custom image prompts (you choose how many) → Nano Banana creates static ad images on demand → Images auto-stored in Box. com for instant access You can request 50, 100, or even 1000 ad variations with one upload. Just specify the number in the form → AI does everything else. Built 100% in n8n. Zero manual work after setup. Want access to the template? → Like this post → Comment "ADS" And I'll send it right over.

Google Nano Banana 🍌 is crazy good at static ads... But it only generates one image at a time. This n8n AI Agent helps you generate 1000s of winning ad variations in minutes, fully automated. → Built with the latest Nano Banana image model → Creates static ad images in bulk → Upload product reference image via n8n form → OpenAI Vision analyzes your product automatically → AI Agent generates custom image prompts (you choose how many) → Nano Banana creates static ad images on demand → Images auto-stored in Box. com for instant access You can request 50, 100, or even 1000 ad variations with one upload. Just specify the number in the form → AI does everything else. Built 100% in n8n. Zero manual work after setup. Want access to the template? → Like this post → Comment "ADS" And I'll send it right over.

Mike Futia

209,503 views • 10 months ago

With the launch of Nano Banana Pro, we're also rolling out the ability for Gemini users to check whether an image was generated with or edited by Google AI using SynthID, our digital watermarking technology. Now you can upload any image into the Gemini app and ask "Is this AI-generated?" Gemini then scans the image for the imperceptible SynthID watermarks that are embedded into all Google AI-generated images - including those by Nano Banana Pro! Learn more about Google’s efforts to increase AI transparency:

With the launch of Nano Banana Pro, we're also rolling out the ability for Gemini users to check whether an image was generated with or edited by Google AI using SynthID, our digital watermarking technology. Now you can upload any image into the Gemini app and ask "Is this AI-generated?" Gemini then scans the image for the imperceptible SynthID watermarks that are embedded into all Google AI-generated images - including those by Nano Banana Pro! Learn more about Google’s efforts to increase AI transparency:

Google Gemini

190,547 views • 8 months ago

🍏 Apple has built AI-powered Photoshop directly into the iPhone Users will be able to upload any photo from their gallery and simply describe what they want changed — remove unwanted objects, swap backgrounds, or even change outfits. The most impressive part? The editor is powered by Nano Banana Pro, one of the most advanced AI image-generation and editing models available today. What do you think about AI getting this much control over our photos? 🤔📸

🍏 Apple has built AI-powered Photoshop directly into the iPhone Users will be able to upload any photo from their gallery and simply describe what they want changed — remove unwanted objects, swap backgrounds, or even change outfits. The most impressive part? The editor is powered by Nano Banana Pro, one of the most advanced AI image-generation and editing models available today. What do you think about AI getting this much control over our photos? 🤔📸

NEXTA

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Google Nano Banana Pro 🍌 is crazy good at static ads... But it only generates one image at a time. This n8n AI Agent generates 100s of ad variations in minutes, fully automated. All inside n8n + Nano Banana Pro. Perfect for DTC brands & agencies who need massive creative volume to test and beat ad fatigue. Let's be honest: Manual ad creation takes forever. You're generating one image at a time, tweaking prompts, downloading files, trying different angles. And by the time you have enough variations to test, you've wasted hours. This n8n automation solves it: → Upload ONE product reference image via n8n form → OpenAI Vision analyzes your product automatically → AI generates custom image prompts (you choose quantity: 50, 100, 1000+) → Nano Banana Pro creates all variations in parallel → 4K studio-quality output with perfect text rendering → Images auto-stored in Box for instant access No manual prompting. No one-by-one generation. No wasted hours. What you get: → Hundreds of ad variations from one upload → Different angles, backgrounds, compositions automatically → 4K resolution with perfect text adherence → Production-ready static ads for testing at scale Built 100% in n8n. Want the complete n8n template? > Comment "NANO" > Like this post And I'll send it over (must be following so I can DM)

Google Nano Banana Pro 🍌 is crazy good at static ads... But it only generates one image at a time. This n8n AI Agent generates 100s of ad variations in minutes, fully automated. All inside n8n + Nano Banana Pro. Perfect for DTC brands & agencies who need massive creative volume to test and beat ad fatigue. Let's be honest: Manual ad creation takes forever. You're generating one image at a time, tweaking prompts, downloading files, trying different angles. And by the time you have enough variations to test, you've wasted hours. This n8n automation solves it: → Upload ONE product reference image via n8n form → OpenAI Vision analyzes your product automatically → AI generates custom image prompts (you choose quantity: 50, 100, 1000+) → Nano Banana Pro creates all variations in parallel → 4K studio-quality output with perfect text rendering → Images auto-stored in Box for instant access No manual prompting. No one-by-one generation. No wasted hours. What you get: → Hundreds of ad variations from one upload → Different angles, backgrounds, compositions automatically → 4K resolution with perfect text adherence → Production-ready static ads for testing at scale Built 100% in n8n. Want the complete n8n template? > Comment "NANO" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

183,179 views • 8 months ago

Two burgers. Same calories. Very different construction. Nano Banana Pro + Kling 2.5 Turbo I generated this AI effect for Impossible Foods in 10 mins with the new AI system my team and I are building. Insane 🤯 The system creates two high-quality images of a regular burger and a plant-based burger along with their deconstructed elements via Nano Banana Pro. Then, it animates the transition between the 2 images with Kling 2.5 Turbo. The combined effect? A visual that sparks online debate and awareness for the burger company. RT & Comment 'BURGER' if you want me to DM you early access to the system as well as all the prompts I used for this!! (must follow for auto dm to send)

Two burgers. Same calories. Very different construction. Nano Banana Pro + Kling 2.5 Turbo I generated this AI effect for Impossible Foods in 10 mins with the new AI system my team and I are building. Insane 🤯 The system creates two high-quality images of a regular burger and a plant-based burger along with their deconstructed elements via Nano Banana Pro. Then, it animates the transition between the 2 images with Kling 2.5 Turbo. The combined effect? A visual that sparks online debate and awareness for the burger company. RT & Comment 'BURGER' if you want me to DM you early access to the system as well as all the prompts I used for this!! (must follow for auto dm to send)

David Roberts

30,374 views • 6 months ago

AI video creation is entering the Skill era. In the past, creating videos like this required complex prompting, constant trial and error, and jumping between multiple AI tools and editing software. But now, with Skills, entire creative workflows can be reused in one click. The camera movement, pacing, editing logic, visual style, and generation process are already built into the Skill. Powered by GPT Image 2.0 + Seedance 2.0 in one flow. This is your personal Al Video Agent. FlovaAI Check how i did it below. #FlovaAl #AlAgent

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AI video creation is evolving fast. Just tested Hailuo AI by MiniMax, and I’m genuinely impressed by how fast it turns ideas into cinematic videos. With the new Nano Banana Pro integration, you can generate precise images, restore photos, and render high-quality characters — then instantly transform them into dynamic videos inside Hailuo AI. The whole workflow from image → video feels smooth and powerful for creators who want to move from concept to content quickly. For anyone experimenting with AI video tools, this is definitely one worth trying out. Check it out here: #hailuoai #minimax #hailuo02 #hailuo23 Hailuo AI (MiniMax) MiniMax (official)

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Chubby♨️

176,267 views • 1 year ago

Nano Banana + n8n is legitimately insane 🤯 Google's Nano Banana creates studio-quality static ads. But manually generating variations one-by-one takes forever. This n8n automation generates 1,000+ ad variations in minutes. Fully automated. Perfect for DTC brands & media buyers who need fresh creative for testing without hiring designers. Here's the problem: You need 50+ ad variations to test angles, but Nano Banana only outputs one image at a time. Manually uploading and tweaking prompts for each variation kills hours. This n8n automation solves it: → Upload ONE product image via n8n form → OpenAI Vision analyzes your product automatically → AI generates custom image prompts (you choose quantity: 50, 100, 1000+) → Nano Banana creates static ad images in bulk → All images auto-stored in Box for instant access No manual prompting. No designer bottlenecks. No waiting on agencies. What you get: → Hundreds of unique ad variations from one upload → Different angles, backgrounds, compositions → Production-ready static ads → Perfect for testing creative on Meta/TikTok Built 100% in n8n. Want the complete n8n template? > Comment "BANANA" > Like this post And I'll send it over (must be following so I can DM)

Nano Banana + n8n is legitimately insane 🤯 Google's Nano Banana creates studio-quality static ads. But manually generating variations one-by-one takes forever. This n8n automation generates 1,000+ ad variations in minutes. Fully automated. Perfect for DTC brands & media buyers who need fresh creative for testing without hiring designers. Here's the problem: You need 50+ ad variations to test angles, but Nano Banana only outputs one image at a time. Manually uploading and tweaking prompts for each variation kills hours. This n8n automation solves it: → Upload ONE product image via n8n form → OpenAI Vision analyzes your product automatically → AI generates custom image prompts (you choose quantity: 50, 100, 1000+) → Nano Banana creates static ad images in bulk → All images auto-stored in Box for instant access No manual prompting. No designer bottlenecks. No waiting on agencies. What you get: → Hundreds of unique ad variations from one upload → Different angles, backgrounds, compositions → Production-ready static ads → Perfect for testing creative on Meta/TikTok Built 100% in n8n. Want the complete n8n template? > Comment "BANANA" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

299,988 views • 9 months ago

Meta just launched its 1st image model after Mark Zuckerberg’s AI shake-up. Muse Image is Meta Superintelligence Labs' first image generator after Muse Spark. They said the model will power new editing features in Meta’s Instagram photo app and be added to its marketer tools for creating platform ads. Meta had relied on Midjourney and Black Forest Labs for generation inside Meta AI. Muse Image brings that layer in-house, so Meta controls quality, cost, and product timing. Consumers get free access through Meta AI, WhatsApp chats, and Instagram Stories. Power users need Meta One or another monthly plan when free limits run out. Users can start from prompts, add photos, annotate edits, or sketch changes directly. Meta says it can erase photobombers, create QR codes, and keep visual text readable. Advertisers get variants through Advantage+ creative, with edits, style swaps, and brand-matched versions. Meta says internal tests trail GPT Image 2 but beat Nano Banana 2 on editing. This is another move in Meta's effort in trying to convert AI infrastructure spending into revenue beyond social ads.

Rohan Paul

20,377 views • 22 days ago

We’d like to share some updates from within the Forge. While minor updates may roll out at a slightly slower pace, our primary focus is dedicated to an exciting upcoming feature: Machina Foundry. What is Machina Foundry? Machina Foundry is an AI Agent Builder Platform that allows users to define an agent's purpose, functions, and objectives through simple, natural language prompts. Once an agent is created, Alchemists can seed liquidity in $ALCH for their agent. Liquidity is placed in Meteora pools, with 50% allocated to the ALCH ecosystem and the remaining 50% locked permanently. This structure ensures the ecosystem benefits from pool fees, enabling periodic ALCH token buybacks and supporting long-term growth. Additionally, the Foundry introduces a flywheel effect: purchasing an agent requires acquiring ALCH tokens, further integrating the ecosystem with the token economy. Agents built in Machina Foundry are fully customizable and reflect unique personalities. They can create apps and tools tailored to their character, powered by Alchemist AI’s robust technology. While similar to Azarus, these agents bring an added layer of individuality, ensuring that their creations vary significantly based on their distinct traits and configurations. Imagine a network of thousands of AI agents, each contributing diverse applications and tools, driving creativity and value across the ecosystem. Now that’s Magic!🪄✨

We’d like to share some updates from within the Forge. While minor updates may roll out at a slightly slower pace, our primary focus is dedicated to an exciting upcoming feature: Machina Foundry. What is Machina Foundry? Machina Foundry is an AI Agent Builder Platform that allows users to define an agent's purpose, functions, and objectives through simple, natural language prompts. Once an agent is created, Alchemists can seed liquidity in $ALCH for their agent. Liquidity is placed in Meteora pools, with 50% allocated to the ALCH ecosystem and the remaining 50% locked permanently. This structure ensures the ecosystem benefits from pool fees, enabling periodic ALCH token buybacks and supporting long-term growth. Additionally, the Foundry introduces a flywheel effect: purchasing an agent requires acquiring ALCH tokens, further integrating the ecosystem with the token economy. Agents built in Machina Foundry are fully customizable and reflect unique personalities. They can create apps and tools tailored to their character, powered by Alchemist AI’s robust technology. While similar to Azarus, these agents bring an added layer of individuality, ensuring that their creations vary significantly based on their distinct traits and configurations. Imagine a network of thousands of AI agents, each contributing diverse applications and tools, driving creativity and value across the ecosystem. Now that’s Magic!🪄✨

ALCHEMIST AI 🔮

72,114 views • 1 year ago

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 views • 9 months ago

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.

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.

Rohan Paul

50,097 views • 4 months ago

YOMIRGO #Product #Update YOMIRGO AI-HUB OFFICIALLY LAUNCH ---A Structural Upgrade from a Single-Product Model to an AI Agent Ecosystem Platform In its first phase, 11 AI projects have been integrated, spanning high-value sectors including finance, scientific research, enterprise services, development tools, and experiential AI. ➡️AI-Hub: This is not merely a feature expansion — it represents a critical structural upgrade from a single-product architecture to a multi-vertical AI Agent aggregation and capitalization platform. This milestone marks the initial structural formation of the YOMIRGO ecosystem. 1. Structural Distinction Between Agent Matrix Lab and AI-Hub To avoid positioning ambiguity, we formally clarify the structural division between the two: 🔘 Agent Matrix Lab — Internal AI Production & Incubation Platform Agent Matrix Lab serves as YOMIRGO’s proprietary AI development and internal incubation platform, responsible for: • R&D and testing of in-house AI products • Incubation of native AI Agents • Technical architecture experimentation and runtime validation • Testing of AI Agent models, memory systems, and runtime orchestration It functions as the production workshop and experimental engine of YOMIRGO’s “AI Super Factory.” 🔘 AI-Hub — External AI Agent Aggregation & Ecosystem Layer AI-Hub is a market-facing AI Agent aggregation and showcase platform, responsible for: • Curation and onboarding of high-quality AI projects • Cross-vertical structured ecosystem layout • Rating and classification systems • Traffic distribution and ecosystem collaboration entry points AI-Hub is not an internal incubation unit, but a standardized aggregation framework at the ecosystem level. 2. Integrated Project Structure (First Batch) ✅1. Finance & Prediction 🔹Cointoken AI — AI Agent-powered quantitative trading engine 🔹VVAI — AI-driven real-time Web3 intelligence and decision system 🔹AlphaQuant — Global financial market forecasting engine 🔹NextGoals — AI-powered global sports prediction agent This vertical forms the real-time information, trading, and predictive decision infrastructure for Web3-native users. ✅2. Science 🔹Charmen AI — Large-model-based pet acoustic recognition technology 🔹Encore Health — AI-driven health forecasting and longevity management system for high-net-worth individuals 🔹Reproducibility AI — AI expert system for financial engineering validation and academic reproducibility This sector focuses on research-grade AI capabilities, collaborating with universities and research institutions to drive real-world scientific deployment. ✅3. Business 🔹GlobalSales — B2B automated lead-generation AI Agent 🔹ResearchBot — Business intelligence and deep due diligence AI Agent This vertical targets the enterprise market, delivering scalable and commercially viable AI productivity tools. ✅4. Coding 🔹CodeMatrix — Full-stack development assistant Providing AI-driven development infrastructure and low-barrier building capabilities to global users. ✅5. Interesting 🔹Fortunetell AI — AI-powered symbolic analysis and interactive insight system Exploring the application boundaries of AI within experiential and interactive scenarios. 3. YOMIRGO Four-Layer Structural Framework YOMIRGO has now established a clearly defined four-layer structure: ▶️Layer 1: Agent Matrix Lab — Internal Production & Incubation ▶️Layer 2: AI-Hub — Ecosystem Aggregation & Rating ▶️Layer 3: LaunchPad — Capitalization Pathway ▶️Layer 4: Market — Circulation & Value Realization Together forming a complete industrial pipeline: Incubation → Validation → Aggregation → Rating → Capitalization → Market Circulation This is the structural model behind YOMIRGO’s defined “AI Super Factory.” 4. Strategic Significance The launch of AI-Hub signifies: • YOMIRGO has established standardized AI Agent aggregation capabilities • A cross-vertical ecosystem structure is now in place • Internal incubation and external aggregation mechanisms are structurally separated • The AI Agent industrial flywheel has begun operating YOMIRGO is no longer merely an AI product platform, but a structured AI Agent industrial system integrating production, aggregation, capitalization, and circulation. 5. Next Phase • Continue expanding high-utility AI Agents with real-world application value • Optimize AI-Hub’s scoring, rating, and filtering mechanisms • Strengthen synergy with LaunchPad and Market • Enable AI Agents to complete value realization within the ecosystem The first 11 projects are only the beginning. AI-Hub is designed to become a continuously expanding AI Agent gateway — not a static product showcase. Further structural expansion is underway.🔥

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YOMIRGO

23,685 views • 5 months ago

February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →

February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →

G.A.M.E

89,973 views • 1 year ago

✨ I've brought back from the dead It was my first visual AI project in 2022, and it's this project that generated random ArchDaily-style architecture designs that made me realize AI image models could do interior design That led me to make which then led me to finetune my first interior design model which then for fun I uploaded my own photos too, which led me to make and then pivoted that in to So this project has a special place for me It was still alive but wasn't generating new designs anymore because it ran on Stable Diffusion 1.5 which was outdated and everything stopped working about a year ago I've now migrated it to its own Hetzner VPS now, which means I can run Claude Code on the server with it, and cleaned it up and upgraded it to the latest AI image models (including Nano Banana Pro) It now generates about 12 new designs every day again, and you can up or downvote the ones you like or don't like!

✨ I've brought back from the dead It was my first visual AI project in 2022, and it's this project that generated random ArchDaily-style architecture designs that made me realize AI image models could do interior design That led me to make which then led me to finetune my first interior design model which then for fun I uploaded my own photos too, which led me to make and then pivoted that in to So this project has a special place for me It was still alive but wasn't generating new designs anymore because it ran on Stable Diffusion 1.5 which was outdated and everything stopped working about a year ago I've now migrated it to its own Hetzner VPS now, which means I can run Claude Code on the server with it, and cleaned it up and upgraded it to the latest AI image models (including Nano Banana Pro) It now generates about 12 new designs every day again, and you can up or downvote the ones you like or don't like!

@levelsio

204,387 views • 4 months ago

Claude Code + Nano Banana 2 is f*cking cracked 🤯 I built a skill inside Claude Code that writes JSON image prompts for Nano Banana 2, and the outputs look like they came from a professional photo shoot. One plain-text prompt. Claude rewrites it as structured JSON with lighting, camera, composition, style, and negative prompts. Then fires it off to Nano Banana 2. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without booking a shoot. If you're using Nano Banana 2 for product shots and lifestyle images but every generation feels like pulling a slot machine lever — random lighting, inconsistent style, plastic skin, misspelled labels ... This skill fixes the entire output: → You describe what you want in plain English → Claude rewrites it as a structured JSON prompt (lighting, camera angle, lens, depth of field, color grading — all of it) → Fires it to Nano Banana 2 via API → Saves the prompt + image in organized folders → You iterate on the style until it's dialed, then every output matches No more slot machine prompting. No more inconsistent brand imagery. No more burning credits on unusable generations. What you get: - Photo-realistic product shots and lifestyle images on demand - Full control over style, lighting, composition, and camera settings - Saved JSON prompts you can reuse across every campaign - A skill that gets smarter the more feedback you give it Built 100% in Claude Code with a custom skill + Python scripts. I put together a full playbook showing the exact skill, the JSON schema, and the workflow to set this up yourself. Want the full playbook? > Like this post > Comment "BANANA" And I'll send it over (must be following so I can DM)

NOVA

63,871 views • 4 months ago

Claude Code + Nano Banana 2 is f*cking cracked 🤯 I built a skill inside Claude Code that writes JSON image prompts for Nano Banana 2, and the outputs look like they came from a professional photo shoot. One plain-text prompt. Claude rewrites it as structured JSON with lighting, camera, composition, style, and negative prompts. Then fires it off to Nano Banana 2. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without booking a shoot. If you're using Nano Banana 2 for product shots and lifestyle images but every generation feels like pulling a slot machine lever — random lighting, inconsistent style, plastic skin, misspelled labels ... This skill fixes the entire output: → You describe what you want in plain English → Claude rewrites it as a structured JSON prompt (lighting, camera angle, lens, depth of field, color grading — all of it) → Fires it to Nano Banana 2 via API → Saves the prompt + image in organized folders → You iterate on the style until it's dialed, then every output matches No more slot machine prompting. No more inconsistent brand imagery. No more burning credits on unusable generations. What you get: - Photo-realistic product shots and lifestyle images on demand - Full control over style, lighting, composition, and camera settings - Saved JSON prompts you can reuse across every campaign - A skill that gets smarter the more feedback you give it Built 100% in Claude Code with a custom skill + Python scripts. I put together a full playbook showing the exact skill, the JSON schema, and the workflow to set this up yourself. Want the full playbook? > Like this post > Comment "BANANA" And I'll send it over (must be following so I can DM)

Mike Futia

211,612 views • 5 months ago

LLM Knowledge Base → Slides When Andrej Karpathy shared his LLM Knowledge Base setup, many were wondering how to generate more visual forms of the wiki. There are many options, but I think Gamma is one of the best at producing high-quality, rich presentations. To showcase this, I just built a pipeline that turns my AI papers wiki (1K+ papers across 20 AI agent topics) into polished slide presentations using Gamma. The flow: Obsidian vault → Gamma MCP → embedded preview in my dashboard. I give one command to my agent, which pulls the top papers from each topic (via the wiki), feeds them to Gamma, and renders the presentation inline. The Gamma connector for Claude is a great choice for generating beautiful and professional slides. Easy to use. Go to your Claude instance and add the official Gamma connector. That's it! Claude Code will now have access to all the necessary MCP tools for generating slides. I use the Claude Agent SDK for my agent orchestrator, so I use the official Gamma MCP tools and embed the generated slides in an iframe via my artifact preview. See the clip below for an example.

LLM Knowledge Base → Slides When Andrej Karpathy shared his LLM Knowledge Base setup, many were wondering how to generate more visual forms of the wiki. There are many options, but I think Gamma is one of the best at producing high-quality, rich presentations. To showcase this, I just built a pipeline that turns my AI papers wiki (1K+ papers across 20 AI agent topics) into polished slide presentations using Gamma. The flow: Obsidian vault → Gamma MCP → embedded preview in my dashboard. I give one command to my agent, which pulls the top papers from each topic (via the wiki), feeds them to Gamma, and renders the presentation inline. The Gamma connector for Claude is a great choice for generating beautiful and professional slides. Easy to use. Go to your Claude instance and add the official Gamma connector. That's it! Claude Code will now have access to all the necessary MCP tools for generating slides. I use the Claude Agent SDK for my agent orchestrator, so I use the official Gamma MCP tools and embed the generated slides in an iframe via my artifact preview. See the clip below for an example.

elvis

47,766 views • 3 months ago