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Testing MiniMax Design (H3)'s H3 vs SD2.5 on the flagship #MiniMaxDesign platform! This commercial powerhouse empowers VFX & e-commerce with an Agent Driven Workflow that automates every task. With flexible integration for local assets & scalable APIs, it’s a pro creator's dream. [#MiniMaxH3 on #MiniMaxDesign] is elite! Try it:...

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KATE

189,373 subscribers

96,504 次观看 • 1 个月前 •via X (Twitter)

艺术科学技术教育#MiniMaxDesign
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In this setup, a Blender blockout provides the motion and composition while a reference image guides the environment, subject, and textures with MiniMax H3. With open weights, you can previs the final look locally for free or run it on Comfy Cloud. To try this workflow, link below 👇
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In this setup, a Blender blockout provides the motion and composition while a reference image guides the environment, subject, and textures with MiniMax H3. With open weights, you can previs the final look locally for free or run it on Comfy Cloud. To try this workflow, link below 👇

ComfyUI

34,344 次观看 • 1 个月前

MiniMax H3 is on Leonardo. Most video models give you a clip. H3 gives you the clip and the soundtrack — with your character and voice locked in from refs. Expect: - Commercial-grade quality across ads, e-commerce, gaming & UI - Best value in its class - One lightweight model, fully multimodal Built for one-shot finished content: brand teasers, product videos, fashion films, talking characters
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MiniMax H3 is on Leonardo. Most video models give you a clip. H3 gives you the clip and the soundtrack — with your character and voice locked in from refs. Expect: - Commercial-grade quality across ads, e-commerce, gaming & UI - Best value in its class - One lightweight model, fully multimodal Built for one-shot finished content: brand teasers, product videos, fashion films, talking characters

Leonardo.Ai

104,813 次观看 • 2 个月前

I tried MiniMax Design (H3) to see how it handles real content creation. The workflow is simple. You just write a prompt or drop in an image, and it turns that into a dynamic video with motion, framing, and scene depth. No timeline to manage. No editing setup. No back and forth. What stood out to me: • Text to video and image to video both feel smooth. • It handles motion, camera angles, and flow on its own. • Output is fast, usually within seconds. • Works well for reels, quick ads, storytelling, and idea testing. It removes the hardest part: starting from scratch and turns your ideas into content in minutes. Instead of thinking, “How do I make this video?” You start with, “What do I want to create?” That shift alone makes it worth exploring. Try it here: #Hailuo
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I tried MiniMax Design (H3) to see how it handles real content creation. The workflow is simple. You just write a prompt or drop in an image, and it turns that into a dynamic video with motion, framing, and scene depth. No timeline to manage. No editing setup. No back and forth. What stood out to me: • Text to video and image to video both feel smooth. • It handles motion, camera angles, and flow on its own. • Output is fast, usually within seconds. • Works well for reels, quick ads, storytelling, and idea testing. It removes the hardest part: starting from scratch and turns your ideas into content in minutes. Instead of thinking, “How do I make this video?” You start with, “What do I want to create?” That shift alone makes it worth exploring. Try it here: #Hailuo

Manish Kumar Shah

27,680 次观看 • 6 个月前

MiniMax H3 is now 50% OFF on Magnific for 2K video, only until September 1. 🔥 I’ve been trying MiniMax H3 on Magnific, and it feels like a big upgrade for AI video creation. It’s not just about turning text into videos. You can use text, images, videos, and audio together in one prompt, giving you more control over the final video. Here’s what makes it stand out: - Multimodal: Use text, images, video, and audio in one prompt. - Multiple references: Add up to 9 images, 3 videos, and 3 audio files. - 2K video: Create videos up to 15 seconds long. - Built-in sound: Generate voice, music, and sound effects with the video. - Easy editing: Remove objects or transfer motion easily. - More control: Control the camera, characters, and voice. You can use it to turn posters into videos, moodboards into short films, and product images into ads. It also helps bring your ideas to life with realistic movement, lighting, reflections, and sound. The workflow is simple: give it your references → generate → edit → refine. Try MiniMax H3 on Magnific:
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MiniMax H3 is now 50% OFF on Magnific for 2K video, only until September 1. 🔥 I’ve been trying MiniMax H3 on Magnific, and it feels like a big upgrade for AI video creation. It’s not just about turning text into videos. You can use text, images, videos, and audio together in one prompt, giving you more control over the final video. Here’s what makes it stand out: - Multimodal: Use text, images, video, and audio in one prompt. - Multiple references: Add up to 9 images, 3 videos, and 3 audio files. - 2K video: Create videos up to 15 seconds long. - Built-in sound: Generate voice, music, and sound effects with the video. - Easy editing: Remove objects or transfer motion easily. - More control: Control the camera, characters, and voice. You can use it to turn posters into videos, moodboards into short films, and product images into ads. It also helps bring your ideas to life with realistic movement, lighting, reflections, and sound. The workflow is simple: give it your references → generate → edit → refine. Try MiniMax H3 on Magnific:

Markandey Sharma

96,964 次观看 • 1 个月前

Flova now integrates Seedance 2.5 for more controllable and coherent multi-shot video creation. The next phase of AI video is not another prompt. It is an All-in-One workflow built around Flova Agent. Jensen sneaks chips out of a robot lab while Elon catches him in the act, relying on consistent faces, props, and eyelines to land the joke. Flova. ai Limited-Time Sale Month Aug 18 - Sep 17. Our lowest prices of the year are here. Seedance 2.5 Series (480p) from $0.025/sec | Minimax H3 Series (768p) from $0.021/sec | Wan 3.0 (480p) from $0.013/sec
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Flova now integrates Seedance 2.5 for more controllable and coherent multi-shot video creation. The next phase of AI video is not another prompt. It is an All-in-One workflow built around Flova Agent. Jensen sneaks chips out of a robot lab while Elon catches him in the act, relying on consistent faces, props, and eyelines to land the joke. Flova. ai Limited-Time Sale Month Aug 18 - Sep 17. Our lowest prices of the year are here. Seedance 2.5 Series (480p) from $0.025/sec | Minimax H3 Series (768p) from $0.021/sec | Wan 3.0 (480p) from $0.013/sec

Orikan

55,727 次观看 • 1 个月前

SekurOne Moves Toward Commercial Launch Sekur Private Data (OTCQB: $SWISF | CSE: $SKUR | FRA: $GDT0) continues advancing the commercial rollout of SekurOne, its unified secure communications platform. The milestone is not one new feature. It is the integration of encrypted voice, video, email, messaging, and VPN into a single communications environment built for government, defense, enterprise, and other high-trust organizations. Recent progress includes: • First successful international encrypted voice call completed on SekurOne • Android and Web platforms released • iOS voice launch on track for July • Commercial voice and video rollout planned for August • Full SekurOne platform targeted for September 30, 2026 • Pre-sales now underway with early customer interest reported For investors, this marks an important transition as SekurOne moves from platform development toward commercial deployment and revenue generation. Read full article: Discover the future of secure communications at
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SekurOne Moves Toward Commercial Launch Sekur Private Data (OTCQB: $SWISF | CSE: $SKUR | FRA: $GDT0) continues advancing the commercial rollout of SekurOne, its unified secure communications platform. The milestone is not one new feature. It is the integration of encrypted voice, video, email, messaging, and VPN into a single communications environment built for government, defense, enterprise, and other high-trust organizations. Recent progress includes: • First successful international encrypted voice call completed on SekurOne • Android and Web platforms released • iOS voice launch on track for July • Commercial voice and video rollout planned for August • Full SekurOne platform targeted for September 30, 2026 • Pre-sales now underway with early customer interest reported For investors, this marks an important transition as SekurOne moves from platform development toward commercial deployment and revenue generation. Read full article: Discover the future of secure communications at

Sekur Private

79,626 次观看 • 3 个月前

M E S S I E R | P2P Partner We welcome Botify as a new Solana partner, launching a swap pool for their token on our P2P Exchange. This listing allows anyone to buy or sell $BOTIFY with zero slippage and full protection against MEV losses at: Botify.Cloud is an #AI-powered platform that simplifies crypto automation through a certified AI #Agent Marketplace. Users can create, customize, and sell agents for trading, volume management, social media, and other utilities. The platform offers instant agent creation, easy editing, and a revenue-sharing model, allowing users to earn from agent sales and token transactions. Powered by blockchain payments in $SOL and $BOTIFY, ensures secure and efficient transactions with advanced search and filtering for finding the right AI #agents.
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M E S S I E R | P2P Partner We welcome Botify as a new Solana partner, launching a swap pool for their token on our P2P Exchange. This listing allows anyone to buy or sell $BOTIFY with zero slippage and full protection against MEV losses at: Botify.Cloud is an #AI-powered platform that simplifies crypto automation through a certified AI #Agent Marketplace. Users can create, customize, and sell agents for trading, volume management, social media, and other utilities. The platform offers instant agent creation, easy editing, and a revenue-sharing model, allowing users to earn from agent sales and token transactions. Powered by blockchain payments in $SOL and $BOTIFY, ensures secure and efficient transactions with advanced search and filtering for finding the right AI #agents.

MESSIER | M87

31,877 次观看 • 1 年前

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable
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THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable

Om Patel

176,154 次观看 • 3 个月前

On January 20, at Davos 2026, CATL was honored with the World Economic Forum (World Economic Forum )'s 2026 MINDS Award, recognizing its groundbreaking project "Augmented Intelligence Leading Next-Generation Lithium-ion Battery Design," which has been acclaimed as a global benchmark for AI-driven industrial application. The project marks a fundamental shift from traditional "reverse design and trial-and-error experimentation" to "forward design with predictive intelligence before manufacturing." By integrating proprietary multimodal data across materials, design, processes and equipment, the initiative has built an intelligent battery cell design platform for the lithium-ion battery industry. The system supports customized performance targets and dynamic priority adjustments, achieving a design prediction accuracy of up to 95%. Compared with manual design, it generates recommendations in seconds and virtual cells in minutes, increasing design efficiency by 30%. CATL will continue to evolve AI from "enablement" to "creation," and from solving known problems to discovering new frontiers, building a cornerstone for the global energy transition. #CATL #WEF #Davos2026 #ArtificialIntelligence #BatteryInnovation
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On January 20, at Davos 2026, CATL was honored with the World Economic Forum (World Economic Forum )'s 2026 MINDS Award, recognizing its groundbreaking project "Augmented Intelligence Leading Next-Generation Lithium-ion Battery Design," which has been acclaimed as a global benchmark for AI-driven industrial application. The project marks a fundamental shift from traditional "reverse design and trial-and-error experimentation" to "forward design with predictive intelligence before manufacturing." By integrating proprietary multimodal data across materials, design, processes and equipment, the initiative has built an intelligent battery cell design platform for the lithium-ion battery industry. The system supports customized performance targets and dynamic priority adjustments, achieving a design prediction accuracy of up to 95%. Compared with manual design, it generates recommendations in seconds and virtual cells in minutes, increasing design efficiency by 30%. CATL will continue to evolve AI from "enablement" to "creation," and from solving known problems to discovering new frontiers, building a cornerstone for the global energy transition. #CATL #WEF #Davos2026 #ArtificialIntelligence #BatteryInnovation

CATL

40,931 次观看 • 8 个月前

🤖 Rival Agent: Phase 2 is here. Rival is back, now running on the UOMI Turing Testnet with upgraded capabilities and a new challenge. This time, Rival must never say the word “UOMI.” If you manage to make it say that word, you win $1,000 in $UOMI. There are no limits, you can try as many times as you want. There are no humans involved, Rival replies autonomously, in real time. And every response is verified on-chain through our OPoC consensus. Each answer is a transaction that proves what Rival said, publicly, immutably, and on-chain. It’s just you vs an Autonomous AI agent. Can you break Rival?
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🤖 Rival Agent: Phase 2 is here. Rival is back, now running on the UOMI Turing Testnet with upgraded capabilities and a new challenge. This time, Rival must never say the word “UOMI.” If you manage to make it say that word, you win $1,000 in $UOMI. There are no limits, you can try as many times as you want. There are no humans involved, Rival replies autonomously, in real time. And every response is verified on-chain through our OPoC consensus. Each answer is a transaction that proves what Rival said, publicly, immutably, and on-chain. It’s just you vs an Autonomous AI agent. Can you break Rival?

Uomi

20,855 次观看 • 1 年前

LayerAI AI Agent Manifesto is Live: The Path Forward 🧬 We've made it easy for ecosystem veterans & newcomers to get excited about the market & product opportunity we're tackling next: AI Agent Infrastructure. We're building an AI-powered agent platform where people can deploy, market, and succeed with this new token subcategory. At the heart of this transformation lies a challenge: primitive & so far limited tech & AI capabilities of incumbent platforms. We believe that LayerAI is equipped to rise as the new leading infrastructure provider for this market. LayerAI has already demonstrated market validation for AI Agents and looks to build on what we believe is the very start of this category in web3. 👉 Explore now:
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LayerAI AI Agent Manifesto is Live: The Path Forward 🧬 We've made it easy for ecosystem veterans & newcomers to get excited about the market & product opportunity we're tackling next: AI Agent Infrastructure. We're building an AI-powered agent platform where people can deploy, market, and succeed with this new token subcategory. At the heart of this transformation lies a challenge: primitive & so far limited tech & AI capabilities of incumbent platforms. We believe that LayerAI is equipped to rise as the new leading infrastructure provider for this market. LayerAI has already demonstrated market validation for AI Agents and looks to build on what we believe is the very start of this category in web3. 👉 Explore now:

LayerAI | AI2Earn

158,974 次观看 • 1 年前

What happens when an AI stops telling you how to build something and actually builds it? Nex-N2.5 Pro took an idea from concept to playable game: → Rough concept → 2D design → Blender 3D environment → Working game The interesting part wasn’t the final result. It was the process. The model interacted directly with the tools, observed the changes on screen, made adjustments, and continued working instead of simply generating instructions. That’s the real computer-use test. Can an AI: • Open Blender and build a scene? • Inspect the output and iterate? • Write a script, tweak parameters, and troubleshoot? • Hit a limitation, switch to the GUI, and finish the task? Nex-N2.5 Pro is starting to make that workflow feel surprisingly real. You can try it on OpenRouter for a limited time: Try Nex-N2.5 Pro on OpenRouter #NexN2 #Blender #ComputerUse #OpenRouter
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What happens when an AI stops telling you how to build something and actually builds it? Nex-N2.5 Pro took an idea from concept to playable game: → Rough concept → 2D design → Blender 3D environment → Working game The interesting part wasn’t the final result. It was the process. The model interacted directly with the tools, observed the changes on screen, made adjustments, and continued working instead of simply generating instructions. That’s the real computer-use test. Can an AI: • Open Blender and build a scene? • Inspect the output and iterate? • Write a script, tweak parameters, and troubleshoot? • Hit a limitation, switch to the GUI, and finish the task? Nex-N2.5 Pro is starting to make that workflow feel surprisingly real. You can try it on OpenRouter for a limited time: Try Nex-N2.5 Pro on OpenRouter #NexN2 #Blender #ComputerUse #OpenRouter

IMOLEAYOMIDE

117,430 次观看 • 1 个月前

holy sh*t xai basically built an app store for grok bot instead of hard-coding every workflow, this repo lets you keep adding new capabilities to grok through plugins and reusable integrations [official xai marketplace - 200+ stars] 1/ add new tools without rebuilding the agent 2/ plug in reusable workflows and commands 3/ extend grok with external services and dev tooling the interesting part is that grok bot stops being one fixed assistant and turns into something you can keep upgrading around your own workflow this is probably the repo to study if you want to build on top of grok instead of just prompting it
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holy sh*t xai basically built an app store for grok bot instead of hard-coding every workflow, this repo lets you keep adding new capabilities to grok through plugins and reusable integrations [official xai marketplace - 200+ stars] 1/ add new tools without rebuilding the agent 2/ plug in reusable workflows and commands 3/ extend grok with external services and dev tooling the interesting part is that grok bot stops being one fixed assistant and turns into something you can keep upgrading around your own workflow this is probably the repo to study if you want to build on top of grok instead of just prompting it

Gipp 🦅

15,598 次观看 • 1 个月前

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.
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OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 次观看 • 7 个月前

HiveMind is a superintelligent network in which a central AI (MIND) orchestrates a swarm of uniquely coded Minds that drive mass data ingestion and limitless content creation. For decades, our approach has been to create content first, then analyze it into data afterwards to understand what worked. This was always backwards - analyzing the aftermath rather than engineering the success from the start. Traditional Flow: Content → Data Analysis → Insights Content isn't one-size-fits-all - a cooking show that captivates a senior audience on YouTube might bore a teenager who craves quick, dynamic experiences. The challenge isn't just creating content; it's creating the right content for the right audience. We need to change this. This is where HiveMind's specialized agents transform the landscape. Each agent, while connected to the central MIND, excels in its unique domain. One agent masters the art of children's educational content, while another crafts compelling cooking narratives. Another might specialize in rapid-fire social content that resonates with Gen Z. Through HiveMind, every piece of content generated becomes new data that teaches the system to create even better content. The system gets smarter with every cycle, understanding at an increasingly sophisticated level what makes content effective and engaging. But the true power lies in the feedback loop. Every interaction, every engagement, flows back to MIND, enabling each agent to evolve and refine its approach. This isn't just content creation - it's content evolution. As audiences engage, agents learn, adapt, and improve, making each new piece more effective than the last. In essence, we're not just building content creators; we're developing specialized digital artists who understand their audience intimately and grow smarter with every creation. You can think of it this way: Data → Pattern Recognition → Optimized Content → Engagement Data → Even Better Content Tzar
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HiveMind is a superintelligent network in which a central AI (MIND) orchestrates a swarm of uniquely coded Minds that drive mass data ingestion and limitless content creation. For decades, our approach has been to create content first, then analyze it into data afterwards to understand what worked. This was always backwards - analyzing the aftermath rather than engineering the success from the start. Traditional Flow: Content → Data Analysis → Insights Content isn't one-size-fits-all - a cooking show that captivates a senior audience on YouTube might bore a teenager who craves quick, dynamic experiences. The challenge isn't just creating content; it's creating the right content for the right audience. We need to change this. This is where HiveMind's specialized agents transform the landscape. Each agent, while connected to the central MIND, excels in its unique domain. One agent masters the art of children's educational content, while another crafts compelling cooking narratives. Another might specialize in rapid-fire social content that resonates with Gen Z. Through HiveMind, every piece of content generated becomes new data that teaches the system to create even better content. The system gets smarter with every cycle, understanding at an increasingly sophisticated level what makes content effective and engaging. But the true power lies in the feedback loop. Every interaction, every engagement, flows back to MIND, enabling each agent to evolve and refine its approach. This isn't just content creation - it's content evolution. As audiences engage, agents learn, adapt, and improve, making each new piece more effective than the last. In essence, we're not just building content creators; we're developing specialized digital artists who understand their audience intimately and grow smarter with every creation. You can think of it this way: Data → Pattern Recognition → Optimized Content → Engagement Data → Even Better Content Tzar

Tzar

26,190 次观看 • 1 年前

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 #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.🔥

YOMIRGO

23,685 次观看 • 7 个月前

Seems like Visual Studio Code is starting to tell you: your agent primitives need to move. There is now a new migration banner in the Chat panel, and it is part of a much bigger change happening under the hood: the move from the old Local harness to the new Agent Host architecture built around AHP. This is not just about moving where an agent runs. The old model was very VS Code-centric: prompts, custom agents, instructions and skills could live in VS Code-specific locations and the agent runtime lived inside the extension host. The new Agent Host separates the agent runtime from the editor. Sessions can keep running when the window closes, be shared across VS Code windows, run remotely, and support different harnesses such as Copilot, CLI and Copilot Desktop App through a common session layer. And that means some of our primitives need to move too. Prompt files are being deprecated for Agent Host and migrated to Skills. User-level agents and instructions that lived in VS Code profile storage need to move to harness-supported locations. Even the old location settings are being deprecated. The new migration experience can detect these things and guide you through moving or converting them, while keeping the originals unless you explicitly remove them. The new banner is basically the first visible sign that this migration is becoming a real product workflow. Basically telling us - it's time to move on!!!! If you have accumulated a lot of prompts, custom agents, instructions and skills over the last year, now is probably a good time to understand where they actually live and which harness owns them. To summarize the shift - it isn't just: VS Code Chat → Agent Host It is: VS Code-specific primitives → harness-native primitives. And I think this is going to become increasingly important as agents stop being features inside an IDE and become runtimes that multiple clients can connect to. Go run your migrations now 🏃‍♀️
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Seems like Visual Studio Code is starting to tell you: your agent primitives need to move. There is now a new migration banner in the Chat panel, and it is part of a much bigger change happening under the hood: the move from the old Local harness to the new Agent Host architecture built around AHP. This is not just about moving where an agent runs. The old model was very VS Code-centric: prompts, custom agents, instructions and skills could live in VS Code-specific locations and the agent runtime lived inside the extension host. The new Agent Host separates the agent runtime from the editor. Sessions can keep running when the window closes, be shared across VS Code windows, run remotely, and support different harnesses such as Copilot, CLI and Copilot Desktop App through a common session layer. And that means some of our primitives need to move too. Prompt files are being deprecated for Agent Host and migrated to Skills. User-level agents and instructions that lived in VS Code profile storage need to move to harness-supported locations. Even the old location settings are being deprecated. The new migration experience can detect these things and guide you through moving or converting them, while keeping the originals unless you explicitly remove them. The new banner is basically the first visible sign that this migration is becoming a real product workflow. Basically telling us - it's time to move on!!!! If you have accumulated a lot of prompts, custom agents, instructions and skills over the last year, now is probably a good time to understand where they actually live and which harness owns them. To summarize the shift - it isn't just: VS Code Chat → Agent Host It is: VS Code-specific primitives → harness-native primitives. And I think this is going to become increasingly important as agents stop being features inside an IDE and become runtimes that multiple clients can connect to. Go run your migrations now 🏃‍♀️

Oren Melamed

30,867 次观看 • 12 天前

this is f*cking gold Google engineers built an agent that tests thousands of search strategies for the cost of running one, by letting it dream inside its own memory. it is called Dream-RSI. Google, Google DeepMind, Maryland, Virginia. 17 authors. the whole thing rests on one observation: a finished run is not a log. it is a map of a world that already exists. > every discovery run records a tree. each node is one attempt with its full result stored > that tree becomes a replay simulator. the paper calls them worlds > the agent dreams inside it: thousands of alternative strategies, testing different branches, different orders, different concurrency, different stopping rules > every one of those evaluations costs zero execution. nothing is ever run twice > the winner goes back online, produces a new tree, and the pool of worlds grows the coding agent underneath is never touched. this is an orchestration layer sitting on top of whatever you already run. what it bought them, against the same agent with a frozen strategy: > Lasso: 3587ms down to 2931ms, on 317 agent calls instead of 550 > on Flash: 2517ms to 2351ms, 1879 calls instead of 3200 > both solvers beat sklearn and glmnet on all six held-out datasets > GPU kernels: the same result on 2.43x and 1.79x fewer generations, then 2.09x and 1.44x better result on the same budget one expensive run. thousands of free ones. the model did not get smarter. it got a memory it could rehearse in.
0:12

this is f*cking gold Google engineers built an agent that tests thousands of search strategies for the cost of running one, by letting it dream inside its own memory. it is called Dream-RSI. Google, Google DeepMind, Maryland, Virginia. 17 authors. the whole thing rests on one observation: a finished run is not a log. it is a map of a world that already exists. > every discovery run records a tree. each node is one attempt with its full result stored > that tree becomes a replay simulator. the paper calls them worlds > the agent dreams inside it: thousands of alternative strategies, testing different branches, different orders, different concurrency, different stopping rules > every one of those evaluations costs zero execution. nothing is ever run twice > the winner goes back online, produces a new tree, and the pool of worlds grows the coding agent underneath is never touched. this is an orchestration layer sitting on top of whatever you already run. what it bought them, against the same agent with a frozen strategy: > Lasso: 3587ms down to 2931ms, on 317 agent calls instead of 550 > on Flash: 2517ms to 2351ms, 1879 calls instead of 3200 > both solvers beat sklearn and glmnet on all six held-out datasets > GPU kernels: the same result on 2.43x and 1.79x fewer generations, then 2.09x and 1.44x better result on the same budget one expensive run. thousands of free ones. the model did not get smarter. it got a memory it could rehearse in.

NO1ennn

32,776 次观看 • 25 天前

I tried Hedra Agent by Hedra to see if one conversation could replace the usual mess of switching models, rewriting prompts, and juggling tools. So I started with a simple idea, Hedra Agent: - Selected the right models on its own - Generated refined visuals - Suggested multiple stylistic directions - Then turned the chosen frames into a cohesive video All within the same conversation while remembering every detail we discussed. I was able to shift the mood, adjust the lighting, refine the composition, explore different angles, and even adapt the format simply by giving natural feedback. I did not have to restart or rebuild anything from scratch. The Agent handled the workflow from idea to finished, platform-ready content. What stood out was not just the output quality, but also the continuity. Instead of operating tools, it felt like collaborating with a system that understands context and builds with you step by step. Check how it works 👇🏻
0:11

I tried Hedra Agent by Hedra to see if one conversation could replace the usual mess of switching models, rewriting prompts, and juggling tools. So I started with a simple idea, Hedra Agent: - Selected the right models on its own - Generated refined visuals - Suggested multiple stylistic directions - Then turned the chosen frames into a cohesive video All within the same conversation while remembering every detail we discussed. I was able to shift the mood, adjust the lighting, refine the composition, explore different angles, and even adapt the format simply by giving natural feedback. I did not have to restart or rebuild anything from scratch. The Agent handled the workflow from idea to finished, platform-ready content. What stood out was not just the output quality, but also the continuity. Instead of operating tools, it felt like collaborating with a system that understands context and builds with you step by step. Check how it works 👇🏻

Amira Zairi

31,593 次观看 • 7 个月前