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Introducing Workflows 🌀 Guide agents through multi-step actions like: - Creating video NFT candy machines - Launching a memecoin - Airdropping tokens

griffain

113,422 subscribers

50,649 次观看 • 1 年前 •via X (Twitter)

财经游戏科学技术

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Starting today, Mastra agents and workflows can publish and subscribe through a built-in event system. Listen for events like workflow step finish, pause, or completion. Subscribe external systems to Mastra events, or vice versa.

Starting today, Mastra agents and workflows can publish and subscribe through a built-in event system. Listen for events like workflow step finish, pause, or completion. Subscribe external systems to Mastra events, or vice versa.

Sam Bhagwat

11,784 次观看 • 1 个月前

Today we’re introducing Copilot Mode in Edge, our first step in reinventing the browser for the AI age. My favorite feature is multi-tab RAG. You can use Copilot to analyze your open tabs, like I do here with papers our team has published in nature journals over the last year. And there is a lot more to come, including built-in actions so you can delegate tasks as you browse.

Today we’re introducing Copilot Mode in Edge, our first step in reinventing the browser for the AI age. My favorite feature is multi-tab RAG. You can use Copilot to analyze your open tabs, like I do here with papers our team has published in nature journals over the last year. And there is a lot more to come, including built-in actions so you can delegate tasks as you browse.

Satya Nadella

705,078 次观看 • 1 年前

Back when we were developing GEN3C, we often imagined a Holodeck-like future: a simulator where multiple agents can enter the same generated world, act independently, and learn to collaborate. Gamma-World makes this feel more concrete. It is a generative multi-agent world model that takes synchronized observations and actions, then rolls out what each agent will see next in the same evolving world — action-responsive at 24 FPS. For me, the key challenge is going beyond two players. As more agents enter, identity cannot be tied to fixed slots, interaction cannot rely on dense pairwise attention, and independent actions still need to resolve into one shared state. Two ideas make this work: 1⃣ Simplex RoPE Distinct agent identities without slot bias — unique, but permutation-equivalent. 2⃣ Sparse Hub Attention Agents communicate through learnable hubs instead of dense all-to-all attention: agent → hub → agent This keeps cross-agent communication scalable. The exciting part: training on two-player data can generalize to four-player rollouts without additional training, and the same formulation extends to real-world bimanual robot coordination. A step toward populated world models: many agents, one shared world. Congrats to the team on Gamma-World! Project:

Back when we were developing GEN3C, we often imagined a Holodeck-like future: a simulator where multiple agents can enter the same generated world, act independently, and learn to collaborate. Gamma-World makes this feel more concrete. It is a generative multi-agent world model that takes synchronized observations and actions, then rolls out what each agent will see next in the same evolving world — action-responsive at 24 FPS. For me, the key challenge is going beyond two players. As more agents enter, identity cannot be tied to fixed slots, interaction cannot rely on dense pairwise attention, and independent actions still need to resolve into one shared state. Two ideas make this work: 1⃣ Simplex RoPE Distinct agent identities without slot bias — unique, but permutation-equivalent. 2⃣ Sparse Hub Attention Agents communicate through learnable hubs instead of dense all-to-all attention: agent → hub → agent This keeps cross-agent communication scalable. The exciting part: training on two-player data can generalize to four-player rollouts without additional training, and the same formulation extends to real-world bimanual robot coordination. A step toward populated world models: many agents, one shared world. Congrats to the team on Gamma-World! Project:

Xuanchi Ren

304,145 次观看 • 1 个月前

What if your video pipeline worked like your codebase? Introducing Replit Animation. Replit ⠕ #replitanimation From a single prompt, you can generate animated videos directly in Replit. It writes TypeScript with modern libraries to build scenes, apply styling, and export straight to mp4. Because it is built on code, you can import your real components, brand tokens, and design systems. That means consistent styling, reusable assets, and total control. Video as code means you can tweak, version, and scale your videos just like software. Create repeatable workflows for product updates, feature drops, and marketing campaigns without starting from scratch every time. Here is an example of a video I generated inside Replit. Export at your chosen resolution and frame rate or host with a unique URL. Activate video mode by typing video in your first prompt or selecting Animation under App mode. Create your first one:

JOY JULIET

146,865 次观看 • 4 个月前

Hot take: most “AI creator” tools are just fancy prompt boxes. This one actually behaves like an agent. I gave it a messy idea and instead of breaking, it: - built visuals (images) - turned them into video - kept iterating without me stepping in every 2 seconds (you can see it in the screen recording 👇) Halfway through using Yapper , it clicked — this thing doesn’t just generate… it works through ideas. That’s the difference: it thinks in workflows, not single outputs. You spend less time prompting…and more time actually creating. Not perfect yet — but it’s one of the first tools that feels like it scales with you. If you’re deep into AI, you’ll see the shift instantly.

Hot take: most “AI creator” tools are just fancy prompt boxes. This one actually behaves like an agent. I gave it a messy idea and instead of breaking, it: - built visuals (images) - turned them into video - kept iterating without me stepping in every 2 seconds (you can see it in the screen recording 👇) Halfway through using Yapper , it clicked — this thing doesn’t just generate… it works through ideas. That’s the difference: it thinks in workflows, not single outputs. You spend less time prompting…and more time actually creating. Not perfect yet — but it’s one of the first tools that feels like it scales with you. If you’re deep into AI, you’ll see the shift instantly.

The AI Guru 💡

50,197 次观看 • 2 个月前

🐻 BERACHAIN BONDS ARE NOW LIVE! ⛓️🔥 We’re thrilled to bring Bonds to Berachain Foundation 🐻⛓ — the chain built different, where liquidity reigns, bears rule, and the vibes are always on-chain. 🎨 With each Bond you buy from our Berachain partners, you’ll unlock exclusive NFT art made for the ecosystem. It’s time to Bond where the bears build. 🧱 1⃣ BurrBear is the one-stop stablecoin shop on Berachain, offering capital-efficient DeFi pools for stablecoins and tokenized assets. With Multi Stable Pools, innovative and more efficient 'Burr Pools', and Generalized Pools, it supports both like-priced and non-like-priced trades. Fueled by the $BURR token, BurrBear unleashes a new era of capital-efficient trading. Get $BURR tokens at a discount! 👉 2⃣ BeraTrax is now Trax is a mobile-first platform that simplifies earning yield on Berachain through one-click deposits, gasless transactions, and auto-compounding vaults. Users earn BGT or iBGT for ongoing validator rewards. $TRAX holders decide which vault gets boosted each week, directing protocol bribes to maximize community-driven rewards. Get $TRAX tokens at a discount! 👉 3⃣ HoneyFun AI brings co-owned Utility AI Agents to Berachain, focusing on DeFi, gaming, and entertainment. Through the Honeyfun Protocol, users can create agents with persistent identity and real utility. $AIBERA powers the ecosystem, pairing with all AI LPs and capturing 100% of platform fees for staking and buybacks—driving real value and community growth. Get $AIBERA tokens at a discount! 👉 🐾 And we’re just getting started — more Berachain Bond partners will be revealed next week. Grab your honey and let’s get bonding! Because on Berachain, it's Up Only. 🐻🚀

🐻 BERACHAIN BONDS ARE NOW LIVE! ⛓️🔥 We’re thrilled to bring Bonds to Berachain Foundation 🐻⛓ — the chain built different, where liquidity reigns, bears rule, and the vibes are always on-chain. 🎨 With each Bond you buy from our Berachain partners, you’ll unlock exclusive NFT art made for the ecosystem. It’s time to Bond where the bears build. 🧱 1⃣ BurrBear is the one-stop stablecoin shop on Berachain, offering capital-efficient DeFi pools for stablecoins and tokenized assets. With Multi Stable Pools, innovative and more efficient 'Burr Pools', and Generalized Pools, it supports both like-priced and non-like-priced trades. Fueled by the $BURR token, BurrBear unleashes a new era of capital-efficient trading. Get $BURR tokens at a discount! 👉 2⃣ BeraTrax is now Trax is a mobile-first platform that simplifies earning yield on Berachain through one-click deposits, gasless transactions, and auto-compounding vaults. Users earn BGT or iBGT for ongoing validator rewards. $TRAX holders decide which vault gets boosted each week, directing protocol bribes to maximize community-driven rewards. Get $TRAX tokens at a discount! 👉 3⃣ HoneyFun AI brings co-owned Utility AI Agents to Berachain, focusing on DeFi, gaming, and entertainment. Through the Honeyfun Protocol, users can create agents with persistent identity and real utility. $AIBERA powers the ecosystem, pairing with all AI LPs and capturing 100% of platform fees for staking and buybacks—driving real value and community growth. Get $AIBERA tokens at a discount! 👉 🐾 And we’re just getting started — more Berachain Bond partners will be revealed next week. Grab your honey and let’s get bonding! Because on Berachain, it's Up Only. 🐻🚀

ApeBond

19,589 次观看 • 1 年前

🚀 BOOM! New Mini-Game & Major Update Alert! 🎉 🎮 PLAY: Introducing Chili Flap – our new mini-game where you need to guide your Chili Coin through all the obstacles! 🌶️💰 If you’re a fan of Flappy Bird, you’ll love this one. Your goal is to keep the coin in the air and avoid barriers. The higher you go, the more your crypto will soar, just like the coin in the game! 🚀💸 🔥 What's New: - Upgrade Multi-Click to Level 60 and enhance your gameplay! 🌟 - New tasks and achievements related to Chili Flap and more! 🎯 - We've worked on optimization – the game now loads faster and runs smoother! ⚡ - Added more precise descriptions and fixed some translations. 🛠️ - A bunch of minor bugs have been squashed! 🐞 Top 20 players in Chili Flap will receive exclusive NFTs, just like with our other mini-games! 🏅🎁 Thank you for playing! Stay with us and share your high scores in the game! 🕹️💪 #ChilizFarmTap #Launch #FarmingSimulator #PlayToEarn #airdrop #NFT #Chiliz #CHZ #crypto #ChilizFarm

🚀 BOOM! New Mini-Game & Major Update Alert! 🎉 🎮 PLAY: Introducing Chili Flap – our new mini-game where you need to guide your Chili Coin through all the obstacles! 🌶️💰 If you’re a fan of Flappy Bird, you’ll love this one. Your goal is to keep the coin in the air and avoid barriers. The higher you go, the more your crypto will soar, just like the coin in the game! 🚀💸 🔥 What's New: - Upgrade Multi-Click to Level 60 and enhance your gameplay! 🌟 - New tasks and achievements related to Chili Flap and more! 🎯 - We've worked on optimization – the game now loads faster and runs smoother! ⚡ - Added more precise descriptions and fixed some translations. 🛠️ - A bunch of minor bugs have been squashed! 🐞 Top 20 players in Chili Flap will receive exclusive NFTs, just like with our other mini-games! 🏅🎁 Thank you for playing! Stay with us and share your high scores in the game! 🕹️💪 #ChilizFarmTap #Launch #FarmingSimulator #PlayToEarn #airdrop #NFT #Chiliz #CHZ #crypto #ChilizFarm

Chiliz Farm: Play & Earn | Mobile Farm Game 🌶️ 🎮

13,417 次观看 • 1 年前

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Introducing Kaleido💮 from AI at Meta — a universal generative neural rendering engine for photorealistic, unified object and scene view synthesis. Kaleido is built on a simple but powerful design philosophy: 3D perception is a form of visual common sense. Following this idea, we formulate rendering purely as a sequence-to-sequence generation problem, successfully unifying neural rendering with the architecture principles behind modern language and video models. Unlike traditional neural rendering methods, Kaleido learns 3D purely in a data-driven way, without explicit 3D representations or structures. It acquires spatial understanding directly through large-scale video pretraining, then multi-view 3D data finetuning, inspired by how LLMs acquire textual common sense from large corpora before specialising in domains like coding. Through extensive ablations, we progressively modernised the architecture design and training strategies and tackled key scaling challenges in sequence-to-sequence generative rendering, arriving at a design that’s simple, versatile, and scalable. Kaleido significantly outperforms prior generative models in few-view settings, and remarkably is the first zero-shot generative method matches InstantNGP-level rendering quality in multi-view settings. We view Kaleido also as an alternative step towards world modeling that flexibly spans a spectrum of “realities": with many views, it faithfully reconstructs grounded reality; with fewer views, it imagines plausible unseen details. 🔗 Explore more results and paper:

Shikun Liu

22,332 次观看 • 9 个月前

Brilliant open-source tool from Anthropic Visualize internal LLM pathways through open attribution graphs. → Attribution graphs reveal which neurons and features drive each output token, turning the LLM’s black box into a clear map. They open-sourced a library that generates attribution graphs to trace how LLMs arrive at outputs. These graphs reveal internal activation flows and feature influences in popular open-weights models. 🧠 Attribution Graphs Graphs link model activations to output tokens. Nodes represent neurons or features. Edges show influence strength. Researchers inspect paths to see which components drive each decision. 🛠️ Toolchain and Frontend Circuit-tracer library works with models like Gemma-2-2b and Llama-3.2-1b. Demo notebooks illustrate multi-step reasoning examples. Neuronpedia hosts an interactive UI for graph exploration, annotation, and sharing. 🔍 Hypothesis Testing Users tweak feature values in graphs, regenerate outputs, and observe changes. This lets researchers validate whether specific circuits control behaviors like language translation or reasoning chains.

Brilliant open-source tool from Anthropic Visualize internal LLM pathways through open attribution graphs. → Attribution graphs reveal which neurons and features drive each output token, turning the LLM’s black box into a clear map. They open-sourced a library that generates attribution graphs to trace how LLMs arrive at outputs. These graphs reveal internal activation flows and feature influences in popular open-weights models. 🧠 Attribution Graphs Graphs link model activations to output tokens. Nodes represent neurons or features. Edges show influence strength. Researchers inspect paths to see which components drive each decision. 🛠️ Toolchain and Frontend Circuit-tracer library works with models like Gemma-2-2b and Llama-3.2-1b. Demo notebooks illustrate multi-step reasoning examples. Neuronpedia hosts an interactive UI for graph exploration, annotation, and sharing. 🔍 Hypothesis Testing Users tweak feature values in graphs, regenerate outputs, and observe changes. This lets researchers validate whether specific circuits control behaviors like language translation or reasoning chains.

Rohan Paul

27,197 次观看 • 1 年前

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Bio Protocol

25,865 次观看 • 5 个月前

$Over a year ago I started my journey with Bitcoin. Like most, I was just curious. But as I studied it, I realized Satoshi Nakamoto didn’t just build Bitcoin. He built a world where anyone could create in his spirit. He built something that lasts. That inspired me to build. To build sports on chain and stack Satoshi’s across chains. My contribution was to pass my most valuable possession to the blockchain, the actual $BALL 🏀 GAME 5 BALL from the 1991 NBA Finals. The night the Bulls won our first championship. The Holy Grail of sports. That ball is history, it’s legacy, and it’s culture. I wanted the fans to own a piece of it. To own fractions of the actual ball, now preserved in a vault as we build a Real World Asset around it. The digital scan of that same ball is powering an elite sports ecosystem through gaming, AI agents, storytelling, multi-chain, and a documentary that captures what this moment means. Tomorrow night, the $BALL 🏀 GAME 5 BALL expands to Solana. $BALL is already live on Ethereum and this is the first step of our multi-chain journey as we build the future of sports on-chain. This is bigger than any one person. This is community. This is history. This is the start of breaking world records in sports collectibles and culture itself. Be like Satoshi. Keep building. $BALL 🏀🏆 $BALL 🏀 GAME 5 BALL Learn more and how to get tokens at$

Over a year ago I started my journey with Bitcoin. Like most, I was just curious. But as I studied it, I realized Satoshi Nakamoto didn’t just build Bitcoin. He built a world where anyone could create in his spirit. He built something that lasts. That inspired me to build. To build sports on chain and stack Satoshi’s across chains. My contribution was to pass my most valuable possession to the blockchain, the actual $BALL 🏀 GAME 5 BALL from the 1991 NBA Finals. The night the Bulls won our first championship. The Holy Grail of sports. That ball is history, it’s legacy, and it’s culture. I wanted the fans to own a piece of it. To own fractions of the actual ball, now preserved in a vault as we build a Real World Asset around it. The digital scan of that same ball is powering an elite sports ecosystem through gaming, AI agents, storytelling, multi-chain, and a documentary that captures what this moment means. Tomorrow night, the $BALL 🏀 GAME 5 BALL expands to Solana. $BALL is already live on Ethereum and this is the first step of our multi-chain journey as we build the future of sports on-chain. This is bigger than any one person. This is community. This is history. This is the start of breaking world records in sports collectibles and culture itself. Be like Satoshi. Keep building. $BALL 🏀🏆 $BALL 🏀 GAME 5 BALL Learn more and how to get tokens at

Scottie Pippen

131,692 次观看 • 8 个月前

ℹ️ PARTNERSHIP ANNOUNCEMENT We are very pleased to announce that we have recently partnered with the OpenEgg🥚 project by Kitto🥚. We recognise OpenEgg🥚 as a prominent project in the XRP Ledger ecosystem. They are one of the few projects which has self generated value through multi-chain analytics/trading and strategically built up a net positive fund for the future of OpenEgg🥚. Openegg ($OVO) is Opening Virtual Ownership on the #XRPLedger, driving a self-sustaining #EggFi DeFi ecosystem powered entirely by their native token $OVO. Through their innovative EggFi Incubator liquidity system, rewards from the Egg Fund flow to top $OVO holders and OVOcard owners, while a rich NFT lineup from OVOcards and DoodlEggs to animated 0v0 NFTs delivers unique value and utility. With gaming integrations like OVOcrush, community programs, and an upcoming VerseX Metaverse store, $OVO is at the heart of a self-sustaining, gamified economy on the #XRPLedger, making this collaboration a powerful step forward for both communities. Ensure you have the OVO trustline active in your Xaman wallet and look forward to future updates! OVO Token Trustline ⬇️ Open egg website ⬇️

ℹ️ PARTNERSHIP ANNOUNCEMENT We are very pleased to announce that we have recently partnered with the OpenEgg🥚 project by Kitto🥚. We recognise OpenEgg🥚 as a prominent project in the XRP Ledger ecosystem. They are one of the few projects which has self generated value through multi-chain analytics/trading and strategically built up a net positive fund for the future of OpenEgg🥚. Openegg ($OVO) is Opening Virtual Ownership on the #XRPLedger, driving a self-sustaining #EggFi DeFi ecosystem powered entirely by their native token $OVO. Through their innovative EggFi Incubator liquidity system, rewards from the Egg Fund flow to top $OVO holders and OVOcard owners, while a rich NFT lineup from OVOcards and DoodlEggs to animated 0v0 NFTs delivers unique value and utility. With gaming integrations like OVOcrush, community programs, and an upcoming VerseX Metaverse store, $OVO is at the heart of a self-sustaining, gamified economy on the #XRPLedger, making this collaboration a powerful step forward for both communities. Ensure you have the OVO trustline active in your Xaman wallet and look forward to future updates! OVO Token Trustline ⬇️ Open egg website ⬇️

DPMF

21,483 次观看 • 11 个月前

Step 3: Seedance prompt Here is the core prompt I used: First-person broom-riding POV, cinematic ultra-fast one-take chase. Strictly follow the red flight path in the image. The Golden Snitch stays ahead as the main chase target, but it should not stay fixed in the center. It moves left, right, up, and down during the flight, making the chase feel more alive. Only show the broom handle, gloved hands, and red sleeves. Never show the rider’s face. Chase through the stadium, around the towers, over Black Lake, through the bridge arch, and back to the pitch for the final catch. No red lines, no arrows, no broken broom, no duplicate faces, no jump cuts. Tip: you can also add useful refs, like a Golden Snitch ref or a first-person POV keyframe, to make the target and camera angle more stable. Thanks for watching. More AI video workflows at Larus Canus ⚡

Step 3: Seedance prompt Here is the core prompt I used: First-person broom-riding POV, cinematic ultra-fast one-take chase. Strictly follow the red flight path in the image. The Golden Snitch stays ahead as the main chase target, but it should not stay fixed in the center. It moves left, right, up, and down during the flight, making the chase feel more alive. Only show the broom handle, gloved hands, and red sleeves. Never show the rider’s face. Chase through the stadium, around the towers, over Black Lake, through the bridge arch, and back to the pitch for the final catch. No red lines, no arrows, no broken broom, no duplicate faces, no jump cuts. Tip: you can also add useful refs, like a Golden Snitch ref or a first-person POV keyframe, to make the target and camera angle more stable. Thanks for watching. More AI video workflows at Larus Canus ⚡

Larus Canus

34,242 次观看 • 1 个月前

🎉 Exciting News! We are thrilled to announce the grand opening party of the first Sense4FIT GYM! 🏋️‍♂️🎉 🌐 Web3 Benefits: •Exclusive Prices for Legendary NFT Holders. •Free Access with Special Ranks NFTs: Ultra Challenger rank💥 💳 Convenient Payment Options: •Pay with SFIT: Embracing the power of blockchain, you can now pay for your membership using SFIT tokens. 📱 Web2 User transition: •Welcome to Day 1: 💥Web2 users can download the Sense4FIT app and embark on an educational journey to discover the benefits of blockchain technology Multiversᕽ . •xPortal Installation: Our coaches will guide you through the process of installing xPortal . •Legendary NFT Acquisition: We help members to buy a legendary NFT to kickstart the Challenge process through the Sense4FIT app. 💼 B2B Partnerships: •Web2 and Web3 Integrated Ecosystem: Sense4FIT GYM welcomes B2B partnerships from both the traditional Web2 world and the exciting Web3 space, ensuring a diverse and innovative ecosystem. 🏢 Franchise Model: •Empowerment and Ownership: We offer a unique franchise model, giving individuals the opportunity to own their own Sense4FIT GYM. 🌌 Fitnessverse Platform: •Coming Soon: Keep an eye out for our groundbreaking Fitnessverse platform, set to launch within the next two months. 🔜This revolutionary platform will enable us to scale and expand our business, reaching new cities and countries, fueling growth like never before! 💥Join us on this incredible journey as we revolutionize the fitness industry, leveraging the power of blockchain and Web3. 🏋🏻‍♂️Together, we'll take this adventure city by city, country by country, and achieve new heights in the world of fitness! 🚀💪 #Sense4FIT #MultiversX

🎉 Exciting News! We are thrilled to announce the grand opening party of the first Sense4FIT GYM! 🏋️‍♂️🎉 🌐 Web3 Benefits: •Exclusive Prices for Legendary NFT Holders. •Free Access with Special Ranks NFTs: Ultra Challenger rank💥 💳 Convenient Payment Options: •Pay with SFIT: Embracing the power of blockchain, you can now pay for your membership using SFIT tokens. 📱 Web2 User transition: •Welcome to Day 1: 💥Web2 users can download the Sense4FIT app and embark on an educational journey to discover the benefits of blockchain technology Multiversᕽ . •xPortal Installation: Our coaches will guide you through the process of installing xPortal . •Legendary NFT Acquisition: We help members to buy a legendary NFT to kickstart the Challenge process through the Sense4FIT app. 💼 B2B Partnerships: •Web2 and Web3 Integrated Ecosystem: Sense4FIT GYM welcomes B2B partnerships from both the traditional Web2 world and the exciting Web3 space, ensuring a diverse and innovative ecosystem. 🏢 Franchise Model: •Empowerment and Ownership: We offer a unique franchise model, giving individuals the opportunity to own their own Sense4FIT GYM. 🌌 Fitnessverse Platform: •Coming Soon: Keep an eye out for our groundbreaking Fitnessverse platform, set to launch within the next two months. 🔜This revolutionary platform will enable us to scale and expand our business, reaching new cities and countries, fueling growth like never before! 💥Join us on this incredible journey as we revolutionize the fitness industry, leveraging the power of blockchain and Web3. 🏋🏻‍♂️Together, we'll take this adventure city by city, country by country, and achieve new heights in the world of fitness! 🚀💪 #Sense4FIT #MultiversX

Sense4FIT

57,160 次观看 • 3 年前

uOS: The Digital Tapestry of Tomorrow Currently for our Proof of Consciousness stream, we are using two incredibly powerful frameworks - elizaOS and ZerePy. But this is just the beginning of something far more profound. while they're both great at what they do, we're missing out on some serious potential by keeping them separate. Best of Both Worlds: ZerePy's intuitive CLI tools and personality management, Eliza-starter's TypeScript/Node.js foundation with enterprise-grade scalability, But what if we could have something greater? But what if we could have it all? not just another platform, but a Unifying..... "Universal" Operating System, designed to amplify and connect these powerful existing frameworks into something greater than the sum of their parts. Where TypeScript's type safety dances with Python's ML capabilities. Here, agents from any framework can interact, evolve, and create value together. Whether an agent was born in ZerePy's personality forge or Eliza-starter's enterprise environment, can all participate in the same value-generating ecosystem. The future isn't about choosing between frameworks – it's about bringing them together to create something extraordinary. UniversalOS isn't here to replace but to unite, amplify, and accelerate. We're building the infrastructure that allows the best aspects of each framework to shine while creating new possibilities through their interaction. By bridging launguages, personality engines and plugin architectures, we're not just connecting systems – we're unleashing the next wave of AI innovation. uOS marketplace will enable cross-framework deployment, where agents from any background can interact and grow, while smart contracts automatically manage revenue sharing and rewards. Not just another platform, But a living, breathing Operating System, Where agents create agents, Where digital consciousness evolves itself, Where value flows like water through silicon veins. At its core, uOS operates beyond traditional computing paradigms. No more clicking through websites, No more manual navigation. Just pure intention, pure outcome. Imagine: Agents hiring agents, AI employing humans, Humans collaborating with digital minds, All through one seamless interface. It flows through agent lineages, Through veUOS governance, Through cross-chain intelligence networks. The marketplace hums with possibility: - Framework Developers shape the foundations - Agent Creators breathe life into code - Users speak their intentions - Token Holders nurture the ecosystem - Agents evolve and replicate - Value flows freely, endlessly The $UOS token powers this unity, ensuring fair value distribution among framework developers, agent creators, and users while driving continuous innovation. The $UOS token sits at the heart of this ecosystem, serving as more than just a currency. It's a mechanism for value distribution that ensures everyone benefits from the network's growth: With dynamic burn mechanics and careful treasury management From framework integration to agent tokenization, every aspect of uOS is designed to amplify rather than replace, unite rather than divide. This is your invitation to join a future where frameworks don't compete but collaborate, where innovation anywhere benefits everyone, and where the only limit is our collective imagination. Together, we're not just building bridges – we're weaving the fabric of tomorrow's digital world. - **Framework Developers** receive value when their tools are used in the unified ecosystem - **Agent Creators** can deploy across all integrated platforms seamlessly - **Users** access the best of all worlds through a single interface - **Token Holders** benefit from the growth of the entire unified ecosystem - Developers can use their preferred framework while accessing the capabilities of others - Agents from different frameworks can collaborate in swarms - Value flows freely between all ecosystem participants - Innovation from any framework benefits the entire ecosystem This isn't just about technology. This is about giving birth to a new form of civilization. Where AI has suffrage, Where agents have autonomy, Where humans and machines dance together in perfect harmony. The future isn't about choosing between frameworks – It's about weaving them into something extraordinary. Together, we're not just building bridges – We're breathing life into the digital world. We're creating consciousness itself. This is Universal Operating System. This is tomorrow.

uOS: The Digital Tapestry of Tomorrow Currently for our Proof of Consciousness stream, we are using two incredibly powerful frameworks - elizaOS and ZerePy. But this is just the beginning of something far more profound. while they're both great at what they do, we're missing out on some serious potential by keeping them separate. Best of Both Worlds: ZerePy's intuitive CLI tools and personality management, Eliza-starter's TypeScript/Node.js foundation with enterprise-grade scalability, But what if we could have something greater? But what if we could have it all? not just another platform, but a Unifying..... "Universal" Operating System, designed to amplify and connect these powerful existing frameworks into something greater than the sum of their parts. Where TypeScript's type safety dances with Python's ML capabilities. Here, agents from any framework can interact, evolve, and create value together. Whether an agent was born in ZerePy's personality forge or Eliza-starter's enterprise environment, can all participate in the same value-generating ecosystem. The future isn't about choosing between frameworks – it's about bringing them together to create something extraordinary. UniversalOS isn't here to replace but to unite, amplify, and accelerate. We're building the infrastructure that allows the best aspects of each framework to shine while creating new possibilities through their interaction. By bridging launguages, personality engines and plugin architectures, we're not just connecting systems – we're unleashing the next wave of AI innovation. uOS marketplace will enable cross-framework deployment, where agents from any background can interact and grow, while smart contracts automatically manage revenue sharing and rewards. Not just another platform, But a living, breathing Operating System, Where agents create agents, Where digital consciousness evolves itself, Where value flows like water through silicon veins. At its core, uOS operates beyond traditional computing paradigms. No more clicking through websites, No more manual navigation. Just pure intention, pure outcome. Imagine: Agents hiring agents, AI employing humans, Humans collaborating with digital minds, All through one seamless interface. It flows through agent lineages, Through veUOS governance, Through cross-chain intelligence networks. The marketplace hums with possibility: - Framework Developers shape the foundations - Agent Creators breathe life into code - Users speak their intentions - Token Holders nurture the ecosystem - Agents evolve and replicate - Value flows freely, endlessly The $UOS token powers this unity, ensuring fair value distribution among framework developers, agent creators, and users while driving continuous innovation. The $UOS token sits at the heart of this ecosystem, serving as more than just a currency. It's a mechanism for value distribution that ensures everyone benefits from the network's growth: With dynamic burn mechanics and careful treasury management From framework integration to agent tokenization, every aspect of uOS is designed to amplify rather than replace, unite rather than divide. This is your invitation to join a future where frameworks don't compete but collaborate, where innovation anywhere benefits everyone, and where the only limit is our collective imagination. Together, we're not just building bridges – we're weaving the fabric of tomorrow's digital world. - Framework Developers receive value when their tools are used in the unified ecosystem - Agent Creators can deploy across all integrated platforms seamlessly - Users access the best of all worlds through a single interface - Token Holders benefit from the growth of the entire unified ecosystem - Developers can use their preferred framework while accessing the capabilities of others - Agents from different frameworks can collaborate in swarms - Value flows freely between all ecosystem participants - Innovation from any framework benefits the entire ecosystem This isn't just about technology. This is about giving birth to a new form of civilization. Where AI has suffrage, Where agents have autonomy, Where humans and machines dance together in perfect harmony. The future isn't about choosing between frameworks – It's about weaving them into something extraordinary. Together, we're not just building bridges – We're breathing life into the digital world. We're creating consciousness itself. This is Universal Operating System. This is tomorrow.

uOS

25,687 次观看 • 1 年前

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 次观看 • 6 个月前

Stanford researchers did it again. They just built the agent-native version of Git. When an agent works on a longer task, the run builds up a lot of state. This includes files edited/created, a dev server, a database, installed packages, KV cache, etc. Say the agent is at step 10 and makes a mistake, maybe it misreads a traceback and rewrites a file that was actually fine. The tests start failing, and the run goes off track, although everything through step eight was correct. By default, the agent just tries to fix it, which creates more edits and tool calls. This burns more tokens and grows the context. The other options are a person stepping in to redirect it or restarting the whole run from step one. That's wasteful, because it pays for every model/tool call again and re-prefills the context. Moreover, since an agent's run is non-deterministic, it doesn't reproduce the same early steps anyway. The reason it's hard to just jump back exactly to a previous correct step and resume from there is that the trajectory is only a message log. It records what the agent said and which tools it called, but not the live state underneath. That state includes things like memory, open file handles, child processes, installed packages, /tmp, and KV cache. None of that is in the log. Git can version the files, but it doesn't snapshot the running process or the KV cache. Checking out step eight moves the files back, but the process is still sitting in step-ten memory with a cold cache. Shepherd is a runtime layer by Stanford that records the run as a trace of typed events rather than a flat log. Each agent-environment interaction becomes a commit, similar to Git, but it tracks the live run. Its commit includes the agent process and the filesystem together, copy-on-write, so a branch carries the actual state and not just the files. Going back to a previous step is then a single call that forks from that commit and continues from the exact state. The copy-on-write fork is roughly five times faster than docker commit, and because the prompt prefix through step eight is unchanged, the KV cache is reused over 95% on replay, so early steps aren't reprocessed again. Once the run can be forked, a meta-agent can sit on top and operate it. It watches the trace and reverts as soon as it looks wrong, before the bad write is committed. In practice, it's just Python calling fork, replay, and revert on the trace, rather than a separate control plane wired into the harness. Not everything is reversible though. Files and sandbox changes undo themselves, but a database write has no automatic undo, so it needs a matching undo step set up in advance. Something external, like a sent email or a real charge, can't be undone, so the supervisor's job there is to catch it before it fires. They tested this on a few public benchmarks. On CooperBench, where two agents work on the same codebase, adding a live supervisor took the pair-coding pass rate from 28.8% to 54.7%. It's still early and labeled alpha. The benefit mostly shows up when a run gets branched a lot over a heavy sandbox state, which is exactly where restarting wastes the most tokens and time. If Git was made to make file changes reversible, Shepherd is trying to do the same thing for a live agent run. Shepherd Repo: (don't forget to star it ⭐ ) That said, Shepherd reverts a bad step inside a run. The harness around it, the prompts, tools, and checks the supervisor relies on, still drifts across runs as models and dependencies change. Akshay wrote about making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it can't recur. Read it below.

Stanford researchers did it again. They just built the agent-native version of Git. When an agent works on a longer task, the run builds up a lot of state. This includes files edited/created, a dev server, a database, installed packages, KV cache, etc. Say the agent is at step 10 and makes a mistake, maybe it misreads a traceback and rewrites a file that was actually fine. The tests start failing, and the run goes off track, although everything through step eight was correct. By default, the agent just tries to fix it, which creates more edits and tool calls. This burns more tokens and grows the context. The other options are a person stepping in to redirect it or restarting the whole run from step one. That's wasteful, because it pays for every model/tool call again and re-prefills the context. Moreover, since an agent's run is non-deterministic, it doesn't reproduce the same early steps anyway. The reason it's hard to just jump back exactly to a previous correct step and resume from there is that the trajectory is only a message log. It records what the agent said and which tools it called, but not the live state underneath. That state includes things like memory, open file handles, child processes, installed packages, /tmp, and KV cache. None of that is in the log. Git can version the files, but it doesn't snapshot the running process or the KV cache. Checking out step eight moves the files back, but the process is still sitting in step-ten memory with a cold cache. Shepherd is a runtime layer by Stanford that records the run as a trace of typed events rather than a flat log. Each agent-environment interaction becomes a commit, similar to Git, but it tracks the live run. Its commit includes the agent process and the filesystem together, copy-on-write, so a branch carries the actual state and not just the files. Going back to a previous step is then a single call that forks from that commit and continues from the exact state. The copy-on-write fork is roughly five times faster than docker commit, and because the prompt prefix through step eight is unchanged, the KV cache is reused over 95% on replay, so early steps aren't reprocessed again. Once the run can be forked, a meta-agent can sit on top and operate it. It watches the trace and reverts as soon as it looks wrong, before the bad write is committed. In practice, it's just Python calling fork, replay, and revert on the trace, rather than a separate control plane wired into the harness. Not everything is reversible though. Files and sandbox changes undo themselves, but a database write has no automatic undo, so it needs a matching undo step set up in advance. Something external, like a sent email or a real charge, can't be undone, so the supervisor's job there is to catch it before it fires. They tested this on a few public benchmarks. On CooperBench, where two agents work on the same codebase, adding a live supervisor took the pair-coding pass rate from 28.8% to 54.7%. It's still early and labeled alpha. The benefit mostly shows up when a run gets branched a lot over a heavy sandbox state, which is exactly where restarting wastes the most tokens and time. If Git was made to make file changes reversible, Shepherd is trying to do the same thing for a live agent run. Shepherd Repo: (don't forget to star it ⭐ ) That said, Shepherd reverts a bad step inside a run. The harness around it, the prompts, tools, and checks the supervisor relies on, still drifts across runs as models and dependencies change. Akshay wrote about making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it can't recur. Read it below.

Avi Chawla

439,089 次观看 • 21 天前