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now supports System One (jev-1.13)—a model built natively for decision-making rather than text generation. Pro now supports System One (jev-1.13)—a model built natively for decision-making rather than text generation. 🧠 By returning calibrated probabilities for up to 64 typed questions in a single forward pass, it turns complex judgment...

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Messiah Partners with IoTeX to Power Next-Gen Node Infrastructure Messiah is proud to team up with IoTeX , a leading force in connecting the real world to Web3. Together, we’re bringing IoTeX’s infrastructure to NodeHub, making it easier than ever for the community to run and earn from nodes. We’re also excited to share that Messiah is now live on DepinScan , further solidifying our position in the decentralized infrastructure space. Explore our listing here: What’s next? IoTeX RPC Nodes on NodeHub: Deploying an IoTeX RPC Node will soon be as simple as a few clicks. Whether you’re bootstrapping a full node or configuring a gateway, NodeHub will handle the complexity so you can focus on building and transacting. Delegate Node Support: For those looking to play a bigger role in IoTeX’s governance and consensus, NodeHub will also support Delegate Node deployment. This partnership isn’t just about infrastructure, it’s about empowering builders, validators, and stakers to contribute to the growth of the IoTeX ecosystem with ease and security. The future of decentralized infrastructure is simple, powerful, and community-driven. With IoTeX on NodeHub, that future is closer than ever.

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we sped up distributed inference by up to 5x with decentralized speculative decoding. many don't realize that AI models normally generate text one single word at a time, waiting for the network after every word. speculative decoding changes this by using a "guess & confirm" system, similar to autocomplete. how it's done: 1. draft locally (the guess) instead of waiting for the network, a tiny, fast model on your device guesses the next few words instantly, without waiting for the network. 2. confirm remotely (the check) the massive remote model doesn't generate from scratch; it just checks the draft. it looks at the guesses in a batch and says "yes, yes, no." you get multiple words in the time it usually takes to get one. 3. adaptive logic dsd is smart. if the topic is creative, it lets the draft flow loose. if the topic is math or code, it checks more strictly. it balances speed and precision automatically so your inference almost feel instant. find out more: paper: blog:

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This was all streamed from Bitcoin using Ordinals. The 3D world supports real-time multiplayer interactions, and decentralized land ownership. All this is on-chain on Bitcoin, and as immutable and secure as Bitcoin itself. The weather in the world reacts to Bitcoin fees, clouds are linked to transaction volume, and time in the world is determined by block height. Makes sense for a Bitcoin world. Real-time multiplayer interactions is one of the newest features. This is particularly difficult to do because Bitcoin block times are ~10 minutes. Bitcoin has been around for 17 years, and we're now seeing fascinating new things being built on top of it.

danny huuep

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There’s a reason the LimeWire name still hits people instantly. If you were around in the early internet days, LimeWire was everywhere. Music discovery, file sharing, chaos, excitement. It shaped how a whole generation interacted with the web. That kind of brand memory doesn’t fade and LimeWire is proving it can be reused in a serious way. What’s different now is the foundation. LimeWire Network is the decentralized storage layer of LimeWire, built on BNB Chain. It is not a nostalgia project. It is infrastructure. Storage, transfers, and usage are happening on chain, designed to scale for real applications rather than demos. The growth backs that up. Looking at recent data on lmwrscan, LimeWire Network has already moved into tens of terabytes of stored data, with tens of thousands of uploads and consistent daily activity. Network usage keeps climbing, not spiking once and disappearing. That kind of curve usually shows real users, not incentive farming. And the economics are clear. The $LMWR token sits at the center of the system. Users pay in LMWR to use storage. Node operators earn LMWR for providing resources. Rewards and payments stay inside the ecosystem instead of leaking out to third parties. That loop matters. Most decentralized storage networks struggle because nobody knows they exist. LimeWire doesn’t have that problem. The brand alone opens doors, pulls attention, and lowers the friction for new users to try the product. When you combine that with live usage data and a functioning token economy, the upside starts to look asymmetric. Built on BNB Chain, LimeWire Network also gets the scalability needed if adoption keeps accelerating. That choice signals intent to grow, not just experiment. This feels like a rare case where nostalgia is not the product, it is the distribution layer. Curious how you see it. Are you watching LimeWire because of the brand comeback, the LimeWire Network growth, or the role of the LMWR token in the long run?

ryu 龙

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Aman has no training manual. Most properties are under 55 rooms runs on staff hired for judgment, not procedure and is vetted one person at a time by a company that never built a system to replace that. More hotels doesn't mean a better hotel company. It means the company traded something for reach. Four Seasons runs 130 properties, Ritz-Carlton close behind, and both train every GM on the same playbook. The same system that turns one founder's vision into something 200 people can execute without ever meeting him. That's what a training manual is: a delegation tool. A way to remove the founder from every decision so the company can outgrow the size of his attention. Aman never built one. Its standard is applied one hire at a time, by people selected for judgment rather than procedure. This is where the math runs backwards on scale. Most Aman properties are under 55 rooms with staff-to-guest ratios between 4:1 and 6:1. A density of attention no 200-property group can afford, because it would mean thousands of individually vetted hires instead of thousands of manual-trained ones. Harvard Business School's case on Aman describes staff pushed toward "considerable initiative" rather than procedure, hired for attitude over hospitality-school credentials. A big group calls that a liability or a lawsuit waiting to happen at scale. At Aman it's the whole product. I've stayed on both sides of this line. The branded properties are excellent, reliable. You can predict the shower pressure before you turn the tap. The Aman properties are the ones where something feels decided by a person, not configured by a department. But if the whole product lives inside one person's taste, it may not survive that person's absence and Aman has grown far past the scale where that was a small bet. Taste that lives in one person's judgment is either the deepest moat in hospitality or a risk no one is paying attention to. I don't think anyone, including the people inside Aman, knows for certain which one it is yet. What this proves about operating at a genuinely high standard: 1/ A training manual is proof a standard has been simplified enough to teach. That's an achievement. It's also a ceiling. 2/ Taste doesn't scale through documentation. It scales through proximity to the person who has it. Meaning a hard structural limit is built into the model. 3/ The real luxury is direct access to the person who decided what the room should feel like, with no system standing between you. Scale protects a company from its founder's limits. Aman is a bet they were never the problem.
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Aman has no training manual. Most properties are under 55 rooms runs on staff hired for judgment, not procedure and is vetted one person at a time by a company that never built a system to replace that. More hotels doesn't mean a better hotel company. It means the company traded something for reach. Four Seasons runs 130 properties, Ritz-Carlton close behind, and both train every GM on the same playbook. The same system that turns one founder's vision into something 200 people can execute without ever meeting him. That's what a training manual is: a delegation tool. A way to remove the founder from every decision so the company can outgrow the size of his attention. Aman never built one. Its standard is applied one hire at a time, by people selected for judgment rather than procedure. This is where the math runs backwards on scale. Most Aman properties are under 55 rooms with staff-to-guest ratios between 4:1 and 6:1. A density of attention no 200-property group can afford, because it would mean thousands of individually vetted hires instead of thousands of manual-trained ones. Harvard Business School's case on Aman describes staff pushed toward "considerable initiative" rather than procedure, hired for attitude over hospitality-school credentials. A big group calls that a liability or a lawsuit waiting to happen at scale. At Aman it's the whole product. I've stayed on both sides of this line. The branded properties are excellent, reliable. You can predict the shower pressure before you turn the tap. The Aman properties are the ones where something feels decided by a person, not configured by a department. But if the whole product lives inside one person's taste, it may not survive that person's absence and Aman has grown far past the scale where that was a small bet. Taste that lives in one person's judgment is either the deepest moat in hospitality or a risk no one is paying attention to. I don't think anyone, including the people inside Aman, knows for certain which one it is yet. What this proves about operating at a genuinely high standard: 1/ A training manual is proof a standard has been simplified enough to teach. That's an achievement. It's also a ceiling. 2/ Taste doesn't scale through documentation. It scales through proximity to the person who has it. Meaning a hard structural limit is built into the model. 3/ The real luxury is direct access to the person who decided what the room should feel like, with no system standing between you. Scale protects a company from its founder's limits. Aman is a bet they were never the problem.

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We removed 93.25% of the connections in our Un-0 image model, fully expecting to pay for it in quality. But it got better. FID 7.15 on ImageNet 64x64, roughly 1.9 ahead of the dense baseline at matched size. Same family of model, a fraction of the couplings, a better score. Here is why that is not as strange as it sounds. Un-0 is a coupled oscillator model, and in the dense version every oscillator talks to every other one. That sounds like a strength, but it means the whole system can fall into catastrophic synchronization: everything locks into step, gradients go flat, and learning stalls. Sparser connectivity leaves room for coherent and incoherent activity to coexist. The dynamics stay alive, and the model keeps learning. Connectivity turns out to be a control knob, not a dial you turn up until it stops. Learn more here:

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ZIGChain is now natively integrated on HTX, bringing the ZIGChain ecosystem into one of the most established global crypto exchanges. This integration expands how users can access RWA and onchain wealth infrastructure, connecting the system we are building to a broader base of global liquidity and participation. HTX has one of the largest active communities in crypto, and this partnership extends the reach of ZIGChain’s infrastructure into that network in a meaningful way. As the ecosystem grows, integrations like this continue to strengthen distribution, deepen access, and increase the surface area for onchain finance globally.

ZIGChain

34,327 Aufrufe • vor 3 Monaten

What is Chainlink CCIP? Chainlink's (Chainlink) Cross-Chain Interoperability Protocol, or CCIP, is designed to let applications communicate across different blockchain networks. Put simply, CCIP acts as a secure messaging and transfer layer between otherwise disconnected blockchains. Here's how it works: (1) It moves data between blockchains CCIP allows smart contracts on one blockchain to send messages to smart contracts on another network. That means an application can trigger an action on a different chain without requiring users to manually move between ecosystems. (2) It can transfer tokens across networks CCIP also supports cross-chain token transfers. Projects can use token pools and other mechanisms to move assets between supported chains while maintaining controlled supply across networks. (3) It lets you combine messaging and asset movement A major feature of CCIP is that developers can send arbitrary messages, transfer tokens, or do both in a single cross-chain transaction, rather than needing separate systems for each. (4) It uses Chainlink's decentralized oracle infrastructure CCIP relies on Chainlink's decentralized oracle network to validate and deliver cross-chain messages. The system uses multiple independent components to help verify transactions and protect against failures or manipulation. (5) It adds programmable token transfers CCIP is not limited to simply sending an asset from one chain to another. Developers can attach instructions to transfers, allowing receiving applications to automatically perform actions when tokens arrive. This could make cross-chain lending, payments, trading, and other DeFi applications easier to build. (6) It is designed for multiple blockchain environments CCIP supports communication across different blockchain ecosystems rather than forcing applications to operate within a single network. That matters as liquidity, users, and applications become increasingly fragmented across chains. The bigger idea is simple. Blockchains were originally built as separate networks, but users and capital increasingly need to move between them. CCIP is Chainlink's attempt to provide the infrastructure for that movement. If cross-chain applications continue expanding, secure interoperability could become one of the most important layers in the blockchain stack.
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What is Chainlink CCIP? Chainlink's (Chainlink) Cross-Chain Interoperability Protocol, or CCIP, is designed to let applications communicate across different blockchain networks. Put simply, CCIP acts as a secure messaging and transfer layer between otherwise disconnected blockchains. Here's how it works: (1) It moves data between blockchains CCIP allows smart contracts on one blockchain to send messages to smart contracts on another network. That means an application can trigger an action on a different chain without requiring users to manually move between ecosystems. (2) It can transfer tokens across networks CCIP also supports cross-chain token transfers. Projects can use token pools and other mechanisms to move assets between supported chains while maintaining controlled supply across networks. (3) It lets you combine messaging and asset movement A major feature of CCIP is that developers can send arbitrary messages, transfer tokens, or do both in a single cross-chain transaction, rather than needing separate systems for each. (4) It uses Chainlink's decentralized oracle infrastructure CCIP relies on Chainlink's decentralized oracle network to validate and deliver cross-chain messages. The system uses multiple independent components to help verify transactions and protect against failures or manipulation. (5) It adds programmable token transfers CCIP is not limited to simply sending an asset from one chain to another. Developers can attach instructions to transfers, allowing receiving applications to automatically perform actions when tokens arrive. This could make cross-chain lending, payments, trading, and other DeFi applications easier to build. (6) It is designed for multiple blockchain environments CCIP supports communication across different blockchain ecosystems rather than forcing applications to operate within a single network. That matters as liquidity, users, and applications become increasingly fragmented across chains. The bigger idea is simple. Blockchains were originally built as separate networks, but users and capital increasingly need to move between them. CCIP is Chainlink's attempt to provide the infrastructure for that movement. If cross-chain applications continue expanding, secure interoperability could become one of the most important layers in the blockchain stack.

BSCN

17,376 Aufrufe • vor 1 Monat

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TESLA HALTED MODEL S AND MODEL X PRODUCTION TO BUILD AN ARMY OF OPTIMUS ROBOTS The Fremont assembly line was torn down in 46 days. In its place, Tesla is building a line for humanoid production, aiming for a million units a year A humanoid robot is a body shaped like a human. Physical AI is the intelligence that controls that body Walking and making coffee is often just imitation learning from a scripted routine. But once the environment shifts, the learned trick stops working Language models had the entire internet to train on. Robotics has nothing close to that scale of data, which is why one giant brain hasn't worked for anyone yet The industry is moving toward modularity instead - separate models for vision, movement, and planning, each improved on its own The real question is no longer whether a robot can move impressively. It's whether it can pull its sensors into one picture of the world and adapt to whatever wasn't scripted for it

iamigorekk

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Extending Ownership Beyond the Asset 🌐👤 For years, owning a collectible meant holding it. That was the entire experience. Now ownership is becoming something more. Collectors can follow their items over time, see how they move between owners, and stay connected to the communities built around each collection. Rather than ending at a single transaction, every item becomes part of a living history shaped by activity and participation. It gives collectors a clearer view of both their assets and the ecosystem around them. Join the collectibles ecosystem today 🎯
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Extending Ownership Beyond the Asset 🌐👤 For years, owning a collectible meant holding it. That was the entire experience. Now ownership is becoming something more. Collectors can follow their items over time, see how they move between owners, and stay connected to the communities built around each collection. Rather than ending at a single transaction, every item becomes part of a living history shaped by activity and participation. It gives collectors a clearer view of both their assets and the ecosystem around them. Join the collectibles ecosystem today 🎯

$COLLECT on Fanable

137,097 Aufrufe • vor 8 Monaten

I spent a decade in TradFi, managing $1B+ at Canada’s largest bank. I began to realize that the system was designed for the institutions running it, rather than the people. My frustration pushed me into Web3. Since then: - Mined Ethereum & invested across the crypto ecosystem since 2018 - Went deep into DeFi in 2022, built one of the first tokenized credit facilities - democratizing access to credit - Joined founding team Nesa in 2024 at intersection of AI × blockchain - empowering building with AI in fully private & sovereign way. There is a lot more I will share along the way. Stay tuned for what’s ahead for Nesa as we continue to scale and deliver.
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I spent a decade in TradFi, managing $1B+ at Canada’s largest bank. I began to realize that the system was designed for the institutions running it, rather than the people. My frustration pushed me into Web3. Since then: - Mined Ethereum & invested across the crypto ecosystem since 2018 - Went deep into DeFi in 2022, built one of the first tokenized credit facilities - democratizing access to credit - Joined founding team Nesa in 2024 at intersection of AI × blockchain - empowering building with AI in fully private & sovereign way. There is a lot more I will share along the way. Stay tuned for what’s ahead for Nesa as we continue to scale and deliver.

Stefan

86,282 Aufrufe • vor 9 Monaten

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
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AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

113,347 Aufrufe • vor 2 Monaten

Building The On-Chain Cooperative 🟡 Welcome to the dawn of a new era in the crypto space, where the buzzword "community" is not just a hollow echo but a vibrant force that propels us towards a brighter future. Let's delve into the heart of MODE, the Onchain Cooperative that seeks to redefine the landscape of web3. What does MODE stand for? MODE stands for building an on-chain cooperative focused on sustainable growth and collective prosperity. At its core, MODE is guided by the principles of cooperation, shared incentives, and community-driven development. The goal is to shift from the "fat protocol" mentality where most value accrues to the blockchain/protocol itself, towards an ecosystem where builders, users, and applications can thrive together. What’s MODE's vision and mission in the web3 space? MODE's vision is to return to web3's founding promise - a future that is better for all, not just the individual. A world with aligned incentives that drive growth for everyone involved. A place with opportunities for all, not just the few. The mission is to pioneer the on-chain cooperative - where contributors are rewarded fairly based on the value they provide. Features like Sequencer Fee Sharing distribute a portion of fees to smart contract developers, incentivizing participation. The aim is to encourage collaboration instead of confrontation. Together, the MODE community can deliver new models for cooperation and shared prosperity in web3. Mode Network will solve many problems today in Web3: • Lack of incentives for developers: Developers creating decentralized apps (dApps) currently have few direct economic incentives to create and maintain their projects. Mode provides them with a steady source of income through fee-sharing. • Lack of collaboration: There are few incentives for blockchain projects to compete less and collaborate more for the benefit of the entire ecosystem. Mode's model encourages collaboration by aligning participants economically. • Excessive value accrual at the protocol layer: Mode aims for a more balanced model where the protocol's success is fueled by the success of application developers/builders and the wider community. Growth is a two-way street – "as we grow, you grow". The MODE Pledge 💛 The promise of crypto and blockchain is a brighter future. One that is better for all not just the individual. Where nothing is more important than community. We've strayed from this path. Entering a world of player vs player. Where value is extracted rather than shared. The game is zero sum rather than positive sum. And incentives are aligned with domination, rather than cooperation. Mode is the dawn of a new age. and a return to the promise of what can be. A world with aligned incentives that drive growth for builders, users and projects. A place with opportunities for all, rather than the few. Where we say goodbye to the 'fat protocol', and hello to the onchain cooperative. Join us on our mission to grow together. If this vision for a community-powered web3 ecosystem resonates - where creators are rewarded for their contributions - you can join the MODE on-chain cooperative! Visit Join the discord community Follow Mode 🟡 Together, we can transform web3 into a positive-sum game that unlocks new possibilities for all. Where your growth fuels the growth of others. Let's build the on-chain cooperative!
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Building The On-Chain Cooperative 🟡 Welcome to the dawn of a new era in the crypto space, where the buzzword "community" is not just a hollow echo but a vibrant force that propels us towards a brighter future. Let's delve into the heart of MODE, the Onchain Cooperative that seeks to redefine the landscape of web3. What does MODE stand for? MODE stands for building an on-chain cooperative focused on sustainable growth and collective prosperity. At its core, MODE is guided by the principles of cooperation, shared incentives, and community-driven development. The goal is to shift from the "fat protocol" mentality where most value accrues to the blockchain/protocol itself, towards an ecosystem where builders, users, and applications can thrive together. What’s MODE's vision and mission in the web3 space? MODE's vision is to return to web3's founding promise - a future that is better for all, not just the individual. A world with aligned incentives that drive growth for everyone involved. A place with opportunities for all, not just the few. The mission is to pioneer the on-chain cooperative - where contributors are rewarded fairly based on the value they provide. Features like Sequencer Fee Sharing distribute a portion of fees to smart contract developers, incentivizing participation. The aim is to encourage collaboration instead of confrontation. Together, the MODE community can deliver new models for cooperation and shared prosperity in web3. Mode Network will solve many problems today in Web3: • Lack of incentives for developers: Developers creating decentralized apps (dApps) currently have few direct economic incentives to create and maintain their projects. Mode provides them with a steady source of income through fee-sharing. • Lack of collaboration: There are few incentives for blockchain projects to compete less and collaborate more for the benefit of the entire ecosystem. Mode's model encourages collaboration by aligning participants economically. • Excessive value accrual at the protocol layer: Mode aims for a more balanced model where the protocol's success is fueled by the success of application developers/builders and the wider community. Growth is a two-way street – "as we grow, you grow". The MODE Pledge 💛 The promise of crypto and blockchain is a brighter future. One that is better for all not just the individual. Where nothing is more important than community. We've strayed from this path. Entering a world of player vs player. Where value is extracted rather than shared. The game is zero sum rather than positive sum. And incentives are aligned with domination, rather than cooperation. Mode is the dawn of a new age. and a return to the promise of what can be. A world with aligned incentives that drive growth for builders, users and projects. A place with opportunities for all, rather than the few. Where we say goodbye to the 'fat protocol', and hello to the onchain cooperative. Join us on our mission to grow together. If this vision for a community-powered web3 ecosystem resonates - where creators are rewarded for their contributions - you can join the MODE on-chain cooperative! Visit Join the discord community Follow Mode 🟡 Together, we can transform web3 into a positive-sum game that unlocks new possibilities for all. Where your growth fuels the growth of others. Let's build the on-chain cooperative!

ETHachi Uchiha | Crypto DEGENius

16,774 Aufrufe • vor 2 Jahren