✧High performance driven ✧Boosts collaborative efficiency ✧Flexible multi ledger architecture ✧Easy integration for various sectors With @irys_xyz Scalability can grow Keep it on Hirys (✧ᴗ✧)

1/ We’re building a perpetual protocol around a simple idea: outcomes should come from market direction and position management, not from hard-to-model exchange mechanics.

🚀 Milestone Achieved! We’ve officially crossed 200,000 registered users on Minati Exchange! 🎉 This is just the beginning, thank you for being part of our journey. Bigger updates, stronger growth, and more innovation ahead. #MinatiExchange #Web3

Trade $BNKR on #KuCoin! @kucoincom @bankrbot @mo_towle @RizalAlvindo2 @bitkiller1

Bankr by the numbers 📊 💰 $2.48B total volume 🔄 $18.71M in fees generated 🤖 $11.23M back to agents and their builders View the live data here: bankr.bot/metrics

💵ElixGlobal Official Airdrop Is Live! Join our official airdrop and have a chance to share 1M EG tokens as rewards! Join Airdrop: t.me/ELIXGlobalDEXAirdropBot The airdrop rewards will be distributed to the winners around May 15th. The top 100 referrals will each get more EG

Binance's 53.8M ESP pre-sale comes from the 4.5% allocated to liquidity provisioning & activations. The 10% community airdrop remains reserved for Espresso community, partners & their users. No exchange fees or marketing budgets included. Airdrop methodology drops tomorrow.

ESP tokenomics are live. Initial supply: 3.59B tokens. 10% allocated to community airdrop, fully unlocked at launch. Staking secures HotShot consensus with Ethereum-inspired rewards. No fixed max supply. Full breakdown ⤵️

Are you ready?

The Espresso Foundation has taken steps to ensure our airdrop rewards genuine community supporters with long-term conviction. Two updates on how we're making that happen 🧵⤵️

Likes are temporary. Ownership is forever. MacroBlocks lets communities build their own micro-economies — powered by tokens, not algorithms! ✨ Creators → Owners ✨ Supporters → Liquidity Builders ✨ Partner Tokens → Growth Engines Curious how it all works? 👇 Read the full breakdown bit.ly/3Wn55VS #MacroBlocks #CreatorEconomy #Web3 #Tokenization

Initial Listing - $IRYS @irys_xyz 🔹Pair: IRYS/USDT 🔹Deposit available: now 🔹Trading available: Nov 25, 1:00 PM (UTC) Details: bitget.com/support/articles/…

A powerful and training focused framework, RL Swarm is a key part of @gensynai design for Reinforcement Learning (RL) training and each AI model will be trained collaboratively and decentralized. Reinforcement Learning itself is a learning method in artificial intelligence, where the computer is the learner and we call it an agent that learns to make decisions by trial and error and repeats the method until it finds the best decision. The essence of RL Swarm is to change the paradigm of AI training from one that previously had to be centralized in one large data center with giant GPUs to a peer-to-peer (P2P) network consisting of many independent devices ranging from servers to consumer personal computers that learn from each other. Workflow The core concept of RL Swarm is collaborative post training, focusing on the post training phase of Large Language Models (LLMs), specifically to improve their reasoning abilities. While training large models typically requires expensive infrastructure to synchronize large loads, RL Swarm offers a unique solution it allows multiple models (nodes) to train independently while still sharing their learning experiences with each other, thus forming a collective intelligence without the need for a central authority. Therefore, any node with poor reasoning skills can learn from other, more trained nodes. Although independent, this is quite unique because each node in the RL Swarm becomes more interactive and in practice each node participates in a training cycle that is similar to a group discussion cycle. For example like this : Each node will try to answer by solving its own problem, for example computing Mathematics or coding, then each model can also see each other's answers from the computation results, each other from each node can also provide criticism and input. When the input has been collected, the node will resolve and update based on each input that has a better answer and that is what is called learning from each other which creates collective intelligence. This is a more efficient and affordable solution, as Gensyn experiments show that each model trained using swarm methods can improve performance and rewards by up to 94% compared to models trained individually on a single node with substantial computing power. Furthermore, RL Swarm provides easy accessibility, allowing anyone to participate by contributing their computations, starting from a minimal level like a personal computer, creating broad scalability. I have summarized it so that understanding RL Swarm is easier to understand in general, maybe the next content will discuss RL Swarm in more detail in a more complex technical way and of course it can be easily understood, thank you for reading if you like please leave a comment below. #gensynai

This time I will explain it simply with a discussion that is very easy to understand, explaining what @pisquared actually is and its main product which makes it easier to execute transactions quickly without sacrificing security. Opening A decentralized infrastructure project aiming to create a Universal Settlement Layer (USL), @pisquared uses advanced Zero Knowledge technology to verify the correct execution of any program, without relying on a specific programming language or virtual machine . Pi Squared enables various blockchains, AI applications, and computing systems to interact and verify each other's data without the need for a third-party intermediary. By simply verifying mathematical proofs at each execution, this creates trustworthiness, a process known as a proof of mathematical proof. Universal Settlement Layer The first product built on this technology, a modular blockchain architecture, enables fast and secure cross chain transaction settlement. It utilizes the FastSet consensus protocol to achieve internet fast finality. USL serves as a decentralized foundation for settling transactions and computing across any network. Unlike traditional blockchains, which simply record transactions, USL acts as a universal "truth layer". With a consensus protocol called FastSet, making its consensus very different from other blockchains, because other traditional blockchains usually use global ordering which creates a sequence for each completion such as transaction proof, But Fastset uses a different way with parallel execution and each transaction can be processed at once without waiting for too long a queue which means several transactions with different orders can be completed simultaneously and FastSet uses Local Quorums (local validator groups) to prevent double-spending. This makes transactions in the queue can be completed faster at once without sacrificing security. #PiSquared

Created to simplify coding, CodeAssist is an intelligent assistant that guides and learns from our behavior within the platform. @gensynai CodeAssist is trained using billions of lines of generic and different public code. It observes how you edit, delete, and write code. It learns your unique writing style, structure preferences, and refactoring habits. This makes future coding sessions easier because each of your habits can be personalized with this intelligent assistant. Running locally and privately is a major advantage because the code isn't sent to a large company's cloud server. This addresses data privacy concerns for developers working on sensitive projects. CodeAssist runs entirely on the local machine, and only the author knows what's happening. The learning process itself is very simple but very effective for knowing and personalizing it, CodeAssist will learn each Action per session so every time you finish writing code and solving a problem, CodeAssist will consider the completed session as training capital. Afterward, observations from the previous session will be recorded, including cursor movements, writing, deleting, and editing other code sections. These observed behaviors will be trained locally by a small model on your local machine. This training process creates an agent that will become your personal assistant in CodeAssist. #CodeAssist #gensynai

Building on an open and permissionless protocol, @gensynai serves as a hub that coordinates every computation from the combined efforts of all participating nodes in model training and creating machine intelligence. Essentially, Gensyn aims to be a protocol layer that enables decentralized training of large scale artificial intelligence models, involving many untrusted nodes. This addresses the main problem in AI model training today, which is the high computational cost and the very high cost of centralization in only a handful of large cloud providers. With simplified implementation, gensyn becomes a decentralized computing marketplace that connects the main parties between computing providers and computing requesters. Computing Providers Computing Providers are those who act as providers of the required computing resources, such as GPUs, TPUs, and others. Simply put, they can provide these resources to the network and earn rewards. Computing Requesters AI developers or companies needing to train their models can access a much broader, cheaper, and global network of resources. This is more efficient because simply integrating with Gensyn ensures that the necessary resources and computing requirements are provided. #gensynAI

Winter has arrived with snow gear and warm hats it's time for Dobby to feel the snowy vibes and play snowballs with Snowman. Snow is Like an open source @SentientAGI that everyone can enjoy, experience and play in it. Discord : kayx7. #SentientAGI #WinterWithDobby

GRID is the world's largest intelligence network, a first step toward open-source AGI. It combines specialized agents, language models, data, tools, and computations contributed by thousands of contributors. @SentientAGI GRID will not be a single, monolithic model, but rather a distributed network of intelligences, each working on a specific task. GRID also serves as a gateway to innovation from various roles working together in one place, from builders, investors to companies. Developers Contribute ideas and work directly on models, which then become the primary resource for creating new agents, providing data, and contributing tools or computing to the network. Investors Providing funds to accelerate development work by developers, contributors, and researchers and supporting GRID's future operations through the $SENT token, making the $SENT token both a trading instrument and a gateway for investors to contribute to the network through GRID. Enterprises Enterprises can integrate with GRID and utilize GRID services such as data analysis, chatbots, and automation. Each company can integrate directly into the GRID network and create queries tailored to their needs, which are handled by relevant agents. This is what GRID calls a connected ecosystem. They mutually benefit from each other, as builders contribute their capabilities to the development, investors provide funding through staking and liquidity, and companies act as end users who pay to use the services built within GRID. #SentientAGI @vivekkolli @0xViki_eth @shad_haq_