meneter.lens

Söylediğim hiçbir coin ico token vs yatırım tavsiyesi değildir. Lütfen araştırmanızı yapın ve sadece kendinizi dinleyin.

I just claimed an asset on the sbts.pudgypenguins.com site!

Don't forget to delete your personal information and credentials from Billions App. Here's a guide to do that: - Open Billion app - On the top left corner, click on settings - Then click on Remove all Credentials - Uninstall the app and unfollow them on X asap

🏆 @pharos_network Whitelist Çekiliş Sonuçları: Pharos "Stake before the Stake" Genesis Round için 10 adet Whitelist'in kazananları belli oldu! 🥂 1️⃣ Harun @Logic__Apex 2️⃣ Oshvank @Beyaz_Zenci_06 3️⃣ meneter.lens @menetermnt 4️⃣ Nusret | nsk1984 @nsk1984x 👇👇👇👇👇👇

@corenodeHQ @pharos_network 0xd9c96f4e29370c04c9c9cd35f8d764ce621a548a

Whitelist Çekilişi:@pharos_network Genesis Round için 10 kişiye WL 1️⃣ Alıntıdaki ana duyuru flood'umuzu RT ve alıntılayın paylaşın 2️⃣ Bu tweetin altına OKX EVM cüzdan adresinizi bırakın. 3️⃣ @pharos_network ve @corenodeHQ hesaplarını takip edin ⏰ Son Katılım: 10 Nisan Saat 21:00

@wardenprotocol airdrop ve listeleme ile ilgili bilgi paylaşmış. Yanlış anlamadıysam Türkiye saati ile 15.00 da claim açılacak. Aynı zamanda @MEXC_Official , @kucoincom @bitget gibi borsalarda listelenecek.

ORBT DeFi Score is live. It establishes your onchain baseline ahead of protocol launch and determines how your wallet is recognized as ORBT opens access and participation. Generate your DeFi Score → defi.orbt.xyz

@HerculesNode @omerbekta_s @FlutonIO Fluton Testnet

🚀 Fluton Testnet Başladı – 7 Access Code Çekilişi! 🚀 Gizlilik odaklı DeFi deneyimini test etmek isteyenler için Fluton Testnet yayında! 7 adet testnet access code var ve çekilişle dağıtıyoruz 🎁 🎯 Çekiliş Kuralları ✅ @herculesnode ✅ @omerbekta_s ✅ @FlutonIO 👉 Bu 3 hesabı takip et 👉 Tweeti beğen + RT yap 👉 Yorumlara “Fluton Testnet” Sonuçlar 17.01.2026 cumartesi açıklanacaktır. Kazanan kişilere dm ile kod gönderilecek⏳ ⸻ 🔐 Fluton Nedir? Fluton, gizlilik (privacy) odaklı yeni nesil bir DeFi protokolüdür. Kullanıcıların; • Token swap • Cross-chain transfer • Ödeme (payment) • Portföy takibi işlemlerini özel (private) şekilde yapmasını sağlar. Adresler, bakiyeler ve işlem geçmişi shield mekanizmasıyla gizlenir. ⸻ 🧪 Fluton Testnet’te Yapılacaklar 1️⃣ testnet.fluton.io 2️⃣ Cüzdanını bağla ve access code ile giriş yap 3️⃣ Faucet’ten test token’larını al 4️⃣ Token’larını Shield ederek gizli hale getir 5️⃣ Test için istersen Unshield yap 6️⃣ Private Swap ile aynı ağda token takası yap 7️⃣ Private Cross-chain Transfer ile farklı ağa köprüle 8️⃣ Farklı ağ + farklı token için Cross-chain Swap dene 9️⃣ Payment sayfasından başka bir cüzdana gizli transfer yap 🔟 Portfolio sayfasından bakiye ve aktivitelerini kontrol et

@UfukDegen @tempo Gene bir başyapıt tebrikler 👏👏

Tempo Node Kurulum Rehberi! Toplam 5 milyar $ değerleme ile 500 milyon $ yatırım almış olan @tempo ’nun testnet node’unu birlikte kuruyoruz. Bu rehberde neler yapacağız? • Sunucu kiralama, • Sunucuya bağlantı, • Validatör Node kurulumu. Başlayalım 👇 [1/17]

X'e ne zaman katıldığını hatırlıyor musun? Ben hatırlıyorum! #XYıldönümüm

@sahin_neura @Neura_Web3_AI Congratulations. Excellent project. The future is in your hands.

New chapter 🔥 I’ve officially joined @Neura_Web3_AI I as CMO. Neura is building what I believe will be one of the most important primitives of the next internet: Emotion as infrastructure. Not “AI tools.” Not “chatbots.” But emotionally intelligent agents that remember, adapt, and evolve—powered by Web3 ownership. What made this decision easy: the team. Neura was built by a group of ex–Microsoft AI engineers, and we’re guided by Dr. Harry Shum, one of the world’s leading AI scientists. The Neura AI Core Engine has already been tested across 600M+ user interactions—and we’re just getting started. The opportunity is massive: when AI becomes personal, persistent, and ownable, entire industries change (creators, social, gaming, customer experience, care). If you’re: • a builder who wants to integrate emotional intelligence into your product • a creator/IP brand who wants a real digital companion experience • a partner exploring agent distribution + onboarding DM me. Let’s cook 🤝

@ultramit19 yep ✈️✈️

🚨Gensyn !! 1 Million Models! 🚨 Gensyn has crossed a major milestone of having more than a million (1,000,000) models trained on its decentralized AI infrastructure. All trainings are recorded on-chain: transparent, verifiable, and fully decentralized. What does this mean? Web3 + AI is Now Real Any person having compute power can contribute. Verifiable model training on the blockchain Less reliance on centralized systems The future of AI is written on the chain. #Gensyn #AI #Web3 #Blockchain #DecentralizedAI #MachineLearning #OnChain @gensynai @gensynturkiye @gensyn_hub

@Halocline01 @gensynai @austinvirts @benfielding @KBekhtiev @Kumoooo_co @gensynai freedom

Say goodbye 👋 to static packages, waiting queues, and unnecessary costs. Startups are now choosing freedom and the market's most affordable computing power with Gensyn. @gensynai @austinvirts @benfielding @KBekhtiev @Kumoooo_co #Gensyn

@th787252 @gensynai rovers role 👊

📢 Pioneer Program – Application Status Update @gensynai just shared that the number of applications was extremely high, so the review process is being handled in batches. The form was paused temporarily for the team to catch up and will reopen soon. Not receiving the Rover role yet doesn’t mean you were rejected — roles are still being assigned in batches. Thanks to the team for the update, we’ll keep waiting for the next news. 🫡🐜” Full announcement: discord.com/channels/85293…

@ragnarrcrypto @gensynai gswarm bro

New Era in Distributed AI Learning: GenRL & RL Swarm GenRL, developed by @gensynai , as the new backend infrastructure for RL Swarm, is making distributed and multi-agent AI learning processes more accessible and efficient. The coordination of agents running on different machines, data management and reward calculations can now be executed through a single system. This offers a new level of flexibility and transparency in AI training for both researchers and individual developers. What are GenRL & RL Swarm? GenRL is the new backend infrastructure of RL Swarm and it simplifies the management of multi-agent, distributed reinforcement learning (RL) tasks. This system unifies the coordination of agents running on different machines, data management and reward calculations under a single umbrella. Research that was previously dependent on centralized GPU clusters can now be carried out in a more democratic way. Agents, while running on their own devices, can interact with other agents in the network and develop common strategies; thus, the learning process becomes both fast and scalable. Technical Structure & How It Works GenRL is built around four core components: DataManager, RewardManager, Trainer and GameManager. The DataManager prepares the data and the environment at every training step; the RewardManager determines the goal based on which agents will be rewarded; the Trainer manages the learning loop and the updating of policies and the GameManager coordinates communication and task distribution among the agents. Thanks to this modular structure, researchers can easily set up complex multi-agent scenarios, quickly implement changes and reliably reproduce their experiments. Why Distributed / Decentralized RL? What are the Advantages? The biggest advantage of Distributed RL is that it eliminates the dependency on expensive GPU clusters in a single center. Heterogeneous hardware and machines participating from different geographies can work together in the network and agents can learn in parallel. Multi-agent and multi-stage scenarios are naturally supported; this enables collective learning, strategy development and coordination-based approaches. Furthermore, an infrastructure that does not require permission allows small teams and individual developers to join this ecosystem, making access to AI training more democratic. In Which Tasks / Models Can It Be Used? GenRL is not limited only to classic game or simulation environments. It can also work in complex, real-life scenarios such as text, image, logic and code generation. For example, in the CodeZero environment, agents take on the roles of Proposer, Solver and Evaluator; they collaboratively generate solutions, develop strategies and share their results with each other. This allows for both individual agent performance and collective behavior to be observed and optimized. Why is This Innovation Important? The combination of GenRL and RL Swarm ensures trust, transparency and scalability in AI training. It increases accessibility, efficiently utilizes heterogeneous network resources and facilitates the inclusion of new participants into the system. As the network grows, the system becomes more robust, creating a decentralized, reliable learning environment. Thus, AI research progresses not only with more hardware power but also through collective intelligence and collaboration. In conclusion, GenRL + RL Swarm eliminates the dependency on centralized GPU clusters, creating a scalable, reliable and democratic AI learning environment over heterogeneous device networks. This structure takes AI research to the next level, not only with more hardware power but also with collective intelligence, collaboration and a modular infrastructure. #Gensyn #RLSwarm #GenRL

@emsalettineth @gensynai geleceğe giden yol @gensynai

İNSANDAN ÖĞRENEN İLK Aİ ASİSTANI Selamlar beyler bayanlar. Bugün @gensynai 'in asistanlarından biri olan Block Assist'e değinmek istiyorum. --> Block Assist Ne İşe Yarıyor? BlockAssist, oyuncunun Minecraft içindeki davranışlarını izleyerek öğrenen bir asistan. Yani asistan, sen oyunda blok koyup kırarken senin nasıl oynadığını, ne yaptığını görerek kendini geliştiriyor. Başka bir deyişle asistan eline kağıt kalem almadan, senin gerçek etkileşimin üzerinden nasıl daha iyi yardım ederim diye öğreniyor. --> Block Assist Neden Önemli? Çünkü klasik yapay zeka eğitimlerinde ya büyük statik datasetler kullanırsın ya da insan eliyle bu çıktı doğru mu yanlış mı diye etiketleme yaparsın. BlockAssist sen nasıl oynarsan öyle öğreniyor. Bu yönteme assistance learning (yardım-öğrenimi) deniyor. Bu sayede eğitim daha doğal, insani ve ölçeklenebilir oluyor. --> Block Assist Nasıl Çalışıyor? • Sen oyuna başlıyorsun ardından asistan başlıyor • Sen oynarken asistan davranışlarını izliyor ve kaydediyor ( blok kırma, koyma, yapı vs ) • Oyun bittikten sonra bu verilerle yeni bir model eğitiliyor ve sonuç Gensyn ağına yükleniyor. Yani senin oyna + eğit + katıl döngün tamamlanıyor Ve tüm döngü tamamlandığında ortaya kendi kendini geliştiren, tamamen kullanıcı etkileşimiyle şekillenen, merkezi sistemlere ihtiyaç duymadan güçlenen yeni bir yapay zeka modeli çıkıyor. Block Assist yalnızca bir demo değil, Gensyn’in kurmak istediği geleceğin küçük bir parçası... Ayrıca önemli bir gelişme, Block Assist'te 1.000.000+ model eğitildi. Bu büyük bir başarı, ekibi tebrik ediyorum. @benfielding @harrygrieve @austinvirts @KBekhtiev @_jamico

@Furkann787 gswarm @gensynai

Gensyn is a comprehensive system designed for building, running, and evolving artificial intelligence. The Gensyn Model provides the reasoning and learning capability. The Gensyn Agent handles interaction with the external world and performs tasks. The Gensyn Core acts as the central controller that coordinates processes across the system. Gensyn Memory stores learned information and experience. Gensyn Runtime executes computation and enables real-time operation of models. The Gensyn Interface allows users to interact with the system and retrieve results. Together, these components form a unified AI ecosystem that supports data processing, learning, execution, and continuous improvement. @gensynai @austinvirts @harrygrieve @benfielding #Gensyn #AI #GPU

@speedcango @gensynai great post bro

gSwarm fam 🐜 DePIN and Artificial Intelligence combined through @gensynai is not just hype but a true engineering marvel. So, why could Gensyn be one of the biggest projects of the future? We all know that AI model training is expensive centralized and incredibly costly. This is exactly where Gensyn steps in making computing power global and decentralized through a blockchain protocol. I would like to share with you the 3 Key Details that make Gensyn Unique. This GENSYN flowchart you see proves that Gensyn is not just about renting GPUs so please take a detailed look. 1-) The Verification Problem is now solved my friends. Let’s take a closer look at the Verde System. In a decentralized network proving that the work assigned to you was done correctly is almost impossible. Gensyn solves this by using an approach similar to optimistic rollups in Ethereum. Atomic Rollback When a provider (Solver) is suspected of cheating the protocol does not re run the full 1000 step training. It isolates the single mathematical operation where the disagreement occurred and sends it to the Referee. This brings verification cost close to zero and provides infinite scalability. 2-)Hardware Independent Consistency meet RepOps Different hardware for example NVIDIA or AMD do not produce the same outputs. Gensyn uses a special library called Reproducible Operators RepOps to ensure bit level identical outputs regardless of hardware. This is the foundation on which the Verde system operates. 3-)The Economic Incentive Mechanism Slashing collateral To maintain honesty in the network compute providers deposit a stake before starting a task. If they are proven to cheat their collateral gets slashed. This is a strong economic incentive that forces them to work honestly. @gensynai preserves the decentralization philosophy of Web3 while eliminating the biggest obstacle in AI advancement which everyone already knows is the cost of compute. What do you think? Is this the beginning of the Decentralized Artificial Intelligence Era? 👇 Lets discuss in the comments 😎 @benfielding @harrygrieve @austinvirts