加密貨幣市場因地緣政治風險下跌6.2%，山寨幣跌幅顯著

加密貨幣總市值24小時下跌6.2%，山寨幣跌幅進一步擴大

吳說獲悉，Coinbase International Exchange 宣布將於 UTC 時間 2025 年 6 月 19 日 9:30（或之後）在 Coinbase International Exchange 與 Coinbase Advanced 平台上線 Resolv 永續合約產品，交易對為 RESOLV-PERP。https://t.co/DfkOnDhCO4

Coinbase 國際站將上線 RESOLV 永續合約

Coinbase在期貨平臺上線山寨幣RESOLV

币安第21期HODLer空投上线，项目为Resolv (RESOLV)，一个以ETH和BTC为支持、锁定美元的USR稳定币协议长期持有BNB的伙伴真是有福，经常打新参与空投，吃豪华猪脚饭，参与打新点击官网进入：现在来说一下RESOLV到底是一个什么项目吧，$RESOLV 作为币安Alpha+现货同步上线的新资产，已进入我的核心观察列表——“稳赛道、低估值、强基本面” 三者叠加，潜力值得持续追踪一、为什么 Resolv 值得重点关注？三大核心逻辑 1.稳定币赛道：机制创新带来结构性优势 传统稳定币（USDC/USDT）：依赖法币储备，收益不共享用户，中心化风险显著 Ethena（USDe）：开创Delta中性稳定币范式（ETH抵押+永续合约对冲），但风险全由协议单一承担 Resolv（USR）的增强设计： 双代币风险分层：基础层 $USR（低风险稳定币）+ 保险层 $RLP（吸收波动的高收益代币），系统性隔离风险，提升协议抗压能力2.估值洼地：对标龙头存在明确预期差Resolv 当前FDV 4.3億美元（流通市值約4800萬美元），而同類項目Ethena FDV超過50億美元，差距超10倍 增長潛力支撐估值：TVL 5個月內從1300萬→5億美元（增長38倍），用戶超5萬，鏈上贖回超17億美元，驗證產品需求3.基本面加速： 收益場景擴張：USR已接入Pendle（收益代幣化）、Sommelier（策略金庫）、Hyperliquid（永續對沖）二、空投策略 空投释放策略： HODLer空投2000萬枚（總量2%）已發放，但早期社區空投（10%）以質押形態（stRESOLV）分發，解質押需14天冷卻期，抑制初期拋壓 解鎖時點預警： 6月27日將解鎖1.3%總量代幣（約1300萬枚），需關注市場承接力。若價格回調，可視為低位加倉機會如果你還沒有币安交易所，可以用我们社区专属返佣邀请链接注册：

本週在 @VestExchange： - 發布了一段關於zkRisk引擎的視頻 - 上市$RESOLV - PWA支援行動裝置（可「新增到主畫面」） - 發布了一篇關於Vest如何能提供無OI上限的文章

RESOLV代幣亮點：收益穩定幣的祕密武器

🚀 RESOLV & HOME 合約已在 Zoomex 上線！🎁 30,000 美元 USDT 獎池等你來拿！🎉 新用戶：首筆交易 = $50 空投🎯 立即交易，還有機會參加 ZWTC 幸運抽獎！🗓 2025 年 6 月 12 日–19 日👉 立即加入：https://t.co/kLVwREBr8F#Zoomex #CryptoTrading #Airdrop #ZWTC2025 https://t.co/IHRm24zFDS

🚀 #RESOLV Token 關鍵要點 @ResolvLabs– USR：由ETH和BTC支持的在區塊鏈上的產生收益的穩定幣– $RESOLV：支持治理、費用分享和增強的鎖定獎勵– 可在以太坊、Base & BNB Chain上組合使用– 無CeFi，但智能合約和市場風險仍存在閱讀完整文章 🔖 https://t.co/AUTLprOf9c#DeFi #XT

Bybit 將上線 RESOLV（RESOLV）

Paradex 已上架 $RESOLV！ 🟢 現已開放交易： • https://t.co/i1nNsIoTLV

好消息！@ResolvLabs 第一季點數兌換現在在 @RumpelLabs 上線了 🚀 💎 要領取你的 $RESOLV，請按照以下簡單的三步流程 👇 https://t.co/LxLN4EEexR

這通電話真是太棒了 🤯$RESOLV 在 15 小時內上漲了近 45%。感謝 @Corgil_ 的遠見 這也提醒了我們，市場仍然聽從 @CorgiCalls 的建議 🤣 https://t.co/uYOzHciqE7

CoinW上線Resolv (RESOLV)，啓動13000美元獎勵活動

$RESOLV，這是正在最快成長的穩定幣協議 @ResolvLabs 的代幣，現在已在 Neverless 上市。 從您銀行帳戶中的任何法定貨幣在 20 秒內轉換為 $RESOLV，以最佳價格且無需支付任何費用。 https://t.co/qOxVZgc3IF

幣安理財、一鍵買幣、閃兌、槓桿上線 Resolv（RESOLV）

Binance平臺新增支持Resolv（RESOLV）多項交易與理財功能

Binance槓桿、理財、一鍵買幣、閃兌上線Resolv（RESOLV）

Binance 很高興宣佈 Resolv (RESOLV) HODLer 空投 – @ResolvLabs $RESOLV。 BNB 持有者，準備好！空投頁面將在 5 小時後於 Binance 空投門戶網站上線。此外，這個代幣將很快在 Binance 上市！ 👉 https://t.co/rUZA8jOT6d https://t.co/FU8VzckApY

🚀 Introducing Fox-1: TensorOpera’s Pioneering Open-Source SLM!We are thrilled to introduce TensorOpera Fox-1, our cutting-edge 1.6B parameter small language model (SLM) designed to advance scalability and ownership in the generative AI landscape. Fox-1 stands out by delivering top-tier performance, surpassing comparable SLMs developed by industry giants such as Apple, Google, and Alibaba.What’s unique about Fox-1?🌟 Outstanding Performance (Small but Smart): Fox-1 was trained from scratch with a 3-stage data curriculum on 3 trillion tokens of text and code data in 8K sequence length. In various benchmarks, Fox-1 is on par or better than other SLMs in its class including Google’s Gemma-2B, Alibaba’s Qwen1.5-1.8B, and Apple’s OpenELM1.1B.🌟 Advanced Architectural Design: With a decoder-only transformer structure, 16 attention heads, and grouped query attention, Fox-1 is notably deeper and more capable than its peers (78% deeper than Gemma 2B, 33% deeper than Qwen1.5 - 1.8B, and 15% deeper than OpenELM 1.1B).🌟Inference Efficiency (Fast): On the TensorOpera serving platform with BF16 precision deployment, Fox-1 processes over 200 tokens per second, outpacing Gemma-2B and matching the speed of Qwen1.5-1.8B.🌟 Versatility Across Platforms: Fox-1's integration into TensorOpera’s platforms enables AI developers to build their models and applications on the cloud via TensorOpera AI Platform, and then deploy, monitor, and fine-tune them on smartphones and AI-enabled PCs via TensorOpera FedML platform. This offers cost efficiency, privacy, and personalized experiences within a unified platform.Why SLMs?1⃣ SLMs provide powerful capabilities with minimal computational and data needs. This “frugality” is particularly advantageous for enterprises and developers seeking to build and deploy their own models across diverse infrastructures without the need for extensive resources.2⃣ SLMs are also engineered to operate with significantly reduced latency and require far less computational power compared to LLMs. This allows them to process and analyze data more quickly, dramatically enhancing both the speed and cost-efficiency of inferencing, as well as responsiveness in generative AI applications.3⃣ SLMs are particularly well-suited for integration into composite AI architectures such as Mixture of Experts (MoE) and model federation systems. These configurations utilize multiple SLMs in tandem to construct a more powerful model that can tackle more complex tasks like multilingual processing and predictive analytics from several data sources.How to get started?We are releasing Fox-1 under the Apache 2.0 license. You can access the model from the TensorOpera AI Platform and Hugging Face.More details in our blogpost: https://t.co/nRemISpsXp…https://t.co/j1EsBS4edl

🎉 Introducing TensorOpera AI, Inc: A New Era in Our Journey! We are thrilled to announce a significant milestone in our journey. Two years ago, we embarked on an ambitious path with FedML, focusing primarily on federated learning. Today, as we look back on the tremendous growth and expansion of our product offerings, it’s clear that we’ve evolved into something much greater. To better represent the breadth and depth of our innovative solutions, we are excited to unveil our new identity: TensorOpera AI, Inc. 🤔 Why TensorOpera AI? Our new name, TensorOpera AI, is a testament to our commitment to blending cutting-edge technology with creativity. The term “Tensor” represents the foundational building blocks of artificial intelligence—emphasizing the critical role of data, computing power, and models in AI operations. “Opera,” on the other hand, brings to mind the rich and diverse world of the arts—encompassing poetry, music, dance, orchestration, and collaboration. This name reflects our vision for a generative AI future, characterized by multi-modality and complex, multi-model AI systems that are as harmonious and coordinated as a grand opera. 📈 Our Expanding Product Suite As TensorOpera AI, we are proud to offer two main product lines that cater to a wide range of needs within the AI community: TensorOpera AI Platform - Accessible at https://t.co/mKbyzriZyQ, this platform is a powerhouse for developers and enterprises aiming to build and scale their generative AI applications. Our platform excels in providing enterprise-grade features that include model deployment, AI agent APIs, serverless and decentralized GPU cloud operations for training and inference, and comprehensive tools for security and privacy. It’s designed to empower users to create, scale, and thrive in the AI ecosystem economically and efficiently. TensorOpera FedML - Available at https://t.co/HWftJA1QPO, this platform remains a leader in federated learning technology. It offers a zero-code, secure, and cross-platform solution that’s perfect for edge computing. The Edge AI SDK, part of TensorOpera FedML, ensures easy deployment across edge GPUs, smartphones, and IoT devices. Additionally, the platform’s MLOps capabilities simplify the decentralization and real-world application of machine learning, backed by years of pioneering research from our co-founders. 🚀 Looking Forward As TensorOpera AI, we remain dedicated to pushing the boundaries of what’s possible in generative AI. Our rebranding is not just a change of name, but a renewal of our promise to you—our community of developers, researchers, and innovators—to provide the tools and technology you need to succeed in this exciting era of AI. We invite you to join us at TensorOpera AI as we continue to orchestrate a smarter, more creative future together.

We are thrilled to announce our partnership with DENSO to empower fully on-premise training, development, and deployment of AI models via @FEDML_AI Nexus AI platform (https://t.co/7cKYybixvQ). As enterprises and organizations move fast toward bringing AI into their products and services, the need for privacy, security, full control, and ownership of the entire AI software stack becomes a critical requirement. This is especially true with the emergence of Generative AI models and applications, as data and AI models have become essential assets for any organization to obtain their competitive advantage. FEDML is committed to helping enterprises navigate the AI revolution with full ownership and control. By deploying FEDML Nexus AI platform on their own infrastructure (whether private cloud, on-premise servers, or hybrid), companies can provide their employees and customers with scalable, state-of-the-art GenAI capabilities, while giving them full control over their data, models, and computing resources. Our partnership with DENSO perfectly embodies our vision of delivering “Your” Generative AI Platform at Scale. Read more here: https://t.co/CMBgqOFrE1 via @VentureBeat

🔥 Start building your own fine-tuned Llama3 on FEDML Nexus AI! Open-sourced Llama3 70B is wildly good: it's on par with the performance of closed-source GPT-4 in Chatbot Arena Leaderboard (as of April 20th, 2024). This provides an excellent opportunity for enterprises and developers to own a high-performance self-hosted LLM customized on their private data. At FEDML, we are very excited to share our zero-code and serverless platform for fine-tuning Llama3-8B/70B, which requires no strong expertise and knowledge in AI and ML Infrastructure. We also have on-demand availability of H100 80GB GPUs at a very low price on FEDML cloud to be used directly when launching your fine-tuning jobs. You just need to: (1) Prepare your own training data (see instructions here: https://t.co/Kqme8Bln9P); (2) Set hyperparameters (or, use the default ones that the platform provides) (3) Click Launch! Read more details and get started here: https://t.co/FTAp1DikHS#Llama3 #serverlessfinetuning #fedml

🚀🚀Llama-3 + FEDML!Llama 3 is now available on FEDML Nexus AI to➤ Access and use APIs with $20 free credit and then atLlama3-8B - $0.1 / 1M TokenLlama3-70B - $0.9 / 1M Token➤ Deploy and serve on your dedicated servers with autoscale and advanced monitoring➤ Create powerful AI Agents with it using a fully integrated RAG pipeline➤ Fine-tune it with one click on FEDML Nexus AI StudioGet started here: https://t.co/cnEfbIfnpk

🚀🚀 @FEDML_AI x @ToyotaMotorCorp We are excited to announce our collaboration with Toyota Motor Corporation to bring Federated Learning into the EV industry.Federated learning has the potential to revolutionize the EV industry by facilitating the development and enhancement of personalized and private AI models. These models learn from a rich array of in-car data, such as the driver's habits—including speed and braking distances—and driving patterns, all while ensuring the privacy of this data. This approach not only improves the user experience by tailoring vehicle performance to individual preferences but also enhances overall vehicle safety and efficiency.Through our recent collaboration with Toyota, we have demonstrated federated training of AI models for accurate battery range estimation in EVs. This is a crucial problem in the EV industry because it enhances driver confidence, aids in efficient route planning, and is essential for overcoming range anxiety. Quite surprisingly, 58% of drivers say that range anxiety prevents them from buying an electric car!In this collaboration, FEDML Nexus AI platform (https://t.co/5AHWXcd9TG) was used to deploy and test centralized, federated, and personalized federated learning scenarios on cars in the lab setting. The results demonstrate that, compared with centralized training, using personalized federated learning:🎯 bandwidth requirement is reduced by 35x!🎯 cloud compute time is reduced by 9x!🎯 personalized model accuracy is improved by 20%!🎯 overall training is reduced by 2x!These results are compelling, demonstrating federated learning's significant impact on improving performance, cost, and privacy in the EV industry.🔥 We are en route to scale up the number of vehicles and set up a larger and more difficult environment to see how the vehicles handle more intense terrain.Read more here: https://t.co/0QpB30zj1p#fedml #toyota #federatedlearning #invehicleAI

🚨Attention, GenAI model builders! FEDML has dedicated H100 available on FEDML Nexus AI at a very competitive price on a month-to-month basis. 🔥 Reach out to us if you are interested!

🚨 @FEDML_AI is a Top Innovator Recipient presenting at Venture Summit West next Wednesday!We'll be presenting in the AI track at Venture Summit West in Silicon Valley on April 10th at 11:40 am. Excited to connect with industry leaders and investors to showcase the potential of FEDML Nexus AI.Find more information here: https://t.co/wCxeaolB3a#FEDML #innovators #VentureSummitWest #SiliconValley

🔥 DBRX by @databricks and Grok1 by @xai are now available for FREE at FEDML Nexus AI!We now offer the Playground, API access, and Private Deployment for the two most recent open-source foundational models by Databricks and xAI on @FEDML_AI Model Hub (https://t.co/XFNqcrdGJR).You can use those models for free in our playground, use the APIs for free, and further create dedicated endpoints for production. 🤖Databricks Instruct (DBRX): This large language model developed by Databricks outperforms many open-source LLMs and even proprietary models like GPT-3.5, thanks to its efficient Mixture-of-Experts architecture. Databricks has open-sourced DBRX, allowing enterprises to customize and improve the model for their specific use cases. 🤖Grok1: This is a remarkable large language model developed by Elon Musk’s xAI, notable for its massive scale, innovative Mixture-of-Experts architecture, open-source availability, and unique personality.Start using these models at FEDML Nexus AI, your Generative AI Platform at Scale (https://t.co/7cKYybj5lo).

#genai #modelserving FEDML’s Five-layer Model Serving Platform! FEDML Nexus AI platform (https://t.co/HWftJA1QPO) provides one of the most advanced model inference services composed of a 5-layer architecture: Layer 0: Deployment and Inference Endpoint. This layer enables HTTPs API, model customization (train/fine-tuning), scalability, scheduling, ops management, logging, monitoring, security (e.g., trust layer for LLM), compliance (SOC2), and on-prem deployment. Layer 1: FEDML Launch Scheduler. It collaborates with the L0 MLOps platform to handle deployment workflow on GPU devices for running serving code and configuration. Layer 2: FEDML Serving Framework. It’s a managed framework for serving scalability and observability. It will load the serving engine and user-level serving code. Layer 3: Model Definition and Inference APIs. Developers can define the model architecture, the inference engine to run the model, and the related schema of the model inference APIs. Layer 4: Inference Engine and Hardware. This is the layer many machine learning system researchers and hardware accelerator companies work to optimize the inference latency & throughput. In our newest technical blog post, we delve into the details of FEDML’ model deployment and serving framework and how developers can start using it: https://t.co/lA6VA01q7E

🚀🚀 FEDML GenAI App is now launched in Discord! @FEDML_AI community members can now create stunning images right within our Discord channel (https://t.co/PkHMWL04qJ) using GenAI models in FEDML Nexus AI model hub, and served in FEDML cloud. This app showcases a glimpse into the capabilities of FEDML Nexus AI platform (https://t.co/7cKYybj5lo) for scalable GenAI model/app serving. Join our thriving Discord community here (https://t.co/PkHMWL04qJ) to play around with this awesome app. Plus, get ready for even more exciting modalities (video, 3D, etc) to be added soon! 🔥🔥

🚀Fun Friday News from FEDML! We’re thrilled to announce the launch of our new in-Slack FEDML GenAI App! ✨ Starting now, FEDML community members can create stunning images right within our Slack channel using GenAI models in FEDML Nexus AI model hub, and served in FEDML cloud. Join our 2000+ Slack community (https://t.co/UaY2SV6QAB) to explore this fun app! Also, stay tuned as we add more exciting modalities to FEDML GenAI App very soon… Our goal for launching this app is to showcase the capabilities of FEDML Nexus AI platform for scalable GenAI model/app serving. Reach us, if you would like to also launch similar applications in your community. #FEDML #GenAI #CreativeAI #SlackApp #HappyFriday

LinkedIn / Twitter post:🚀 Exciting News! 🚀#pretraining #finetuning #llm #GaLore #FEDML🌟 FEDML Nexus AI platform now unlocks the pre-training and fine-tuning of LLaMA-7B on geo-distributed RTX4090s!📈By supporting the newly developed GaLore as a ready-to-launch job in FEDML Nexus AI, we have enabled the pre-training and fine-tuning of models like LLaMA 7B with a token batch size of 256 on a single RTX 4090, without additional memory optimization.🔗 Meaning? We're scaling up the training of heavy LLMs on more accessible GPUs across the world.💡 The magic behind it? Introducing FedLLM and UnitedLLM: our twin titans for collaborative learning. FedLLM harnesses geo-distributed data while maintaining privacy, and UnitedLLM taps into the collective strength of community GPUs for decentralized model training. Together, they're transforming the AI training landscape!For more details, please read our blog at https://t.co/dXMiEI5Be1

🚀Join us for our post GTC event on Thursday at 5pm in our office "The Lucky Building"🤞In the holy rooms previously home of companies like @Google , @PayPal and recently @FEDML_AI , "The Lucky Building" (165 University Avenue, Palo Alto) is in the prime location in the hearth of Silicon Valley.We look forward to welcoming generative AI founders, partners and investors to our space, having exciting discussions, and a couple of drinks together. RSVP here: https://t.co/orSjCdxMq7#GTC24 #GDC24 #GenerativeAI #SiliconValley

#llm #training #finetuning #genai #ml #ai #machinelearning We are excited to introduce our Serverless Training Cloud Service on FEDML Nexus AI with Seamless Experimental Tracking. It provides a variety of GPU types (A100, H100, A6000, RTX4090, etc.) for developers to train your model at any time in a serverless manner. Developers only pay per usage. It includes the following features: 1. Cost-effective training: Developers do not need to rent or purchase GPUs, developers can initiate serverless training tasks at any time, and developers only need to pay according to the usage time; 2. Flexible Resource Management: Developers can also create a cluster to use fixed machines and support the cluster autostop function (such as automatic shutdown after 30 minutes) to help you save the cost loss caused by forgetting to shut down the idle resources; 3. Simplified Code Setup: You do not need to modify your python training source code, you only need to specify the path of the code, environment installation script, and the main entrance through the YAML file 4. Experimental Tracking: The training process includes rich experimental tracking functions, including Run Overview, Metrics, Logs, Hardware Monitoring, Model, Artifacts, and other tracking capabilities. You can use the API provided by FEDML Python Library for experimental tracking, such as fedml.log(); 5. GPU Availability: There are many GPU types to choose from. You can go to Secure Cloud or Community Cloud to view the type and set it in the YAML file to use it. We will introduce how simple it is as follows: - Zero-code Serverless LLM Training on FEDML Nexus AI- Training More GenAI Models with FEDML Launch and Pre-built Job Store- Experiment Tracking for Large-scale Distributed Training- Train on Your Own GPU cluster https://t.co/GfkcLi4LB8

Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker https://t.co/Mlfr8vwkG4

FEDML’s Recent Advances in Federated Learning (2023-2024)As a pioneer in the field of federated learning, FEDML initially focused on an AI platform dedicated to federated learning. Over time, it evolved into a comprehensive "Your Generative AI Platform at Scale". While making this transformation, we still kept making strong progress and achieving significant milestones in the federated learning domain. In this post, we'll reflect on our perspectives regarding federated learning within the Generative AI (GenAI) landscape and recap the strides we've made over the previous year.https://t.co/WeCTIkXWcO

🎇 🎉 🚀 FEDML Nexus AI is the scalable GenAI platform for developers, startups, and enterprises to run applications easily and economically. To bring innovations from research to production rapidly, today we are very excited to announce the release of three innovative open-sourced GenAI models into production as easy-to-use HTTPs APIs: LLaVa-13B, SQLCoder-70B, and InstantID. https://t.co/HWftJA1QPO 💽 1. SQLCoder-70B: write SQL like a database expert Stop struggling with complex SQL queries! SQLCoder takes your natural language questions and instantly generates the perfect SQL code to answer them. No more writing code yourself - just ask SQLCoder, and it will handle the heavy lifting.  🖼 2. LLaVa-13B: large language and vision model LLaVA represents a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding, achieving impressive chat capabilities mimicking spirits of the multimodal GPT-4 and setting a new state-of-the-art accuracy on Science QA. 📸 3. InstantID: instantly generate your high-fidelity personal image with a single reference image Want to create personalized images in seconds? InstantID is a revolutionary AI tool that lets you transform a single photo into a variety of poses and styles, all while preserving your identity.  No more needing a massive dataset of images - InstantID works its magic with just one! #InstantID #SQLCoder #llava #ImageStylization #CodeGeneration #VisualUnderstanding

🚀🚀🚀 Introducing FEDML Launch - Run Any GenAI Jobs on Globally Distributed GPU Cloud: Pre-training, Fine-tuning, Federated Learning, and Beyond. It's powered by FEDML Nexus AI, your generative AI platform at scale Platform: https://t.co/HWftJA1QPOGitHub: https://t.co/RPdIvl2tGdDocumentation: https://t.co/Ff9rxdUZxcArtificial General Intelligence (AGI) promises a transformative leap in technology, fundamentally requiring the scalability of both models and data to unleash its full potential. Organizations such as OpenAI and Meta have been at the forefront, advancing the field by adhering to the "scaling laws" of AI. These laws posit that larger machine learning models, equipped with more parameters and trained with more data, yield superior performance. Nonetheless, the current approach, centered around massive GPU clusters within a single data center, poses a significant challenge for many AI practitioners.Our vision is to provide a scalable AI platform to democratize access to distributed AI systems, fostering the next wave of advancements in foundational models. By leveraging a greater number of GPUs and tapping into geo-distributed data, we aim to amplify these models' collective intelligence. To make this a reality, the ability to seamlessly run AI jobs from a local laptop to a distributed GPU cloud or onto on-premise clusters is essential—particularly when utilizing GPUs spread across multiple regions, clouds, or providers. It is a crucial step for AI practitioners to have such a product at their fingertips, toward a more inclusive and expansive future for AGI development.At FEDML, we developed FEDML Launch, a super launcher that can run any generative AI jobs (pre-training, fine-tuning, federated learning, etc.) on a globally distributed GPU cloud. It swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. It supports a range of compute-intensive jobs for generative AI and LLMs, such as large-scale training, fine-tuning, serverless deployments, and vector DB searches. FEDML Launch also facilitates on-premise cluster management and deployment on private or hybrid clouds.Learn more at https://t.co/BoAoOrGBUV and check out our blog post for more details: https://t.co/ena26jHdr6#scalableAI #machinelearning #generatieveai #FEDML #distributedcomputing

Just got off a call with @FEDML_AI and super excited for our future. RNP-007 is here for some light reading on what has been voted on: